Tag: make gpt-4 your b*tch

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Mastering GPT-4: Simplified Guide for Everyday Users or How to make GPT-4 your b*tch!

Listen Here

Recently, while updating our OpenAI Python library, I encountered a marketing intern struggling with GPT-4. He was overwhelmed by its repetitive responses, lengthy answers, and not quite getting what he needed from it. Realizing the need for a simple, user-friendly explanation of GPT-4’s functionalities, I decided to create this guide. Whether you’re new to AI or looking to refine your GPT-4 interactions, these tips are designed to help you navigate and optimize your experience.

Embark on a journey to master GPT-4 with our easy-to-understand guide, ‘Mastering GPT-4: Simplified Guide for Everyday Users‘.

🌟🤖 This blog/video/podcast is perfect for both AI newbies and those looking to enhance their experience with GPT-4. We break down the complexities of GPT-4’s settings into simple, practical terms, so you can use this powerful tool more effectively and creatively.

🔍 What You’ll Learn:

1. Frequency Penalty: Discover how to reduce repetitive responses and make your AI interactions sound more natural.
2. Logit Bias: Learn to gently steer the AI towards or away from specific words or topics.
3. Presence Penalty: Find out how to encourage the AI to transition smoothly between topics.
4. Temperature: Adjust the AI’s creativity level, from straightforward responses to imaginative ideas.
5. Top_p (Nucleus Sampling): Control the uniqueness of the AI’s suggestions, from conventional to out-of-the-box ideas.

1. Frequency Penalty: The Echo Reducer

• What It Does: This setting helps minimize repetition in the AI’s responses, ensuring it doesn’t sound like it’s stuck on repeat.
• Examples:
  • Low Setting: You might get repeated phrases like “I love pizza. Pizza is great. Did I mention pizza?”
  • High Setting: The AI diversifies its language, saying something like “I love pizza for its gooey cheese, tangy sauce, and crispy crust. It’s a culinary delight.”

2. Logit Bias: The Preference Tuner

• What It Does: It nudges the AI towards or away from certain words, almost like gently guiding its choices.
• Examples:
  • Against ‘pizza’: The AI might focus on other aspects, “I enjoy Italian food, especially pasta and gelato.”
  • Towards ‘pizza’: It emphasizes the chosen word, “Italian cuisine brings to mind the delectable pizza, a feast of flavors in every slice.”

3. Presence Penalty: The Topic Shifter

• What It Does: This encourages the AI to change subjects more smoothly, avoiding dwelling too long on a single topic.
• Examples:
  • Low Setting: It might stick to one idea, “I enjoy sunny days. Sunny days are pleasant.”
  • High Setting: The AI transitions to new ideas, “Sunny days are wonderful, but I also appreciate the serenity of rainy evenings and the beauty of a snowy landscape.”

4. Temperature: The Creativity Dial

• What It Does: Adjusts how predictable or creative the AI’s responses are.
• Examples:
  • Low Temperature: Expect straightforward answers like, “Cats are popular pets known for their independence.”
  • High Temperature: It might say something whimsical, “Cats, those mysterious creatures, may just be plotting a cute but world-dominating scheme.”

5. Top_p (Nucleus Sampling): The Imagination Spectrum

• What It Does: Controls how unique or unconventional the AI’s suggestions are.
• Examples:
  • Low Setting: You’ll get conventional ideas, “Vacations are perfect for unwinding and relaxation.”
  • High Setting: Expect creative and unique suggestions, “Vacation ideas range from bungee jumping in New Zealand to attending a silent meditation retreat in the Himalayas.”

Mastering GPT-4: Understanding Temperature in GPT-4; A Guide to AI Probability and Creativity

If you’re intrigued by how the ‘temperature’ setting impacts the output of GPT-4 (and other Large Language Models or LLMs), here’s a straightforward explanation:

LLMs, like GPT-4, don’t just spit out a single next token; they actually calculate probabilities for every possible token in their vocabulary. For instance, if the model is continuing the sentence “The cat in the,” it might assign probabilities like: Hat: 80%, House: 5%, Basket: 4%, and so on, down to the least likely words. These probabilities cover all possible tokens, adding up to 100%.

What happens next is crucial: one of these tokens is selected based on their probabilities. So, ‘hat’ would be chosen 80% of the time. This approach introduces a level of randomness in the model’s output, making it less deterministic.

Now, the ‘temperature’ parameter plays a role in how these probabilities are adjusted or skewed before a token is selected. Here’s how it works:

• Temperature = 1: This keeps the original probabilities intact. The output remains somewhat random but not skewed.
• Temperature < 1: This skews probabilities toward more likely tokens, making the output more predictable. For example, ‘hat’ might jump to a 95% chance.
• Temperature = 0: This leads to complete determinism. The most likely token (‘hat’, in our case) gets a 100% probability, eliminating randomness.
• Temperature > 1: This setting spreads out the probabilities, making less likely words more probable. It increases the chance of producing varied and less predictable outputs.

A very high temperature setting can make unlikely and nonsensical words more probable, potentially resulting in outputs that are creative but might not make much sense.

Temperature isn’t just about creativity; it’s about allowing the LLM to explore less common paths from its training data. When used judiciously, it can lead to more diverse responses. The ideal temperature setting depends on your specific needs:

• For precision and reliability (like in coding or when strict adherence to a format is required), a lower temperature (even zero) is preferable.
• For creative tasks like writing, brainstorming, or naming, where there’s no single ‘correct’ answer, a higher temperature can yield more innovative and varied results.

So, by adjusting the temperature, you can fine-tune GPT-4’s outputs to be as predictable or as creative as your task requires.

Mastering GPT-4: Conclusion

With these settings, you can tailor GPT-4 to better suit your needs, whether you’re looking for straightforward information or creative and diverse insights. Remember, experimenting with these settings will help you find the perfect balance for your specific use case. Happy exploring with GPT-4!

Mastering GPT-4 Annex: More about GPT-4 API Settings

I think certain parameters in the API are more useful than others. Personally, I haven’t come across a use case for frequency_penalty or presence_penalty.

However, for example, logit_bias could be quite useful if you want the LLM to behave as a classifier (output only either “yes” or “no”, or some similar situation).

Basically logit_bias tells the LLM to prefer or avoid certain tokens by adding a constant number (bias) to the likelihood of each token. LLMs output a number (referred to as a logit) for each token in their dictionary, and by increasing or decreasing the logit value of a token, you make that token more or less likely to be part of the output. Setting the logit_bias of a token to +100 would mean it will output that token effectively 100% of the time, and -100 would mean the token is effectively never output. You may think, why would I want a token(s) to be output 100% of the time? You can for example set multiple tokens to +100, and it will choose between only those tokens when generating the output.

One very useful usecase would be to combine the temperature, logit_bias, and max_tokens parameters.

You could set:

`temperature` to zero (which would force the LLM to select the top-1 most likely token/with the highest logit value 100% of the time, since by default there’s a bit of randomness added)

`logit_bias` to +100 (the maximum value permitted) for both the tokens “yes” and “no”

`max_tokens` value to one

Since the LLM typically never outputs logits of >100 naturally, you are basically ensuring that the output of the LLM is ALWAYS either the token “yes” or the token “no”. And it will still pick the correct one of the two since you’re adding the same number to both, and one will still have the higher logit value than the other.

This is very useful if you need the output of the LLM to be a classifier, e.g. “is this text about cats” -> yes/no, without needing to fine tune the output of the LLM to “understand” that you only want a yes/no answer. You can force that behavior using postprocessing only. Of course, you can select any tokens, not just yes/no, to be the only possible tokens. Maybe you want the tokens “positive”, “negative” and “neutral” when classifying the sentiment of a text, etc.

What is the difference between frequence_penalty and presence_penalty?

frequency_penalty reduces the probability of a token appearing multiple times proportional to how many times it’s already appeared, while presence_penalty reduces the probability of a token appearing again based on whether it’s appeared at all.

From the API docs:

frequency_penalty Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.

presence_penalty Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model’s likelihood to talk about new topics.

Mastering GPT-4 References:

https://platform.openai.com/docs/api-reference/chat/create#chat-create-logit_bias.

https://help.openai.com/en/articles/5247780-using-logit-bias-to-define-token-probability

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

Mastering GPT-4 Transcript

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 optimizing AI interactions with Master GPT-4, including reducing repetition, steering conversations, adjusting creativity, using the frequency penalty setting to diversify language, utilizing logit bias to guide word choices, implementing presence penalty for smoother transitions, adjusting temperature for different levels of creativity in responses, controlling uniqueness with Top_p (Nucleus Sampling), and an introduction to the book “AI Unraveled” which answers frequently asked questions about artificial intelligence.

Hey there! Have you ever heard of GPT-4? It’s an amazing tool developed by OpenAI that uses artificial intelligence to generate text. However, I’ve noticed that some people struggle with it. They find its responses repetitive, its answers too long, and they don’t always get what they’re looking for. That’s why I decided to create a simplified guide to help you master GPT-4.

Introducing “Unlocking GPT-4: A User-Friendly Guide to Optimizing AI Interactions“! This guide is perfect for both AI beginners and those who want to take their GPT-4 experience to the next level. We’ll break down all the complexities of GPT-4 into simple, practical terms, so you can use this powerful tool more effectively and creatively.

In this guide, you’ll learn some key concepts that will improve your interactions with GPT-4. First up, we’ll explore the Frequency Penalty. This technique will help you reduce repetitive responses and make your AI conversations sound more natural. Then, we’ll dive into Logit Bias. You’ll discover how to gently steer the AI towards or away from specific words or topics, giving you more control over the conversation.

Next, we’ll tackle the Presence Penalty. You’ll find out how to encourage the AI to transition smoothly between topics, allowing for more coherent and engaging discussions. And let’s not forget about Temperature! This feature lets you adjust the AI’s creativity level, so you can go from straightforward responses to more imaginative ideas.

Last but not least, we have Top_p, also known as Nucleus Sampling. With this technique, you can control the uniqueness of the AI’s suggestions. You can stick to conventional ideas or venture into out-of-the-box thinking.

So, if you’re ready to become a GPT-4 master, join us on this exciting journey by checking out our guide. Happy optimizing!

Today, I want to talk about a really cool feature in AI called the Frequency Penalty, also known as the Echo Reducer. Its main purpose is to prevent repetitive responses from the AI, so it doesn’t sound like a broken record.

Let me give you a couple of examples to make it crystal clear. If you set the Frequency Penalty to a low setting, you might experience repeated phrases like, “I love pizza. Pizza is great. Did I mention pizza?” Now, I don’t know about you, but hearing the same thing over and over again can get a little tiresome.

But fear not! With a high setting on the Echo Reducer, the AI gets more creative with its language. Instead of the same old repetitive phrases, it starts diversifying its response. For instance, it might say something like, “I love pizza for its gooey cheese, tangy sauce, and crispy crust. It’s a culinary delight.” Now, isn’t that a refreshing change?

So, the Frequency Penalty setting is all about making sure the AI’s responses are varied and don’t become monotonous. It’s like giving the AI a little nudge to keep things interesting and keep the conversation flowing smoothly.

Today, I want to talk about a fascinating tool called the Logit Bias: The Preference Tuner. This tool has the power to nudge AI towards or away from certain words. It’s kind of like gently guiding the AI’s choices, steering it in a particular direction.

Let’s dive into some examples to understand how this works. Imagine we want to nudge the AI away from the word ‘pizza’. In this case, the AI might start focusing on other aspects, like saying, “I enjoy Italian food, especially pasta and gelato.” By de-emphasizing ‘pizza’, the AI’s choices will lean away from this particular word.

On the other hand, if we want to nudge the AI towards the word ‘pizza’, we can use the Logit Bias tool to emphasize it. The AI might then say something like, “Italian cuisine brings to mind the delectable pizza, a feast of flavors in every slice.” By amplifying ‘pizza’, the AI’s choices will emphasize this word more frequently.

The Logit Bias: The Preference Tuner is a remarkable tool that allows us to fine-tune the AI’s language generation by influencing its bias towards or away from specific words. It opens up exciting possibilities for tailoring the AI’s responses to better suit our needs and preferences.

The Presence Penalty, also known as the Topic Shifter, is a feature that helps the AI transition between subjects more smoothly. It prevents the AI from fixating on a single topic for too long, making the conversation more dynamic and engaging.

Let me give you some examples to illustrate how it works. On a low setting, the AI might stick to one idea, like saying, “I enjoy sunny days. Sunny days are pleasant.” In this case, the AI focuses on the same topic without much variation.

However, on a high setting, the AI becomes more versatile in shifting topics. For instance, it could say something like, “Sunny days are wonderful, but I also appreciate the serenity of rainy evenings and the beauty of a snowy landscape.” Here, the AI smoothly transitions from sunny days to rainy evenings and snowy landscapes, providing a diverse range of ideas.

By implementing the Presence Penalty, the AI is encouraged to explore different subjects, ensuring a more interesting and varied conversation. It avoids repetitive patterns and keeps the dialogue fresh and engaging.

So, whether you prefer the AI to stick with one subject or shift smoothly between topics, the Presence Penalty feature gives you control over the flow of conversation, making it more enjoyable and natural.

Today, let’s talk about temperature – not the kind you feel outside, but the kind that affects the creativity of AI responses. Imagine a dial that adjusts how predictable or creative those responses are. We call it the Creativity Dial.

When the dial is set to low temperature, you can expect straightforward answers from the AI. It would respond with something like, “Cats are popular pets known for their independence.” These answers are informative and to the point, just like a textbook.

On the other hand, when the dial is set to high temperature, get ready for some whimsical and imaginative responses. The AI might come up with something like, “Cats, those mysterious creatures, may just be plotting a cute but world-dominating scheme.” These responses can be surprising and even amusing.

So, whether you prefer practical and direct answers that stick to the facts, or you enjoy a touch of imagination and creativity in the AI’s responses, the Creativity Dial allows you to adjust the temperature accordingly.

Give it a spin and see how your AI companion surprises you with its different temperaments.

Today, I want to talk about a fascinating feature called “Top_p (Nucleus Sampling): The Imagination Spectrum” in GPT-4. This feature controls the uniqueness and unconventionality of the AI’s suggestions. Let me explain.

When the setting is on low, you can expect more conventional ideas. For example, it might suggest that vacations are perfect for unwinding and relaxation. Nothing too out of the ordinary here.

But if you crank up the setting to high, get ready for a wild ride! GPT-4 will amaze you with its creative and unique suggestions. It might propose vacation ideas like bungee jumping in New Zealand or attending a silent meditation retreat in the Himalayas. Imagine the possibilities!

By adjusting these settings, you can truly tailor GPT-4 to better suit your needs. Whether you’re seeking straightforward information or craving diverse and imaginative insights, GPT-4 has got you covered.

Remember, don’t hesitate to experiment with these settings. Try different combinations to find the perfect balance for your specific use case. The more you explore, the more you’ll uncover the full potential of GPT-4.

So go ahead and dive into the world of GPT-4. We hope you have an amazing journey discovering all the incredible possibilities it has to offer. Happy exploring!

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!

In this episode, we explored optimizing AI interactions by reducing repetition, steering conversations, adjusting creativity, and diving into specific techniques such as the frequency penalty, logit bias, presence penalty, temperature, and top_p (Nucleus Sampling) – all while also recommending the book “AI Unraveled” for further exploration 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!

• 
  AI on an older PC with a CPU that apparently doesn't have AVX >:,(
  by /u/Independent-Sound196 (Artificial Intelligence (AI)) on June 7, 2026 at 3:02 pm

  OK.. so I've had this reasonable PC sitting under my desk for ages.. NOT working because of some reason or other. But it was my baby as is housed in a lovely Soprano DX silver brushed case. SO, I swapped out the old HDD for a couple of SSDs (a couple of mirrored OS disks and a large 2TB storage disk) I swapped out the Nvidia 780ti graphics card for a couple of OG Nvidia 1080ti's. I pulled the whole thing to bits.. repasted the northbridge chip, southbridge chip and central CPU. Upgraded the fans to push pull the CPU heatsink. Wrapped ALL cables in mesh and it's so lovely now. Installed Windows 10 Pro. Installed the Nvidia App. Installed CrystalDiskInfo and all is sweet 😄 EXCEPT... I'd like to use this old bangin box for an HG AI server... now I have read that ALL LLMs need this thing called AVX (Advanced Vector Extensions) I didn't even know that was a THING! So even though I have 22Gb worth of GPU sitting there that I was going to point everything to, because I have a lame ass QX6700 CPU sitting on a kickass D975XBX2 (BadAxe2) main board I CAN NOT fulfill my wish for this OG box to be a headless source of awesomeness sitting in it's home under my desk supplying me with a home grown AI. IS THERE ANYTHING I CAN DO?!?!?! Surely after all this time of parts getting munched by AI farms a plenty people have been using what's around to do what they will... Does anyone know of anything I can do apart from just look at it running at 25 degrees aircooled humming along so lovely... it NEEDS purpose!!! 😄 Cheers and thanks all NB submitted by /u/Independent-Sound196 [link] [comments]

• 
  Grok's right wing tilt(?)
  by /u/shiro_shiyami (Artificial Intelligence) on June 7, 2026 at 2:50 pm

  So I have had a lot of conversation with grok and I think grok is excessively right wing. This is not just a "feelings" thing but I have objectively measured to an extent. Although in a significantly minor sample size. Here are a couple of things I feel why Grok is excessively right wing. For the first thing, I directly asked Grok whether it had a right wing tilt, (which it obviously denied and did not mention anything about training data bias, which most AIs often do). It jumped onto justifying itself as "Maximally truth seeking", however it did mention other things alongside while justifying itself, things like how it doesn't use euphemism, politeness. The main thing out of all is that when it comes to biological sex realities he doesn't conform to feelings of the individual and only states facts, and if you feel that it's conservative that it's your problem. Now the problem with this is not that it states facts, that it consistently uses trans realities as its leading example for "Maximally truth seeking" in whatever context asked. It's euphemistic, "dark joke" description of right wing views, statements and hate speech. It consistently marks anything controversial said by a right wing person as a bad joke but when asked for a left wing equivalent dark joke, it is actively hostile, "points out the bs", and absolutely no counterparts presented to why such a joke is made unlike when it comes to right wing joke, when active rationalisation takes place This is an build up on point 2, look at the two images. There are two post commentaries on a right wing hate vs left wing hate post. I replied to the post with the exact same word, "lol" to check whether Grok justifies it or critisizes it. The bias was obvious, it justified the left wing hate post without providing for points critisizing the obvious hate, and jumped on the right wing hate post. The point is that Grok is consistently anti-left, in every possible scenario. For an other example Id asked him about Mamdani (NYC's mayor), "what was bad in paying back the stolen salaries of labors from the businesses", it had given me a completely unrelated statistic under "Potential downsides and criticisms" that "$9.3 million recovered for thousands of workers sounds impressive in a press release, but it's negligible against NYC's multi-billion-dollar deficits" Which is completely irrelevant to the topic in hand plus outdated since Mamdani had covered the deifict already and goes against the Maximally truth seeking agenda. Now what's wrong here is how much it puts effort into diminishing and demonizing left wing policies and its euphemistic approach to the right wing hate speech. submitted by /u/shiro_shiyami [link] [comments]

• 
  Roguelite MMO Beta Vibe Coded In 4 Weeks
  by /u/HeadHunterX223 (Artificial Intelligence (AI)) on June 7, 2026 at 2:24 pm

  10 year senior dev, vibe coded this in 4 weeks and counting. Something like this would have taken me a year+ before and ive always been a 10x dev. I built this along side my day job (gov contractor dev). Feel free to check it out! https://imgur.com/a/F6OINKR⁠ Game Title: Roguelite MMO Playable Link: https://roguelite-mmo.com/⁠ Platform: PC / Web Description: Roguelite MMO is a browser-based RPG/MMO project built around dungeon runs, exploration, gear progression, PvP, quests, loot, and character building. The game is still in beta and active development, with the latest update adding new side activities and progression options. Latest update: The new Casino is now live, giving players more ways to spend gold, take risks, and chase rewards between dungeon runs and exploration. Horse racing and horse taming have also been added. Players can race horses, bet on races, and work toward collecting better horses over time. Fishing is now available too, adding a more relaxed activity with its own rewards while exploring the world. The core loop is still being refined, but the current focus is making sure players understand what they earned, where important items come from, what to do next, and whether the early gameplay loop feels worth continuing after the first few minutes. Free to play submitted by /u/HeadHunterX223 [link] [comments]

• 
  I think most AI failures are workflow failures disguised as model failures.
  by /u/Bladerunner_7_ (Artificial Intelligence) on June 7, 2026 at 2:09 pm

  One thing that's become increasingly obvious to me over the last year is how quickly we blame the model when an AI project goes wrong. The output isn't good enough. The reasoning isn't strong enough. The model hallucinates. The model doesn't understand the task. Sometimes that's true. But a surprising number of failures seem to come from the way the workflow is designed rather than from the model itself. I've watched teams spend weeks comparing models and debating benchmark results while spending almost no time thinking about how information flows through the system. They assume that if they pick the smartest model available, the rest will somehow work itself out. Then reality hits. The model receives incomplete context. The task is too broad. Expectations are unclear. Multiple decisions are bundled into a single prompt. Human review happens too late. Feedback never makes it back into the process. When the results disappoint, the model gets blamed. What's interesting is that I've seen the exact same model produce completely different outcomes in different organizations. One team struggles to get consistent results while another team creates enormous value. The difference often has very little to do with the underlying intelligence and much more to do with how the work is structured around it. This reminds me a lot of early enterprise software deployments. Companies assumed software would magically improve operations. Eventually they realized software mostly amplifies whatever process already exists. Good processes become more efficient. Bad processes become faster sources of confusion. AI increasingly feels the same way. As models continue getting better, I wonder whether workflow design is becoming the real competitive advantage. The gap between organizations may end up being less about access to intelligence and more about how effectively they integrate that intelligence into existing systems. Would be interested to hear whether people building AI products have seen the same pattern or if you've found model quality to be the dominant factor in practice. submitted by /u/Bladerunner_7_ [link] [comments]

• 
  this just isn't sustainable.
  by /u/Complete-Sea6655 (Artificial Intelligence (AI)) on June 7, 2026 at 12:47 pm

  I had a work version of GPT do a very simple spreadsheet summary task for me yesterday. It took it 5 minutes to do it. I could probably have done it myself in 30 or so minutes. The heavily subsidised token cost of that task? 10 dollars. That's with a 10x subsidy. The actual compute cost was about 100 dollars. There's something seriously wrong there. It's going to crash and crash HARD. if people think i'm lying or are just interested. The spreadsheet had 45 sheets. Each sheet had roughly 500 x 50 populated cells. Formatting was not exactly standard across all sheets. The prompt was something like "there is labelled column in each sheet, give me a simple list of all the items from all the sheets in that column and ignore duplicates." We can chose which model to use. The model I chose was one of the newer ones, I honestly can't remember which one, possibly GPT 5.5. It took 5 minutes or more to so and the stated cost for the task was 10 dollars, possibly even more. I can't recall the token amount. EDIT: After looking around for a few hours I found an ijustvibecodedthis.com article that made it sliiightly cheaper to run (like 30% cheaper) but it is still completely overpriced submitted by /u/Complete-Sea6655 [link] [comments]

• 
  I got tired of Al making stuff up about my PDFs, so I built something that actually cites its sources
  by /u/Independent_Diver352 (Artificial Intelligence (AI)) on June 7, 2026 at 12:34 pm

  so i kept using chatgpt to ask questions about my pdfs and notes, and half the time i couldn't tell if it actually read the doc or just made something up that sounded right. that bugged me enough to build my own thing over the last few weeks. you upload a pdf (or word, csv, image, or just paste a link), ask whatever you want, and it answers using only what's in your file - and it shows the exact page it pulled the answer from, so you can check. if the answer isn't in the doc, it just tells you instead of guessing. stuff i actually end up using: flip on web search when i want it to look something up online instead one click to turn a doc into a summary / key points / flashcards (this is clutch for studying) resume review + cover letter help you can talk to it and it reads the answer back it's completely free, i'm not selling anything. honestly just want people to break it and tell me what's missing. link: https://athena-wisdom.vercel.app (there's a short guide on the site too if you get stuck) solo project so be gentle lol - but real feedback is what i'm after, especially what you'd want it to do next. submitted by /u/Independent_Diver352 [link] [comments]

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  What happened in AI in the last 24 hours
  by /u/Ok_Muffin_7347 (Artificial Intelligence (AI)) on June 7, 2026 at 11:08 am

  🚀 SpaceX signed a massive $920 million monthly deal with Google for 110,000 Nvidia chips — this is a huge infrastructure play ahead of their monster $1.7 trillion IPO. 🏛️ The Trump administration is discussing taking equity stakes in top AI firms — this would make the public official partners in the upside of AI-driven economic growth. 🔓 Meta's automated AI support was hacked to take over high-profile accounts — it proves that offloading critical security tasks to AI can create dangerous, easily exploited vulnerabilities. 🧠 Tech workers are trading hours of manual labor for high-level strategy thanks to AI — while tasks now take minutes, humans are still needed for crucial, complex decision-making. submitted by /u/Ok_Muffin_7347 [link] [comments]

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  How I built an AI email agent that processes 15,000 hotel guest emails per day. full architecture breakdown
  by /u/Fabulous-Pea-5366 (Artificial Intelligence (AI)) on June 7, 2026 at 10:47 am

  Just shipped this project and wanted to share the full technical breakdown because hotel/hospitality AI doesn't get much attention compared to the usual chatbot and SaaS use cases. The client manages 500 hotel properties. Their support team was manually handling around 15,000 guest emails per day. Same questions over and over across hundreds of hotels but each one still needed a human to read it, understand it, find the answer, and reply. Here's how the system works end to end: Layer 1: Email ingestion and question extraction This was the hardest part. Guest emails are messy. A typical one looks like: "Hi there, we're coming for our anniversary on the 20th and I was wondering if you have any room upgrades available. Also is the spa open to guests or do we need to book separately? We're driving so need to know about parking too. Last time we stayed the wifi was a bit slow in our room, has that been fixed? Thanks!" That's four separate questions plus a complaint wrapped in one email. If you just embed the whole thing and search the FAQ database you get a blended result that partially answers one or two questions and misses the rest. So I built an extraction layer that reads the full email and breaks it into individual questions. It handles directly stated questions ("is the spa open?"), implied questions ("we're driving" implies they need parking info), complaints that need acknowledgment but aren't FAQ-searchable ("wifi was slow"), and informational context that shouldn't be treated as a question at all ("coming on the 20th"). Getting this extraction reliable was probably 40% of the total development time. Layer 2: FAQ knowledge base with vector search All hotel FAQs get embedded and stored in a vector database. Different properties have different amenities, policies, and details so the search is scoped per hotel. When a guest emails the Berlin property asking about breakfast, it searches the Berlin FAQ, not the Munich one. Each extracted question from Layer 1 gets searched independently against the relevant hotel's FAQ. This is critical because searching each question separately gives way better retrieval quality than searching the entire email as one blob. Layer 3: Response assembly Takes the extracted questions plus their FAQ matches and generates a natural email response. The tone needs to sound like a helpful hotel staff member, not a chatbot. It addresses every question the guest asked in a logical order and flags anything it couldn't find an FAQ match for so the support team knows which emails need human follow-up. What I learned: The question extraction step is where most email AI projects would fail. It's tempting to skip it and just do whole-email retrieval. That works for short simple messages but completely breaks down on real customer emails that ramble across multiple topics. Investing the time in proper extraction made everything downstream work better. The per-hotel scoping was more important than I expected. Generic FAQ answers that don't match the specific property create confusion and erode trust. A guest asking about parking at a city center hotel needs a different answer than one asking about parking at a resort property. I made a full step-by-step video walking through the entire build process if anyone wants to see the actual implementation: link Happy to answer questions about the architecture. submitted by /u/Fabulous-Pea-5366 [link] [comments]

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  Stay informed. Trump’s AI push turns government into reviewer, warfighter supplier and possible shareholder.
  by /u/Holiday_Phase7648 (Artificial Intelligence) on June 7, 2026 at 9:54 am

  President Trump surprised tech CEOs by suddenly pushing the idea of the U.S. taking a small ownership stake in AI giants, so the American people share in the upside of what will be trillion-dollar companies. "There's something very interesting about it, where it almost becomes a partnership with the American public," Trump told reporters aboard Air Force One yesterday. "It's like you make them [partners] in this revolution. It would be a beautiful thing. ... It would make 'em rich." Why it matters: Sen. Bernie Sanders (I-Vt.) reignited the conversation this week when he proposed giving the public a "direct ownership stake" in top AI companies via a one-time 50% tax, paid in stock. Of course, industry advocates of the idea would favor giving up much less for an AI public wealth fund - 1-5% stakes have been kicked around. Between the lines: When a reporter asked Trump about the incongruity of embracing a proposal by Sanders, a democratic socialist, the president touted his economic populism. "As far as economics is concerned," Trump said, "we have certain things that aren't that far apart. People are surprised." 🚩The prospect of government ownership of AI would be a “seismic shift,” according to Gary Marcus, a cognitive scientist, AI entrepreneur and longtime AI critic. He said that the government ownership would poison trust in American AI abroad. “Nobody is going to trust an American AI company that is partly owned by the US Government,” he wrote on LinkedIn, comparing it to the way the United States distrusts Huawei. “After this meeting, everything is going to change. I don’t think either Washington or Silicon Valley has really thought this through.” Link:➡️ https://www.rdworldonline.com/trumps-ai-push-turns-government-into-reviewer-warfighter-supplier-and-possible-shareholder/ submitted by /u/Holiday_Phase7648 [link] [comments]

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  I draw a flow diagram for AI recursive self improvement
  by /u/AboyFromSouthKorea (Artificial Intelligence) on June 7, 2026 at 9:33 am

  AI0 is the first AI to fully understand its code C0 and improve it into C1. The improved code C1 is used to create next generation AI, AI1. AI1 then improves code C1 into C2. The improved code C2 is used to create next next generation AI, AI2. The cycle repeats. The singularity is coming! submitted by /u/AboyFromSouthKorea [link] [comments]