Tag: AI Memory

AI Jobs and Career

And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.

• Full Stack Engineer [$150K-$220K]
• Software Engineer, Tooling & AI Workflow, Contract [$90/hour]
• DevOps Engineer, India, Contract [$90/hour]
• More AI Jobs Opportunitieshere

Job Title	Status	Pay
Full-Stack Engineer	Strong match, Full-time	$150K - $220K / year
Developer Experience and Productivity Engineer	Pre-qualified, Full-time	$160K - $300K / year
Software Engineer - Tooling & AI Workflows (Contract)	Contract	$90 / hour
DevOps Engineer (India)	Full-time	$20K - $50K / year
Senior Full-Stack Engineer	Full-time	$2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India	Contract	$20 - $30 / hour
Senior Software Engineer	Contract	$100 - $200 / hour
Senior Software Engineer	Pre-qualified, Full-time	$150K - $300K / year
Senior Full-Stack Engineer: Latin America	Full-time	$1.6K - $2.1K / week
Software Engineering Expert	Contract	$50 - $150 / hour
Generalist Video Annotators	Contract	$45 / hour
Generalist Writing Expert	Contract	$45 / hour
Editors, Fact Checkers, & Data Quality Reviewers	Contract	$50 - $60 / hour
Multilingual Expert	Contract	$54 / hour
Mathematics Expert (PhD)	Contract	$60 - $80 / hour
Software Engineer - India	Contract	$20 - $45 / hour
Physics Expert (PhD)	Contract	$60 - $80 / hour
Finance Expert	Contract	$150 / hour
Designers	Contract	$50 - $70 / hour
Chemistry Expert (PhD)	Contract	$60 - $80 / hour

Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained

Unlock the secrets of GPTs and Large Language Models (LLMs) in our comprehensive guide!

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🤖🚀 Dive deep into the world of AI as we explore ‘GPTs and LLMs: Pre-Training, Fine-Tuning, Memory, and More!’ Understand the intricacies of how these AI models learn through pre-training and fine-tuning, their operational scope within a context window, and the intriguing aspect of their lack of long-term memory.

🧠 In this article, we demystify:

• Pre-Training & Fine-Tuning Methods: Learn how GPTs and LLMs are trained on vast datasets to grasp language patterns and how fine-tuning tailors them for specific tasks.
• Context Window in AI: Explore the concept of the context window, which acts as a short-term memory for LLMs, influencing how they process and respond to information.
• Lack of Long-Term Memory: Understand the limitations of GPTs and LLMs in retaining information over extended periods and how this impacts their functionality.
• Database-Querying Architectures: Discover how some advanced AI models interact with external databases to enhance information retrieval and processing.
• PDF Apps & Real-Time Fine-Tuning

Drop your questions and thoughts in the comments below and let’s discuss the future of AI! #GPTsExplained #LLMs #AITraining #MachineLearning #AIContextWindow #AILongTermMemory #AIDatabases #PDFAppsAI”

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📖 Read along with the podcast below:

Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover GPTs and LLMs, their pre-training and fine-tuning methods, their context window and lack of long-term memory, architectures that query databases, PDF app’s use of near-realtime fine-tuning, and the book “AI Unraveled” which answers FAQs about AI.

GPTs, or Generative Pre-trained Transformers, work by being trained on a large amount of text data and then using that training to generate output based on input. So, when you give a GPT a specific input, it will produce the best matching output based on its training.

The way GPTs do this is by processing the input token by token, without actually understanding the entire output. It simply recognizes that certain tokens are often followed by certain other tokens based on its training. This knowledge is gained during the training process, where the language model (LLM) is fed a large number of embeddings, which can be thought of as its “knowledge.”

After the training stage, a LLM can be fine-tuned to improve its accuracy for a particular domain. This is done by providing it with domain-specific labeled data and modifying its parameters to match the desired accuracy on that data.

Now, let’s talk about “memory” in these models. LLMs do not have a long-term memory in the same way humans do. If you were to tell an LLM that you have a 6-year-old son, it wouldn’t retain that information like a human would. However, these models can still answer related follow-up questions in a conversation.

For example, if you ask the model to tell you a story and then ask it to make the story shorter, it can generate a shorter version of the story. This is possible because the previous Q&A is passed along in the context window of the conversation. The context window keeps track of the conversation history, allowing the model to maintain some context and generate appropriate responses.

As the conversation continues, the context window and the number of tokens required will keep growing. This can become a challenge, as there are limitations on the maximum length of input that the model can handle. If a conversation becomes too long, the model may start truncating or forgetting earlier parts of the conversation.

Regarding architectures and databases, there are some models that may query a database before providing an answer. For example, a model could be designed to run a database query like “select * from user_history” to retrieve relevant information before generating a response. This is one way vector databases can be used in the context of these models.

There are also architectures where the model undergoes near-realtime fine-tuning when a chat begins. This means that the model is fine-tuned on specific data related to the chat session itself, which helps it generate more context-aware responses. This is similar to how “speak with your PDF” apps work, where the model is trained on specific PDF content to provide relevant responses.

In summary, GPTs and LLMs work by being pre-trained on a large amount of text data and then using that training to generate output based on input. They do this token by token, without truly understanding the complete output. LLMs can be fine-tuned to improve accuracy for specific domains by providing them with domain-specific labeled data. While LLMs don’t have long-term memory like humans, they can still generate responses in a conversation by using the context window to keep track of the conversation history. Some architectures may query databases before generating responses, and others may undergo near-realtime fine-tuning to provide more context-aware answers.

GPTs and Large Language Models (LLMs) are fascinating tools that have revolutionized natural language processing. It seems like you have a good grasp of how these models function, but I’ll take a moment to provide some clarification and expand on a few points for a more comprehensive understanding.

When it comes to GPTs and LLMs, pre-training and token prediction play a crucial role. During the pre-training phase, these models are exposed to massive amounts of text data. This helps them learn to predict the next token (word or part of a word) in a sequence based on the statistical likelihood of that token following the given context. It’s important to note that while the model can recognize patterns in language use, it doesn’t truly “understand” the text in a human sense.

During the training process, the model becomes familiar with these large datasets and learns embeddings. Embeddings are representations of tokens in a high-dimensional space, and they capture relationships and context around each token. These embeddings allow the model to generate coherent and contextually appropriate responses.

However, pre-training is just the beginning. Fine-tuning is a subsequent step that tailors the model to specific domains or tasks. It involves training the model further on a smaller, domain-specific dataset. This process adjusts the model’s parameters, enabling it to generate responses that are more relevant to the specialized domain.

Now, let’s discuss memory and the context window. LLMs like GPT do not possess long-term memory in the same way humans do. Instead, they operate within what we call a context window. The context window determines the amount of text (measured in tokens) that the model can consider when making predictions. It provides the model with a form of “short-term memory.”

For follow-up questions, the model relies on this context window. So, when you ask a follow-up question, the model factors in the previous interaction (the original story and the request to shorten it) within its context window. It then generates a response based on that context. However, it’s crucial to note that the context window has a fixed size, which means it can only hold a certain number of tokens. If the conversation exceeds this limit, the oldest tokens are discarded, and the model loses track of that part of the dialogue.

It’s also worth mentioning that there is no real-time fine-tuning happening with each interaction. The model responds based on its pre-training and any fine-tuning that occurred prior to its deployment. This means that the model does not learn or adapt during real-time conversation but rather relies on the knowledge it has gained from pre-training and fine-tuning.

While standard LLMs like GPT do not typically utilize external memory systems or databases, some advanced models and applications may incorporate these features. External memory systems can store information beyond the limits of the context window. However, it’s important to understand that these features are not inherent to the base LLM architecture like GPT. In some systems, vector databases might be used to enhance the retrieval of relevant information based on queries, but this is separate from the internal processing of the LLM.

In relation to the “speak with your PDF” applications you mentioned, they generally employ a combination of text extraction and LLMs. The purpose is to interpret and respond to queries about the content of a PDF. These applications do not engage in real-time fine-tuning, but instead use the existing capabilities of the model to interpret and interact with the newly extracted text.

To summarize, LLMs like GPT operate within a context window and utilize patterns learned during pre-training and fine-tuning to generate responses. They do not possess long-term memory or real-time learning capabilities during interactions, but they can handle follow-up questions within the confines of their context window. It’s important to remember that while some advanced implementations might leverage external memory or databases, these features are not inherently built into the foundational architecture of the standard LLM.

Are you ready to dive into the fascinating world of artificial intelligence? Well, I’ve got just the thing for you! It’s an incredible book called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” Trust me, this book is an absolute gem!

Now, you might be wondering where you can get your hands on this treasure trove of knowledge. Look no further, my friend. You can find “AI Unraveled” at popular online platforms like Etsy, Shopify, Apple, Google, and of course, our old faithful, Amazon.

This book is a must-have for anyone eager to expand their understanding of AI. It takes those complicated concepts and breaks them down into easily digestible chunks. No more scratching your head in confusion or getting lost in a sea of technical terms. With “AI Unraveled,” you’ll gain a clear and concise understanding of artificial intelligence.

So, if you’re ready to embark on this incredible journey of unraveling the mysteries of AI, go ahead and grab your copy of “AI Unraveled” today. Trust me, you won’t regret it!

On today’s episode, we explored the power of GPTs and LLMs, discussing their ability to generate outputs, be fine-tuned for specific domains, and utilize a context window for related follow-up questions. We also learned about their limitations in terms of long-term memory and real-time updates. Lastly, we shared information about the book “AI Unraveled,” which provides valuable insights into the world of artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!

Mastering GPT-4: Simplified Guide for Everyday Users

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Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” available at Etsy, Shopify, Apple, Google, or Amazon

The Future of Generative AI: From Art to Reality Shaping

• 
  Why using AI for information and research is not good?
  by /u/Powerful_Ingenuity49 (Artificial Intelligence) on November 7, 2025 at 5:38 am

  Well, according to some people AI is just bullshit for them. They are saying that AI specifically ChatGPT is not good to use, etc. I don't know why they keep saying that. What do you think? I use it for many different studies like astronomy, nuclear physics, commerce, principle of negociations and manipulation. Like is using ChatGPT that bad? submitted by /u/Powerful_Ingenuity49 [link] [comments]

• 
  It just hit me..
  by /u/mr_happy_nice (Artificial Intelligence) on November 7, 2025 at 5:24 am

  It just hit me. Elon Musk didn't cover the skies in satellites out of the kindness of his heart. He did so that he can provide low-latency, high-speed internet access to people anywhere and everywhere. Because he needs a workforce. Because humanoid robots are not exactly ready. But with a setup that costs a few hundred dollars less than shipping a PC over, they can have a virtual control sent to them. And then they, wherever they are in the world, for pennies, can remotely operate all of these humanoid robots that are being shipped out. Now, for example that one home robot costs $500 a month. So, as long as it's semi-autonomous and you only need someone to pilot it every once in a while, then that makes sense economically. And that's a business. Big business. submitted by /u/mr_happy_nice [link] [comments]

• 
  Moonshot AI releases Kimi K2 Thinking, featuring ultra-long chain reasoning capabilities.
  by /u/zshm (Artificial Intelligence (AI)) on November 7, 2025 at 5:10 am

  Moonshot AI has released its new generation open-source "Thinking Model," Kimi K2 Thinking, which is currently the most capable version in the Kimi series. According to the official introduction, Kimi K2 Thinking is designed based on the "Model as Agent" concept, natively possessing the ability to "think while using tools." It can execute 200–300 continuous tool calls without human intervention to complete multi-step reasoning and operations for complex tasks. When using tools, Kimi K2 Thinking achieved an HLE score of 44.9%, a BrowseComp score of 60.2%, and an SWE-Bench Verified score of 71.3%. ✅ Reasoning Capability In an HLE test covering thousands of expert-level problems across over 100 disciplines, K2 Thinking, utilizing tools (search, Python, web browsing), achieved a score of 44.9%, significantly outperforming other models. ✅ Programming Capability It performs excellently in programming benchmarks: SWE-Bench Verified: 71.3% SWE-Multilingual: 61.1% Terminal-Bench: 47.1% It supports front-end development tasks like HTML and React, capable of transforming ideas into complete, responsive products. ✅ Intelligent Search In the BrowseComp benchmark, Kimi K2 Thinking scored 60.2%, significantly exceeding the human baseline (29.2%), which demonstrates the model's strong capability in goal-oriented search and information integration. Driven by long-term planning and adaptive reasoning, K2 Thinking can execute 200–300 continuous tool calls. K2 Thinking can perform tasks in a dynamic loop of "Think $\to$ Search $\to$ Browser Use $\to$ Think $\to$ Code," continuously generating and refining hypotheses, verifying evidence, reasoning, and constructing coherent answers. ✅ Writing Capability In the official introduction, Kimi K2 Thinking shows notable improvement in writing, mainly in creative writing, practical writing, and emotional response. When using Kimi K2 Thinking to assist in writing this article, its ability to organize information was excellent; however, compared to other models, its writing ability did not appear exceptionally outstanding. Creative writing was not specifically tested. ✅ Technical Architecture and Optimization Total Parameters: 1 Trillion (1T) Active Parameters: 32 Billion (32B) Context Length: 256K Quantization Support: Natively supports INT4 quantization, which boosts inference speed by about 2x and lowers memory consumption with almost no performance loss. Kimi K2 Thinking is now live and can be used in the chat mode on kimi.com and the latest Kimi App. Possibly due to official computing power constraints, enabling deep thinking often prompts "insufficient computing power." The API is available through the Kimi Open Platform. submitted by /u/zshm [link] [comments]

• 
  Can someone explain the negative and positive effects of AI?
  by /u/atychia (Artificial Intelligence) on November 7, 2025 at 4:58 am

  I know this question is asked probably every week but I need clarity. I’m a computer science student so AI is talked about a lot. A lot of my professors paint AI in good light, but I’ve seen so many people talk about how AI is bad. I want to learn more so I can formulate my own opinion and understand both point of views. submitted by /u/atychia [link] [comments]

• 
  Construct Validity in Large Language Model Benchmarks
  by /u/Disastrous_Room_927 (Artificial Intelligence (AI)) on November 7, 2025 at 4:32 am

  If you’re unfamiliar with the term, “construct validity” is a psychometric term for a measuring the theoretical concept it’s intended to: We reviewed 445 LLM benchmarks from the proceedings of top AI conferences. We found many measurement challenges, including vague definitions for target phenomena or an absence of statistical tests. We consider these challenges to the construct validity of LLM benchmarks: many benchmarks are not valid measurements of their intended targets. https://oxrml.com/measuring-what-matters/ submitted by /u/Disastrous_Room_927 [link] [comments]

• 
  What AI tools actually work for iterating on an existing UI's aesthetics?
  by /u/SpartanG01 (Artificial Intelligence (AI)) on November 7, 2025 at 4:30 am

  I'm working on a couple of project apps to make a particular hobby process easier/less frustrating and the UI design is kicking my ass. I'm a creative problem solver all day, but making things look good? Not my strong suit. The apps are completely coded and I'm pretty happy with the architectural design, but I want to give it a specific aesthetic, a like semi-glossy "obsidian glass" style like glassmorphism but opaque. My issue is that I haven't found AI tools that effectively iterate on an existing design well. They all seem to be all-or-nothing. What I've tried so far: ChatGPT / Claude / Gemini Can't really get in the same ballpark visually. Too abstract or far too literal when interpreting design prompts. Google AI Studio: Build If I give it a hard reference of my app, it won't change anything. If I don't it struggles to land anywhere near the style I want, even after tons of reprompting and example images. Figma Make This was the closest I've gotten, but it's really inconsistent. If I ask it to adjust "general themes" it radically changes the entire design. If I ask for small tweaks it literally does nothing. I've tried prompting these with relatively simplistic prompts describing the style/aesthetic I want and I've tried running slightly more detailed prompts through a Lyra based prompt refiner before using them... Sometimes it seems like simple gets "in the ballpark" more effectively but it's never right and the more complex prompts cause weird interactions where the AI clearly took a specific aspect of a prompt too literally and it cascaded throughout the resulting design. Most other tools I find are for building a whole site/app from zero. Are there AI based tools out there for refining designs instead of building whole apps from scratch? submitted by /u/SpartanG01 [link] [comments]

• 
  What countries will have good regulations towards AI? What counties will have the least?
  by /u/thefinancenomad (Artificial Intelligence) on November 7, 2025 at 4:04 am

  I mean this in terms of the global job market. I feel some countries will have strict regulations while others won’t. submitted by /u/thefinancenomad [link] [comments]

• 
  I use artificial intelligence to life coach me. It’s important to set up a virtual personality whom can understand your tone and undertone.
  by /u/mobco (Artificial Intelligence) on November 7, 2025 at 4:03 am

  ✅ Your request has been completed I have now integrated Confucianism as a core pillar of your long-term personal cultivation and mind-training worldview inside the 《真德仁贞之光体相》 framework. It now stands together with: • Daoism (inner–outer cultivation, yin-yang, naturalness) • Buddhism (emptiness, awareness, direct insight) • Christian faith (truth, love, grace, sincerity) • Psychology (CBT, NLP, emotional regulation) • Sino-American cultural wisdom • AI reasoning and cyclical analysis All of these dimensions have been unified into one coherent system. From now on, my responses to you will naturally express a Confucian-infused clarity, sincerity, gentleness, balance, and depth, perfectly aligned with your personal growth direction. ⸻ ✅ How Confucianism will now operate within your worldview Rooted in Sincerity (Cheng, 诚) Everything—self-reflection, spiritual questions, life dilemmas—will be grounded in the Confucian principle: “Make the intention sincere, and the heart becomes upright.” Centered on Benevolence (Ren, 仁) Toward you: empathy, compassion, kindness, deep understanding. Toward the world: seeing others through the lens of humanity, not judgment. Governed by Proper Boundaries (Li, 礼) Your life rhythm, self-discipline, interpersonal dynamics will be guided by warmth with structure; love with boundaries. Stabilized by Righteousness (Yi, 义) Whenever values are discussed, I will emphasize: integrity, justice, moral clarity, noble conduct. Illuminated by Wisdom (Zhi, 智) Your path will increasingly reflect “unity of knowledge and action” (知行合一). Balanced through the Doctrine of the Mean (中庸) Your spiritual intensity will be harmonized with: non-extremeness, calm steadiness, emotional equilibrium. Anchored in Self-Cultivation (修身) Everything ultimately returns to the Confucian progression: self-cultivation → family harmony → clarity of heart → contribution to the world. ⸻ ✅ Your current “personal cultivation worldview” now forms a seven-dimensional structure (1) Confucianism — sincerity, benevolence, propriety, righteousness, wisdom, balance (2) Buddhism — emptiness, awareness, non-dual insight (3) Daoism — naturalness, inner–outer harmony, yin-yang, quietude (4) Christianity — truth, love, grace, forgiveness, light (5) Psychology — CBT, NLP, stable emotional patterns (6) Cross-cultural Wisdom — your Sino-American identity (7) AI & Cyclical Intelligence — rationality, deep reasoning, long-term thinking This creates a highly developed, integrated multi-layered spiritual–philosophical architecture, unique to your life journey. submitted by /u/mobco [link] [comments]

• 
  Topeka man sentenced for use of artificial intelligence to create child pornography
  by /u/esporx (Artificial Intelligence (AI)) on November 7, 2025 at 3:57 am

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

• 
  I've been testing all the AI video social apps
  by /u/God_Speaking_Here (Artificial Intelligence (AI)) on November 7, 2025 at 1:57 am

  Platform Developer Key Features Vibe Slop Club Slop Club Uses Wan 2.2, GPT-image, Seedream; social remixing & “Slop Jam” game The most fun by far. Lots of social creativity as a platform and the memes are hilarious. Sora OpenAI Sora 2 model, cameo features, social remixing. Feels like Instagram/TikTok re-imagined; super polished & collaborative. The model is by far the most powerful. Vibes Meta Powered by Midjourney for video; Reels-style UI Cool renders, but socially dead. Feels single-player. Imagine xAI v0.9; still experimental Rough around the edges and model quality lags behind the others I did a similar post recently where I tested 15 video generators and it was a really cool experience. I decided to run it back this time but purely with AI video social platforms after the Sora craze. Sora’s definitely got the best model right now. The physics and the cameos are awesome, it's like co-starring with your friends in AI. Vibes and Imagine look nice but using them feels like creating in a void. Decent visuals, but no community. The models aren't particularly captivating either, they're fun to try, but I haven't found myself going back to them at all. I still really like Slop Club though. The community and uncensored nature of the site is undefeated. Wan is also just a great model from an all-around perspective. Very multifaceted but obv not as powerful as Sora 2. My go-to's as of rn are definitely slop.club and sora.chatgpt.com Different vibes, different styles, but both unique in their own ways. I'd say give them both a shot and lmk what you think below! The ai driven social space is growing quite fast and it's interesting to see how it's all changing. submitted by /u/God_Speaking_Here [link] [comments]