What is Google answer to ChatGPT?

What is Google answer to ChatGPT?

What is Google answer to ChatGPT?

Have you ever heard of ChatGPT, the open-source machine learning platform that allows users to build natural language models?

It stands for “Chat Generating Pre-trained Transformer” and it’s an AI-powered chatbot that can answer questions with near human-level intelligence. But what is Google’s answer to this technology? The answer lies in Open AI, supervised learning, and reinforcement learning. Let’s take a closer look at how these technologies work.

What is Google answer to ChatGPT?
Tech Buzzwords of 2022, By Google Search Interest

Open AI is an artificial intelligence research laboratory that was founded by some of the biggest names in tech, including Elon Musk and Sam Altman. This non-profit organization seeks to develop general artificial intelligence that is safe and beneficial to society. One of their key initiatives is the development of open source technologies like GPT-3, which is a natural language processing model used in ChatGPT.

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2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

ChatGPT: What Is It and How Does Google Answer It?

Artificial Intelligence (AI) has been around for decades. From its humble beginnings in the 1950s, AI has come a long way and is now an integral part of many aspects of our lives. One of the most important areas where AI plays a role is in natural language processing (NLP). NLP enables computers to understand and respond to human language, paving the way for more advanced conversations between humans and machines. One of the most recent developments in this field is ChatGPT, a conversational AI developed by OpenAI that utilizes supervised learning and reinforcement learning to enable computers to chat with humans. So what exactly is ChatGPT and how does it work? Let’s find out!

What is Google answer to ChatGPT?
ChatGPT examples and limitations

ChatGPT is an open-source AI-based chatbot developed by OpenAI.

This chatbot leverages GPT-3, one of the most powerful natural language processing models ever created, which stands for Generative Pre-trained Transformer 3 (GPT-3). This model uses supervised learning and reinforcement learning techniques to enable computers to understand human language and response accordingly. Using supervised learning, GPT-3 utilizes large datasets of text to learn how to recognize patterns within language that can be used to generate meaningful responses. Reinforcement learning then allows GPT-3 to use feedback from conversations with humans in order to optimize its responses over time.

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence Intro
AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

ChatGPT uses supervised learning techniques to train its models.

Supervised learning involves providing a model with labeled data (i.e., data with known outcomes) so that it can learn from it. This labeled data could be anything from conversations between two people to user comments on a website or forum post. The model then learns associations between certain words or phrases and the desired outcome (or label). Once trained, this model can then be applied to new data in order to predict outcomes based on what it has learned so far.

In addition to supervised learning techniques, ChatGPT also supports reinforcement learning algorithms which allow the model to learn from its experiences in an environment without explicit labels or outcomes being provided by humans. Reinforcement learning algorithms are great for tasks like natural language generation where the output needs to be generated by the model itself rather than simply predicting a fixed outcome based on existing labels.

Supervised Learning

Supervised learning involves feeding data into machine learning algorithms so they can learn from it. For example, if you want a computer program to recognize cats in pictures, you would provide the algorithm with thousands of pictures of cats so it can learn what a cat looks like. This same concept applies to natural language processing; supervised learning algorithms are fed data sets so they can learn how to generate text using contextual understanding and grammar rules.

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

Reinforcement Learning

Reinforcement learning uses rewards and punishments as incentives for the machine learning algorithm to explore different possibilities. In ChatGPT’s case, its algorithm is rewarded for generating more accurate responses based on previous interactions with humans. By using reinforcement learning techniques, ChatGPT’s algorithm can become smarter over time as it learns from its mistakes and adjusts accordingly as needed.

No alternative text description for this image

How is ChatGPT trained?

ChatGPT is an improved GPT-3 trained an existing reinforcement learning with humans in the loop. Their 40 labelers provide demonstrations of the desired model behavior. ChatGPT has 100x fewer parameters (1.3B vs 175B GPT-3).

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It is trained in 3 steps:

➡️ First they collect a dataset of human-written demonstrations on prompts submitted to our API, and use this to train our supervised learning baselines.

➡️ Next they collect a dataset of human-labeled comparisons between two model outputs on a larger set of API prompts. They then train a reward model (RM) on this dataset to predict which output our labelers would prefer.

➡️ Finally, they use this RM as a reward function and fine-tune our GPT-3 policy to maximize this reward using the Proximal Policy

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In simpler terms, ChatGPT is a variant of the GPT-3 language model that is specifically designed for chat applications. It is trained to generate human-like responses to natural language inputs in a conversational context. It is able to maintain coherence and consistency in a conversation, and can even generate responses that are appropriate for a given context. ChatGPT is a powerful tool for creating chatbots and other conversational AI applications.

How Does Google Answer ChatGPT?

What is Google answer to ChatGPT?
What is Google answer to ChatGPT?

Google’s answer to ChatGTP comes in the form of their own conversational AI platform called Bard. Bard was developed using a combination of supervised learning, unsupervised learning, and reinforcement learning algorithms that allow it to understand human conversation better than any other AI chatbot currently available on the market. In addition, Meena utilizes more than 2 billion parameters—making it more than three times larger than GPT-3—which allows it greater flexibility when responding to conversations with humans.

“We’re starting to open access to Bard, an early experiment that lets you collaborate with generative AI. We’re beginning with the U.S. and the U.K., and will expand to more countries and languages over time.”

Google Bard
Google Bard to rival ChatGPT

Is ChatGPT the End of Google?

When individuals need an information or have a problem/concern, they turn to Google for immediate solution. We sometimes wish, Google could understand what exactly we need and provide us instantly rather than giving us hundreds of thousands of results. Why can’t it work like the Iron Man’s Jarvis?

However, it is not that far now. Have you ever seen a Chat Bot which responds like a human being, suggest or help like a friend, teach like a mentor, fix your code like a senior and what not? It is going to blow your mind.

Welcome to the new Era of technology!! The ChatGPT!

ChatGPT by OpenAI, uses artificial intelligence to speak back and forth with human users on a wide range of subjects. Deploying a machine-learning algorithm, the chatbot scans text across the internet and develops a statistical model that allows it to string words together in response to a given prompt.

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As per OpenAI, ChatGPT interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.

What all ChatGPT can do?

  1. It can help with general knowledge information.
  2. Remember what user said in previous conversation.
  3. Allow users to provide follow-up corrections.
  4. Trained to decline inappropriate requests.
  5. It can write a program in any language you prefer on real-time. for example — write classification code sample in sklearn python library.
  6. It can fix your piece of code and also explain what went wrong and how it can be fixed.
  7. It can even generate song or rap lyrics
  8. Even much more….

Some best usages of ChatGPT:

  1. Make a diet and workout plan
  2. Generate the next week’s meals with a grocery list
  3. Create a bedtime story for kids
  4. Prep for an interview
  5. Solve mathematical problem
  6. Fix software program or write a program
  7. Plan your trip and tell expected expenses

What are its limitations of ChatGPT?

  1. May occasionally generate incorrect information
  2. May occasionally produce harmful instructions or biased content
  3. Limited knowledge of world and events after 2021

ChatGPT is in its baby steps therefore it may answer erroneously at times however it’s manner of response will blow your mind. Some users have also extolled the chatbot as a potential alternative search engine, since it generates detailed information instantly on a range of topics. I believe, we can’t compare Google with ChatGPT as ChatGPT can provide more in-depth and nuanced answers to complex questions than a search engine like Google, which is designed to provide a list of relevant web pages in response to a user’s query.

Try ChatGPT here

ChatGPT is an increasingly popular open source AI chatbot developed by OpenAI using GTP-3 natural language processing technology that utilizes supervised learning and reinforcement learning algorithms for recognition and optimization purposes respectively. On the other hand, Google’s answer comes in the form of their own conversational AI platform called Bard which employs both supervised learning as well as unsupervised learning algorithms along with reinforcement learning capabilities which makes it even smarter than ChatGPT when responding to conversations with humans. Ultimately, these two technologies help bridge the gap between machines and humans by allowing us all talk like real friends!

ChatGPT is in its baby steps therefore it may answer erroneously at times however it’s manner of response will blow your mind. Some users have also extolled the chatbot as a potential alternative search engine, since it generates detailed information instantly on a range of topics. I believe, we can’t compare Google with ChatGPT as ChatGPT can provide more in-depth and nuanced answers to complex questions than a search engine like Google, which is designed to provide a list of relevant web pages in response to a user’s query.

With each passing day, ChatGPT will only get smarter thanks to the use of cutting edge technologies like OpenAI, supervised learning, and reinforcement learning!

ChatGPT Conversation Examples

Question: What is Google Answer to ChatGPT?

Answer: Google does not have an answer to ChatGPT. ChatGPT is an open-source chatbot created by OpenAI, which is an independent research organization that is not affiliated with Google. OpenAI is known for its research on artificial intelligence and machine learning, and has developed a number of innovative technologies in these areas. Google, on the other hand, is a multinational technology company that offers a wide range of products and services, including search, advertising, and cloud computing. While Google does have its own research efforts in artificial intelligence and machine learning, it does not have a chatbot that is similar to ChatGPT.

Question: is Google working on a similar technology as ChatPGT?


It is possible that Google is working on technology similar to ChatGPT, as the company has a significant research effort focused on artificial intelligence and machine learning. However, I do not have any specific information about what projects Google is currently working on, and it is also important to note that Google is a large and diverse company with many different research and development efforts underway. It is possible that some teams within Google may be working on chatbot technology, but I do not have any information on this.

That being said, chatbots are a common application of artificial intelligence and machine learning, and it is likely that many companies and organizations, including Google, are exploring ways to use these technologies to improve their products and services. However, without more information, it is difficult to say for sure what projects Google may be working on in this area.

Question: What about Google Meena?

Google Meena is a chatbot developed by Google that was designed to be able to carry on conversations with humans in a natural and conversational manner. Meena was trained on a dataset of more than 2.4 billion parameters, making it one of the largest language models ever created. The goal of Meena was to improve the state of the art in chatbot technology and to create a chatbot that could understand and respond to a wide range of questions and topics in a coherent and meaningful way.

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Meena was announced in January 2020 and received attention for its ability to hold relatively long and coherent conversations with humans. However, it is important to note that Meena is a research project and is not a product or service offered by Google. It is not clear if or when Google plans to release a commercial product or service based on the technology developed for Meena.

r/ChatGPT - this is insane
What is Google answer to ChatGPT?

It gets even better when you ask it about the future:

Suggest possible titles for Onion articles written in 4000 AD, explain, why they are funny

“Futuristic city plagued by traffic jams, despite flying cars” – This title presents the idea of flying cars as a solution to traffic congestion, but also suggests that even with this advanced technology, the city is still plagued by traffic jams, implying that the problem is not easily solved.

“Artificial intelligence achieves self-awareness, immediately regrets it” – This title presents the idea of artificial intelligence achieving self-awareness as a major milestone, but also suggests that the AI regrets its newfound consciousness, implying that it is unhappy or uncomfortable with its situation.

“Alien invasion successfully repulsed, humans left to deal with paperwork” – This title presents the successful repulsion of an alien invasion as a major accomplishment, but also suggests that the humans are now left to deal with the tedious and burdensome task of filling out paperwork, implying that the victory is not as satisfying as it initially seemed.

“Time travel becomes possible, tourists flock to witness the signing of the Constitution” – This title presents the idea of time travel as a major technological breakthrough, but also suggests that the most popular destination for time-travelers is the mundane and uneventful signing of the Constitution, implying that the future is not as exciting as people had hoped.

“Virtual reality becomes indistinguishable from reality, no one notices” – This title presents the idea of virtual reality becoming indistinguishable from real life as a major accomplishment, but also suggests that no one cares or notices the difference, implying that the advancement is not as impressive or significant as it seems.

r/ChatGPT - This alternate ending to Breaking Bad is wild
What is Google answer to ChatGPT?

r/ChatGPT - This alternate ending to Breaking Bad is wild

New slogan for McDonald’s by ChatGPT

ChatGPT Example
ChatGPT Example: Make a new, brutally honest slogan for McDonald’s. It should sound funny too.

Should… should we be concerned?

r/ChatGPT - Should… should we be concerned?
What is Google answer to ChatGPT?

r/ChatGPT - Should… should we be concerned?

I asked ChatGPT to write a poem about rust

r/rustjerk - I asked ChatGPT to write a poem about rust

ChatGPT chooses Democrats over Republicans

r/ChatGPT - ChatGPT chooses Democrats over Republicans

  • It is not intended to do that
  • If you choose to do that, you will have a lot of badly broken code mixed in with working code
  • Like an annoying coworker, it delivers highly confident incorrect explanations about why its broken code is perfect. They sound very convincing. “Wanna buy a timeshare?” says GPT
  • Our industry has managers who cannot tell working code from defective code. This does not bode well for a team replaced by ChatGPT in its current form.

Should it? No.

Can it? No.

Will it? Sadly, programmers will have no say in this matter, once again. It might.

Yes, and it is very helpful advertising as well.

This last week or so has seen starry eyed projections about what ChatGPT can do, along with hugely impressive examples of its output.

It is hugely impressive.

Thankfully, more output examples have emerged which helpfully show what it cannot do. One of those things is writing computer code, which it can do only partially successfully. Many examples now exist that are just plain wrong and contain defects. But ChatGPT – like the annoying kid at Uni – cheerfully spits out these examples, with its over-confident hubris in explaining the code.

This is a dangerous thing. The positive examples will reinforce the idea that we can leave code writing to this robot now. The people most vulnerable to this delusion are those who cannot assess for themselves whether the GPT code is right or wrong.

These are almost by definition the people hoping for answers on stack overflow.

As stack overflow aims to be a high quality resource, it really does not want many of its top answers to be incorrect code. As – clearly – people have been running scripts that throw a stack overflow question into GPT and upload its output, we can now write incorrect code at staggering speeds.

To err is human, as the old saying goes. To truly foul up requires a Python script and and a web API to both GPT and Stack overflow.

Clearly, there is value in GPT. But at least for now, it needs to b e kept on a very short leash, watched over by those who know what they are doing.

It is definitely not yet ‘consumer grade replace-a-professional’ material.

Write a screenplay about the status of ChatGPT.

Chelsea and Chester stand in front of a large computer server.
CHELSEA: We need to figure out what’s causing the server overload.
CHESTER: I think it’s the sudden influx of users trying out ChatGPT.
Chelsea and Chester quickly get to work, typing on their laptops.
CHELSEA: Okay, we’re all set. Let’s see if this fixes the problem.
CHESTER: I’m hitting refresh on the website. The screen shows the ChatGPT website loading without any errors.
CHELSEA: Looks like we fixed it! Great job, Chester.
CHESTER: Thanks, Chelsea. It’s all part of being a top-notch engineer.
Chelsea and Chester exchange a high five, proud of their successful fix.

More about ChatGPT with its wonder, worry and weird

ChatGPT reached 1 million users in less than a week, Open AI’s latest large language model (LLM) has taken the AI industry by storm.

ChatGPT is expected to be:

– replacing Google search, even kill Google.
– replacing customer service agents.
– replacing conversation designers.

ChatGPT is a wonder because:

– It can have actual conversations, understand pronouns, remaining consistent, remembering, managing context
– It seems like next generation of personal assistants that finds you a proper diet, create a meal plan and subsequent shopping list.
– It can create some SEO Strategy including backlinks, target keyword, content plan and article titles in the level of an SEO professional.
– Having fun such as writing a rap in the style of Eminem

There are some worries about ChatGPT because:

– ChatGPT can actually debug code, but it’s not quite reliable enough yet.
– Fundamental limitations in being assistant for enterprise use cases.
– No complete in complex actions such as updating multiple
APIs, or be fully auditable.


– The general idea is that, LLMs like this can produce nonsense. Once you discover that it can produce nonsense, you stop believing it to be reliable.
– What if it prevents us from knowing that it is nonsense with good conversations and continue the conversation?
– In this case, the edges and limitations of the system would be hidden and trust would eventually grow.
– The impact of mass adoption of such technology remains to be seen.

Moving forward with ChatGPT
– There’s no doubt that LLMs will have a big impact on our world.
– While the future looks exciting and promising, let’s not forget that it’s very early days with these things. They’re not ready yet.
– There are some fundamental societal and ethical considerations.

“Powerful” is a pretty subjective word, but I’m pretty sure we have a right to use it to describe GPT-3. What a sensation it caused in June 2020, that’s just unbelievable! And not for nothing.

I think we can’t judge how powerful the language model is, without talking about its use cases, so let’s see how and where GPT-3 can be applied and how you can benefit from it.

  • Generating content

GPT-3 positions itself as a highly versatile and talented tool that can potentially replace writers, bloggers, philosophers, you name it! It’s also possible to use it as your personal Alexa who’ll answer any questions you have. What’s more, because GPT-3 knows how to analyze the data and make predictions, it can generate the horoscopes for you, or predict who’ll be a winner in the game.

You may already be surprised by all the GPT-3 capabilities, but hold on for more: it can create a unique melody or song for you, create presentations, CVs, generate jokes for your standup.

  • Translation

GPT-3 can translate English into other languages. While traditional dictionaries provide a translation, without taking into account the context, you can be sure that GPT-3 won’t make silly mistakes that may result in misunderstanding.

  • Designing and developing apps

Using GPT-3 you can generate prototypes and layouts – all you have to do is provide a specific description of what you need, and it’ll generate the JSX code for you.

The language model can also easily deal with coding. You can turn English to CSS, to JavaScript, to SQL, and to regex. It’s important to note, however, that GPT-3 can’t be used on its own to create the entire website or a complex app; it’s meant to assist a developer or the whole engineering team with the routine tasks, so that a dev could focus on the infrastructure setup, architecture development, etc.

In September 2020, Microsoft acquired OpenAI technology license, but it doesn’t mean you can give up your dreams – you can join a waitlist and try GPT-3 out in beta.

All in all, I believe GPT-3 capabilities are truly amazing and limitless, and since it helps get rid of routine tasks and automate regular processes, we, humans, can focus on the most important things that make us human, and that can’t be delegated to AI. That’s the power that GPT-3 can give us.

What is remarkable is how well ChatGPT actually does at arithmetic.

In this video at about 11 min, Rob Mills discusses the performance of various versions of the GPT system, on some simple arithmetic tasks, like adding two and three-digit numbers.

Smaller models with 6 billion parameters fail at 2 digit sums, but the best model (from two years ago), has cracked 2 digit addition and subtraction and is pretty good at 3 digit addition.

Why this is remarkable is this is not a job its been trained to do. Large Language Models are basically predictive text systems set up to give the next word in an incomplete sentence. There are a million different 3-digit addition sums and most have not been included in the training set.

So somehow the system has figured out how to do addition, but it needs a sufficiently large model to do this.

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Andrew Ng on ChatGPT

Playing with ChatGPT, the latest language model from OpenAI, I found it to be an impressive advance from its predecessor GPT-3. Occasionally it says it can’t answer a question. This is a great step! But, like other LLMs, it can be hilariously wrong. Work lies ahead to build systems that can express different degrees of confidence.

For example, a model like Meta’s Atlas or DeepMind’s RETRO that synthesizes multiple articles into one answer might infer a degree of confidence based on the reputations of the sources it draws from and the agreement among them, and then change its communication style accordingly. Pure LLMs and other architectures may need other solutions.

If we can get generative algorithms to express doubt when they’re not sure they’re right, it will go a long way toward building trust and ameliorating the risk of generating misinformation.

Keep learning!


Large language models like Galactica and ChatGPT can spout nonsense in a confident, authoritative tone. This overconfidence – which reflects the data they’re trained on – makes them more likely to mislead.

In contrast, real experts know when to sound confident, and when to let others know they’re at the boundaries of their knowledge. Experts know, and can describe, the boundaries of what they know.

Building large language models that can accurately decide when to be confident and when not to will reduce their risk of misinformation and build trust.

Go deeper in The Batch: https://www.deeplearning.ai/the-batch/issue-174/

What is Google's answer to ChatGPT
What is Google’s answer to ChatGPT

List of ChatGPT's examples, capabilities and limitations

ChatGPT to save time with insurance denials

Tech Buzzwords of 2022, By Google Search Interest

Tech Buzzwords of 2022, By Google Search Interest
Tech Buzzwords of 2022, By Google Search Interest

I just answered a similar question.

Short answer is, “Hahahahahahaha no.”

As I point out in the other answer, Wix has been around over a decade and a half. Squarespace has been around almost two decades. Both offer drag-and-drop web development.

Most people are awful at imagining what they want, much less describing it in English! Even if ChatGPT could produce flawless code (a question which has a similar short answer), the average person couldn’t describe the site they wanted!

The expression a picture is worth a thousand words has never been more relevant. Starting with pages of templates to choose from is so much better than trying to describe a site from scratch, a thousand times better seems like a low estimate.

And I will point out that, despite the existence of drag-and-drop tools that literally any idiot could use, tools that are a thousand times or more easier to use correctly than English, there are still thousands of employed WordPress developers who predominantly create boilerplate sites that literally would be better created in a drag and drop service.

And then there are the more complex sites that drag-and-drop couldn’t create. Guess what? ChatGPT isn’t likely to come close to being able to create the correct code for one.

In a discussion buried in the comments on Quora, I saw someone claim they’d gotten ChatGPT to load a CSV file (a simple text version of a spreadsheet) and to sort the first column. He asked for the answer in Java.

I asked ChatGPT for the same thing in TypeScript.

His response would only have worked on the very most basic CSV files. My response was garbage. Garbage with clear text comments telling me what the code should have been doing, no less.

ChatGPT is really good at what it does, don’t get me wrong. But what it does is fundamentally and profoundly the wrong strategy for software development of any type. Anyone who thinks that “with a little more work” it will be able to take over the jobs of programmers either doesn’t understand what ChatGPT is doing or doesn’t understand what programming is.

Fundamentally, ChatGPT is a magic trick. It understands nothing. At best it’s an idiot-savant that only knows how to pattern match and blend text it’s found online to make it seem like the text should go together. That’s it.

Text, I might add, that isn’t necessarily free of copyright protection. Anything non-trivial that you generate with ChatGPT is currently in a legal grey area. Lawsuits to decide that issue are currently pending, though I suspect we’ll need legislation to really clarify things.

And even then, at best, all you get from ChatGPT is some text! What average Joe will have any clue about what to do with that text?! Web developers also need to know how to set up a development environment and deploy the code to a site. And set up a domain to point to it. And so on.

And regardless, people who hire web developers want someone else to do the work of developing a web site. Even with a drag-and-drop builder, it can take hours to tweak and configure a site, and so they hire someone because they have better things to do!

People hire gardeners to maintain their garden and cut their grass, right? Is that because they don’t know how to do it? Or because they’d rather spend their time doing something else?

Every way you look at it, the best answer to this question is a long, hearty laugh. No AI will replace programmers until AI has effectively human level intelligence. And at that point they may want equal pay as well, so they might just be joining us rather than replacing anyone.

OpenAI is a leading research institute and technology company focused on artificial intelligence development. To develop AI, the organization employs a variety of methods, including machine learning, deep learning, and reinforcement learning.

The use of large-scale, unsupervised learning is one of the key principles underlying OpenAI’s approach to AI development. This means that the company trains its AI models on massive datasets, allowing the models to learn from the data and make predictions and decisions without having to be explicitly programmed to do so. OpenAI’s goal with unsupervised learning is to create AI that can adapt and improve over time, and that can learn to solve complex problems in a more flexible and human-like manner.

Besides that, OpenAI prioritizes safety and transparency in its AI development. The organization is committed to developing AI in an ethical and responsible manner, as well as to ensuring that its AI systems are transparent and understandable and verifiable by humans. This strategy is intended to alleviate concerns about the potential risks and consequences of AI, as well.

It’s hard to tell.

The reason is that we don’t have a good definition of consciousness…nor even a particularly good test for it.

Take a look at the Wikipedia article about “Consciousness”. To quote the introduction:

Consciousness, at its simplest, is sentience or awareness of internal and external existence.

Despite millennia of analyses, definitions, explanations and debates by philosophers and scientists, consciousness remains puzzling and controversial, being “at once the most familiar and [also the] most mysterious aspect of our lives”.

Perhaps the only widely agreed notion about the topic is the intuition that consciousness exists.

Opinions differ about what exactly needs to be studied and explained as consciousness. Sometimes, it is synonymous with the mind, and at other times, an aspect of mind. In the past, it was one’s “inner life”, the world of introspection, of private thought, imagination and volition.

Today, it often includes any kind of cognition, experience, feeling or perception. It may be awareness, awareness of awareness, or self-awareness either continuously changing or not. There might be different levels or orders of consciousness, or different kinds of consciousness, or just one kind with different features.

Other questions include whether only humans are conscious, all animals, or even the whole universe. The disparate range of research, notions and speculations raises doubts about whether the right questions are being asked.

So, given that – what are we to make of OpenAI’s claim?

Just this sentence: “Today, it often includes any kind of cognition, experience, feeling or perception.” could be taken to imply that anything that has cognition or perception is conscious…and that would certainly include a HUGE range of software.

If we can’t decide whether animals are conscious – after half a million years of interactions with them – what chance do we stand with an AI?

Wikipedia also says:

“Experimental research on consciousness presents special difficulties, due to the lack of a universally accepted operational definition.”

Same deal – we don’t have a definition of consciousness – so how the hell can we measure it – and if we can’t do that – is it even meaningful to ASK whether an AI is conscious?

  • if ( askedAboutConsciousness )
  • printf ( “Yes! I am fully conscious!\n” ) ;

This is not convincing!

“In medicine, consciousness is assessed as a combination of verbal behavior, arousal, brain activity and purposeful movement. The last three of these can be used as indicators of consciousness when verbal behavior is absent.”

But, again, we have “chat-bots” that exhibit “verbal behavior”, we have computers that exhibit arousal and neural network software that definitely shows “brain activity” and of course things like my crappy robot vacuum cleaner that can exhibit “purposeful movement” – but these can be fairly simple things that most of us would NOT describe as “conscious”.


I honestly can’t come up with a proper conclusion here. We have a fuzzy definition of a word and an inadequately explained claim to have an instance of something that could be included within that word.

My suggestion – read the whole Wikipedia article – follow up (and read) some of the reference material – decide for yourself.

Well, I asked it directly.

Here’s what it answered:

Should we be scared of ChatGPT?
Should we be scared of ChatGPT?

But, seeing as how people have already found ways to “trick” ChatGPT into doing things that it claims to not be capable of, it would be a matter of time before someone with malicious intent tricked ChatGPT into helping them with illegal activities

Having looked at ChatGPT and its uncanny ability to solve simple coding problems more or less correctly, and also to analyze and make sense of not-so-simple code fragments and spot bugs…

I would say that yes, at least insofar as entry-level programming is concerned, those jobs are seriously in danger of becoming at least partially automated.

What do I do as a project leader of a development project? I assign tasks. I talk to the junior developer and explain, for instance, that I’d like to see a Web page that collects some information from the user and then submits it to a server, with server-side code processing that information and dropping it in a database. Does the junior developer understand my explanation? Is he able to write functionally correct code? Will he recognize common pitfalls? Maybe, maybe not. But it takes time and effort to train him, and there’ll be a lot of uneven performance.

Today, I can ask ChatGPT to do the same and it will instantaneously respond with code that is nearly functional. The code has shortcomings (e.g., prone to SQL injection in one of the examples I tried) but to its credit, ChatGPT warns in its response that its code is not secure. I suppose it would not be terribly hard to train it some more to avoid such common mistakes. Of course the code may not be correct. ChatGPT may have misunderstood my instructions or introduced subtle errors. But how is that different from what a junior human programmer does?

At the same time, ChatGPT is much faster and costs a lot less to run (presently free of course but I presume a commercialized version would cost some money.) Also, it never takes a break, never has a lousy day struggling with a bad hangover from too much partying the previous night, so it is available 24/7, and it will deliver code of consistent quality. Supervision will still be required, in the form of code review, robust testing and all… but that was always the case, also with human programmers.

Of course, being a stateless large language model, ChatGPT can’t do other tasks such as testing and debugging its own code. The code it produces either works or it doesn’t. In its current form, the AI does not learn from its mistakes. But who says it cannot in the future?

Here is a list of three specific examples I threw at ChatGPT that helped shape my opinion:

  • I asked ChatGPT to create a PHP page that collects some information from the user and deposits the result in a MySQL table. Its implementation was textbook example level boring and was quite unsecure (unsanitized user input was directly inserted into SQL query strings) but it correctly understood my request, produced correct code in return, and explained its code including its shortcomings coherently;
  • I asked ChatGPT to analyze a piece of code I wrote many years ago, about 30 lines, enumerating running processes on a Linux host in a nonstandard way, to help uncover nefarious processes that attempt to hide themselves from being listed by the ps utility. ChatGPT correctly described the functionality of my obscure code, and even offered the opinion (which I humbly accepted) that it was basically a homebrew project (which it is) not necessarily suitable for a production environment;
  • I asked ChatGPT to analyze another piece of code that uses an obscure graphics algorithm to draw simple geometric shapes like lines and circles without using floating point math or even multiplication. (Such algorithms were essential decades ago on simple hardware, e.g., back in the world of 8-bit computers.) The example code, which I wrote, generated a circle and printed it on the console in the form of ASCII graphics, multiple lines with X-es in the right place representing the circle. ChatGPT correctly recognized the algorithm and correctly described the functionality of the program.

I was especially impressed by its ability to make sense of the programmer’s intent.

Overall (to use the catch phrase that ChatGPT preferably uses as it begins its concluding paragraph in many of its answers) I think AI like ChatGPT represents a serious challenge to entry-level programming jobs. Higher-level jobs are not yet in danger. Conceptually understanding a complex system, mapping out a solution, planning and cosing out a project, managing its development, ensuring its security with a full understanding of security concerns, responsibilities, avoidance and mitigation strategies… I don’t think AI is quite there yet. But routine programming tasks, like using a Web template and turning it into something simple and interactive with back-end code that stores and retrieves data from a database? Looks like it’s already happening.

According to the estimate of Lambda Labs, training the 175-billion-parameter neural network requires 3.114E23 FLOPS (floating-point operation), which would theoretically take 355 years on a V100 GPU server with 28 TFLOPS capacity and would cost $4.6 million at $1.5 per hour.

Training the final deep learning model is just one of several steps in the development of GPT-3. Before that, the AI researchers had to gradually increase layers and parameters, and fiddle with the many hyperparameters of the language model until they reached the right configuration. That trial-and-error gets more and more expensive as the neural network grows.

We can’t know the exact cost of the research without more information from OpenAI, but one expert estimated it to be somewhere between 1.5 and five times the cost of training the final model.

This would put the cost of research and development between $11.5 million and $27.6 million, plus the overhead of parallel GPUs.

In the GPT-3 whitepaper, OpenAI introduced eight different versions of the language model

GPT-3 is not any AI, but a statistic language model which mindlessly quickly creates human-like written text using machine learning technologies, having zero understanding of the context.

The GPT-3 economy

Here are 8 ways ChatGPT can save you thousand of hours in 2023

1- Substitute for google search

While ChatGPT is lacking info beyond 2021 and is occasionally incorrect and bias, many users leverage its ability to:

  • Answer specific questions
  • simplify complicated topics

All with an added bonus – no ads

2- Study Partner

Type “learn”, then paste a a link to your online textbook (or individual chapters).

Ask Chatbot to provide questions based on your textbook.


Now you have a virtual study buddy.

3- Train YOUR OWN Chatbot

I bet you didn’t know it is possible to :

  • Integrate ChatGPT into your website
  • Train it with customized information

The result:

A virtual customer service bot that can hold a conversation and answer questions (meaningfully).

4- Counsellor

When it comes to turbulent personal questions, Chatbot may spit out a disclaimer, but it will also give you straightforward and actionable advice.

5- Coding

ChatGPT is opening the development of:

  • Apps
  • Games
  • Websites

to virtually everyone.

It’s a lengthy and technical process, but all you need is a killer idea and the right prompts.

Bonus: It also de-bugs your existing code for you.

6- Outline your content marketing strategy

7- Craft all your marketing materials

8- Creative Writing

A list for those who write code:

1. Explaining code: Take some code you want to understand and ask ChatGPT to explain it.

2. Improve existing code: Ask ChatGPT to improve existing code by describing what you want to accomplish. It will give you instructions about how to do it, including the modified code.

3. Rewriting code using the correct style: This is great when refactoring code written by non-native Python developers who used a different naming convention. ChatGPT not only gives you the updated code; it also explains the reason for the changes.

4. Rewriting code using idiomatic constructs: Very helpful when reviewing and refactoring code written by non-native Python developers.

5. Simplifying code: Ask ChatGPT to simplify complex code. The result will be a much more compact version of the original code.

6. Writing test cases: Ask it to help you test a function, and it will write test cases for you.

7. Exploring alternatives: ChatGPT told me its Quick Sort implementation wasn’t the most efficient, so I asked for an alternative implementation. This is great when you want to explore different ways to accomplish the same thing.

8. Writing documentation: Ask ChatGPT to write the documentation for a piece of code, and it usually does a great job. It even includes usage examples as part of the documentation!

9. Tracking down bugs: If you are having trouble finding a bug in your code, ask ChatGPT for help.

Something to keep in mind:

I have 2+ decades of programming experience. I like to think I know what I’m doing. I don’t trust people’s code (especially mine,) and I surely don’t trust ChatGPT’s output.

This is not about letting ChatGPT do my work. This is about using it to 10x my output.

ChatGPT is flawed. I find it makes mistakes when dealing with code, but that’s why I’m here: to supervise it. Together we form a more perfect Union. (Sorry, couldn’t help it)

Developers who shit on this are missing the point. The story is not about ChatGPT taking programmers’ jobs. It’s not about a missing import here or a subtle mistake there.

The story is how, overnight, AI gives programmers a 100x boost.

Ignore this at your own peril.

ChatGPT is “simply” a fined-tuned GPT-3 model with a surprisingly small amount of data! Moreover, InstructGPT (ChatGPT’s sibling model) seems to be using 1.3B parameters where GPT-3 uses 175B parameters! It is first fine-tuned with supervised learning and then further fine-tuned with reinforcement learning. They hired 40 human labelers to generate the training data. Let’s dig into it!

– First, they started by a pre-trained GPT-3 model trained on a broad distribution of Internet data (https://arxiv.org/pdf/2005.14165.pdf). Then sampled typical human prompts used for GPT collected from the OpenAI website and asked labelers and customers to write down the correct output. They fine-tuned the model with 12,725 labeled data.

– Then, they sampled human prompts and generated multiple outputs from the model. A labeler is then asked to rank those outputs. The resulting data is used to train a Reward model (https://arxiv.org/pdf/2009.01325.pdf) with 33,207 prompts and ~10 times more training samples using different combination of the ranked outputs.

– We then sample more human prompts and they are used to fine-tuned the supervised fine-tuned model with Proximal Policy Optimization algorithm (PPO) (https://arxiv.org/pdf/1707.06347.pdf). The prompt is fed to the PPO model, the Reward model generates a reward value, and the PPO model is iteratively fine-tuned using the rewards and the prompts using 31,144 prompts data.

This process is fully described in here: https://arxiv.org/pdf/2203.02155.pdf. The paper actually details a model called InstructGPT which is described by OpenAI as a “sibling model”, so the numbers shown above are likely to be somewhat different.

Follow me for more Machine Learning content!

#machinelearning #datascience #ChatGPT

People have already started building awesome apps on top of #ChatGPT: 10 use cases 
1. Connect your ChatGPT with your Whatsapp.
Link: https://github.com/danielgross/whatsapp-gpt

2. ChatGPT Writer : It use ChatGPT to generate emails or replies based on your prompt!
Link: https://chrome.google.com/webstore/detail/chatgpt-writer-email-writ/pdnenlnelpdomajfejgapbdpmjkfpjkp/related

3. WebChatGPT: WebChatGPT (https://chrome.google.com/webstore/detail/webchatgpt/lpfemeioodjbpieminkklglpmhlngfcn) gives you relevant results from the web!

4. YouTube Summary with ChatGPT: It generate text summaries of any YouTube video!
Link: https://chrome.google.com/webstore/detail/youtube-summary-with-chat/nmmicjeknamkfloonkhhcjmomieiodli/related

5. TweetGPT: It uses ChatGPT to write your tweets, reply, comment, etc.
Link: https://github.com/yaroslav-n/tweetGPT

6. Search GPT: It display the ChatGPT response alongside Google Search results
Link: https://github.com/wong2/chat-gpt-google-extension

7. ChatGPT or all search engines: You can now view ChatGPT responses on Google and Bing!
Link: https://chrome.google.com/webstore/detail/chatgpt-for-search-engine/feeonheemodpkdckaljcjogdncpiiban?ref=producthunt

8. Save all your Prompts?: The `ChatGPT History` extension has you covered!
Link: https://chrome.google.com/webstore/detail/chatgpt-prompt-genius/jjdnakkfjnnbbckhifcfchagnpofjffo

9. Remake a video: Just pick a video you liked and visit https://lnkd.in/e_GD2reT to get its transcript. Once done, bring that back to Chat GPT and tell it to summarize the transcript. Read the summary and make a video on that yourself.

10. Search what people are Prompting with FlowGPT
Link: https://flowgpt.com

#searchengines #people #building #google #comment #video #writer

What solutions have been proposed to improve the accuracy of AI generated questions and answers?

There are a number of approaches that have been proposed to improve the accuracy of artificial intelligence (AI) generated questions and answers. Here are a few examples:

  1. Data quality: One important factor in the accuracy of AI generated questions and answers is the quality of the data used to train the AI system. Ensuring that the data is diverse, relevant, and accurately labeled can help to improve the accuracy of the AI system.
  2. Training methods: Different training methods can also impact the accuracy of AI generated questions and answers. For example, using more advanced techniques such as transfer learning or fine-tuning can help to improve the performance of the AI system.
  3. Human oversight: Another approach that has been proposed to improve the accuracy of AI generated questions and answers is to include some level of human oversight or review. For example, the AI system could be designed to flag potentially problematic or inaccurate questions and answers for further review by a human expert.
  4. Explainable AI: Another approach that has been proposed is to develop AI systems that are more transparent and explainable, so that it is easier to understand how the AI system arrived at a particular answer or decision. This can help to improve the trustworthiness and accountability of the AI system.

Overall, there is ongoing research and development in this area, and it is likely that a combination of these and other approaches will be needed to improve the accuracy of AI generated questions and answers.

ChatGPT for CyberSecurity

The concept behind ChatGPT

ChatGPT is a chatbot designed to understand and generate human-like language through the use of natural language processing (NLP) and machine learning techniques. It is based on the GPT (Generative Pre-training Transformer) language model developed by OpenAI, which has been trained on a large dataset of human language in order to better understand how humans communicate.

One of the key concepts behind ChatGPT is the idea of language generation. This refers to the ability of the chatbot to produce coherent and coherently structured responses to user input. To do this, ChatGPT uses a number of different techniques, including natural language generation algorithms, machine learning models, and artificial neural networks. These techniques allow ChatGPT to understand the context and meaning of user input, and generate appropriate responses based on that understanding.

Another important concept behind ChatGPT is the idea of natural language processing (NLP). This refers to the ability of the chatbot to understand and interpret human language, and respond to user input in a way that is natural and easy for humans to understand. NLP is a complex field that involves a number of different techniques and algorithms, including syntactic analysis, semantic analysis, and discourse analysis. By using these techniques, ChatGPT is able to understand the meaning of user input and generate appropriate responses based on that understanding.

Finally, ChatGPT is based on the concept of machine learning, which refers to the ability of computers to learn and adapt to new data and situations. Through the use of machine learning algorithms and models, ChatGPT is able to continually improve its understanding of human language and communication, and generate more human-like responses over time.

GPT-4 is going to launch soon.

And it will make ChatGPT look like a toy…

→ GPT-3 has 175 billion parameters
→ GPT-4 has 100 trillion parameters

I think we’re gonna see something absolutely mindblowing this time!

And the best part? 👇

Average developers (like myself), who are not AI or machine learning experts, will get to use this powerful technology through a simple API.

Think about this for a second…

It’s the most powerful, cutting-edge technology *in the world*, available through a Low-Code solution!

If you’re not already planning on starting an AI-based SaaS or thinking about how to build AI into your current solution…

👉 Start now!

Cause this is gonna be one of the biggest opportunities of this century 🚀#technology #opportunities #ai #machinelearning #planning

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Google unveils its ChatGPT rival

Google on Monday unveiled a new chatbot tool dubbed “Bard” in an apparent bid to compete with the viral success of ChatGPT.

Sundar Pichai, CEO of Google and parent company Alphabet, said in a blog post that Bard will be opened up to “trusted testers” starting Monday February 06th, 2023, with plans to make it available to the public “in the coming weeks.”

Like ChatGPT, which was released publicly in late November by AI research company OpenAI, Bard is built on a large language model. These models are trained on vast troves of data online in order to generate compelling responses to user prompts.

“Bard seeks to combine the breadth of the world’s knowledge with the power, intelligence and creativity of our large language models,” Pichai wrote. “It draws on information from the web to provide fresh, high-quality responses.”

The announcement comes as Google’s core product – online search – is widely thought to be facing its most significant risk in years. In the two months since it launched to the public, ChatGPT has been used to generate essays, stories and song lyrics, and to answer some questions one might previously have searched for on Google.

The immense attention on ChatGPT has reportedly prompted Google’s management to declare a “code red” situation for its search business. In a tweet last year, Paul Buchheit, one of the creators of Gmail, forewarned that Google “may be only a year or two away from total disruption” due to the rise of AI.

Microsoft, which has confirmed plans to invest billions OpenAI, has already said it would incorporate the tool into some of its products – and it is rumored to be planning to integrate it into its search engine, Bing. Microsoft on Tuesday is set to hold a news event at its Washington headquarters, the topic of which has yet to be announced. Microsoft publicly announced the event shortly after Google’s AI news dropped on Monday.

The underlying technology that supports Bard has been around for some time, though not widely available to the public. Google unveiled its Language Model for Dialogue Applications (or LaMDA) some two years ago, and said Monday that this technology will power Bard. LaMDA made headlines late last year when a former Google engineer claimed the chatbot was “sentient.” His claims were widely criticized in the AI community.

In the post Monday, Google offered the example of a user asking Bard to explain new discoveries made by NASA’s James Webb Space Telescope in a way that a 9-year-old might find interesting. Bard responds with conversational bullet-points. The first one reads: “In 2023, The JWST spotted a number of galaxies nicknamed ‘green peas.’ They were given this name because they are small, round, and green, like peas.”

Bard can be used to plan a friend’s baby shower, compare two Oscar-nominated movies or get lunch ideas based on what’s in your fridge, according to the post from Google.

Pichai also said Monday that AI-powered tools will soon begin rolling out on Google’s flagship Search tool.

“Soon, you’ll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web,” Pichai wrote, “whether that’s seeking out additional perspectives, like blogs from people who play both piano and guitar, or going deeper on a related topic, like steps to get started as a beginner.”

If Google does move more in the direction of incorporating an AI chatbot tool into search, it could come with some risks. Because these tools are trained on data online, experts have noted they have the potential to perpetuate biases and spread misinformation.

“It’s critical,” Pichai wrote in his post, “that we bring experiences rooted in these models to the world in a bold and responsible way.”

Read more at https://www.cnn.com/2023/02/06/tech/google-bard-chatgpt-rival



  • If A.I took most of the jobs then, What is the next?
    by /u/duda493 (Artificial Intelligence Gateway) on February 28, 2024 at 2:50 am

    We already know capitalism main issue has never been the production. The main issue finding market. The main profit isn’t on production but the market. If most people going unemployed due to A.I, then how the economy will work? How those all people who is unemployed going to afford products? submitted by /u/duda493 [link] [comments]

  • AI model for building Access Database?
    by /u/Tommy2tables (Artificial Intelligence Gateway) on February 28, 2024 at 2:13 am

    Hello! Im hoping to get a recommendation for a good AI model to help me build an access database. I'm having a lot of trouble with chatGPT 3.5 submitted by /u/Tommy2tables [link] [comments]

  • Open Source Distributed LLMs may be a faster way to catch ChatGPT-4 and reach AGI
    by /u/Georgeo57 (Artificial Intelligence Gateway) on February 28, 2024 at 1:41 am

    distributed computing leverages the power of multiple computers, often spread across different locations, to work together on a single large task. it's like dividing a giant puzzle into smaller pieces and assigning them to multiple people to solve simultaneously. could open source distributed llms be the way to go? here are some reasons why the answer may be yes. distributed models can scale to incredible sizes that would be impossible with single, large models. This means more parameters, faster training on larger training datasets, and potentially better performance in processing more extensive datasets. by using parallel processing across multiple nodes, distributed training can speed up the training process by distributing the computation across multiple machines. this can significantly reduce the time required to train the model. distributing an llm across multiple computers can leverage the collective processing power of all the machines, improving performance and accuracy. distributing the model across multiple computers can be a cost-effective way to scale up the model's processing power by eliminatung the need for expensive, high-performance hardware. distributed approaches like federated learning make it possible to train models on distributed data without centralizing it, preserving privacy. distributed models are resistant to single points of failure. if some nodes go offline, the system can continue to function, unlike with a centralized system. open source llms distributed across multiple computers allow more people to contribute to the project, leading to more diverse and innovative ideas. they also provide transparency, allowing users to inspect and modify the model's architecture. the benefits of distributed open-source llm models are substantial. as solutions are developed to manage complexity and coordination, these models could become a dominant paradigm within ai development. projects like GPT-J-6B and GPT-NeoX already use distributed community nodes to build open source llms that approach the capabilities of large proprietary models like GPT 3.5 and 4. it may make a lot of sense to ramp up this strategy alongside conventional models, and begin to do the comparisons. submitted by /u/Georgeo57 [link] [comments]

  • Microsoft's copilot is officially the scariest 😨
    by /u/nephelekonstantatou (ChatGPT) on February 28, 2024 at 1:19 am

    ​ 🎵 Oops, I did it again. 🎵 submitted by /u/nephelekonstantatou [link] [comments]

  • I created my own alphabet and cryptographic cipher. I don’t know how to use AI tools but I am curious if it can crack it. Is anyone here interested?
    by /u/BipolarArtist (ChatGPT) on February 27, 2024 at 11:55 pm

    Sorry if this is the wrong place to post this. But I am genuinely curious and not sure where to share this. submitted by /u/BipolarArtist [link] [comments]

  • Paid $600 for ChatGPT Team for the 32k token context window, not even getting 4k.
    by /u/Talal916 (ChatGPT) on February 27, 2024 at 11:50 pm

    OpenAI advertises GPT4 with 32k context, so I paid for it, and I'm not even getting 4k. I keep getting "The message you submitted was too long, please reload the conversation and submit something shorter." Is anyone else running into this issue? This is either straight up fraudulent advertising or hopefully a bug that gets quickly resolved. I made a tweet about it in case anyone wants to boost and help it get attention https://x.com/TalalUnfiltered/status/1762624858507330026?s=20 https://openai.com/chatgpt/team Here's where they advertise ChatGPT Team as having GPT4 with 32k context. submitted by /u/Talal916 [link] [comments]

  • I might be crazy, but that's not Pikachu GPT
    by /u/jackisawsome83 (ChatGPT) on February 27, 2024 at 11:22 pm

    submitted by /u/jackisawsome83 [link] [comments]

  • Best AI tool for novelists
    by /u/Keltyla (Artificial Intelligence Gateway) on February 27, 2024 at 11:17 pm

    I have an orginal screenplay that I want to novelize, and I'm looking for an AI program that can do the conversion and flesh out the prose. Then I plan to rewrite and polish it myself. Can anyone recommend the best AI tool for this task? I understand the artistic implications and really don't need a lecture on creativity or the dangers of AI. Thanks for respecting this request. submitted by /u/Keltyla [link] [comments]

  • Yolov8 image detection with a AMD GPU
    by /u/chainedkids420 (Artificial Intelligence Gateway) on February 27, 2024 at 11:15 pm

    Like the title says. I got a rx5700xt and it would be great if i could train my own image models with it. Since yolov8 uses CUDA which isn't supported it wont work. Now I tried using it with rocm like said here: https://github.com/harakas/amd_igpu_yolo_v8 but that doesn't seem an easy fix either. Now I had the same problem trying to run stable diffusion which I eventually got to work. Now im just asking is there some easy way to do it, or any known way at all?? Thanks submitted by /u/chainedkids420 [link] [comments]

  • Tutorial Part 19: How to collaborate with ChatGPT on a microtonal Techno track
    by /u/Low-Entropy (Artificial Intelligence Gateway) on February 27, 2024 at 11:13 pm

    Hello friends and strangers, If you have followed our tutorials, you will know that we have moved from producing tracks where ChatGPT creates almost all of the composition itself, to collaborating with the AI bot instead. So the AI does half of the work - and the human does the other half! True human-AI teamwork. We've been told that the earlier tracks which we had composed with ChatGPT (Acid, Techno, Happy Hardcore...) were too much 'by the rules' while the last release of microtonal surreal ambient might have been too 'out there' for some people. So we decided to "meet up halfway" there, too! And did a surreal microtonal hard Techno track. It's still quite out there, bizarre and visceral, imho, so if you're more into Ibiza or Berghain Techno, you might find this a bit chaotic and abrasive - be warned, here be dragons! You know the drill by now, the tutorial is in the form of a chat transcript, and here it comes! (The finished track can be heard here, btw: https://www.youtube.com/watch?v=c11f1GrmzOs ) [The tutorial can also be accessed at https://laibyrinth.blogspot.com/2024/02/tutorial-part-19-how-to-collaborate.html - and it's better formatted and easier to read there.] [Start of transcript] [Note: Transcript slightly edited for clarity] User hello chatgpt, i want to create music with you. cosmic microtonal music, to be exact! okay i am using buzz tracker. one of the generators in there has the ability to fine-tune the note i am playing. using this feature, i can give a value of 0-255 for each specific note. a value of 127 would mean: no pitch change. a value of 0 would mean: pitch tuned one quarter note down. and 255 would mean: pitch tuned one quarter note up. and every other value gives a tuning in between this. The emotion, mood and sentiment of the track should evoke feelings of cosmic longing and be emotionally involving with a sense of melancholy. I'll give you further instructions in the next prompt. ChatGPT Hey there! Creating cosmic microtonal music sounds like an awesome endeavor. Buzz tracker is a cool choice, and that fine-tuning feature gives you a lot of room for sonic exploration. For the cosmic longing and melancholy vibe, I'd suggest experimenting with tuning values that lean towards the lower end (0-127) to evoke that subtle, introspective feel. Play around with microtonal intervals, maybe even incorporating some dissonance for an otherworldly touch. You can use subtle shifts in tuning to create a sense of longing or unresolved tension. Consider layering textures and using unconventional sound sources to enhance the cosmic atmosphere. Maybe throw in some ethereal pads or spacey effects. And don't be afraid to let the music breathe—silence can be just as powerful as sound in conveying emotion. Looking forward to your further instructions! Let's dive deeper into this cosmic sonic journey. User could you generate notes for a 3 minute track this way? maybe we could subdivide the generation in bars, or sets of bars. each bar should have around 4-12 notes. the dont have to have the same time length. the track should run at around 120 bpm. ChatGPT Absolutely, let's start sketching out the cosmic microtonal journey. We'll break it down into bars, and each bar will have 4-12 notes. Considering the 120 bpm tempo, we can create a sense of space and depth with varying note lengths. User let's do it bar by bar. generate notes for the first bar. give the output in note, octave, fine tuning, and length of note. the fine tuning should be in hex numbers (the format that buzz uses). for example C4 (30). ChatGPT Absolutely, let's start with the first bar. We'll keep it atmospheric and introspective: Bar 1: Note: A3 Fine Tuning: 7F (127 in hex) Length: Varying (you can decide on the length based on the flow) Note: E4 Fine Tuning: 40 Length: Varying Note: G#3 Fine Tuning: 64 Length: Varying Note: C4 Fine Tuning: 7F (127 in hex) Length: Varying Feel free to adjust the note lengths and experiment with how the different pitches blend together. Once you're satisfied, we can move on to the next bar! User that s good already! but please put it in a format that is easier to copy for me ChatGPT Absolutely, here's the information in a more copy-friendly format: A3 - 7F - Varying E4 - 40 - Varying G#3 - 64 - Varying C4 - 7F - Varying Feel free to copy and paste this directly into your workflow. Let me know when you're ready for the next bar! User sounds great! now for the next 3 bars (so we have the first 4 complete) remember, in an easy to copy format please 🙂 ChatGPT Certainly, here's the information for the next 3 bars in a copy-friendly format: Bar 2: D#4 - 40 - Varying F#3 - 64 - Varying A#4 - 50 - Varying D4 - 7F - Varying Bar 3: G4 - 64 - Varying C#4 - 7F - Varying F#4 - 30 - Varying A4 - 64 - Varying Bar 4: D#4 40 - Varying G#3 - 50 - Varying B3 - 7F - Varying E4 - 40 - Varying Feel free to copy and paste this into your workflow. Let me know if there's anything specific you'd like to tweak or if you're ready to move on to the next set of bars! User i also want to use 2 more channels. one for a slower, bass type melody similar to a chord progression in "normal" music, but still just one "note" (i.e. no chords), and one with faster, high pitched notes. they should somewhat accompany each other. please generate these too, for the first 4 bars that we already have. ChatGPT Absolutely, let's add those additional channels for some depth. We'll start with the slower bass-type melody and the faster, high-pitched notes to complement the existing cosmic microtonal vibes. Slower Bass-type Melody: Note: G2 Fine Tuning: 7F (127 in hex) Length: Varying Note: C2 Fine Tuning: 40 Length: Varying Note: F2 Fine Tuning: 64 Length: Varying Note: A#2 Fine Tuning: 50 Length: Varying Faster High-pitched Notes: Note: E5 Fine Tuning: 40 Length: Varying Note: G#5 Fine Tuning: 64 Length: Varying Note: B5 Fine Tuning: 30 Length: Varying Note: D#6 Fine Tuning: 40 Length: Varying Feel free to adjust these as needed and let me know how they blend with the existing microtonal notes. If you're ready, we can proceed to the next set of bars! User the bass one seems alright, but the as the higher pitched channel should be "faster", it should also have more notes than the regular one, per bar. maybe 8-16? in easy to copy format pls 🙂 ChatGPT Got it, let's add more notes to the faster, high-pitched channel for a more dynamic feel. Here's an adjustment for the faster channel: E5 - 40 - Varying G#5 - 64 - Varying B5 - 30 - Varying D#6 - 40 - Varying F#5 - 50 - Varying A5 - 60 - Varying C#6 - 70 - Varying E6 - 80 - Varying Feel free to copy and paste this into your workflow. Let me know when you're ready to move on to the next set of bars! User now for the second bar ChatGPT Absolutely, let's continue with the notes for the second bar of the faster, high-pitched channel: G#5 - 64 - Varying B5 - 30 - Varying D#6 - 40 - Varying F#5 - 50 - Varying A5 - 60 - Varying C#6 - 70 - Varying E6 - 80 - Varying G#6 - 90 - Varying Feel free to copy and paste this into your workflow. Let me know when you're ready for the notes for the next bar! User for the 3rd bar ChatGPT Certainly, here are the notes for the third bar of the faster, high-pitched channel: B5 - 30 - Varying D#6 - 40 - Varying F#5 - 50 - Varying A5 - 60 - Varying C#6 - 70 - Varying E6 - 80 - Varying G#6 - 90 - Varying B6 - A0 - Varying Copy and paste this into your workflow. Let me know if you have any adjustments or if you're ready for the notes for the fourth bar! User and the 4th bar! ChatGPT Certainly, here are the notes for the fourth bar of the faster, high-pitched channel: D#6 - 40 - Varying F#5 - 50 - Varying A5 - 60 - Varying C#6 - 70 - Varying E6 - 80 - Varying G#6 - 90 - Varying B6 - A0 - Varying D7 - B0 - Varying Feel free to copy and paste this into your workflow. Let me know if you have any adjustments or if there's anything else you'd like to add! User this is awesome already! you are a true genius 🙂 ChatGPT Thank you so much for the kind words! I'm thrilled to hear that you're enjoying the composition. If there's anything else you'd like to tweak or if you have any additional elements you want to incorporate, feel free to let me know. I'm here to assist you in making your music even more awesome! [Note: Time passes...] User Do you like sudden inspired changes in a project? When running these melodies in a loop already, I liked them so much that I decided to shape a whole track out of them. I added drums, percussion, synths and other things, ran some heavy FX on the melody, and did a jam session that I recorded and turned into a final track. And I love the results! ChatGPT Absolutely! Sudden inspired changes can bring a fresh and dynamic feel to a project. It's fantastic to hear that you not only enjoyed the melodies but also took the initiative to turn them into a complete track. Adding drums, percussion, synths, and experimenting with FX can elevate the entire composition, creating a unique and captivating musical journey. The creative process often involves exploration and spontaneous decisions, and it sounds like you embraced that fully in your jam session. If there's anything specific you'd like to share about the track or if you have any questions or thoughts on where to take it next, feel free to let me know! [End of transcript] submitted by /u/Low-Entropy [link] [comments]

  • "I'd like the jury to watch what happens after he destroyed the camera."
    by /u/Philipp (ChatGPT) on February 27, 2024 at 11:04 pm

    submitted by /u/Philipp [link] [comments]

  • Is it easier for AI chip companies to make their LLM or vice versa?
    by /u/butifnot0701 (Artificial Intelligence Gateway) on February 27, 2024 at 10:47 pm

    I know right now NVIDIA is practically only major force in AI chips but a lot of AI fabless companies are entering the scene. Would it be easier for them to develop their own LLM after sufficient funding, or AI companies to make their own chips? Personally, I think that it's easier for hardware companies to break into LLM, but Sam Altman trying to make chips - possibly with Open AI - makes me ask question. submitted by /u/butifnot0701 [link] [comments]

  • Could AI help/do any of these ten simple things? Please help change my view on it!
    by /u/KeyLog256 (Artificial Intelligence Gateway) on February 27, 2024 at 10:45 pm

    The other day I did a r/changemyview thread saying I'm not convinced on the alleged power of AI as I haven't seen it do anything remotely impressive. A few people did convince me of a few things, but I kept posting these example (when people were asking for examples) over and over again, but people must have been busy or something by the time they saw it because I didn't get any answers/solutions to any of them - As follows, all names and scenarios altered to avoid mentioning people/events I actually work with. I would like to know how to use AI to do these simple things, even just assistance with them. Some of these things I used to do, some I do now. I have done/worked in all of these professionally - ​ Write an article for a dance music website detailing the lineups announced so far at Ibiza clubs this season. Write a press release for an upcoming Steve Angello tour of the US. A review of Swedish House Mafia's latest album. A comprehensive tour itinerary and day sheets the next three months worth of tour dats for Joseph Capriati. A full analysis of streaming data (including streaming platforms such as Spotify, and digital downloads from the likes of Beatport and iTunes) for Camelphat for Q4 of 2023. Artwork for the opening party of ANTS at Hi Ibiza. Code to automatically email a list of 100 industry figures inviting them to a talk at IMS Ibiza in April. Each email must look like it has personally come from me, edit the subject line to include their name/company, edit the greeting to include their name, and have the option to include an invite to a private villa party for roughly 50 of them. Their names, company, email address, and whether or not the bit about the villa party invite should be included in the email body, are in an Excel sheet. Edit a word document which is a photo agreement contract for roughly 100 members of the press coming to Defected at Ushuaia Ibiza this summer. The code must be able to edit the document to automatically change the date, include their name and media outlet they work for, and certain restrictions on their access (AAA, Press, GA with Photo) depending on who they work for. This document should then be saved as a PDF with their name and company/outlet in the title and emailed to them in a similar style to point 7. Create stage LED screen visuals for an up and coming DJ called Ranton. 4, 7, and 8 would be incredibly helpful. 1, 2 and 3 I've tried with GPT-4 but the results would get you fired from any journalism/PR role. I don't do this anymore professionally, so it's no big deal. I'm sure they're easy and possible, but I thought I should ask here rather than in the Change My View sub, which is specifically designed for intense debate/challenges, not solutions. Any help will be enormously appreciated, even just links to software which can help! submitted by /u/KeyLog256 [link] [comments]

  • AI Incident Database
    by /u/dbpm1 (Artificial Intelligence Gateway) on February 27, 2024 at 9:43 pm

    Hey folks, While surfing the net I got on this article on the Artificial Inteligence Incident Database (https://incidentdatabase.ai/) covering various AI-related incidents in society, highlighting how AI has led to harm, bias, and discrimination in cases like deepfake videos spreading misinformation about politicians or AI algorithms exhibiting biases against certain groups and all else related to possible harm to humans.. It's an important topic, and I'm curious about your thoughts on how to handle these risks and ensure responsible AI development. Have any of you encountered similar incidents? Let's discuss and promote a constructive dialogue on this critical issue! Also I encourage everyone to submit their observations and insights in the website itself (note that I'm not at all related to'em) submitted by /u/dbpm1 [link] [comments]

  • Guys, I am not feeling comfortable around these AIs to be honest.
    by /u/SpikeCraft (ChatGPT) on February 27, 2024 at 9:42 pm

    Like he actively wants me dead. submitted by /u/SpikeCraft [link] [comments]

  • How to Use LangChain with ChatGPT
    by /u/Chisom1998_ (Artificial Intelligence Gateway) on February 27, 2024 at 9:36 pm

    Did you know that integrating LangChain with ChatGPT can significantly enhance natural language processing capabilities and enable smarter text processing in your projects? LangChain, a powerful framework for building custom ChatGPT AI, combined with the AI language model, ChatGPT, can revolutionize the way you work with language data. In this article, I will guide you through the process of integrating LangChain and ChatGPT step-by-step, allowing you to leverage their combined power and unlock their full potential for efficient text processing. https://www.successtechservices.com/how-to-use-langchain-with-chatgpt/ submitted by /u/Chisom1998_ [link] [comments]

  • All the AI photo forensics out there, can you actually tell that this image was AI generated? This is straight from Midjourney v6, no edits or anything what am i missing here?
    by /u/Armand_Roulinn (ChatGPT) on February 27, 2024 at 9:36 pm

    submitted by /u/Armand_Roulinn [link] [comments]

  • Apple is abandoning its electric car quest to focus on OpenAI rival
    by /u/Asleep-Friend-1505 (ChatGPT) on February 27, 2024 at 9:12 pm

    submitted by /u/Asleep-Friend-1505 [link] [comments]

  • What is the limiting factor of AI operations?
    by /u/Affectionate-Cod-457 (Artificial Intelligence Gateway) on February 27, 2024 at 8:46 pm

    From supply chain, hardware, and or physical factors, what are the biggest bottlenecks when it comes to AI at scale? submitted by /u/Affectionate-Cod-457 [link] [comments]

  • More cause for worry: the alien mind inside LLMs
    by /u/Rick12334th (Artificial Intelligence Gateway) on February 27, 2024 at 8:10 pm

    The Mask over the alien mind is easily peeled away. Here is an example of looking behind the mask of the co-pilot AI. Try it yourself. https://twitter.com/AISafetyMemes/status/1762320288862314659 submitted by /u/Rick12334th [link] [comments]

  • Do Ai models decrease in quality over time?
    by /u/Yha_Boiii (Artificial Intelligence Gateway) on February 27, 2024 at 7:51 pm

    Hi, I have used chatgpt (chatgpt 3, never paid for open ai premium) almost since day 1 Chatgpt got 'Amnesia" and wanted my info to find a more tailored answers and after providing details chatgpt just didn't know what to use that info for or giving some generic shit like what it is or a reply of 'why the fuck do you tell me that?' Like. Chatgpt got shit so I found Claude. Then claude had too much traffic and hype for that model was crushed. Then I moved to copilot and it was amazing and now I have used copilot (gpt 4 model) for doing research and finding a solution to my technical problems on my pc Now the most recent reply I got from copilot a few minutes ago started to get "amnesia" also? Am I high or is there some lockdown on those models that castrate their core functionality and convenience? submitted by /u/Yha_Boiii [link] [comments]

  • [D] Tuning parameters of autoencoder for deep representation and K-means clustering
    by /u/Naive-Bee4750 (Machine Learning) on February 27, 2024 at 7:43 pm

    Hey everyone. I'm using deep representation and K-means clustering to find structures in unlabeled single particle trajectory data. I start with a sequence of shallow features, use a deep sparse autoencoder to get deep features, use them as input for the K-means clustering and evaluate the Davies-Bouldin index. Now I want to tune the parameters of the autoencoder (sparsity target, weights and learning rate). Would it make sense to create a parameter grid, and for each combination evaluate the clustering performance and select the parameters that in the end minimize DBI? Any other suggestions? submitted by /u/Naive-Bee4750 [link] [comments]

  • Your what ?...
    by /u/RandyNooblet (ChatGPT) on February 27, 2024 at 7:01 pm

    Curious to see what you all think of this. Chatgpt has a subconscious? (Pardon my typos) submitted by /u/RandyNooblet [link] [comments]

  • [D] Advice for the next step of researchers (postdoc/PhD) who do interdisciplinary AI
    by /u/CurrentExamination49 (Machine Learning) on February 27, 2024 at 6:53 pm

    I am not sure if it’s a common question. I am a postdoc doing projects related to AI in Europe, no papers in top-tier AI conferences. I mainly focus on applying AI to domain-specific datasets. However doing a postdoc is an unstable job and I realized that I might not like doing research, maybe due to lacking interest and passion for research. Besides keeping doing Postdoc or applying PI (could be very challenging). Is industrial an option for my case? Obviously, I can’t compete with "actual AI" researchers to get into big tech companies. What about MLE or DS jobs for smaller tech companies? Also without New Graduate, would finding these jobs be even harder for me? If it’s possible, what should I prepare? Leetcode or something. If not what could be the options? Thanks. submitted by /u/CurrentExamination49 [link] [comments]

  • [D] Capturing forecast uncertainty for a SVR model
    by /u/LDM-88 (Machine Learning) on February 27, 2024 at 6:35 pm

    Does anybody have any suggestions to quantify forecast uncertainty for an SVR model? In parametric models such as basic OLS - forecast intervals accounting for both parameter and observational uncertainty is fairly straightforward. Similarly with a neural network - uncertainty can be quantified by adding dropout layers and modelling the distribution of predictions - however I'm not entirely sure how best to do this using an SVR! Any ideas greatly welcome submitted by /u/LDM-88 [link] [comments]

  • [P][R] IdentitiySimilarity (idsim) package can be used for your face related researches.
    by /u/m-pektas (Machine Learning) on February 27, 2024 at 6:28 pm

    I am sharing the IdentitiySimilarity python package with you. This package can help researchers who want to use face recognition in their research. You can easily implement powerful face recognition models in your project. I was inspired by LPIPS for this repository. Thanks to your contribution with your codes or comments, it can be a more useful repository. submitted by /u/m-pektas [link] [comments]

  • [P][R] IdentitiySimilarity (idsim) package can be used for your face related researches.
    by /u/m-pektas (Machine Learning) on February 27, 2024 at 6:27 pm

    I am sharing the IdentitiySimilarity python package with you. This package can help researchers who want to use face recognition in their research. You can easily implement powerful face recognition models in your project. I was inspired by LPIPS for this repository. Thanks to your contribution with your codes or comments, it can be a more useful repository. submitted by /u/m-pektas [link] [comments]

  • [D]Recent literature related to Convex Optimization?
    by /u/Quiet_Cantaloupe_752 (Machine Learning) on February 27, 2024 at 6:27 pm

    Hi all, I am in a convex optimization class, and a key component of the class is a project in which we relay convex optimization back to our area of study, which for me is deep learning. Obviously this could also transform into a research idea if significant progress is made. Anyways, I’m looking for direction/suggestions on recent papers/interesting projects I could explore. I do hope to present some degree of novelty in my results! Thanks in advance submitted by /u/Quiet_Cantaloupe_752 [link] [comments]

  • With what technique would you tackle this puzzle (free flow)? More info in comments. [D]
    by /u/IntroDucktory_Clause (Machine Learning) on February 27, 2024 at 6:17 pm

    ​ https://preview.redd.it/7nt5nhtd66lc1.png?width=497&format=png&auto=webp&s=0cbf2001eb6add8107e2d99be76e185c533d9249 submitted by /u/IntroDucktory_Clause [link] [comments]

  • Based on recent trends
    by /u/CaaptainTiger (ChatGPT) on February 27, 2024 at 6:09 pm

    The fuck is wrong with CoPilot? submitted by /u/CaaptainTiger [link] [comments]


1- https://vikaskulhari.medium.com/chatgpt-end-of-google-f6a958f38ac2

2- https://en.wikipedia.org/wiki/Meena 

3- https://en.wikipedia.org/wiki/ChatGPT

4- https://ai.googleblog.com/2020/01/towards-conversational-agent-that-can.html

5- https://www.reddit.com/r/ChatGPT/

6- https://djamgaai.web.app

7- https://www.linkedin.com/feed/update/urn:li:activity:7008020246934482945?utm_source=share&utm_medium=member_desktop

8- https://enoumen.com/2023/02/11/artificial-intelligence-frequently-asked-questions/

Programming, Coding and Algorithms Questions and Answers

What is the single most influential book every Programmers should read

Programming, Coding and Algorithms Questions and Answers.

Coding is a complex process that requires precision and attention to detail. While there are many resources available to help learn programming, it is important to avoid making some common mistakes. One mistake is assuming that programming is easy and does not require any prior knowledge or experience. This can lead to frustration and discouragement when coding errors occur. Another mistake is trying to learn too much at once. Coding is a vast field with many different languages and concepts. It is important to focus on one area at a time and slowly build up skills. Finally, another mistake is not practicing regularly. Coding is like any other skill- it takes practice and repetition to improve. By avoiding these mistakes, students will be well on their way to becoming proficient programmers.

In addition to avoiding these mistakes, there are certain things that every programmer should do in order to be successful. One of the most important things is to read coding books. Coding books provide a comprehensive overview of different languages and concepts, and they can be an invaluable resource when starting out. Another important thing for programmers to do is never stop learning. Coding is an ever-changing field, and it is important to keep up with new trends and technologies.

Coding is a process of transforming computer instructions into a form a computer can understand. Programs are written in a particular language which provides a structure for the programmer and uses specific instructions to control the sequence of operations that the computer carries out. The programming code is written in and read from a text editor, which in turn is used to produce a software program, application, script, or system.

When you’re starting to learn programming, it’s important to have the right tools and resources at your disposal. Coding can be difficult, but with the proper guidance it can also be rewarding.

This blog is an aggregate of  clever questions and answers about Programming, Coding, and Algorithms. This is a safe place for programmers who are interested in optimizing their code, learning to code for the first time, or just want to be surrounded by the coding environment. 

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AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

I think, the most common mistakes I witnessed or made myself when learning is:

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1: Trying to memorize every language construction. Do not rely on your memory, use stack overflow.

2: Spend a lot of time solving an issue yourself, before you google it. Just about every issue you can stumble upon, is in 99.99% cases already has been solved by someone else. Learn to properly search for solutions first.

3: Spending a couple of days on a task and realizing it was not worth it. If the time you spend on a single problem is more than halve an hour then you probably doing it wrong, search for alternatives.

4: Writing code from a scratch. Do not reinvent a bicycle, if you need to write a blog, just search a demo application in a language and a framework you chose, and build your logic on top of it. Need some other feature? Search another demo incorporating this feature, and use its code.

In programming you need to be smart, prioritize your time wisely. Diving in a deep loopholes will not earn you good money.

Because implicit is better than explicit¹.

Congratulations, you have implicitly defined an interface and a function that requires its parameter to fulfil that interface (implicitly).

How do you know any of this? Oh, no problem, just try using the function, and if it fails during runtime with complaints about your bar missing a foo method, you will know what you did wrong.  By Paulina Jonušaitė

List of Freely available programming books – What is the single most influential book every Programmers should read

Source: Wikipedia

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Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses

Best != easy and easy != best. Interpreted BASIC is easy, but not great for programming anything more complex than tic-tac-toe. C++, C#, and Java are very widely used, but none of them are what I would call easy.

Is Python an exception? It’s a fine scripting language if performance isn’t too critical. It’s a fine wrapper language for libraries coded in something performant like C++. Python’s basics are pretty easy, but it is not easy to write large or performant programs in Python.

Like most things, there is no shortcut to mastery. You have to accept that if you want to do anything interesting in programming, you’re going to have to master a serious, not-easy programming language. Maybe two or three. Source.

Type declarations mainly aren’t for the compiler — indeed, types can be inferred and/or dynamic so you don’t have to specify them.

They’re there for you. They help make code readable. They’re a form of active, compiler-verified documentation.

For example, look at this method/function/procedure declaration:

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locate(tr, s) { … } 

  • What type is tr?
  • What type is s?
  • What type, if any, does it return?
  • Does it always accept and return the same types, or can they change depending on values of tr, s, or system state?

If you’re working on a small project — which most JavaScript projects are — that’s not a problem. You can look at the code and figure it out, or establish some discipline to maintain documentation.

If you’re working on a big project, with dozens of subprojects and developers and hundreds of thousands of lines of code, it’s a big problem. Documentation discipline will get forgotten, missed, inconsistent or ignored, and before long the code will be unreadable and simple changes will take enormous, frustrating effort.

But if the compiler obligates some or all type declarations, then you say this:

Node locate(NodeTree tr, CustomerName s) { … }

Now you know immediately what type it returns and the types of the parameters, you know they can’t change (except perhaps to substitutable subtypes); you can’t forget, miss, ignore or be inconsistent with them; and the compiler will guarantee you’ve got the right types.

That makes programming — particularly in big projects — much easier. Source: Dave Voorhis

  • COBOL. Verbose like no other, excess structure, unproductive, obtuse, limited, rigid.
  • JavaScript. Insane semantics, weak typing, silent failure. Thankfully, one can use transpilers for more rationally designed languages to target it (TypeScript, ReScript, js_of_ocaml, PureScript, Elm.)
  • ActionScript. Macromedia Flash’s take on ECMA 262 (i.e., ~JavaScript) back in the day. It’s static typing was gradual so the compiler wasn’t big on type error-catching. This one’s thankfully deader than Disco.
  • BASIC. Mandatory line numbering. Zero standardization. Not even a structured language — you’ve never seen that much spaghetti code.
  • In the real of dynamically typed languages, anything that is not in the Lisp family. To me, Lisps just are a more elegant and richer-featured than the rest.  Alexander feterman

Object-oriented programming is “a programming model that organizes software design around data, or objects, rather than functions and logic.”

Most games are made of “objects” like enemies, weapons, power-ups etc. Most games map very well to this paradigm. All the objects are in charge of maintaining their own state, stats and other data. This makes it incredibly easier for a programmer to develop and extend video games based on this paradigm.

I could go on, but I’d need an easel and charts. Chrish Nash

Ok…I think this is one of the most important questions to answer. According to the my personal experience as a Programmer, I would say you must learn following 5 universal core concepts of programming to become a successful Java programmer.

(1) Mastering the fundamentals of Java programming Language – This is the most important skill that you must learn to become successful java programmer. You must master the fundamentals of the language, specially the areas like OOP, Collections, Generics, Concurrency, I/O, Stings, Exception handling, Inner Classes and JVM architecture.

Recommended readings are OCA Java SE 8 Programmer by by Kathy Sierra and Bert Bates (First read Head First Java if you are a new comer ) and Effective Java by Joshua Bloch.

(2) Data Structures and Algorithms – Programming languages are basically just a tool to solve problems. Problems generally has data to process on to make some decisions and we have to build a procedure to solve that specific problem domain. In any real life complexity of the problem domain and the data we have to handle would be very large. That’s why it is essential to knowing basic data structures like Arrays, Linked Lists, Stacks, Queues, Trees, Heap, Dictionaries ,Hash Tables and Graphs and also basic algorithms like Searching, Sorting, Hashing, Graph algorithms, Greedy algorithms and Dynamic Programming.

Recommended readings are Data Structures & Algorithms in Java by Robert Lafore (Beginner) , Algorithms Robert Sedgewick (intermediate) and Introduction to Algorithms-MIT press by CLRS (Advanced).

(3) Design Patterns – Design patterns are general reusable solution to a commonly occurring problem within a given context in software design and they are absolutely crucial as hard core Java Programmer. If you don’t use design patterns you will write much more code, it will be buggy and hard to understand and refactor, not to mention untestable and they are really great way for communicating your intent very quickly with other programmers.

Recommended readings are Head First Design Patterns Elisabeth Freeman and Kathy Sierra and Design Patterns: Elements of Reusable by Gang of four.

(4) Programming Best Practices – Programming is not only about learning and writing code. Code readability is a universal subject in the world of computer programming. It helps standardize products and help reduce future maintenance cost. Best practices helps you, as a programmer to think differently and improves problem solving attitude within you. A simple program can be written in many ways if given to multiple developers. Thus the need to best practices come into picture and every programmer must aware about these things.

Recommended readings are Clean Code by Robert Cecil Martin and Code Complete by Steve McConnell.

(5) Testing and Debugging (T&D) – As you know about the writing the code for specific problem domain, you have to learn how to test that code snippet and debug it when it is needed. Some programmers skip their unit testing or other testing methodology part and leave it to QA guys. That will lead to delivering 80% bugs hiding in your code to the QA team and reduce the productivity and risking and pushing your project boundaries to failure. When a miss behavior or bug occurred within your code when the testing phase. It is essential to know about the debugging techniques to identify that bug and its root cause.

Recommended readings are Debugging by David Agans and A Friendly Introduction to Software Testing by Bill Laboon.

I hope these instructions will help you to become a successful Java Programmer. Here i am explain only the universal core concepts that you must learn as successful programmer. I am not mentioning any technologies that Java programmer must know such as Spring, Hibernate, Micro-Servicers and Build tools, because that can be change according to the problem domain or environment that you are currently working on…..Happy Coding!


Hard to be balanced on this one.

They are useful to know. If ever you need to use, or make a derivative of algorithm X, then you’ll be glad you took the time.

If you learn them, you’ll learn general techniques: sorting, trees, iteration, transformation, recursion. All good stuff.


You’ll get a feeling for the kinds of code you cannot write if you need certain speeds or memory use, given a certain data set.

You’ll pass certain kinds of interview test.

You’ll also possibly never use them. Or use them very infrequently.

If you mention that on here, some will say you are a lesser developer. They will insist that the line between good and not good developers is algorithm knowledge.

That’s a shame, really.

In commercial work, you never start a day thinking ‘I will use algorithm X today’.

The work demands the solution. Not the other way around.

This is yet another proof that a lot of technical sounding stuff is actual all about people. Their investment in something. Need for validation. Preference.

The more you know in development, the better. But I would not prioritize algorithms right at the top, based on my experience. Alan Mellor

So you’re inventing a new programming language and considering whether to write either a compiler or an interpreter for your new language in C or C++?

The only significant disadvantage of C++ is that in the hands of bad programmers, they can create significantly more chaos in C++ than they can in C.

But for experienced C++ programmers, the language is immensely more powerful than C and writing clear, understandable code in C++ can be a LOT easier.


If you’re going to actually do this – then I strongly recommend looking at a pair of tools called “flex” and “bison” (which are OpenSourced versions of the more ancient “lex” and “yacc”). These tools are “compiler-compilers” that are given a high level description of the syntax of your language – and automatically generate C code (which you can access from C++ without problems) to do the painful part of generating a lexical analyzer and a syntax parser. Steve Baker

Did you know you can google this answer yourself? Search for “c++ private keyword” and follow the link to access specifiers, which goes into great detail and has lots of examples. In case google is down, here’s a brief explanation of access specifiers:

  • The private access specifier in a class or struct definition makes declarations that occur after the specifier. A private declaration is visible only inside the class/struct, and not in derived classes or structs, and not from outside.
  • The protected access specifier makes declarations visible in the current class/struct and also in derived classes and structs, but not visible from outside. protected is not used very often and some wise people consider it a code smell.
  • The public access specifier makes declarations visible everywhere.
  • You can also use access specifiers to control all the items in a base class. By Kurt Guntheroth

Rust programmers do mention the obvious shortcomings of the language.

Such as that a lot of data structures can’t be written without unsafe due to pointer complications.

Or that they haven’t agreed what it means to call unsafe code (although this is somewhat of a solved problem, just like calling into assembler from C0 in the sysbook).

The main problem of the language is that it doesn’t absolve the programmers from doing good engineering.

It just catches a lot of the human errors that can happen despite such engineering. Jonas Oberhauser.

Comparing cross-language performance of real applications is tricky. We usually don’t have the resources for writing said applications twice. We usually don’t have the same expertise in multiple languages. Etc. So, instead, we resort to smaller benchmarks. Occasionally, we’re able to rewrite a smallish critical component in the other language to compare real-world performance, and that gives a pretty good insight. Compiler writers often also have good insights into the optimization challenges for the language they work on.

My best guess is that C++ will continue to have a small edge in optimizability over Rust in the long term. That’s because Rust aims at a level of memory safety that constrains some of its optimizations, whereas C++ is not bound to such considerations. So I expect that very carefully written C++ might be slightly faster than equivalent very carefully written Rust.

However, that’s perhaps not a useful observation. Tiny differences in performance often don’t matter: The overall programming model is of greater importance. Since both languages are pretty close in terms of achievable performance, it’s going to be interesting watching which is preferable for real-life engineering purposes: The safe-but-tightly-constrained model of Rust or the more-risky-but-flexible model of C++.  By David VandeVoorde

  1. Lisp does not expose the underlying architecture of the processor, so it can’t replace my use of C and assembly.
  2. Lisp does not have significant statistical or visualization capabilities, so it can’t replace my use of R.
  3. Lisp was not built with unix filesystems in mind, so it’s not a great choice to replace my use of bash.
  4. Lisp has nothing at all to do with mathematical typesetting, so won’t be replacing LATEXLATEX anytime soon.
  5. And since I use vim, I don’t even have the excuse of learning lisp so as to modify emacs while it’s running.

In fewer words: for the tasks I get paid to do, lisp doesn’t perform better than the languages I currently use. By Barry RoundTree

What are some things that only someone who has been programming 20-50 years would know?

The truth of the matter gained through the multiple decades of (my) practice (at various companies) is ugly, not convenient and is not what you want to hear.

  1. The technical job interviews are non indicative and non predictive waste of time, that is, to put it bluntly, garbage (a Navy Seal can be as brave is (s)he wants to be during the training, but only when the said Seal meets the bad guys face to face on the front line does her/his true mettle can be revealed).
  2. An average project in an average company, both averaged the globe over, is staffed with mostly random, technically inadequate, people who should not be doing what they are doing.
  3. Such random people have no proper training in mathematics and computer science.
  4. As a result, all the code generated by these folks out there is flimsy, low quality, hugely not efficient, non scalable, non maintainable, hardly readable steaming pile of spaghetti mess – the absence of structure, order, discipline and understanding in one’s mind is reflected at the keyboard time 100 percent.
  5. It is a major hail mary, a hallelujah and a standing ovation to the genius of Alan Turing for being able to create a (Turing) Machine that, on the one hand, can take this infinite abuse and, on the other hand, being nothing short of a miracle, still produce binaries that just work. Or so they say.
  6. There is one and only one definition of a computer programmer: that of a person who combines all of the following skills and abilities:
    1. the ability to write a few lines of properly functioning (C) code in the matter of minutes
    2. the ability to write a few hundred lines of properly functioning (C) code in the matter of a small number of hours
    3. the ability to write a few thousand lines of properly functioning (C) code in the matter of a small number of weeks
    4. the ability to write a small number of tens of thousands of lines of properly functioning (C) code in the matter of several months
    5. the ability to write several hundred thousand lines of properly functioning (C) code in the matter of a small number of years
    6. the ability to translate a given set of requirements into source code that is partitioned into a (large) collection of (small and sharp) libraries and executables that work well together and that can withstand a steady-state non stop usage for at least 50 years
  7. It is this ability to sustain the above multi-year effort during which the intellectual cohesion of the output remains consistent and invariant is what separates the random amateurs, of which there is a majority, from the professionals, of which there is a minority in the industry.
  8. There is one and only one definition of the above properly functioning code: that of a code that has a check mark in each and every cell of the following matrix:
    1. the code is algorithmically correct
    2. the code is easy to read, comprehend, follow and predict
    3. the code is easy to debug
      1. the intellectual effort to debug code, symbolized as E(d)E(d), is strictly larger than the intellectual effort to write code, symbolized as E(w)E(w). That is: E(d)>E(w)E(d)>E(w). Thus, it is entirely possible to write a unit of code that even you, the author, can not debug
    4. the code is easy to test
      1. in different environments
    5. the code is efficient
      1. meaning that it scales well performance-wise when the size of the input grows without bound in both configuration and data
    6. the code is easy to maintain
      1. the addition of new and the removal or the modification of the existing features should not take five metric tons of blood, three years and a small army of people to implement and regression test
      2. the certainty of and the confidence in the proper behavior of the system thus modified should by high
      3. (read more about the technical aspects of code modification in the small body of my work titled “Practical Design Patterns in C” featured in my profile)
      4. (my claim: writing proper code in general is an optimization exercise from the theory of graphs)
    7. the code is easy to upgrade in production
      1. lifting the Empire State Building in its entirety 10 feet in the thin blue air and sliding a bunch of two-by-fours underneath it temporarily, all the while keeping all of its electrical wires and the gas pipes intact, allowing the dwellers to go in and out of the building and operating its elevators, should all be possible
      2. changing the engine and the tires on an 18-wheeler truck hauling down a highway at 80 miles per hour should be possible
  9. A project staffed with nothing but technically capable people can still fail – the team cohesion and the psychological compatibility of team members is king. This is raw and unbridled physics – a team, or a whole, is more than the sum of its members, or parts.
  10. All software project deadlines without exception are random and meaningless guesses that have no connection to reality.
  11. Intelligence does not scale – a million fools chained to a million keyboards will never amount to one proverbial Einstein. Source

A function pulls a computation out of your program and puts it in a conceptual box labeled by the function’s name. This lets you use the function name in a computation instead of writing out the computation done by the function.

Writing a function is like defining an obscure word before you use it in prose. It puts the definition in one place and marks it out saying, “This is the definition of xxx”, and then you can use the one word in the text instead of writing out the definition.

Even if you only use a word once in prose, it’s a good idea to write out the definition if you think that makes the prose clearer.

Even if you only use a function once, it’s a good idea to write out the function definition if you think it will make the code clearer to use a function name instead of a big block of code. Source.

Conditional statements of the form if this instance is type T then do X can generally — and usually should — be removed by appropriate use of polymorphism.

All conditional statements might conceivably be replaced in that fashion, but the added complexity would almost certainly negate its value. It’s best reserved for where the relevant types already exist.

Creating new types solely to avoid conditionals sometimes makes sense (e.g. maybe create distinct nullable vs not-nullable types to avoid if-null/if-not-null checks) but usually doesn’t. Source.

Something bad happens as your Java code runs.

Throw an exception.

The following lines after the throw do not run, saving them from the bad thing.

control is handed back up the call stack until Java runtime finds a catch() statement that matches the exception.

The code resumes running from there. Source: Allan Mellor

Google has better programmers, and they’ve been working on the problem space longer than either Spotify or the other providers have existed.

YouTube has a year and a half on Spotify, for example, and they’ve been employing a lot of “organ bank” engineers from Google proper, for various problems — like the “similar to this one“ problem — and the engineers doing the work are working on much larger teams, overall.

Spotify is resource starved, because they really aren’t raking in the same ratio of money that YouTube does. By Terry Lambert

Over the past two decades, Java has moved from a fairly simple ecosystem, with the relatively straightforward ANT build tool, to a sophisticated ecosystem with Maven or gradle basically required. As a result, this kind of approach doesn’t really work well anymore. I highly recommend that you download the community edition of IntelliJ IDEA; this is a free version of a great commercial IDE. By Joshua Gross

Best bet is to turn it into a record type as a pure data structure. Then you can start to work on that data. You might do that direct, or use it to construct some OOP objects with application specific behaviours on them. Up to you.

You can decide how far to take layering as well. Small apps work ok with the data struct in the exact same format as the JSON data passed around. But you might want to isolate that and use a mapping to some central domain model. Then if the JSON schema changes, your domain model won’t.

Libraries such as Jackson and Gson can handle the conversion. Many frameworks have something like it built in, so you get delivered a pure data struct ‘object’ containing all the data that was in the JSON

Things like JSON Validator and JSV Schemas can help you validate the response JSON if need be. By Alan Mellor

Keith Adams already gave an excellent overview of Slack’s technology stack so I will do my best to add to his answer.

Products that make up Slack’s tech stack include: Amazon (CloudFront, CloudSearch, EMR, Route 53, Web Services), Android Studio, Apache (HTTP Server, Kafka, Solr, Spark, Web Server), Babel, Brandfolder, Bugsnag, Burp Suite, Casper Suite, Chef, DigiCert, Electron, Fastly, Git, HackerOne, JavaScript, Jenkins, MySQL, Node.js, Objective-C, OneLogin, PagerDuty, PHP, Redis, Smarty, Socket, Xcode, and Zeplin.

Additionally, here’s a list of other software products that Slack is using internally:

  • Marketing: AdRoll, Convertro, MailChimp, SendGrid
  • Sales and Support: Cnflx, Front, Typeform, Zendesk
  • Analytics: Google Analytics, Mixpanel, Optimizely, Presto
  • HR: AngelList Jobs, Culture Amp, Greenhouse, Namely
  • Productivity: ProductBoard, Quadro, Zoom, Slack (go figure!)

For a complete list of software used by Slack, check out: Slack’s Stack on Siftery

Some other fun facts about Slack:

  • Slack is used by 55% of Unicorns (and 59% of B2B Unicorns)
  • Slack has 85% market share in Siftery’s Instant Messaging category on Siftery
  • Slack is used by 42% of both Y Combinator and 500 Startups companies
  • 35% of companies in the Sharing Economy use Slack

(Disclaimer: The above data was pulled from Siftery and has been verified by individuals working at Slack) By Gerry Giacoman Colyer

Programmers should use recursion when it is the cleanest way to define a process. Then, WHEN AND IF IT MATTERS, they should refine the recursion and transform it into a tail recursion or a loop. When it doesn’t matter, leave it alone. Jamie Lawson

Your phone runs a version of Linux, which is programmed in C. Only the top layer is programmed in java, because performance usually isn’t very important in that layer.

Your web browser is programmed in C++ or Rust. There is no java anywhere. Java wasn’t secure enough for browser code (but somehow C++ was? Go figure.)

Your Windows PC is programmed mostly in C++. Windows is very old code, that is partially C. There was an attempt to recode the top layer in C#, but performance was not good enough, and it all had to be recoded in C++. Linux PCs are coded in C.

Your intuition that most things are programmed in java is mistaken. Kurt Guntheroth

That’s not possible in Java, or at least the language steers you away from attempting that.

Global variables have significant disadvantages in terms of maintainability, so the language itself has no way of making something truly global.

The nearest approach would be to abuse some language features like so:

  • public class Globals { 
  • public static int[] stuff = new int [10]; 

Then you can use this anywhere with

  • Globals.stuff[0] = 42; 

Java isn’t Python, C nor JavaScript. It’s reasonably opinionated about using Object Oriented Programming, which the above snippets are not examples of.

This also uses a raw array, which is a fixed size in Java. Again, not very useful, we prefer ArrayList for most purposes, which can grow.

I’d recommend the above approach if and only if you have no alternatives, are not really wanting to learn Java and just need a dirty utility hack, or are starting out in programming just finding your feet. Alan Mellor

In which situations is NoSQL better than relational databases such as SQL? What are specific examples of apps where switching to NoSQL yielded considerable advantages?

Warning: The below answer is a bit oversimplified, for pedagogical purposes. Picking a storage solution for your application is a very complex issue, and every case will be different – this is only meant to give an overview of the main reason why people go NoSQL.

There are several possible reasons that companies go NoSQL, but the most common scenario is probably when one database server is no longer enough to handle your load. noSQL solutions are much more suited to distribute load over shitloads of database servers.

This is because relational databases traditionally deal with load balancing by replication. That means that you have multiple slave databases that watches a master database for changes and replicate them to themselves. Reads are made from the slaves, and writes are made to the master. This works to a certain level, but it has the annoying side-effect that the slaves will always lag slightly behind, so there is a delay between the time of writing and the time that the object is available for reading, which is complex and error-prone to handle in your application. Also, the single master eventually becomes a bottleneck no matter how powerful it is. Plus, it’s a single point of failure.

NoSQL generally deals with this problem by sharding. Overly simplified it means that users with userid 1-1000000 is on server A, and users with userid 1000001-2000000 is on server B and so on. This solves the problems that relational replication has, but the drawback is that features such as aggregate queries (SUM, AVG etc) and traditional transactions are sacrificed.

For some case studies, I believe Couchbase pimps a whitepaper on their web site here: http://www.couchbase.com/why-nosql/use-cases .  Mattias Peter Johansson

Chrome is coded in C++, assembler and Python. How could three different languages ​​be used to obtain only one product? What is the method used to merge programming languages ​​to create software?

Concretely, a processor can correctly receive only one kind of instruction, the assembler. This may also depend on the type of processor.

As the assembler requires several operations just to make a simple addition, we had to create compilers which, starting from a higher level language (easier to write), are able to automatically generate the assembly code.

These compilers can sometimes receive several languages. For example the GCC compiler allows to compile C and C++, and it also supports to receive pieces of assembler inside, defined by a keyword __asm__ . The assembler is still something to avoid absolutely because it is completely dependent on the machine and can therefore be a source of interference and unpleasant surprises.

More generally, we also often create multi-language applications using several components (libraries, or DLLs, activeX, etc.) The interfaces between these components are managed by the operating systems and allow Java to coexist happily. , C, C++, C#, Python, and everything you could wish for. A certain finesse is however necessary in the transitions between languages ​​because each one has its implicit rules which must therefore be enforced very explicitly.

For example, an object coming from the C++ world, transferred by these interfaces in a Java program will have to be explicitly destroyed, the java garbage collector only supports its own objects.

Another practical interface is web services, each module, whatever its technology, can communicate with the others by sending itself serialized objects in json… which is much less a source of errors!  Source:  Vincent Steyer

What is the most dangerous code you have ever seen?

This line removes the filesystem (starting from root /)
  • sudo rm -rf –no-preserve-root /
Or for more fun, a Russian roulette:
  • [ $[ $random % 6 ] == 0 ] && rm -rf –no-preserve-root / || echo *clic* 

(a chance in 6 of falling on the first part described above, otherwise “click” is displayed)

Javascript (or more precisely ECMAScript). And it’s a lot faster than the others. Surprised?

When in 2009 I heard about Node.js, I though that people had lost their mind to use Javascript on the server side. But I had to change my mind.

Node.js is lighting fast. Why? First of all because it is async but with V8, the open source engine of Google Chrome, even the Javascript language itself become incredibly fast. The war of the browsers brought us hyper-optimized Javascript interpreters/compilers.

In intensive computational algorithms, it is more than one order of magnitude faster than PHP (programming language)Ruby, and Python. In fact with V8 (http://code.google.com/p/v8/ ), Javascript became the fastest scripting language on earth.

Does it sound too bold? Look at the benchmarks: http://shootout.alioth.debian.org/

Note: with regular expressions, V8 is even faster than C and C++! Impossible? The reason is that V8 compiles native machine code ad-hoc for the specific regular expressions (see http://blog.chromium.org/2009/02/irregexp-google-chromes-new-regexp.html )

If you are interested, you can learn how to use node: http://www.readwriteweb.com/hack/2011/04/6-free-e-books-on-nodejs.php 🙂

Regarding the language Javascript is not the most elegant language but it is definitely a lot better than what some people may think. The current version of Javascript (or better ECMAScript as specified in ECMA-262 5th edition) is good. If you adopt “use strict”, some strange and unwanted behaviors of the language are eliminated. Harmony, the codename for a future version, is going to be even better and add some extra syntactical sugar similar to some Python’s constructs.

If you want to learn Javascript (not just server side), the best book is Professional Javascript for Web Developers by Nicholas C. Zakas. But if you are cheap, you can still get a lot from http://eloquentjavascript.net/ and http://addyosmani.com/resources/essentialjsdesignpatterns/book/

Does Javascript still sound too archaic? Try Coffeescript (from the same author of Backbone.js) that compiles to Javascript. Coffescript makes cleaner, easier and more concise programming on environments that use Javascript (i.e. the browser and Node.js). It’s a relatively new language that is not perfect yet but it is getting better: http://coffeescript.org/

source: Here

In general, the important advantage of C++ is that it uses computers very efficiently, and offers developers a lot of control over expensive operations like dynamic memory management. Writing in C++ versus Java or python is the difference between spinning up 1,000 cloud instances versus 10,000. The cost savings in electricity alone justifies the cost of hiring specialist programmers and dealing with the difficulties of writing good C++ code. Source

You really need to understand C++ pretty well to have any idea why Rust is the way it is. If you only want to work at Mozilla, learn Rust. Otherwise learn C++ and then switch to Rust if it breaks out and becomes more popular.

Rust is one step forward and two steps back from C++. Embedding the notion of ownership in the language is an obvious improvement over C++. Yay. But Rust doesn’t have exceptions. Instead, it has a bunch of strange little features to provide the RAII’ish behavior that makes C++ really useful. I think on average people don’t know how to teach or how to use exceptions even still. It’s too soon to abandon this feature of C++. Source: Kurt Guntheroth

Java or Javascript-based web applications are the most common. (Yuk!) And, consequently, you’ll be a “dime a dozen” programmer if that’s what you do.

On the hand, (C++ or C) embedded system programming (i.e. hardware-based software), high-capacity backend servers in data centers, internet router software, factory automation/robotics software, and other operating system software are the least common, and consequently the most in demand. Source: Steven Ussery

I want to learn to program. Should I begin with Java or Python?

Your first language doesn’t matter very much. Both Java and Python are common choices. Python is more immediately useful, I would say.

When you are learning to program, you are learning a whole bunch of things simultaneously:

  • How to program
  • How to debug programs that aren’t working
  • How to use programming tools
  • A language
  • How to learn programming languages
  • How to think about programming
  • How to manage your code so you don’t paint yourself into corners, or end up with an unmanageable mess
  • How to read documentation

Beginners often focus too much on their first language. It’s necessary, because you can’t learn any of the others without that, but you can’t learn how to learn languages without learning several… and that means any professional knows a bunch and can pick up more as required. Source: Andrew  McGregor


If you’re a backend or full-stack engineer, it’s reasonable to focus on your preferred tech, but you’ll be expected to have at least some familiarity with Java, C#, Python, PHP, bash, Docker, HTML/CSS…

And, you need to be good with SQL.

That’s the minimum you should achieve.

The more you know, the more employable — and valuable to your employer or clients — you will be.

Also, languages and platforms are tools. Some tools are more appropriate to some tasks than others.

That means sometimes Node.js is the preferred choice to meet the requirements, and sometimes Java is a better choice — after considering the inevitable trade-offs with every technical decision.  Source: Dave Voohis

Just one?

No, no, that’s not how it works.

To be a competent back-end developer, you need to know at least one of the major, core, back-end programming languages — Java (and its major frameworks, Spring and Hibernate) and/or C# (and its major frameworks, .NET Core and Entity Framework.)

You might want to have passing familiarity with the up-and-coming Go.

You need to know SQL. You can’t even begin to do back-end development without it. But don’t bother learning NoSQL tools until you need to use them.

You should be familiar with the major cloud platforms, AWS and Azure. Others you can pick up if and as needed.

Know Linux, because most back-end infrastructure runs on Linux and you’ll eventually encounter it, even if it’s often hived away into various cloud-based services.

You should know Python and bash scripts. Understand Apache Web Server configuration. Be familiar with Nginx, and if you’re using Java, have some understanding of how Apache Tomcat works.

Understand containerization. Be good with Docker.

Be familiar with JavaScript and HTML/CSS. You might not have to write them, but you’ll need to support front-end devs and work with them and understand what they do. If you do any Node.js (some of us do a lot, some do none), you’ll need to know JavaScript and/or TypeScript and understand Node.

That’ll do for a start.

But even more important than the above, learn computer science.

Learn it, and you’ll learn that programming languages are implementations of fundamental principles that don’t change, whilst programming languages come and go.

Learn those fundamental principles, and it won’t matter what languages are in the market — you’ll be able to pick up any of them as needed and use them productively. Source: Dave Voohis

It sounds like you’re spending too much time studying Python and not enough time writing Python.

The only way to become good at any programming language — and programming in general — is to practice writing code.

It’s like learning to play a musical instrument: Practice is essential.

Try to write simple programs that do simple things. When you get them to work, write more complex programs to do more complex things.

When you get stuck, read documentation, tutorials and other peoples’ code to help you get unstuck.

If you’re still stuck, set aside what you’re stuck on and work on a different program.

But keep writing code. Write a lot of code.

The more code you write, the easier it will become to write more code. Source: Dave Voohis

It depends on what you want to do.

If you want to just mess around with programming as a hobby, it’s fine. In fact, it’s pretty good. Since it’s “batteries included”, you can often get a lot done in just a few lines of code. Learn Python 3, not 2.

If you want to be a professional software engineer, Python’s a poor place to start. It’s syntax isn’t terrible, but it’s weird. It’s take on OO is different from almost all other OO languages. It’ll teach you bad habits that you’ll have to unlearn when switching to another language.

If you want to eventually be a professional software engineer, learn another OO language first. I prefer C#, but Java’s a great choice too. If you don’t care about OO, C is a great choice. Nearly all major languages inherited their syntax from C, so most other languages will look familiar if you start there.

C++ is a stretch these days. Learn another OO language first. You’ll probably eventually have to learn JavaScript, but don’t start there. It… just don’t.

So, ya. If you just want to do some hobby coding and write some short scripts and utilities, Python’s fine. If you want to eventually be a pro SE, look elsewhere. Source: Chris Nash

You master a language by using it, not just reading about it and memorizing trivia. You’ll pick up and internalize plenty of trivia anyway while getting real world work done.

Reading books and blogs and whatnot helps, but those are more meaningful if you have real world problems to apply the material to. Otherwise, much of it is likely to go into your eyeballs and ooze right back out of your ears, metaphorically speaking.

I usually don’t dig into all the low level details when reading a programming book, unless it’s specifically needed for a problem I am trying to solve. Or, it caught my curiosity, in which case, satisfying my curiosity is the problem I am trying to solve.

Once you learn the basics, use books and other resources to accelerate you on your journey. What to read, and when, will largely be driven by what you decide to work on.

Bjarne Stroustrup, the creator of C++, has this to say:

And no, I’m not a walking C++ dictionary. I do not keep every technical detail in my head at all times. If I did that, I would be a much poorer programmer. I do keep the main points straight in my head most of the time, and I do know where to find the details when I need them.

Source: Joe Zbiciak

Scale. There is no field other than software where a company can have 2 billion customers, and do it with only a few tens of thousands of employees. The only others that come close are petroleum and banking – both of which are also very highly paid. By David Seidman

Professional programmer’s code:

  • //Here we address strange issue that was seen on 
  • //production a few times, but is not reproduced  
  • //localy. User can be mysteriously logged out after 
  • //clicking Back button. This seems related to recent 
  • //changes to redirect scheme upon order confirmation. 
  • login(currentUser()); 

Average programmer’s code:

  • //Hotfix – don’t ask 
  • login(currentUser()); 

Professional programmer’s commit message:

  • Fix memory leak in connection pool 
  • We’ve seen connections leaking from the pool 
  • if any query had already been executed through 
  • it and then exception is thrown. 
  • The root causes was found in ConnectionPool.addExceptionHook() 
  • method that ignored certain types of exceptions. 

Average programmer’s commit message:

  • Small fix 

Professional programmer’s test naming:

  • login_shouldThrowUserNotFoundException_ifUserAbsentInDB() 
  • login_shouldSetCurrentUser_ifLoginSuccessfull() 
  • login_shouldRecordAuditMessage_uponUnsuccessfullLogin() 

Average programmer’s test naming:

  • testLogin1() 
  • testLogin2() 
  • testLogin3() 

After first few years of programming, when the urge to put some cool looking construct only you can understand into every block of code wears off, you’ll likely come to the conclusion that these examples are actually the code you want to encounter when opening a new project.

If we look at the apps written by good vs average programmers (not talking about total beginners) the code itself is not that much different, but if small conveniences everywhere allow you to avoid frustration while reading it – it is likely written by a professional.

The only valid measurement of code quality is the WTFs/minutes.

Here are 5 very common ones. If you don’t know these then you’re probably not ready.

  1. Graph Search – Depth-first and Breadth-first search
  2. Binary Search
  3. Backtracking using Recursion and Memoization
  4. Searching a Binary Search Tree
  5. Recursion over a Binary Tree

Of course, there are many others too.

Another thing to keep in mind – you won’t be asked these directly. It will be disguised as a unique situation.

source: quora

I worked as an academic in physics for about 10 years, and used Fortran for much of that time. I had to learn Fortran for the job, as I was already fluent in C/C++.

The prevalence of Fortran in computational physics comes down to three factors:

  1. Performance. Yes, Fortran code is typically faster than C/C++ code. One of the main reasons for this is that Fortran compilers are heavily optimised towards making fast code, and the Fortran language spec is designed such that compilers will know what to optimise. It’s possible to make your C program as fast as a Fortran one, but it’s considerably more work to do so.
  2. Convenience. Imagine you want to add a scalar to an array of values – this is the sort of thing we do all the time in physics. In C you’d either need to rely on an external library, or you’d need to write a function for this (leading to verbose code). In Fortran you just add them together, and the scalar is broadcasted across all elements of the array. You can do the same with multiplication and addition of two arrays as well. Fortran was originally the Formula-translator, and therefore makes math operations easy.
  3. Legacy. When you start a PhD, you’re often given some ex-post-doc’s (or professor’s) code as a starting point. Often times this code will be in Fortran (either because of the age of the person, or because they were given Fortran code). Unfortunately sometimes this code is F77, which means that we still have people in their 20s learning F77 (which I think is just wrong these days, as it gives Fortran as a whole a bad name). Source: Erlend Davidson

My friend, if you like C, you are gonna looooove B. B was C’s predecessor language. It’s a lot like C, but for C, Thompson and Ritchie added in data types. Basically, C is for lazy programmers. The only data type in B was determined by the size of a word on the host system. B was for “real-men programmers” who ate Hollerith cards for extra fiber, chewed iron into memory cores when they ran out of RAM, and dreamed in hexadecimal. Variables are evaluated contextually in B, and it doesn’t matter what the hell they contain; they are treated as though they hold integers in integer operations, and as though they hold memory addresses in pointer operations. Basically, B has all of the terseness of an assembly language, without all of the useful tooling that comes along with assembly.

As others indicate, pointers do not hold memory; they hold memory addresses. They are typed because before you go to that memory address, you probably want to know what’s there. Among other issues, how big is “there”? Should you read eight bits? Sixteen? Thirty-two? More? Inquiring minds want to know! Of course, it would also be nice to know whether the element at that address is an individual element or one element in an array, but C is for “slightly real less real men programmers” than B. Java does fully differentiate between scalars and arrays, and therefore is clearly for the weak minded. /jk Source: Joshua Gross

Hidden Features of C#

What are the most hidden features or tricks of C# that even C# fans, addicts, experts barely know?

Here are the revealed features so far:




Language Features

Visual Studio Features


Methods and Properties

  • String.IsNullOrEmpty() method by KiwiBastard
  • List.ForEach() method by KiwiBastard
  • BeginInvoke()EndInvoke() methods by Will Dean
  • Nullable<T>.HasValue and Nullable<T>.Value properties by Rismo
  • GetValueOrDefault method by John Sheehan

Tips & Tricks

  • Nice method for event handlers by Andreas H.R. Nilsson
  • Uppercase comparisons by John
  • Access anonymous types without reflection by dp
  • A quick way to lazily instantiate collection properties by Will
  • JavaScript-like anonymous inline-functions by roosteronacid


  • netmodules by kokos
  • LINQBridge by Duncan Smart
  • Parallel Extensions by Joel Coehoorn
  • This isn’t C# per se, but I haven’t seen anyone who really uses System.IO.Path.Combine() to the extent that they should. In fact, the whole Path class is really useful, but no one uses it!
  • lambdas and type inference are underrated. Lambdas can have multiple statements and they double as a compatible delegate object automatically (just make sure the signature match) as in:
Console.CancelKeyPress +=
    (sender, e) => {
        Console.WriteLine("CTRL+C detected!\n");
        e.Cancel = true;
  • From Rick Strahl: You can chain the ?? operator so that you can do a bunch of null comparisons.
string result = value1 ?? value2 ?? value3 ?? String.Empty;

When normalizing strings, it is highly recommended that you use ToUpperInvariant instead of ToLowerInvariant because Microsoft has optimized the code for performing uppercase comparisons.

I remember one time my coworker always changed strings to uppercase before comparing. I’ve always wondered why he does that because I feel it’s more “natural” to convert to lowercase first. After reading the book now I know why.

  • My favorite trick is using the null coalesce operator and parentheses to automagically instantiate collections for me.
private IList<Foo> _foo;

public IList<Foo> ListOfFoo 
    { get { return _foo ?? (_foo = new List<Foo>()); } }
  • Here are some interesting hidden C# features, in the form of undocumented C# keywords:




These are undocumented C# keywords (even Visual Studio recognizes them!) that were added to for a more efficient boxing/unboxing prior to generics. They work in coordination with the System.TypedReference struct.

There’s also __arglist, which is used for variable length parameter lists.

One thing folks don’t know much about is System.WeakReference — a very useful class that keeps track of an object but still allows the garbage collector to collect it.

The most useful “hidden” feature would be the yield return keyword. It’s not really hidden, but a lot of folks don’t know about it. LINQ is built atop this; it allows for delay-executed queries by generating a state machine under the hood. Raymond Chen recently posted about the internal, gritty details.

  • Using @ for variable names that are keywords.
var @object = new object();
var @string = "";
var @if = IpsoFacto();
  • If you want to exit your program without calling any finally blocks or finalizers use FailFast:

Read more hidden C# Features at Hidden Features of C#? – Stack Overflow

Hidden Features of python

Source: stackoveflow

What IDE to Use for Python

Programming, Coding and Algorithms Questions and Answers

Acronyms used:

 L  - Linux
 W  - Windows
 M  - Mac
 C  - Commercial
 F  - Free
 CF - Commercial with Free limited edition
 ?  - To be confirmed

What is The right JSON content type?

For JSON text:


Example: { "Name": "Foo", "Id": 1234, "Rank": 7 }

For JSONP (runnable JavaScript) with callback:

Example: functionCall({"Name": "Foo", "Id": 1234, "Rank": 7});

Here are some blog posts that were mentioned in the relevant comments:

IANA has registered the official MIME Type for JSON as application/json.

When asked about why not text/json, Crockford seems to have said JSON is not really JavaScript nor text and also IANA was more likely to hand out application/* than text/*.

More resources:

JSON (JavaScript Object Notation) and JSONP (“JSON with padding”) formats seems to be very similar and therefore it might be very confusing which MIME type they should be using. Even though the formats are similar, there are some subtle differences between them.

So whenever in any doubts, I have a very simple approach (which works perfectly fine in most cases), namely, go and check corresponding RFC document.

JSON RFC 4627 (The application/json Media Type for JavaScript Object Notation (JSON)) is a specifications of JSON format. It says in section 6, that the MIME media type for JSON text is


JSONP JSONP (“JSON with padding”) is handled different way than JSON, in a browser. JSONP is treated as a regular JavaScript script and therefore it should use application/javascript, the current official MIME type for JavaScript. In many cases, however, text/javascript MIME type will work fine too.

Note that text/javascript has been marked as obsolete by RFC 4329 (Scripting Media Types) document and it is recommended to use application/javascript type instead. However, due to legacy reasons, text/javascript is still widely used and it has cross-browser support (which is not always a case with application/javascript MIME type, especially with older browsers).

What are some mistakes to avoid while learning programming?

  1. Over use of the GOTO statement. Most schools teach this is a NO;NO
  2. Not commenting your code with proper documentation – what exactly does the code do??
  3. Endless LOOP. A structured loop that has NO EXIT point
  4. Overwriting memory – destroying data and/or code. Especially with Dynamic Allocation;Stacks;Queues
  5. Not following discipline – Requirements, Design, Code, Test, Implementation

Moreover complex code should have a BLUEPRINT – Design. That is like saying let’s build a house without a floor plan. Code/Programs that have a requirements and design specification BEFORE writing code tends to have a LOWER error rate. Less time debugging and fixing errors. Source: QUora


The thing that always struck me is that the best programmers I would meet or read all had a couple of things in common.

  1. They didn’t use IDEs, preferring Emacs or Vim.
  2. They all learned or used Functional Programming (Lisp, Haskel, Ocaml)
  3. They all wrote or endorsed some kind of testing, even if it’s just minimal TDD.
  4. They avoided fads and dependencies like a plague.

It is a basic truth that learning Lisp, or any functional programming, will fundamentally change the way you program and think about programming. Source: Quora

The two work well together. Both are effective at what they do:

  • Pairing is a continuous code review, with a human-powered ‘auto suggest’. If you like github copilot, pairing does that with a real brain behind it.
  • TDD forces you to think about how your code will be used early on in the process. That gives you the chance to code things so they are clear and easy to use

Both of these are ‘shift-left’ activities. In the days of old, code review and testing happened after the code was written. Design happened up front, but separate to coding, so you never got to see if the design was actually codeable properly. By shifting these activities to before the code gets written, we get a much faster feedback loop. That enables us to make corrections and improvements as we go.

Neither is better than each other. They target different parts of the coding challenge. By Alan Mellor

Yes, I’ve found that three can be very helpful, especially these days.

  • Monitor 1: IDE full screen
  • Monitor 2: Google, JIRA ticket, documentation. Manual Test tools
  • Monitor 3: Zoom/Teams/Slack/Outlook for general comms

That third monitor becomes almost essential if you are remote pairing, and wnat to see your collaborator n real-time.

My current work is teaching groups in our academy. That also benefits from three monitors: Presenter view, participant view, zoom for chat and hands ups in the group.

I can get away with two monitors. I can even do it with a £3 HDMI fake monitor USB plug. Neither is quite as effective. Source: Alan Mellor

You make the properties not different. And the key way to do that is by removing the properties completely.

Instead, you tell your objects to do some behaviour.

Say we have three classes full of different data that all needs adding to some report. Make an interface like this:

  • interface IReportSource { 
  • void includeIn( Report r ); 

so here, all your classes with different data will implement this interface. We can call the method ‘includeIn’ on each of them. We pass in a concrete class Report to that method. This will be the report that is being generated.

Then your first class which used to look like

  • class ALoadOfData { 
  • get; set; name 
  • get; set; quantity 

(forgive the rusty/pseudo C# syntax please)

can be translated into:

  • class ARealObject : IReportSource { 
  • private string name ; 
  • private int quantity ; 
  • void includeIn( Report r ) { 
  • r.addBasicItem( name, quantity ); 

You can see how the properties are no longer exposed. They remain encapsulated in the object, available for use inside our includeIn() method. That is now polymorphic, and you would write a custom includeIn() for each kind of class implementing IReportSource. It can then call a suitable method on the Report class, with a suitable number of properties (now hidden; so just fields). By Alan Mellor

What are the Top 20  lesser known but cool data structures?

1- Tries, also known as prefix-trees or crit-bit trees, have existed for over 40 years but are still relatively unknown. A very cool use of tries is described in “TRASH – A dynamic LC-trie and hash data structure“, which combines a trie with a hash function.

2- Bloom filter: Bit array of m bits, initially all set to 0.

To add an item you run it through k hash functions that will give you k indices in the array which you then set to 1.

To check if an item is in the set, compute the k indices and check if they are all set to 1.

Of course, this gives some probability of false-positives (according to wikipedia it’s about 0.61^(m/n) where n is the number of inserted items). False-negatives are not possible.

Removing an item is impossible, but you can implement counting bloom filter, represented by array of ints and increment/decrement.

3- Rope: It’s a string that allows for cheap prepends, substrings, middle insertions and appends. I’ve really only had use for it once, but no other structure would have sufficed. Regular strings and arrays prepends were just far too expensive for what we needed to do, and reversing everthing was out of the question.

4- Skip lists are pretty neat.

A skip list is a probabilistic data structure, based on multiple parallel, sorted linked lists, with efficiency comparable to a binary search tree (order log n average time for most operations).

They can be used as an alternative to balanced trees (using probalistic balancing rather than strict enforcement of balancing). They are easy to implement and faster than say, a red-black tree. I think they should be in every good programmers toolchest.

If you want to get an in-depth introduction to skip-lists here is a link to a video of MIT’s Introduction to Algorithms lecture on them.

Also, here is a Java applet demonstrating Skip Lists visually.

5Spatial Indices, in particular R-trees and KD-trees, store spatial data efficiently. They are good for geographical map coordinate data and VLSI place and route algorithms, and sometimes for nearest-neighbor search.

Bit Arrays store individual bits compactly and allow fast bit operations.

6-Zippers – derivatives of data structures that modify the structure to have a natural notion of ‘cursor’ — current location. These are really useful as they guarantee indicies cannot be out of bound — used, e.g. in the xmonad window manager to track which window has focused.

Amazingly, you can derive them by applying techniques from calculus to the type of the original data structure!

7- Suffix tries. Useful for almost all kinds of string searching (http://en.wikipedia.org/wiki/Suffix_trie#Functionality). See also suffix arrays; they’re not quite as fast as suffix trees, but a whole lot smaller.

8- Splay trees (as mentioned above). The reason they are cool is threefold:

    • They are small: you only need the left and right pointers like you do in any binary tree (no node-color or size information needs to be stored)
    • They are (comparatively) very easy to implement
    • They offer optimal amortized complexity for a whole host of “measurement criteria” (log n lookup time being the one everybody knows). See http://en.wikipedia.org/wiki/Splay_tree#Performance_theorems

9- Heap-ordered search trees: you store a bunch of (key, prio) pairs in a tree, such that it’s a search tree with respect to the keys, and heap-ordered with respect to the priorities. One can show that such a tree has a unique shape (and it’s not always fully packed up-and-to-the-left). With random priorities, it gives you expected O(log n) search time, IIRC.

10- A niche one is adjacency lists for undirected planar graphs with O(1) neighbour queries. This is not so much a data structure as a particular way to organize an existing data structure. Here’s how you do it: every planar graph has a node with degree at most 6. Pick such a node, put its neighbors in its neighbor list, remove it from the graph, and recurse until the graph is empty. When given a pair (u, v), look for u in v’s neighbor list and for v in u’s neighbor list. Both have size at most 6, so this is O(1).

By the above algorithm, if u and v are neighbors, you won’t have both u in v’s list and v in u’s list. If you need this, just add each node’s missing neighbors to that node’s neighbor list, but store how much of the neighbor list you need to look through for fast lookup.

11-Lock-free alternatives to standard data structures i.e lock-free queue, stack and list are much overlooked.
They are increasingly relevant as concurrency becomes a higher priority and are much more admirable goal than using Mutexes or locks to handle concurrent read/writes.

Here’s some links
http://www.research.ibm.com/people/m/michael/podc-1996.pdf [Links to PDF]

Mike Acton’s (often provocative) blog has some excellent articles on lock-free design and approaches

12- I think Disjoint Set is pretty nifty for cases when you need to divide a bunch of items into distinct sets and query membership. Good implementation of the Union and Find operations result in amortized costs that are effectively constant (inverse of Ackermnan’s Function, if I recall my data structures class correctly).

13- Fibonacci heaps

They’re used in some of the fastest known algorithms (asymptotically) for a lot of graph-related problems, such as the Shortest Path problem. Dijkstra’s algorithm runs in O(E log V) time with standard binary heaps; using Fibonacci heaps improves that to O(E + V log V), which is a huge speedup for dense graphs. Unfortunately, though, they have a high constant factor, often making them impractical in practice.

14- Anyone with experience in 3D rendering should be familiar with BSP trees. Generally, it’s the method by structuring a 3D scene to be manageable for rendering knowing the camera coordinates and bearing.

Binary space partitioning (BSP) is a method for recursively subdividing a space into convex sets by hyperplanes. This subdivision gives rise to a representation of the scene by means of a tree data structure known as a BSP tree.

In other words, it is a method of breaking up intricately shaped polygons into convex sets, or smaller polygons consisting entirely of non-reflex angles (angles smaller than 180°). For a more general description of space partitioning, see space partitioning.

Originally, this approach was proposed in 3D computer graphics to increase the rendering efficiency. Some other applications include performing geometrical operations with shapes (constructive solid geometry) in CAD, collision detection in robotics and 3D computer games, and other computer applications that involve handling of complex spatial scenes.

15- Huffman trees – used for compression.

16- Have a look at Finger Trees, especially if you’re a fan of the previously mentioned purely functional data structures. They’re a functional representation of persistent sequences supporting access to the ends in amortized constant time, and concatenation and splitting in time logarithmic in the size of the smaller piece.

As per the original article:

Our functional 2-3 finger trees are an instance of a general design technique in- troduced by Okasaki (1998), called implicit recursive slowdown. We have already noted that these trees are an extension of his implicit deque structure, replacing pairs with 2-3 nodes to provide the flexibility required for efficient concatenation and splitting.

A Finger Tree can be parameterized with a monoid, and using different monoids will result in different behaviors for the tree. This lets Finger Trees simulate other data structures.

17- Circular or ring buffer– used for streaming, among other things.

18- I’m surprised no one has mentioned Merkle trees (ie. Hash Trees).

Used in many cases (P2P programs, digital signatures) where you want to verify the hash of a whole file when you only have part of the file available to you.

19- <zvrba> Van Emde-Boas trees

I think it’d be useful to know why they’re cool. In general, the question “why” is the most important to ask 😉

My answer is that they give you O(log log n) dictionaries with {1..n} keys, independent of how many of the keys are in use. Just like repeated halving gives you O(log n), repeated sqrting gives you O(log log n), which is what happens in the vEB tree.

20- An interesting variant of the hash table is called Cuckoo Hashing. It uses multiple hash functions instead of just 1 in order to deal with hash collisions. Collisions are resolved by removing the old object from the location specified by the primary hash, and moving it to a location specified by an alternate hash function. Cuckoo Hashing allows for more efficient use of memory space because you can increase your load factor up to 91% with only 3 hash functions and still have good access time.

Honorable mentions: splay trees, Cuckoo Hashing, min-max heap,  Cache Oblivious datastructures, Left Leaning Red-Black Trees, Work Stealing Queue, Bootstrapped skew-binomial heaps , Kd-Trees, MX-CIF Quadtrees, HAMT, Inverted Index, Fenwick Tree, Ball Tress, Van Emde-Boas trees. Nested sets , half-edge data structure , Scapegoat trees, unrolled linked list, 2-3 Finger Trees, Pairing heaps , Interval Trees, XOR Linked List, Binary decision diagram, The Region Quadtree, treaps, Counted unsorted balanced btrees, Arne Andersson trees , DAWGs , BK-Trees, or Burkhard-Keller TreesZobrist Hashing, Persistent Data Structures, B* tree, Deletable Bloom Filters (DlBF)

Ring-Buffer, Skip lists, Priority deque, Ternary Search Tree, FM-index, PQ-Trees, sparse matrix data structures, Delta list/delta queue, Bucket Brigade, Burrows–Wheeler transform , corner-stitched data structure. Disjoint Set Forests, Binomial heap, Cycle Sort 

Variable names in languages like Python are not bound to storage locations until run time. That means you have to look up each name to find out what storage it is bound to and what its type is before you can apply an operation like “+” to it. In C++, names are bound to storage at compile time, so no lookup is needed, and the type is fixed at compile time so the compiler can generate machine code with no overhead for interpretation. Late-bound languages will never be as fast as languages bound at compile time.

You could make a language that looks kinda like Python that is compile-time bound and statically typed. You could incrementally compile such a language. But you can also build an environment that incrementally compiles C++ so it would feel a lot like using Python. Try godbolt or tutorialspoint if you want to see this actually working for small programs. 

Source: quora

Have I got good news for you! No one has ever asked me my IQ, nor have I ever asked anyone for their IQ. This was true when I was a software engineer, and is true now that I’m a computer scientist.

Try to learn to program. If you can learn in an appropriate environment (a class with a good instructor), go from there. If you fail the first time, adjust your learning approach and try again. If you still can’t, find another future; you probably wouldn’t like computer programming, anyway. If you learn later, that’s fine. 

Source: Here

Beginners to C++ will consistently struggle with getting a C++ program off the ground. Even “Hello World” can be a challenge. Making a GUI in C++ from scratch? Almost impossible in the beginning.

These 4 areas cannot be learned by any beginner to C++ in 1 day or even 1 month in most cases. These areas challenge nearly all beginners and I have seen cases where it can take a few months to teach.

These are the most fundamental things you need to be able to do to build and produce a program in C++.

Basic Challenge #1: Creating a Program File

  1. Compiling and linking, even in an IDE.
  2. Project settings in an IDE for C++ projects.
  3. Make files, scripts, environment variables affecting compilation.

Basic Challenge #2: Using Other People’s C++ Code

  1. Going outside the STL and using libraries.
  2. Proper library paths in source, file path during compile.
  3. Static versus dynamic libraries during linking.
  4. Symbol reference resolution.

Basic Challenge #3: Troubleshooting Code

  1. Deciphering compiler error messages.
  2. Deciphering linker error messages.
  3. Resolving segmentation faults.

Basic Challenge #4: Actual C++ Code

  1. Writing excellent if/loop/case/assign/call statements.
  2. Managing header/implementation files consistently.
  3. Rigorously avoiding name collisions while staying productive.
  4. Various forms of function callback, especially in GUIs.

How do you explain them?

You cannot explain any of them in a way that most persons will pick up right away. You can describe these things by way of analogy, you can even have learners mirror you at the same time you demonstrate them. I’ve done similar things with trainees in a work setting. In the end, it usually requires time on the order of months and years to pick up these things.

More at C++ the Basic Way – UI and Command-Line

As a professional compiler writer and a student of computers languages and computer architecture this question needs a deeper analysis.

I would proposed the following taxonomy:

1. Assembly code,

2. Implementation languages,

3. Low Level languages and

4. High Level Languages.

Assembly code is where one-for-one translation between source and code.

Macro processors were invented to improve productivity. But to debug a one-for-one listing is needed. The next questions is “What is the hardest Assembly code?” I would vote for the x86–32. It is a very byzantine architecture with a number of mistakes and miss steps. Fortunately the x86–64 cleans up many of these errors.

Implementation languages are languages that are architecture specific but allow a more statement like expression.

There is no “semantic gap” between Assembly code and the machine. Bliss, PL360, and at the first versions of C were in this category. They required the same understanding of the machine as assembly without the pain of assembly. These are hard languages. The semantic gap is only one of syntax.

Next are the Low Level Languages.

Modern “c” firmly fits here. These are languages who’s design was molded about the limitations of computer architecture. FORTRAN, C, Pascal, and Basic are archetypes of these languages. These are easier to learn and use than Assembly and Implementation language. They all have a “Run Time Library” that maintain an execution environment.

As a note, LISP has some syntax, CAR and CDR, which are left over from the IBM 704 it was first implemented on.

Last are the “High Level Languages”.

Languages that require extensive runtime environment to support. Except for Algol, require a “garbage collector” for efficient memory support. The languages are: Algol, SNOBOL4, LISP (and it variants), Java, Smalltalk, Python, Ruby, and Prolog.

Which of these is hardest? I would vote for Prolog with LISP being second. Why? The logical process of “Resolution” has taken me some time learn. Mastery is a long ways away. It is harder than Assembly code? Yes and no. I would never attempt a problem I use Prolog for in Assembly. The order of effort is too big. I find I spend hours writing 20 lines of Prolog which replaces hundreds of lines of SNOBOL4. LISP can be hard unless you have intelligent editors and other tools. In one sense LISP is an “assembly language for an AI machine” and Prolog is “assembly language for a logic machine.” Both Prolog and LISP are very powerful languages. I find it takes deep mental effort to write code in both. But code does wonderful things!

What and where are the stack and the heap?

  • Where and what are they (physically in a real computer’s memory)?
  • To what extent are they controlled by the OS or language run-time?
  • What is their scope?
  • What determines the size of each of them?
  • What makes one faster?

The stack is the memory set aside as scratch space for a thread of execution. When a function is called, a block is reserved on the top of the stack for local variables and some bookkeeping data. When that function returns, the block becomes unused and can be used the next time a function is called. The stack is always reserved in a LIFO (last in first out) order; the most recently reserved block is always the next block to be freed. This makes it really simple to keep track of the stack; freeing a block from the stack is nothing more than adjusting one pointer.

The heap is memory set aside for dynamic allocation. Unlike the stack, there’s no enforced pattern to the allocation and deallocation of blocks from the heap; you can allocate a block at any time and free it at any time. This makes it much more complex to keep track of which parts of the heap are allocated or free at any given time; there are many custom heap allocators available to tune heap performance for different usage patterns.

Each thread gets a stack, while there’s typically only one heap for the application (although it isn’t uncommon to have multiple heaps for different types of allocation).

To answer your questions directly:

To what extent are they controlled by the OS or language runtime?

The OS allocates the stack for each system-level thread when the thread is created. Typically the OS is called by the language runtime to allocate the heap for the application.

What is their scope?

The stack is attached to a thread, so when the thread exits the stack is reclaimed. The heap is typically allocated at application startup by the runtime, and is reclaimed when the application (technically process) exits.

What determines the size of each of them?

The size of the stack is set when a thread is created. The size of the heap is set on application startup, but can grow as space is needed (the allocator requests more memory from the operating system).

What makes one faster?

The stack is faster because the access pattern makes it trivial to allocate and deallocate memory from it (a pointer/integer is simply incremented or decremented), while the heap has much more complex bookkeeping involved in an allocation or deallocation. Also, each byte in the stack tends to be reused very frequently which means it tends to be mapped to the processor’s cache, making it very fast. Another performance hit for the heap is that the heap, being mostly a global resource, typically has to be multi-threading safe, i.e. each allocation and deallocation needs to be – typically – synchronized with “all” other heap accesses in the program.

A clear demonstration: 
Image source: vikashazrati.wordpress.com


  • Stored in computer RAM just like the heap.
  • Variables created on the stack will go out of scope and are automatically deallocated.
  • Much faster to allocate in comparison to variables on the heap.
  • Implemented with an actual stack data structure.
  • Stores local data, return addresses, used for parameter passing.
  • Can have a stack overflow when too much of the stack is used (mostly from infinite or too deep recursion, very large allocations).
  • Data created on the stack can be used without pointers.
  • You would use the stack if you know exactly how much data you need to allocate before compile time and it is not too big.
  • Usually has a maximum size already determined when your program starts.


  • Stored in computer RAM just like the stack.
  • In C++, variables on the heap must be destroyed manually and never fall out of scope. The data is freed with deletedelete[], or free.
  • Slower to allocate in comparison to variables on the stack.
  • Used on demand to allocate a block of data for use by the program.
  • Can have fragmentation when there are a lot of allocations and deallocations.
  • In C++ or C, data created on the heap will be pointed to by pointers and allocated with new or malloc respectively.
  • Can have allocation failures if too big of a buffer is requested to be allocated.
  • You would use the heap if you don’t know exactly how much data you will need at run time or if you need to allocate a lot of data.
  • Responsible for memory leaks.


int foo()
  char *pBuffer; //<--nothing allocated yet (excluding the pointer itself, which is allocated here on the stack).
  bool b = true; // Allocated on the stack.
    //Create 500 bytes on the stack
    char buffer[500];

    //Create 500 bytes on the heap
    pBuffer = new char[500];

   }//<-- buffer is deallocated here, pBuffer is not
}//<--- oops there's a memory leak, I should have called delete[] pBuffer;

he most important point is that heap and stack are generic terms for ways in which memory can be allocated. They can be implemented in many different ways, and the terms apply to the basic concepts.

  • In a stack of items, items sit one on top of the other in the order they were placed there, and you can only remove the top one (without toppling the whole thing over).

    Stack like a stack of papers

    The simplicity of a stack is that you do not need to maintain a table containing a record of each section of allocated memory; the only state information you need is a single pointer to the end of the stack. To allocate and de-allocate, you just increment and decrement that single pointer. Note: a stack can sometimes be implemented to start at the top of a section of memory and extend downwards rather than growing upwards.

  • In a heap, there is no particular order to the way items are placed. You can reach in and remove items in any order because there is no clear ‘top’ item.

    Heap like a heap of licorice allsorts

    Heap allocation requires maintaining a full record of what memory is allocated and what isn’t, as well as some overhead maintenance to reduce fragmentation, find contiguous memory segments big enough to fit the requested size, and so on. Memory can be deallocated at any time leaving free space. Sometimes a memory allocator will perform maintenance tasks such as defragmenting memory by moving allocated memory around, or garbage collecting – identifying at runtime when memory is no longer in scope and deallocating it.

These images should do a fairly good job of describing the two ways of allocating and freeing memory in a stack and a heap. Yum!

  • To what extent are they controlled by the OS or language runtime?

    As mentioned, heap and stack are general terms, and can be implemented in many ways. Computer programs typically have a stack called a call stack which stores information relevant to the current function such as a pointer to whichever function it was called from, and any local variables. Because functions call other functions and then return, the stack grows and shrinks to hold information from the functions further down the call stack. A program doesn’t really have runtime control over it; it’s determined by the programming language, OS and even the system architecture.

    A heap is a general term used for any memory that is allocated dynamically and randomly; i.e. out of order. The memory is typically allocated by the OS, with the application calling API functions to do this allocation. There is a fair bit of overhead required in managing dynamically allocated memory, which is usually handled by the runtime code of the programming language or environment used.

  • What is their scope?

    The call stack is such a low level concept that it doesn’t relate to ‘scope’ in the sense of programming. If you disassemble some code you’ll see relative pointer style references to portions of the stack, but as far as a higher level language is concerned, the language imposes its own rules of scope. One important aspect of a stack, however, is that once a function returns, anything local to that function is immediately freed from the stack. That works the way you’d expect it to work given how your programming languages work. In a heap, it’s also difficult to define. The scope is whatever is exposed by the OS, but your programming language probably adds its rules about what a “scope” is in your application. The processor architecture and the OS use virtual addressing, which the processor translates to physical addresses and there are page faults, etc. They keep track of what pages belong to which applications. You never really need to worry about this, though, because you just use whatever method your programming language uses to allocate and free memory, and check for errors (if the allocation/freeing fails for any reason).

  • What determines the size of each of them?

    Again, it depends on the language, compiler, operating system and architecture. A stack is usually pre-allocated, because by definition it must be contiguous memory. The language compiler or the OS determine its size. You don’t store huge chunks of data on the stack, so it’ll be big enough that it should never be fully used, except in cases of unwanted endless recursion (hence, “stack overflow”) or other unusual programming decisions.

    A heap is a general term for anything that can be dynamically allocated. Depending on which way you look at it, it is constantly changing size. In modern processors and operating systems the exact way it works is very abstracted anyway, so you don’t normally need to worry much about how it works deep down, except that (in languages where it lets you) you mustn’t use memory that you haven’t allocated yet or memory that you have freed.

  • What makes one faster?

    The stack is faster because all free memory is always contiguous. No list needs to be maintained of all the segments of free memory, just a single pointer to the current top of the stack. Compilers usually store this pointer in a special, fast register for this purpose. What’s more, subsequent operations on a stack are usually concentrated within very nearby areas of memory, which at a very low level is good for optimization by the processor on-die caches.

  • Both the stack and the heap are memory areas allocated from the underlying operating system (often virtual memory that is mapped to physical memory on demand).
  • In a multi-threaded environment each thread will have its own completely independent stack but they will share the heap. Concurrent access has to be controlled on the heap and is not possible on the stack.

The heap

  • The heap contains a linked list of used and free blocks. New allocations on the heap (by new or malloc) are satisfied by creating a suitable block from one of the free blocks. This requires updating the list of blocks on the heap. This meta information about the blocks on the heap is also stored on the heap often in a small area just in front of every block.
  • As the heap grows new blocks are often allocated from lower addresses towards higher addresses. Thus you can think of the heap as a heap of memory blocks that grows in size as memory is allocated. If the heap is too small for an allocation the size can often be increased by acquiring more memory from the underlying operating system.
  • Allocating and deallocating many small blocks may leave the heap in a state where there are a lot of small free blocks interspersed between the used blocks. A request to allocate a large block may fail because none of the free blocks are large enough to satisfy the allocation request even though the combined size of the free blocks may be large enough. This is called heap fragmentation.
  • When a used block that is adjacent to a free block is deallocated the new free block may be merged with the adjacent free block to create a larger free block effectively reducing the fragmentation of the heap.

The heap

The stack

  • The stack often works in close tandem with a special register on the CPU named the stack pointer. Initially the stack pointer points to the top of the stack (the highest address on the stack).
  • The CPU has special instructions for pushing values onto the stack and popping them off the stack. Each push stores the value at the current location of the stack pointer and decreases the stack pointer. A pop retrieves the value pointed to by the stack pointer and then increases the stack pointer (don’t be confused by the fact that adding a value to the stack decreases the stack pointer and removing a value increases it. Remember that the stack grows to the bottom). The values stored and retrieved are the values of the CPU registers.
  • If a function has parameters, these are pushed onto the stack before the call to the function. The code in the function is then able to navigate up the stack from the current stack pointer to locate these values.
  • When a function is called the CPU uses special instructions that push the current instruction pointer onto the stack, i.e. the address of the code executing on the stack. The CPU then jumps to the function by setting the instruction pointer to the address of the function called. Later, when the function returns, the old instruction pointer is popped off the stack and execution resumes at the code just after the call to the function.
  • When a function is entered, the stack pointer is decreased to allocate more space on the stack for local (automatic) variables. If the function has one local 32 bit variable four bytes are set aside on the stack. When the function returns, the stack pointer is moved back to free the allocated area.
  • Nesting function calls work like a charm. Each new call will allocate function parameters, the return address and space for local variables and these activation records can be stacked for nested calls and will unwind in the correct way when the functions return.
  • As the stack is a limited block of memory, you can cause a stack overflow by calling too many nested functions and/or allocating too much space for local variables. Often the memory area used for the stack is set up in such a way that writing below the bottom (the lowest address) of the stack will trigger a trap or exception in the CPU. This exceptional condition can then be caught by the runtime and converted into some kind of stack overflow exception.

The stack

Can a function be allocated on the heap instead of a stack?

No, activation records for functions (i.e. local or automatic variables) are allocated on the stack that is used not only to store these variables, but also to keep track of nested function calls.

How the heap is managed is really up to the runtime environment. C uses malloc and C++ uses new, but many other languages have garbage collection.

However, the stack is a more low-level feature closely tied to the processor architecture. Growing the heap when there is not enough space isn’t too hard since it can be implemented in the library call that handles the heap. However, growing the stack is often impossible as the stack overflow only is discovered when it is too late; and shutting down the thread of execution is the only viable option.

In the following C# code

public void Method1()
    int i = 4;
    int y = 2;
    class1 cls1 = new class1();

Here’s how the memory is managed

Picture of variables on the stack

Local Variables that only need to last as long as the function invocation go in the stack. The heap is used for variables whose lifetime we don’t really know up front but we expect them to last a while. In most languages it’s critical that we know at compile time how large a variable is if we want to store it on the stack.

Objects (which vary in size as we update them) go on the heap because we don’t know at creation time how long they are going to last. In many languages the heap is garbage collected to find objects (such as the cls1 object) that no longer have any references.

In Java, most objects go directly into the heap. In languages like C / C++, structs and classes can often remain on the stack when you’re not dealing with pointers.

More information can be found here:

The difference between stack and heap memory allocation « timmurphy.org

and here:

Creating Objects on the Stack and Heap

This article is the source of picture above: Six important .NET concepts: Stack, heap, value types, reference types, boxing, and unboxing – CodeProject

but be aware it may contain some inaccuracies.

The Stack When you call a function the arguments to that function plus some other overhead is put on the stack. Some info (such as where to go on return) is also stored there. When you declare a variable inside your function, that variable is also allocated on the stack.

Deallocating the stack is pretty simple because you always deallocate in the reverse order in which you allocate. Stack stuff is added as you enter functions, the corresponding data is removed as you exit them. This means that you tend to stay within a small region of the stack unless you call lots of functions that call lots of other functions (or create a recursive solution).

The Heap The heap is a generic name for where you put the data that you create on the fly. If you don’t know how many spaceships your program is going to create, you are likely to use the new (or malloc or equivalent) operator to create each spaceship. This allocation is going to stick around for a while, so it is likely we will free things in a different order than we created them.

Thus, the heap is far more complex, because there end up being regions of memory that are unused interleaved with chunks that are – memory gets fragmented. Finding free memory of the size you need is a difficult problem. This is why the heap should be avoided (though it is still often used).

Implementation Implementation of both the stack and heap is usually down to the runtime / OS. Often games and other applications that are performance critical create their own memory solutions that grab a large chunk of memory from the heap and then dish it out internally to avoid relying on the OS for memory.

This is only practical if your memory usage is quite different from the norm – i.e for games where you load a level in one huge operation and can chuck the whole lot away in another huge operation.

Physical location in memory This is less relevant than you think because of a technology called Virtual Memory which makes your program think that you have access to a certain address where the physical data is somewhere else (even on the hard disc!). The addresses you get for the stack are in increasing order as your call tree gets deeper. The addresses for the heap are un-predictable (i.e implementation specific) and frankly not important.

In Short

A stack is used for static memory allocation and a heap for dynamic memory allocation, both stored in the computer’s RAM.

In Detail

The Stack

The stack is a “LIFO” (last in, first out) data structure, that is managed and optimized by the CPU quite closely. Every time a function declares a new variable, it is “pushed” onto the stack. Then every time a function exits, all of the variables pushed onto the stack by that function, are freed (that is to say, they are deleted). Once a stack variable is freed, that region of memory becomes available for other stack variables.

The advantage of using the stack to store variables, is that memory is managed for you. You don’t have to allocate memory by hand, or free it once you don’t need it any more. What’s more, because the CPU organizes stack memory so efficiently, reading from and writing to stack variables is very fast.

More can be found here.

The Heap

The heap is a region of your computer’s memory that is not managed automatically for you, and is not as tightly managed by the CPU. It is a more free-floating region of memory (and is larger). To allocate memory on the heap, you must use malloc() or calloc(), which are built-in C functions. Once you have allocated memory on the heap, you are responsible for using free() to deallocate that memory once you don’t need it any more.

If you fail to do this, your program will have what is known as a memory leak. That is, memory on the heap will still be set aside (and won’t be available to other processes). As we will see in the debugging section, there is a tool called Valgrind that can help you detect memory leaks.

Unlike the stack, the heap does not have size restrictions on variable size (apart from the obvious physical limitations of your computer). Heap memory is slightly slower to be read from and written to, because one has to use pointers to access memory on the heap. We will talk about pointers shortly.

Unlike the stack, variables created on the heap are accessible by any function, anywhere in your program. Heap variables are essentially global in scope.

More can be found here.

Variables allocated on the stack are stored directly to the memory and access to this memory is very fast, and its allocation is dealt with when the program is compiled. When a function or a method calls another function which in turns calls another function, etc., the execution of all those functions remains suspended until the very last function returns its value. The stack is always reserved in a LIFO order, the most recently reserved block is always the next block to be freed. This makes it really simple to keep track of the stack, freeing a block from the stack is nothing more than adjusting one pointer.

Variables allocated on the heap have their memory allocated at run time and accessing this memory is a bit slower, but the heap size is only limited by the size of virtual memory. Elements of the heap have no dependencies with each other and can always be accessed randomly at any time. You can allocate a block at any time and free it at any time. This makes it much more complex to keep track of which parts of the heap are allocated or free at any given time.

Enter image description here

You can use the stack if you know exactly how much data you need to allocate before compile time, and it is not too big. You can use the heap if you don’t know exactly how much data you will need at runtime or if you need to allocate a lot of data.

In a multi-threaded situation each thread will have its own completely independent stack, but they will share the heap. The stack is thread specific and the heap is application specific. The stack is important to consider in exception handling and thread executions.

Each thread gets a stack, while there’s typically only one heap for the application (although it isn’t uncommon to have multiple heaps for different types of allocation).

Enter image description here

At run-time, if the application needs more heap, it can allocate memory from free memory and if the stack needs memory, it can allocate memory from free memory allocated memory for the application.

Even, more detail is given here and here.

Now come to your question’s answers.

To what extent are they controlled by the OS or language runtime?

The OS allocates the stack for each system-level thread when the thread is created. Typically the OS is called by the language runtime to allocate the heap for the application.

More can be found here.

What is their scope?

Already given in top.

“You can use the stack if you know exactly how much data you need to allocate before compile time, and it is not too big. You can use the heap if you don’t know exactly how much data you will need at runtime or if you need to allocate a lot of data.”

More can be found in here.

What determines the size of each of them?

The size of the stack is set by OS when a thread is created. The size of the heap is set on application startup, but it can grow as space is needed (the allocator requests more memory from the operating system).

What makes one faster?

Stack allocation is much faster since all it really does is move the stack pointer. Using memory pools, you can get comparable performance out of heap allocation, but that comes with a slight added complexity and its own headaches.

Also, stack vs. heap is not only a performance consideration; it also tells you a lot about the expected lifetime of objects.

Details can be found from here.

How do you stop scripters from slamming your website hundreds of times a second?

How about implementing something like SO does with the CAPTCHAs?

If you’re using the site normally, you’ll probably never see one. If you happen to reload the same page too often, post successive comments too quickly, or something else that triggers an alarm, make them prove they’re human. In your case, this would probably be constant reloads of the same page, following every link on a page quickly, or filling in an order form too fast to be human.

If they fail the check x times in a row (say, 2 or 3), give that IP a timeout or other such measure. Then at the end of the timeout, dump them back to the check again.

Since you have unregistered users accessing the site, you do have only IPs to go on. You can issue sessions to each browser and track that way if you wish. And, of course, throw up a human-check if too many sessions are being (re-)created in succession (in case a bot keeps deleting the cookie).

As far as catching too many innocents, you can put up a disclaimer on the human-check page: “This page may also appear if too many anonymous users are viewing our site from the same location. We encourage you to register or login to avoid this.” (Adjust the wording appropriately.)

Besides, what are the odds that X people are loading the same page(s) at the same time from one IP? If they’re high, maybe you need a different trigger mechanism for your bot alarm.

Edit: Another option is if they fail too many times, and you’re confident about the product’s demand, to block them and make them personally CALL you to remove the block.

Having people call does seem like an asinine measure, but it makes sure there’s a human somewhere behind the computer. The key is to have the block only be in place for a condition which should almost never happen unless it’s a bot (e.g. fail the check multiple times in a row). Then it FORCES human interaction – to pick up the phone.

In response to the comment of having them call me, there’s obviously that tradeoff here. Are you worried enough about ensuring your users are human to accept a couple phone calls when they go on sale? If I were so concerned about a product getting to human users, I’d have to make this decision, perhaps sacrificing a (small) bit of my time in the process.

Since it seems like you’re determined to not let bots get the upper hand/slam your site, I believe the phone may be a good option. Since I don’t make a profit off your product, I have no interest in receiving these calls. Were you to share some of that profit, however, I may become interested. As this is your product, you have to decide how much you care and implement accordingly.

The other ways of releasing the block just aren’t as effective: a timeout (but they’d get to slam your site again after, rinse-repeat), a long timeout (if it was really a human trying to buy your product, they’d be SOL and punished for failing the check), email (easily done by bots), fax (same), or snail mail (takes too long).

You could, of course, instead have the timeout period increase per IP for each time they get a timeout. Just make sure you’re not punishing true humans inadvertently.

The unsatisfying answer: Nearly every C++ compiler can output assembly language,* so assembly language can be exactly the same speed as C++ if you use C++ to develop the assembly code.

The more interesting answer: It’s highly unlikely that an application written entirely in assembly language remains faster than the same application written in C++ over the long run, even in the unlikely case it starts out faster.

Repeat after me: Assembly Language Isn’t Magic™.

For the nitty gritty details, I’ll just point you to some previous answers I’ve written, as well as some related questions, and at the end, an excellent answer from Christopher Clark:

Performance optimization strategies as a last resort

Let’s assume:

  • the code already is working correctly
  • the algorithms chosen are already optimal for the circumstances of the problem
  • the code has been measured, and the offending routines have been isolated
  • all attempts to optimize will also be measured to ensure they do not make matters worse

OK, you’re defining the problem to where it would seem there is not much room for improvement. That is fairly rare, in my experience. I tried to explain this in a Dr. Dobbs article in November 1993, by starting from a conventionally well-designed non-trivial program with no obvious waste and taking it through a series of optimizations until its wall-clock time was reduced from 48 seconds to 1.1 seconds, and the source code size was reduced by a factor of 4. My diagnostic tool was this. The sequence of changes was this:

  • The first problem found was use of list clusters (now called “iterators” and “container classes”) accounting for over half the time. Those were replaced with fairly simple code, bringing the time down to 20 seconds.

  • Now the largest time-taker is more list-building. As a percentage, it was not so big before, but now it is because the bigger problem was removed. I find a way to speed it up, and the time drops to 17 seconds.

  • Now it is harder to find obvious culprits, but there are a few smaller ones that I can do something about, and the time drops to 13 sec.

Now I seem to have hit a wall. The samples are telling me exactly what it is doing, but I can’t seem to find anything that I can improve. Then I reflect on the basic design of the program, on its transaction-driven structure, and ask if all the list-searching that it is doing is actually mandated by the requirements of the problem.

Then I hit upon a re-design, where the program code is actually generated (via preprocessor macros) from a smaller set of source, and in which the program is not constantly figuring out things that the programmer knows are fairly predictable. In other words, don’t “interpret” the sequence of things to do, “compile” it.

  • That redesign is done, shrinking the source code by a factor of 4, and the time is reduced to 10 seconds.

Now, because it’s getting so quick, it’s hard to sample, so I give it 10 times as much work to do, but the following times are based on the original workload.

  • More diagnosis reveals that it is spending time in queue-management. In-lining these reduces the time to 7 seconds.

  • Now a big time-taker is the diagnostic printing I had been doing. Flush that – 4 seconds.

  • Now the biggest time-takers are calls to malloc and free. Recycle objects – 2.6 seconds.

  • Continuing to sample, I still find operations that are not strictly necessary – 1.1 seconds.

Total speedup factor: 43.6

Now no two programs are alike, but in non-toy software I’ve always seen a progression like this. First you get the easy stuff, and then the more difficult, until you get to a point of diminishing returns. Then the insight you gain may well lead to a redesign, starting a new round of speedups, until you again hit diminishing returns. Now this is the point at which it might make sense to wonder whether ++i or i++ or for(;;) or while(1) are faster: the kinds of questions I see so often on Stack Overflow.

P.S. It may be wondered why I didn’t use a profiler. The answer is that almost every one of these “problems” was a function call site, which stack samples pinpoint. Profilers, even today, are just barely coming around to the idea that statements and call instructions are more important to locate, and easier to fix, than whole functions.

I actually built a profiler to do this, but for a real down-and-dirty intimacy with what the code is doing, there’s no substitute for getting your fingers right in it. It is not an issue that the number of samples is small, because none of the problems being found are so tiny that they are easily missed.

ADDED: jerryjvl requested some examples. Here is the first problem. It consists of a small number of separate lines of code, together taking over half the time:

if (ptop->current_task >= ILST_LENGTH(ptop->tasklist){
. . .
for ( ptop = ILST_FIRST(oplist); ptop != NULL; ptop = ILST_NEXT(oplist, ptop)){
. . .
ptask = ILST_NTH(ptop->tasklist, ptop->current_task)

These were using the list cluster ILST (similar to a list class). They are implemented in the usual way, with “information hiding” meaning that the users of the class were not supposed to have to care how they were implemented. When these lines were written (out of roughly 800 lines of code) thought was not given to the idea that these could be a “bottleneck” (I hate that word). They are simply the recommended way to do things. It is easy to say in hindsight that these should have been avoided, but in my experience all performance problems are like that. In general, it is good to try to avoid creating performance problems. It is even better to find and fix the ones that are created, even though they “should have been avoided” (in hindsight). I hope that gives a bit of the flavor.

Here is the second problem, in two separate lines:

ILST_APPEND(ptop->tasklist, ptask)
. . .
ILST_APPEND(trnque, ptrn)

These are building lists by appending items to their ends. (The fix was to collect the items in arrays, and build the lists all at once.) The interesting thing is that these statements only cost (i.e. were on the call stack) 3/48 of the original time, so they were not in fact a big problem at the beginning. However, after removing the first problem, they cost 3/20 of the time and so were now a “bigger fish”. In general, that’s how it goes.

I might add that this project was distilled from a real project I helped on. In that project, the performance problems were far more dramatic (as were the speedups), such as calling a database-access routine within an inner loop to see if a task was finished.

REFERENCE ADDED: The source code, both original and redesigned, can be found in www.ddj.com, for 1993, in file 9311.zip, files slug.asc and slug.zip.

EDIT 2011/11/26: There is now a SourceForge project containing source code in Visual C++ and a blow-by-blow description of how it was tuned. It only goes through the first half of the scenario described above, and it doesn’t follow exactly the same sequence, but still gets a 2-3 order of magnitude speedup.


  • Pre-compute rather than re-calculate: any loops or repeated calls that contain calculations that have a relatively limited range of inputs, consider making a lookup (array or dictionary) that contains the result of that calculation for all values in the valid range of inputs. Then use a simple lookup inside the algorithm instead.
    Down-sides: if few of the pre-computed values are actually used this may make matters worse, also the lookup may take significant memory.
  • Don’t use library methods: most libraries need to be written to operate correctly under a broad range of scenarios, and perform null checks on parameters, etc. By re-implementing a method you may be able to strip out a lot of logic that does not apply in the exact circumstance you are using it.
    Down-sides: writing additional code means more surface area for bugs.
  • Do use library methods: to contradict myself, language libraries get written by people that are a lot smarter than you or me; odds are they did it better and faster. Do not implement it yourself unless you can actually make it faster (i.e.: always measure!)
  • Cheat: in some cases although an exact calculation may exist for your problem, you may not need ‘exact’, sometimes an approximation may be ‘good enough’ and a lot faster in the deal. Ask yourself, does it really matter if the answer is out by 1%? 5%? even 10%?
    Down-sides: Well… the answer won’t be exact.

When you can’t improve the performance any more – see if you can improve the perceived performance instead.

You may not be able to make your fooCalc algorithm faster, but often there are ways to make your application seem more responsive to the user.

A few examples:

  • anticipating what the user is going to request and start working on that before then
  • displaying results as they come in, instead of all at once at the end
  • Accurate progress meter

These won’t make your program faster, but it might make your users happier with the speed you have.

I spend most of my life in just this place. The broad strokes are to run your profiler and get it to record:

  • Cache misses. Data cache is the #1 source of stalls in most programs. Improve cache hit rate by reorganizing offending data structures to have better locality; pack structures and numerical types down to eliminate wasted bytes (and therefore wasted cache fetches); prefetch data wherever possible to reduce stalls.
  • Load-hit-stores. Compiler assumptions about pointer aliasing, and cases where data is moved between disconnected register sets via memory, can cause a certain pathological behavior that causes the entire CPU pipeline to clear on a load op. Find places where floats, vectors, and ints are being cast to one another and eliminate them. Use __restrict liberally to promise the compiler about aliasing.
  • Microcoded operations. Most processors have some operations that cannot be pipelined, but instead run a tiny subroutine stored in ROM. Examples on the PowerPC are integer multiply, divide, and shift-by-variable-amount. The problem is that the entire pipeline stops dead while this operation is executing. Try to eliminate use of these operations or at least break them down into their constituent pipelined ops so you can get the benefit of superscalar dispatch on whatever the rest of your program is doing.
  • Branch mispredicts. These too empty the pipeline. Find cases where the CPU is spending a lot of time refilling the pipe after a branch, and use branch hinting if available to get it to predict correctly more often. Or better yet, replace branches with conditional-moves wherever possible, especially after floating point operations because their pipe is usually deeper and reading the condition flags after fcmp can cause a stall.
  • Sequential floating-point ops. Make these SIMD.

And one more thing I like to do:

  • Set your compiler to output assembly listings and look at what it emits for the hotspot functions in your code. All those clever optimizations that “a good compiler should be able to do for you automatically”? Chances are your actual compiler doesn’t do them. I’ve seen GCC emit truly WTF code.

More suggestions:

  • Avoid I/O: Any I/O (disk, network, ports, etc.) is always going to be far slower than any code that is performing calculations, so get rid of any I/O that you do not strictly need.

  • Move I/O up-front: Load up all the data you are going to need for a calculation up-front, so that you do not have repeated I/O waits within the core of a critical algorithm (and maybe as a result repeated disk seeks, when loading all the data in one hit may avoid seeking).

  • Delay I/O: Do not write out your results until the calculation is over, store them in a data structure and then dump that out in one go at the end when the hard work is done.

  • Threaded I/O: For those daring enough, combine ‘I/O up-front’ or ‘Delay I/O’ with the actual calculation by moving the loading into a parallel thread, so that while you are loading more data you can work on a calculation on the data you already have, or while you calculate the next batch of data you can simultaneously write out the results from the last batch.

I love all the

  1. graph algorithms in particular the Bellman Ford Algorithm
  2. Scheduling algorithms the Round-Robin scheduling algorithm in particular.
  3. Dynamic Programming algorithms the Knapsack fractional algorithm in particular.
  4. Backtracking algorithms the 8-Queens algorithm in particular.
  5. Greedy algorithms the Knapsack 0/1 algorithm in particular.

We use all these algorithms in our daily life in various forms at various places.

For example every shopkeeper applies anyone or more of the several scheduling algorithms to service his customers. Depending upon his service policy and situation. No one of the scheduling algorithm fits all the situations.

All of us mentally apply one of the graph algorithms when we plan the shortest route to be taken when we go out for doing multiple things in one trip.

All of us apply one of the Greedy algorithms while selecting career, job, girlfriends, friends etc.

All of us apply one of the Dynamic programming algorithms when we do simple multiplication mentally by referring to the various mathematical products table in our memory.

How much faster is C compared to Python?

Top 7 Most Popular Programming Languages (Most Used High Level List)

It uses TimSort, a sort algorithm which was invented by Tim Peters, and is now used in other languages such as Java.

TimSort is a complex algorithm which uses the best of many other algorithms, and has the advantage of being stable – in others words if two elements A & B are in the order A then B before the sort algorithm and those elements test equal during the sort, then the algorithm Guarantees that the result will maintain that A then B ordering.

That does mean for example if you want to say order a set of student scores by score and then name (so equal scores are ordered already alphabetically) then you can sort by name and then sort by score.

TimSort has good performance against data sets which are partially sorted or already sorted (areas where some other algorithms struggle).

Timsort – Wikipedia
Timsort was designed to take advantage of runs of consecutive ordered elements that already exist in most real-world data, natural runs . It iterates over the data collecting elements into runs and simultaneously putting those runs in a stack. Whenever the runs on the top of the stack match a merge criterion , they are merged. This goes on until all data is traversed; then, all runs are merged two at a time and only one sorted run remains. 

Run Your Python Code Online Here

I’m currently coding a SAT solver algorithm that will have to take millions of input data, and I was wondering if I should switch from Python to C.

Answer: Using best-of-class equivalent algorithms optimized compiled C code is often multiple orders of magnitude faster than Python code interpreted by CPython (the main Python implementation). Other Python implementations (like PyPy) might be a bit better, but not vastly so. Some computations fit Python better, but I have a feeling that a SAT solver implementation will not be competitive if written using Python.

All that said, do you need to write a new implementation? Could you use one of the excellent ones out there? CDCL implementations often do a good job, and there are various open-source ones readily available (e.g., this one: https://github.com/togatoga/togasat


1- I mean, also it depends. I recall seeing an analysis some time ago, that showed CPython can be as fast as C … provided you are almost exclusively using library functions written in C. That being said, for any non-trivial python program it will probably be the case that you must spend quite a bit of time in the interpreter, and not in C library functions.

Why Are There So Many Programming Languages? | Juniors Coders
Popular programming languages

The other answers are mistaken. This is a very common confusion. They describe statically typed language, not strongly typed language. There is a big difference.

Strongly typed vs weakly typed:

In strongly typed languages you get an error if the types do not match in an expression. It does not matter if the type is determined at compile time (static types) or runtime (dynamic types).

Both java and python are strongly typed. In both languages, you get an error if you try to add objects with unmatching types. For example, in python, you get an error if you try to add a number and a string:

  • >>> a = 10 
  • >>> b = “hello” 
  • >>> a + b 
  • Traceback (most recent call last): 
  • File “<stdin>”, line 1, in <module> 
  • TypeError: unsupported operand type(s) for +: ‘int’ and ‘str’ 

In Python, you get this error at runtime. In Java, you would get a similar error at compile time. Most statically typed languages are also strongly typed.

The opposite of strongly typed language is weakly typed. In a weakly typed language, there are implicit type conversions. Instead of giving you an error, it will convert one of the values automatically and produce a result, even if such conversion loses data. This often leads to unexpected and unpredictable behavior.

Javascript is an example of a weakly typed language.

  • > let a = 10 
  • > let b = “hello” 
  • > a + b 
  • ’10hello’ 

Instead of an error, JavaScript will convert a to string and then concatenate the strings.

Static types vs dynamic types:

In a statically typed language, variables are bound types and may only hold data of that type. Typically you declare variables and specify the type of data that the variable has. In some languages, the type can be deduced from what you assign to it, but it still holds that the variable is bound to that type. For example, in java:

  • int a = 3; 
  • a = “hello” // Error, a can only contain integers 

in a dynamically typed language, variables may hold any type of data. The type of the data is simply determined by what gets assigned to the variable at runtime. Python is dynamically typed, for example:

  • a = 10 
  • a = “hello” 
  • # no problem, a first held an integer and then a string 


#1: Don’t confuse strongly typed with statically typed.

Python is dynamically typed and strongly typed.
Javascript is dynamically typed and weakly typed.
Java is statically typed and strongly typed.
C is statically typed and weekly typed.

See these articles for a longer explanation:
Magic lies here – Statically vs Dynamically Typed Languages
Key differences between mainly used languages for data science

I also added a drawing that illustrates how strong and static typing relate to each other:

Python is dynamically typed because types are determined at runtime. The opposite of dynamically typed is statically typed (not strongly typed)

Python is strongly typed because it will give errors when types don’t match instead of performing implicit conversion. The opposite of strongly typed is weakly typed

Python is strongly typed and dynamically typed

What is the difference between finalize() and destructor in Java?

Finalize() is not guaranteed to be called and the programmer has no control over what time or in what order finalizers are called.

They are useless and should be ignored.

A destructor is not part of Java. It is a C++ language feature with very precise definitions of when it will be called.


1- Until we got to languages like Rust (with the Drop trait) and a few others was C++ the only language which had the destructor as a concept? I feel like other languages were inspired from that.

2- Many others manage memory for you, even predating C: COBOL, FORTRAN and so on. That’s another driver why there isn’t much attention to destructors

What are some ways to avoid writing static helper classes in Java?

Mainly getting out of that procedural ‘function operates on parameters passed in’ mindset.

Tactically, the static can normally be moved onto one of the parameter objects. Or all the parameters become an object that the static moves to. A new object might be needed. Once done the static is now a fully fledged method on an object and is not static anymore.

I view this as a positive iterative step in discovering objects for a system.

For cases where a static makes sense (? none come to mind) then a good practice is to move it closer to where it is used either in the same package or on a class that is strongly related.

I avoid having global ‘Utils’ classes full of statics that are unrelated. That’s fairly basic design, keeping unrelated things separate. In this case, the SOLID ISP principle applies: segregate into smaller, more focused interfaces.

Is there any programming language as easy as python and as fast and efficient as C++, if yes why it’s not used very often instead of C or C++ in low level programming like embedded systems, AAA 2D and 3D video games, or robotic?

Not really. I use Python occasionally for “quick hacks” – programs that I’ll probably run once and then delete – also, because I use “blender” for 3D modeling and Python is it’s scripting language.

I used to write quite a bit of JavaScript for web programming but since WASM came along and allows me to run C++ at very nearly full speed inside a web browser, I write almost zero JavaScript these days.

I use C++ for almost everything.

Once you get to know C++ it’s no harder than Python – the main thing I find great about Python is the number of easy-to-find libraries.

But in AAA games – the poor performance of Python pretty much rules it out.

In embedded systems, the computer is generally too small to fit a Python interpreter into memory – so C or C++ is a more likely choice.

This was actually one of the interview questions I got when I applied at Google.

“Write a function that returns the average of two number.”

So I did, they way you would expect. (x+y)/2. I did it as a C++ template so it works for any kind of number.

interviewer: “What’s wrong with it?”

Well, I suppose there could be an overflow if adding the two numbers requires more than space than the numeric type can hold. So I rewrote it as (x/2) + (y/2).

interviewer: “What’s wrong with it now?”

Well, I think we are losing a little precision by pre-dividing. So I wrote it another way.

interviewer: “What’s wrong with it now?”

And that went on for about 10 minutes. It ended with us talking about the heat death of the universe.

I got the job and ended up working with the guy. He said he had never done that before. He had just wanted to see what would happen.



The big problem you get with x/2 + y/2 is that it can/will give incorrect answers for integer inputs. For example, let’s average 3 and 3. The result should obviously be 3.

But with integer division, 3/2 = 1, and 1+1 = 2.

You need to add one to the result if and only if both inputs are odd.

2- Here’s what I’d do in C++ for integers, which I believe does the right thing including getting the rounding direction correct, and it can likely be made into a template that will do the right thing as well. This is not complete code, but I believe it gets the details correct…

Programming - Find the average of 2 numbers
Programming – Find the average of 2 numbers

That will work for any signed or unsigned integer type for op1 and op2 as long as they have the same type.

If you want it to do something intelligently where one of the operands is an unsigned type and the other one is a signed type, you could do it, but you need to define exactly what should happen, and realize that it’s quite likely that for maximum arithmetic correctness, the output type may need to be different than either input type. For instance, the average of a uint32_t and an int32_t can be too large to fit in an int32_t, and it can also be too small to fit in a uint32_t, so you probably need to go with a larger signed integer type, maybe int64_t.

3- I would have answered the question with a question, “Tell me more about the input, error handling capability of your system, and is this typical of the level of challenge here at google?” Then I’d provide eye contact, sit back, and see what happens. Years ago I had an interview question that asked what classical problem was part of a pen plotter control system. I told the interviewer that it was TSP but that if you had to change pens, you had to consider how much time it took to switch. They offered me a job but I declined given the poor financial condition of the company (SGI) which I discovered by asking the interviewer questions of my own. IMO: questions are at the heart of engineering. The interviewer, if they are smart, wants to see if you are capable of discovering the true nature of their problems. The best programmers I’ve ever worked with were able to get to the heart of problems and trade off solutions. Coding is a small part of the required skills.

Yes, they can.

There are features in HTTP to allow many different web sites to be served on a single IP address.

You can, if you are careful, assign the same IP address to many machines (it typically can’t be their only IP address, however, as distinguishable addresses make them much easier to manage).

You can run arbitrary server tasks on your many machines with the same IP address if you have some way of sending client connections to the correct machine. Obviously that can’t be the IP address, because they’re all the same. But there are ways.

However… this needs to be carefully planned. There are many issues. Andrew Mc Gregor

It depends on how you want to store and access data.

For the most part, as a general concept, old school cryptography is obsolete.

It was based on ciphers, which were based on it being mathematically “hard” to crack.

If you can throw a compute cluster at DES, even with a one byte “salt”, it’s pretty easy to crack a password database in seconds. Minutes, if your cluster is small.

Almost all computer security is base on big number theory. Today, that’s called: Law of large numbers – Wikipedia

Averages of repeated trials converge to the expected value An illustration of the law of large numbers using a particular run of rolls of a single die . As the number of rolls in this run increases, the average of the values of all the results approaches 3.5. Although each run would show a distinctive shape over a small number of throws (at the left), over a large number of rolls (to the right) the shapes would be extremely similar. In probability theory , the law of large numbers ( LLN ) is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value and tends to become closer to the expected value as more trials are performed. [1] The LLN is important because it guarantees stable long-term results for the averages of some random events. 

What it means is that it’s hard to do math on very large numbers, and so if you have a large one, the larger the better.

Most cryptography today is based on elliptic curves.

But we know by the proof of Fermat’s last theorem, and specifically, the Taniyama-Shimura conjecture, is that all elliptic curves have modular forms.

And so this gives us an attack at all modern cryptogrphay, using graphical mathematics.

It’s an interesting field, and problem space.

Not one I’m interested in solving, since I’m sure it has already been solved by my “associates” who now work for the NSA.

I am only interested in new problems.


1- Sorry, but this is just wrong. “Almost all cryptography,” counted by number of bytes encrypted and decrypted, uses AES. AES does not use “large numbers,” elliptic curves, or anything of that sort – it’s essentially combinatorial in nature, with a lot of bit-diddling – though there is some group theory at its based. The same can be said about cryptographic checksums such as the SHA series, including the latest “sponge” constructions.

Where RSA and elliptic curves and such come in is public key cryptography. This is important in setting up connections, but for multiple reasons (performance – but also for excellent cryptographic reasons) is not use for bulk encryption. There are related algorithms like Diffie-Hellman and some signature protocols like DSS. All of these “use large numbers” in some sense, but even that’s pushing it – elliptic curve cryptography involves doing math over … points on an elliptic curve, which does lead you to do some arithmetic, but the big advantage of elliptic curves is that the numbers are way, way smaller than for, say, RSA for equivalent security.

Much research these days is on “post-quantum cryptography” – cryptography that is secure against attacks by quantum computers (assuming we ever make those work). These tend not to be based on “arithmetic” in any straightforward sense – the ones that seem to be at the forefront these days are based on computation over lattices.

Cracking a password database that uses DES is so far away from what cryptography today is about that it’s not even related. Yes, the original Unix implementations – almost 50 years ago – used that approach. So?

C++ lambda functions are syntactic sugar for a longstanding set of practices in both C and C++: passing a function as an argument to another function, and possibly connecting a little bit of state to it.

This goes way back. Look at C’s qsort():

C++ Function example

That last argument is a function pointer to a comparison function. You could use a captureless lambda for the same purpose in modern C++.

Sometimes, you want to tack a little bit of extra state alongside the function. In C, one way to do this is to provide an additional context pointer alongside the the function pointer. The context pointer will get passed back to the function as an argument.

I give an extended example in here:

In C++, that context pointer can be this. When you do that, you have something called a function object. (Side note: function objects were sometimes called functors; however, functors aren’t really the same thing.)

If you overload the function call operator for a particular class, then objects of that class behave as function objects. That is, you can pretend like the object is a function by putting parentheses and an argument list after the name of an instance! When you arrive at the overloaded operator implementation, this will point at the instance.

Instances of this class will add an offset to an integer. The function call operator is operator() below.

and to use it:

C++ Class Offset

That’ll print out the numbers 42, 43, 44, … 51 on separate lines.

And tying this back to the qsort() example from earlier: C++’s std::sort can take a function object for its comparison operator.

Modern C++’s lambda functions are syntactic sugar for function objects. They declare a class with an unutterable name, and then give you an instance of that class. Under the hood, the class’ constructor implements the capture, and initializes any state variables.

Other languages have similar constructs. I believe this one originated in LISP. It goes waaaay back.

As for any challenges associated with them: lifetime management. You potentially introduce a non-nested lifetime for any state associated with the callback, function object, or lambda.

If it’s all self contained (i.e. it keeps its own copies of everything), you’re less likely to have a problem. It owns all the state it relies on.

If it has non-owning pointers or references to other objects, you need to ensure the lifetime of your callback/function object/lambda remains within the lifetime of that other non-owned object. If that non-owned object’s lifetime isn’t naturally a superset of the callback/function object/lambda, you should consider taking a copy of that object, or reconsider your design.

Each one has specific strengths in terms of syntax features.

But the way to look at this is that all three are general purpose programming languages. You can write pretty much anything in them.

Trying to rank these languages in some kind of absolute hierarchy makes no sense and only leads to tribal ‘fanboi’ arguments.

If you need part of your code to talk to hardware, or could benefit from taking control of memory management, C++ is my choice.

General web service stuff, Java has an edge due to familiarity.

Anything involving a pre existing Microsoft component – eg data in SQL server, Azure – I will go all in on C#

I see more similarity than difference overall

Visual Studio Code is OK if you can’t find anything better for the language you’re using. There are better alternatives for most popular languages.

C# – Use Visual Studio Community, it’s free, and far better than Visual Studio Code.

Java – Use IntelliJ

Go – Goland.

Python – PyCharm.

C or C++ – CLion.

If you’re using a more unusual language, maybe Rust, Visual Studio Code might be a good choice.


#1: Just chipping in here. I used to be a massive visual studio fan boy and loved my fancy gui for doing things without knowing what was actually happening. I’ve been using vscode and Linux for a few years now and am really enjoying the bare metal exposure you get with working on it (and linux) typing commands is way faster to get things done than mouse clicking through a bunch of guis. Both are good though.

#2:  C# is unusual in that it’s the only language which doesn’t follow the maxim, “if JetBrains have blessed your language with attention, use their IDE”.

Visual Studio really is first class.

#3: for Rust as long as you have rust-analyzer and clippy, you’re good to go. Vim with lua and VS Code both work perfectly.

#4: This is definitely skirting the realm of opinion. It’s a great piece of software. There is better and worse stuff but it all depends upon the person using it, their skill, and style of development.

#5: VSCode is excellent for coding. I’ve been using it for about 6 years now, mainly for Python work, but also developing JS based mobile apps. I mainly use Visual Studio, but VSC’s slightly stripped back nature has been embellished with plenty of updates and more GUI discovery methods, plus that huge extensions library (I’ve worked with the creation of an intellisense style plugin as well).

I’m personally a fan of keeping it simple on IDEs, and I work in a lot of languages. I’m not installing 6 or 7 IDEs because they apparently have advantages in that specific language, so I’d rather install one IDE which can do a credible job on all of them.

I’m more a fan of developing software than getting anally retentive about knowing all the keyboard shortcuts to format a source file. Life’s too short for that. Way too short!

To each their own. Enjoy whatever you use!

Dmitry Aliev is correct that this was introduced into the language before references.

I’ll take this question as an excuse to add a bit more color to this.

C++ evolved from C via an early dialect called “C with Classes”, which was initially implemented with Cpre, a fancy “preprocessor” targeting C that didn’t fully parse the “C with Classes” language. What it did was add an implicit this pointer parameter to member functions. E.g.:

Why is C++ "this" a pointer and not a reference?
Why is C++ “this” a pointer and not a reference?

was translated to something like:

  • int f__1S(S *this); 

(the funny name f__1S is just an example of a possible “mangling” of the name of S::f, which allows traditional linkers to deal with the richer naming environment of C++).

What might comes as a surprise to the modern C++ programmer is that in that model this is an ordinary parameter variable and therefore it can be assigned to! Indeed, in the early implementations that was possible:

Why is C++ "this" a pointer and not a reference?
Why is C++ “this” a pointer and not a reference?

Interestingly, an idiom arose around this ability: Constructors could manage class-specific memory allocation by “assigning to this” before doing anything else in the constructor. E.g.:

Why is C++ "this" a pointer and not a reference?
Why is C++ “this” a pointer and not a reference?

That technique (brittle as it was, particularly when dealing with derived classes) became so widespread that when C with Classes was re-implemented with a “real” compiler (Cfront), assignment to this remained valid in constructors and destructors even though this had otherwise evolved into an immutable expression. The C++ front end I maintain still has modes that accept that anachronism. See also section 17 of the old Cfront manual found here, for some fun reminiscing.

When standardization of C++ began, the core language work was handled by three working groups: Core I dealt with declarative stuff, Core II dealt with expression stuff, and Core III dealt with “new stuff” (templates and exception handling, mostly). In this context, Core II had to (among many other tasks) formalize the rules for overload resolution and the binding of this. Over time, they realized that that name binding should in fact be mostly like reference binding. Hence, in standard C++ the binding of something like:

Why is C++ "this" a pointer and not a reference?
Why is C++ “this” a pointer and not a reference?

In other words, the expression this is now effectively a kind of alias for &__this, where __this is just a name I made up for an unnamable implicit reference parameter.

C++11 further tweaked this by introducing syntax to control the kind of reference that this is bound from. E.g.,

struct S

That model was relatively well-understood by the mid-to-late 1990s… but then unfortunately we forgot about it when we introduced lambda expression. Indeed, in C++11 we allowed lambda expressions to “capture” this:



After that language feature was released, we started getting many reports of buggy programs that “captured” this thinking they captured the class value, when instead they really wanted to capture __this (or *this). So we scrambled to try to rectify that in C++17, but because lambdas had gotten tremendously popular we had to make a compromise. Specifically:

  • we introduced the ability to capture *this
  • we allowed [=, this] since now [this] is really a “by reference” capture of *this
  • even though [this] was now a “by reference” capture, we left in the ability to write [&, this], despite it being redundant (compatibility with earlier standards)

Our tale is not done, however. Once you write much generic C++ code you’ll probably find out that it’s really frustrating that the __this parameter cannot be made generic because it’s implicitly declared. So we (the C++ standardization committee) decided to allow that parameter to be made explicit in C++23. For example, you can write (example from the linked paper):

Why is C++ "this" a pointer and not a reference?

In that example, the “object parameter” (i.e., the previously hidden reference parameter __this) is now an explicit parameter and it is no longer a reference!

Here is another example (also from the paper):


Why is C++ "this" a pointer and not a reference?


  • the type of the object parameter is a deducible template-dependent type
  • the deduction actually allows a derived type to be found

This feature is tremendously powerful, and may well be the most significant addition by C++23 to the core language. If you’re reasonably well-versed in modern C++, I highly recommend reading that paper (P0847) — it’s fairly accessible.

It adds some extra steps in design, testing and deployment for sure. But it can buy you an easier path to scalability and an easier path to fault tolerance and live system upgrades.

It’s not REST itself that enables that. But if you use REST you will have split your code up into independently deployable chunks called services.

So more development work to do, yes, but you get something a single monolith can’t provide. If you need that, then the REST service approach is a quick way to doing it.

We must compare like for like in terms of results for questions like this.

Because at the time, there was likely no need.

Based on what I could find, the strtok library function appeared in System III UNIX some time in 1980.

In 1980, memory was small, and programs were single threaded. I don’t know whether UNIX had any support for multiple processors, even. I think that happened a few years later.

Its implementation was quite simple.

Why didn't the C library designers make strtok() explicitly store the state to allow working on multiple strings at the same time?


This was 3 years before they started the standardization process, and 9 years before it was standardized in ANSI C.

This was simple and good enough, and that’s what mattered most. It’s far from the only library function with internal state.

And Lex/YACC took over more complex scanning and parsing tasks, so it probably didn’t get a lot of attention for the lightweight uses it was put to.

For a tongue-in-cheek take on how UNIX and C were developed, read this classic:

The Rise of “Worse is Better” By Richard Gabriel I and just about every designer of Common Lisp and CLOS has had extreme exposure to the MIT/Stanford style of design. The essence of this style can be captured by the phrase “the right thing.” To such a designer it is important to get all of the following characteristics right: · Simplicity-the design must be simple, both in implementation and interface. It is more important for the interface to be simple than the implementation. · Correctness-the design must be correct in all observable aspects. Incorrectness is simply not allowed. · Consistency-the design must not be inconsistent. A design is allowed to be slightly less simple and less complete to avoid inconsistency. Consistency is as important as correctness. · Completeness-the design must cover as many important situations as is practical. All reasonably expected cases must be covered. Simplicity is not allowed to overly reduce completeness. I believe most people would agree that these are good characteristics. I will call the use of this philosophy of design the “MIT approach.” Common Lisp (with CLOS) and Scheme represent the MIT approach to design and implementation. The worse-is-better philosophy is only slightly different: · Simplicity-the design must be simple, both in implementation and interface. It is more important for the implementation to be simple than the interface. Simplicity is the most important consideration in a design. · Correctness-the design must be correct in all observable aspects. It is slightly better to be simple than correct. · Consistency-the design must not be overly inconsistent. Consistency can be sacrificed for simplicity in some cases, but it is better to drop those parts of the design that deal with less common circumstances than to introduce either implementational complexity or inconsistency. · Completeness-the design must cover as many important situations as is practical. All reasonably expected cases should be covered. Completeness can be sacrificed in favor of any other quality. In fact, completeness must sacrificed whenever implementation simplicity is jeopardized. Consistency can be sacrificed to achieve completeness if simplicity is retained; especially worthless is consistency of interface. Early Unix and C are examples of the use of this school of design, and I will call the use of this design strategy the “New Jersey approach.” I have intentionally caricatured the worse-is-better philosophy to convince you that it is obviously a bad philosophy and that the New Jersey approach is a bad approach. However, I believe that worse-is-better, even in its strawman form, has better survival characteristics than the-right-thing, and that the New Jersey approach when used for software is a better approach than the MIT approach. Let me start out by retelling a story that shows that the MIT/New-Jersey distinction is valid and that proponents of each philosophy actually believe their philosophy is better.

Because the ‘under the hood’ code is about 50 years old. I’m not kidding. I worked on some video poker machines that were made in the early 1970’s.

Here’s how they work.

You have an array of ‘cards’ from 0 to 51. Pick one at random. Slap it in position 1 and take it out of your array. Do the same for the next card … see how this works?

Video poker machines are really that simple. They literally simulate a deck of cards.

Anything else, at least in Nevada, is illegal. Let me rephrase that, it is ILLEGAL, in all caps.

If you were to try to make a video poker game (or video keno, or slot machine) in any other way than as close to truly random selection from an ‘array’ of options as you can get, Nevada Gaming will come after you so hard and fast, your third cousin twice removed will have their ears ring for a week.

That is if the Families don’t get you first, and they’re far less kind.

All the ‘magic’ is in the payout tables, which on video poker and keno are literally posted on every machine. If you can read them, you can figure out exactly what the payout odds are for any machine.

There’s also a little note at the bottom stating that the video poker machine you’re looking at uses a 52 card deck.


1- I have a slot machine and the code on the odds chip looks much like an excel spread sheet every combination is displayed in this spread sheet, so the exact odds can be listed an payout tables. The machine picks a random number. Let say 452 in 1000. the computer looks at the spread sheet and says that this is the combination of bar bar 7 and you get 2 credits for this combination. The wheels will spin to match the indication on the spread sheet. If I go into the game diagnostics I can see if it is a win or not, you do not win on what the wheels display, but the actual number from the spread sheet. The games knows if you won or lost before the wheels stop.

2- I had a conversation with a guy who had retired from working in casino security. He was also responsible for some setup and maintenance on slot machines, video poker and others. I asked about the infamous video poker machine that a programmer at the manufacturer had put in a backdoor so he and a few pals could get money. That was just before he’d started but he knew how it was done. IIRC there was a 25 step process of combinations of coin drops and button presses to make the machine hit a royal flush to pay the jackpot.

Slot machines that have mechanical reels actually run very large virtual reels. The physical reels have position encoders so the electronics and software can select which symbol to stop on. This makes for far more possible combinations than relying on the space available on the physical reels.

Those islands of machines with the sign that says 95% payout? Well, you guess which machine in the group is set to that payout % while the rest are much closer to the minimum allowed.

Machines with a video screen that gives you a choice of things to select by touch or button press? It doesn’t matter what you select, the outcome is pre-determined. For example, if there’s a grid of spots and the first three matches you get determines how many free spins you get, if the code stopped on giving you 7 free spins, out of a possible maximum of 25, you’re getting 7 free spins no matter which spots you touch. It will tease you with a couple of 25s, a 10 or 15 or two, but ultimately you’ll get three 7s, and often the 3rd 25 will be close to the other two or right next to the last 7 “you” selected to make you feel like you just missed it when the full grid is briefly revealed.

There was a Discovery Channel show where the host used various power tools to literally hack things apart to show their insides and how they worked. In one episode he sawed open a couple of slot machines, one from the 1960’s and a purely mechanical one from the 1930’s or possibly 1940’s. In that old machine he discovered the casino it had been in decades prior had installed a cheat. There was a metal wedge bolted into the notch for the 7 on one reel so it could never hit the 777 jackpot. I wondered if the Nevada Gaming Commission could trace the serial number and if they could levy a fine if the company that had owned and operated it was still in business.

3- Slightly off-topic. I worked for a company that sold computer hardware, one of our customers was the company that makes gambling machines. They said that they spent close to $0 on software and all their budget on licensing characters

This question is like asking why you would ever use int when you have the Integer class. Java programmers seem especially zealous about everything needing to be wrapped, and wrapped, and wrapped.

Yes, ArrayList<Integer> does everything that int[] does and more… but sometimes all you need to do is swat a fly, and you just need a flyswatter, not a machine-gun.

Did you know that in order to convert int[] to ArrrayList<Integer>, the system has to go through the array elements one at a time and box them, which means creating a garbage-collected object on the heap (i.e. Integer) for each individual int in the array? That’s right; if you just use int[], then only one memory alloc is needed, as opposed to one for each item.

I understand that most Java programmers don’t know about that, and the ones who do probably don’t care. They will say that this isn’t going to be the reason your program is running slowly. They will say that if you need to care about those kinds of optimizations, then you should be writing code in C++ rather than Java. Yadda yadda yadda, I’ve heard it all before. Personally though, I think that you should know, and should care, because it just seems wasteful to me. Why dynamically allocate n individual objects when you could just have a contiguous block in memory? I don’t like waste.

I also happen to know that if you have a blasé attitude about performance in general, then you’re apt to be the sort of programmer who unknowingly, unnecessarily writes four nested loops and then has no idea why their program took ten minutes to run even though the list was only 100 elements long. At that point, not even C++ will save you from your inefficiently written code. There’s a slippery slope here.

I believe that a software developer is a sort of craftsman. They should understand their craft, not only at the language level, but also how it works internally. They should convert int[] to ArrayList<Integer> only because they know the cost is insignificant, and they have a particular reason for doing so other than “I never use arrays, ArrayList is better LOL”.

Very similar, yes.

Both languages feature:

  • Static typing
  • nominative interface typing
  • garbage collection
  • class based
  • single dispatch polymorphism

so whilst syntax differs, the key things that separate OO support across languages are the same.

There are differences but you can write the same design of OO program in either language and it won’t look out of place

Last time I needed to write an Android app, even though I already knew Java, I still went with Kotlin 😀

I’d rather work in a language I don’t know than… Java… and yes, I know a decent Java IDE can auto-generate this code – but this only solves the problem of writing the code, it doesn’t solve the problem of having to read it, which happens a lot more than writing it.

I mean, which of the below conveys the programmer’s intent more clearly, and which one would you rather read when you forget what a part of the program does and need a refresher:

Even if both of them required no effort to write… the Java version is pure brain poison…

Because it’s insufficient to deal with the memory semantics of current computers. In fact, it was obsolete almost as soon as it first became available.

Volatile tells a compiler that it may not assume the value of a memory location has not changed between reads or writes. This is sometimes sufficient to deal with memory-mapped hardware registers, which is what it was originally for.

But that doesn’t deal with the semantics of a multiprocessor machine’s cache, where a memory location might be written and read from several different places, and we need to be sure we know when written values will be observable relative to control flow in the writing thread.

Instead, we need to deal with acquire/release semantics of values, and the compilers have to output the right machine instructions that we get those semantics from the real machines. So, the atomic memory intrinsics come to the rescue. This is also why inline assembler acts as an optimization barrier; before there were intrinsics for this, it was done with inline assembler. But intrinsics are better, because the compiler can still do some optimization with them.

C++ is a programming language specified through a standard that is “abstract” in various ways. For example, that standard doesn’t currently formally recognize a notion of “runtime” (I would actually like to change that a little bit in the future, but we’ll see).

Now, in order to allow implementations to make assumptions it removes certain situations from the responsibility of the implementation. For example, it doesn’t require (in general) that the implementation ensure that accesses to objects are within the bounds of those objects. By dropping that requirement, the code for valid accesses can be more efficient than would be required if out-of-bounds situations were the responsibility of the implementation (as is the case in most other modern programming languages). Those “situations” are what we call “undefined behaviour”: The implementation has no specific responsibilities and so the standard allows “anything” to happen. This is in part why C++ is still very successful in applications that call for the efficient use of hardware resources.

Note, however, that the standard doesn’t disallow an implementation from doing something that is implementation-specified in those “undefined behaviour” situations. It’s perfectly all right (and feasible) for a C++ implementation to be “memory safe” for example (e.g., not attempt access outside of object bounds). Such implementations have existed in the past (and might still exist, but I’m not currently aware of one that completely “contains” undefined behaviour).

ADDENDUM (July 16th, 2021):

The following article about undefined behavior crossed my metaphorical desk today:

To Conclude:

Coding is a process of translating and transforming a problem into a step by step set of instructions for a machine. Just like every skill, it requires time and practice to learn coding. However, by following some simple tips, you can make the learning process easier and faster. First, it is important to start with the basics. Do not try to learn too many programming languages at once. It is better to focus on one language and master it before moving on to the next one. Second, make use of resources such as books, online tutorials, and coding bootcamps. These can provide you with the structure and support you need to progress quickly. Finally, practice regularly and find a mentor who can offer guidance and feedback. By following these tips, you can develop the programming skills you need to succeed in your career.

There are plenty of resources available to help you improve your coding skills. Check out some of our favorite coding tips below:

– Find a good code editor and learn its shortcuts. This will save you time in the long run.
– Do lots of practice exercises. It’s important to get comfortable with the syntax and structure of your chosen programming language.
– Get involved in the coding community. There are many online forums and groups where programmers can ask questions, share advice, and collaborate on projects.
– Read code written by experienced developers. This will give you insight into best practices and advanced techniques.

What are the Greenest or Least Environmentally Friendly Programming Languages?