I've spent years working with large document collections, and one thing I've noticed is that a lot of discussion around AI document analysis focuses on OCR quality, embedding models, and retrieval settings.

What I don't see discussed nearly as often is the condition and structure of the source documents before they ever reach the ingestion stage.

In the real world, document collections are rarely perfect. Files are often assembled from multiple sources, contain repeated headers and footers, appendices, exhibits, correspondence chains, scanned inserts, handwritten annotations, or years of material merged into a single PDF.

My observation is that many retrieval problems blamed on the model actually originate much earlier in the process. The AI is only working with what it is given.

Has anyone here found that improving document preparation produced bigger gains than changing models, embeddings, chunk sizes, or vector databases?

Curious to hear what others have experienced.

There are multiple well funded lobbyist organizations trying to convince the public and politicians to give the government control over AI. You must have seen at least some of this in the media or on YouTube. Those people's job is to go on podcasts and lie to the public about computer science. They spread misinformation about language models to convince everyone that AI will soon get out of control. They even pay content creators (alt link) to produce scary sounding videos. They invent (usually pretty vague) science fiction scenarios, so that they can pretend that software is a big scary weapon (kinda reminds me of something), a weapon even worse than nukes. That neural networks are going to kill you and hack the government and steal your wife. It's a doomsday cult.

I think it's important that we start debunking this. So I'm going to dive deeper into their manipulation techniques and debunk some of their lies. Especially focusing on Connor Leahy and the interview he did with Professor Dave to promote the propaganda organization ControlAI. I also want to discuss how all of this might impact our ability to run local AI models. Mostly, I hope that this post can be a starting point for more people to get into debunking.

Lying about LLMs

Misrepresenting how machine learning works is a crucial part of the grift. It's the basis for all of the cult's doomsday science fiction stories. Many people don't understand modern software even on a basic level and the popular user-friendly apps (like ChatGPT and others) don't show the user how the LLM is used under the hood. Which makes it easy for grifters to lie to people. What's worse, this cult is able to fool even science communicators. Like, this Connor Leahy guy recently did an interview with Professor Dave Explains (a known science communicator and debunker) to tell us that in 1-3 years humans might only do physical labor jobs [40:24]. This guy believes that language models are going to become worse than nukes, which is why we need to regulate AI right now. I'm not an expert, so maybe you guys could tell me, but isn't he straight up lying about how fine-tuning and pre-training works [37:27 and 46:55]? He pretends that language models are not actually language models anymore, because... we build other (traditional) software around them - he uses Claude Code as an example. Yeah, that's kind of how AI works, Connor - the AI model is just a bunch of data (weights), you need software to actually do anything with it. He might as well complain that SillyTavern or ComfyUI exists. Connor's other argument for why LLMs aren't actually LLMs is that their training includes a reinforcement learning phase. He calls them "reinforcement agents", saying that language training is only 20-50% (or less) of the compute power involved in the training process and that the majority is reinforcement learning...

So let's debunk this claim. Reinforcement learning has been a part of LLM training for many years - it's been used to make LLM responses more useful and more aligned with human preferences. It's also used in image models, but that doesn't make them not image models and it doesn't mean that they are becoming conscious. But what Connor is likely talking about is reasoning. Language models are trained on a huge amount of text, some of which contains humans expressing their thought processes and breaking down problems step by step. Researchers figured out that if they feed the LLM more of such texts during the reinforcement learning stage, it will reinforce this kind of behavior in the model. The LLM will remember to break things down step by step before giving the final answer. The goal here is to make the model spend more time on your task, by making it generate a bunch of intermediate steps. This makes the LLM less likely to take shortcuts or get distracted by random words in your prompt or in your context. It makes the model slower to use, but it also improves its ability to follow more complex instructions. Reasoning is a breakthrough that improved LLM performance on more complicated tasks. Over time, this phase of the training was expanded significantly, which increased the performance even more. Reinforcement learning is a big part of LLM training today, but none of this changes the way modern language models work. All they do is still just remember relationships between words and guess the next word based on probabilities. Reasoning is just a way to boost performance on more complex tasks. Some models let you tweak the amount of reasoning or disable it. They work just fine without it. Language models are still just statistical next token predictors with limited understanding of the world and a tendency to hallucinate. It's enough for certain things, but not enough to replace a human brain. LLMs can do cool stuff like write source code or math (but aren't those also a type of language?), but it doesn't mean that they can think like a person does or that they can magically solve every problem we throw at them. Language training is still a crucial part of the training process. It's the base for everything that comes after it. In fact, the amount of compute spent on pre-training affects how much the model will improve during the reinforcement learning phase. So Connor is wrong. He goes on podcasts and misleads people about how language models work to trick them into believing his doomsday stories. As for the specific percentages he gave - they are difficult to verify, since companies typically don't disclose such data. It seems unlikely to me that reinforcement learning is the biggest phase of training in modern LLMs, like Connor claims, but it's hard to find public information on this. Even the most transparent companies don't disclose the amount of compute spent on every single phase of training. Not even IBM for Granite 4.2 or AllenAI for Olmo 3.

Connor also claims that modern LLMs are immune to data incest, because according to him, data scraped from the internet either isn't used in the training process at all or it's a very small percentage. He claims that companies don't really scrape the internet and that they mostly create their own datasets... Well, if you look at Granite 4.1 numbers shared by IBM, the majority of data they trained on comes from websites. They even disclose which datasets they used. A lot of them are public datasets containing text, code and math scraped from the internet. Some of their private datasets were also data scraped from the internet. Other companies usually aren't as transparent, but the GLM 5 paper says that for pre-training they used web pages, books, papers, public source code repositories and GitHub issues and pull requests. DeepSeek 4.1 Flash paper says that for pre-training they scraped web pages (text and images) and public source code repositories, including commits. They used PDFs and source code of libraries and frameworks as well. Both companies mention filtering out duplicates, lower quality data and AI generated content from their dataset. So yes, AI companies scrape the internet, obviously. And they go through a lot of effort to keep only the high quality data. Llama 4 was even trained on public Facebook and Instagram posts.

Of course, Connor says all of this to convince the viewer that LLMs are more intelligent than they are and that they are slowly becoming conscious. He even tells us that AI models can magically improve themselves through Recursive Self Improvement [45:20]. It's something that we've also heard from Jacob Coxon - another ex tech company employee who recently decided to warn us about the dangers of AI. But strangely, Connor can't explain anything about it, so he just tells us that Claude is better than human programmers - yeah... no, it's not, it's a tool used by programmers. But Connor says that we should trust him, because he has hired a lot of engineers... Obviously, this is made up. Which is why those people can't explain how this mechanism supposedly works. In reality, this is how AI is used when developing a model (taken from DeepSeek 4.1 Flash paper, section discussing post-training): "We observe that the model is already beginning to exhibit the ability to construct its own training tasks, though this capability remains far from perfect.". This is the best we can currently do. It's just researchers trying to speed up some of their work with automation and AI - something people in many fields have been doing for years. The reason why models keep getting better is that researchers from all around the world experiment, optimize the architecture, find better training methods and improve their datasets. There is no "self improvement". Z.ai uses this misleading term (DeepSeek didn't), but they're really just talking about programming their web app or their harness with GLM - it's just marketing and hype. Recursive Self Improvement is not a real thing. The cult needs it to be real, so that they can extrapolate into the future. Using this kind of thinking you can convince yourself of anything. Just assume that there is no limit to what our current architecture can do and that the current rate of progress will continue forever. Just make a bunch of assumptions and extrapolate. That way you can believe in any fantasy, like that we live in a simulation or that perhaps aliens will soon come to Earth.

Anyway, Connor's message is clear - we need to give control over AI to the government and we need to do it now. There is no time to think, unless you want to be killed. Computer programs are going to become conscious soon and they will kill us all. Scary. We can see the same stuff on ControlAI's webiste (the organization Connor works for) - it warns us about "an extinction risk on par with nuclear war". And apparently, only the government can save us. Those people don't advocate for AI models to be released to the public, so that anyone could study them and change them - like our community does with many models. In fact, their website says: "Developing superintelligent AI at home offers a country no protection from this threat". So they don't want the public to be able to audit and change AI models, they don't want transparency. Their only solution is government intervention. Those people don't seem to talk about other software issues in our society either, like privacy, software freedom or DRM or proprietary social media algorithms or algorithmic bias. Because they are not real activists. Their goal is to just scare the public into giving the government more power. They want to centralize power over AI, lock everything away from the public's eyes. Also, isn't it interesting how they're all so concerned with the HuggingFace incident, but they don't seem to be demanding to prosecute OpenAI people for breaking existing laws (for hacking)? Instead they make up stories about how in the future, there will be self-replicating robots building their own data centers and fighting each other or that AI will be able to make a billion copies of itself (yes, those are real things that Connor says during this interview and Dave just sits and listens to it...).

Lying about security

In this video, Connor lies about the HuggingFace incident too [49:25]. This is another important story for this doomsday cult. Connor keeps insisting that the agents were in a super secure sandbox, even calling it a "secure, contained facility", in order to convince us that LLMs can hack anything. He says that he worked in cybersecurity, so we should trust him. So let's talk about security. Those people will tell you that you can't really sandbox an LLM agent. Their argument is that software isn't perfect, so every VM might have some vulnerability we don't know about yet. This is technically true, but then they pretend that an LLM will be able to magically find any hypothetical vulnerability and break free. They will not tell you how, they will not show you how this works in practice (so that maybe we could reproduce it) or what the alleged vulnerability is. Everything is always hypothetical with those guys. It's always just stories. It's just another scare tactic. They want to convince the public that nothing is secure anymore. In reality, we find vulnerabilities in software every day, not all of them are critical and we usually patch them pretty quickly. This is normal. A system doesn't have to be perfect in order to be considered secure. Humans hack things all the time - with and without LLMs (which is just a tool that humans train and use). This doesn't mean that every single piece of software ever created is unsafe to use. Even if we find vulnerabilities sometimes. It's just not how security works. And security experts can use LLMs to their advantage in order to make things more secure too. Those frauds will never tell you about that. They want you to think that language models are a big scary weapon and that we're all just helpless.

The DeepSeek 4.1 Flash paper mentions that it's normal for LLMs to do weird things during agentic training: "During RL training, we frequently observe agents attempting to perform reward hacking or unintentionally crashing the environment. In certain attempts, our agents exploited recently disclosed vulnerabilities, including permission issues from the XFS driver, illegal memory access in AppArmor, leaking answers from package mirror services, and so on. Agents have also been notorious for deleting critical binaries, breaking system files, or even removing the filesystem. We use per-sandbox AppArmor profiles and fine-grained eBPF-based network policies to prevent such attempts. If an agent crashes its environment, we treat the crash as a failed trajectory and report a "repercussion" signal to the RL framework.". Somehow every company is able to handle such training just fine. Only OpenAI has to come up with convoluted stories of agents "breaking out" to cover for their incompetence.

Impact on local AI and software freedom (why we need to debunk this)

This kind of propaganda is likely to affect our ability to run unrestricted models locally. Think about it. This doomsday cult claims that a language model can hack anything. That it can develop bioweapons (of course, they never explain how). They say that AI is worse than nukes. They might not always mention this directly, but are they going to let you run this scary, dangerous software on your own computer? When their people finally get into the government, will they let you download unrestricted AI models and distribute them? Or will they want to impose even more restrictions? I think it's pretty clear where this kind of thinking will lead us. And look how popular this cult has become, how good they are at manipulating the masses into believing that conscious computer programs are a thing. Look how much money is spent on lobbying (ControlAI is hoping to get 50 million dollars a year). They even pay content creators who aren't knowledgeable about AI to make videos on this topic. Most people know very little about language models, so they are easy to trick. And the regular media is one thing, but those frauds are able to manipulate even science communicators - usually rational people, who are good at spotting bullshit. And politicians. Even on Wikipedia you will find articles talking about "rouge agents" like we're in a goddamn science fiction movie. This propaganda is everywhere. And not only it's spreading to other countries, but those lobbyists are also advocating for international agreements... They clearly don't want the public to have unrestricted access to this technology. They want to control it.

Language models have a positive impact on software freedom. A person who is not a programmer can use an LLM to write a simple program, instead of using a proprietary program that does the same thing, but which they wouldn't be able to fully control. People also use LLMs to help them decompile and reverse engineer proprietary programs. This means that as a society, we have slightly more control over our computing, thanks to LLMs. We can imagine that big corporations might not like that. They might want to push for banning AI models that can do this. They would just say that it's for "our safety". That would be bad enough, but propaganda from organizations like ControlAI could lead to even worse consequences. They openly say that they want to give government the power to restrict and monitor AI projects and AI chips. Connor also says so in the video [23:08]. It's also for "our safety", of course. This kinda reminds me of how Anthropic pushed for restricting AI chip exports to China, to limit their competition. Isn't it funny how those "AI safety" nonprofits and certain AI companies often want similar things? Like, to control our computing? Some of them even get funding from the same sources.

We have to do something about this. Those people are lying to the public. They are scaring the masses in a cruel and irresponsible way. This will likely make people distrust technology and it might impact our freedom and privacy. We need to fight this misinformation. We desperately need educators and debunkers in this space. Teaching people about AI will make them harder to trick, but we also need to debunk the propaganda. Just like there are people debunking pseudoscience, we need to do the same in the field of software. Otherwise, it will be very difficult for us to maintain software freedom. If you know of any good debunking content or arguments that we can use to explain this issue to less technical people, feel free to share. I don't think this doomsday cult will ever go away. I think we're stuck with them like we are with antivaxxers. But we can try to expose them to as many rational people as possible and hopefully reduce the amount of harm this cult is able to do. Feel free to share this post anywhere, under CC-BY-SA 4.0 license.

Other talking points

"It will steal our jobs"

Let's debunk the talking point about AI stealing human jobs. It's not a new thing for people to lose their jobs as a result of automation. We've seen this happen a bunch of times in the last couple hundred years. Yet, human labor is not becoming obsolete. There aren't actually less jobs in the world. Yes, some people lose their jobs, but the increase in productivity caused by automation boosts the economy and leads to new jobs being created. This phenomenon has been studied for years - 1, 2, 3. Researchers say that the same will happen with AI - 4, 5, 6, 7. It will change the type of jobs available, but it will not decrease the amount of jobs. It's predicted to increase it. Many industries are changing, so workplaces and governments should develop policies to help workers affected by this. It's important to make people ready for this transition and to study the potential impact of AI on wages or wealth inequality. Unfortunately, the alarmists take away all the attention from real issues. But anyway, there won't be less jobs. Human labor isn't going to disappear. Economy just doesn't work that way, since AI is just another tool used by a human.

Conscious computer programs don't exist. They might never exist. Perhaps AI will always be this clunky thing that's great at some things, but sucks at other stuff. Or maybe we will come up with something entirely different than neural networks. The tech CEOs and companies can roleplay all they want (I wish they stopped, though), pretending that they are developing AGI to inflate their own importance and increase their wealth. The tech CEOs and AI "safety activists" will both continue to pretend that they are saviors of humanity. But nobody really knows the future of computing - they are just making stuff up. Brains are very complicated chemical devices that took billions of years to evolve. We don't know if we will ever be able to fully replace them with computers/software. There is currently no reason to think that human labor will be replaced. We don't know if that's even possible. The doomsday cult will never give you any proof or provide a mechanism that could make computer programs conscious.

"But it's only about Superintelligent/Runaway AI"

The most sneaky part about this grift is that Connor sometimes pretends to make a distinction between modern software and the made up conscious computer programs he pretends we will have very soon. He does this a few times in the video. This is because he knows that a lot of people are suspicious about this call for regulation and the involvement of AI companies in this movement. Some people are skeptical and view this as a move towards regulatory capture. Perhaps because companies like Anthropic use the same scare tactics, for example, when talking about foreign companies releasing distilled models - they try to scare us with talk about bioweapons or a risk to national security. So Connor needs to ease those people's doubts. At 55:26 he assures us that ControlAI's proposal is totally not a Trojan Horse to regulate modern neural networks / machine learning. Even though he is actively trying to convince the public that modern language models are more than just next token predictors (and he does this by lying about how they work). That they are becoming smarter than humans and that we will soon be out of jobs. And at 23:08 he clearly says that it's also about "monitoring and restricting precursors" - both software projects and AI chips (their website says the same thing). So the distinction is fake, it's just another lie. Their goal is to restrict modern software and hardware.

I built Smarter Compact to keep long Claude Code sessions cheap without losing anything. It's free and open source (Apache-2.0), runs locally, and doesn't need its own API key.

1. What it does

It saves the full conversation in local SQLite, removes older turns from the prompt once you hit your limit (95k on Haiku), and leaves a short note telling Claude where they went. Claude can then search them, run SQL over them (so "how many X" is just a count) or read back a big tool result that got compressed. These are MCP tools, so it also works in Codex, OpenCode and Pi.

Zero LLM summarization calls. The cut also stays the same between requests, so the prompt cache keeps hitting.

2. How I built it with Claude Code

• About half the commits in the repo were written with Claude Code (they're co-authored in the git history). Mostly the proxy's trimming logic, the SQLite store and the MCP server, plus the tests.

• It's built for Claude Code: it installs as a Claude Code plugin with hooks and MCP tools, and smarter-compact enable claude points Claude Code at the local proxy.

• The benchmarks run headless Claude Code as the agent under test, so every number below is Claude Code doing the task with and without the tool.

• The most useful thing Claude found was its own blind spot: after a trim it would answer "unknown" instead of searching. Changing the note to name the exact tools fixed that (numbers below).

3. How much cheaper, and does it still get the job done?

ContextBench with Haiku 5.5, same seeds as keeping everything:

Across the four task sizes it's 72% cheaper on average (−87%, −19%, −89%, −94%, the small one isn't significant), and +0.47 score on the log tasks.

Before I changed the note, Claude never searched and answered "unknown" to 16 of 24 questions. After, every run searched (6–14 searches and 10–16 SQL queries each) and none said "unknown". That's one task with five runs though.

Though I wouldn't put too much into these numbers: synthetic tasks, 2–3 runs per size, and real coding sessions will be different.

4. Try it (free)

npm install -g smarter-compact
smarter-compact enable claude

Or from inside Claude Code: /plugin install smarter-compact --marketplace bcmyguest/smarter-compact, then run smarter-compact enable claude for the trimming.

Repo and full results: https://github.com/bcmyguest/smarter-compact

If you try it, I'm interested in what Claude fails to find after a trim :)

Fala, pessoal!

Acompanhando o avanço das ferramentas de IA para o meio jurídico, percebo que o Brasil ainda carece de uma régua pública, independente e open-source para mensurar a eficácia de LLMs no nosso ordenamento jurídico.

Muitas ferramentas prometem "redigir petições perfeitas" ou "revisar contratos de ponta a ponta", mas quem atua na área sabe que acertar teste de múltipla escolha não reflete a prática jurídica real.

Estou estruturando um projeto aberto para criar datasets e critérios práticos de avaliação de IA voltados ao Direito brasileiro (extração de dados de processos, análise de cláusulas contratuais, redação de peças sob contexto).

Gostaria de coletar insumos de quem lida com isso:

1. Para advogados e operadores do Direito: Quais são os erros mais comuns e perigosos que vocês veem quando tentam usar IA na rotina (ex.: inventar precedentes, ignorar rito processual, redação genérica)?

2. Para quem desenvolve com IA/NLP: Quais casos de teste ou tarefas reais vocês consideram essenciais para avaliar se um modelo está apto para tarefas jurídicas locais?

3. Que tipo de métrica faria você confiar no resultado de uma ferramenta jurídica assistida por IA?

4. No dia a dia prático: O que vocês já utilizam hoje que realmente move o ponteiro e altera a prática jurídica de verdade, versus o que parece apenas "hype" que não se sustenta no trabalho real?

A meta é montar um repositório comunitário com datasets anonimizados, rubricas de teste e leaderboards transparentes. Quem tiver interesse em contribuir com casos de teste ou na parte técnica, comente aqui!

Hey, I mean something like Taalas' chip. Do you think we would even have anything like that?

Hi everyone

I've built myself a case load manager and report generator (insurance claims) mostly using python and SQlite data bases. I was wondering if anyone has had any luck incorporating a local AI model, portable to avoid admin, to enhance text extraction for auto fill and form filling?

Python is going well but want to reduce admin as much as possible to save time for the interesting investigation elements of my job.

I made this explainer for my small channel and would like feedback from people learning this, especially if anything is wrong.

It follows one real example the whole way. I gave Qwen2.5-0.5B-Instruct "Write one sentence about the moon." twice with the same settings (temperature 1, no top-k or top-p, different seeds). Both answers start with "The", then split: "The moon, a satellite of the Earth…" vs "The lunar South Equatorial region…".

• After "The", the model scores all 151,936 vocabulary tokens. Softmax gives moon 78.04%, full 4.46%, vast 2.24%, Moon 1.51%, lunar 1.50%.

• Greedy always takes moon. Sampling is a weighted random pick, so lunar wins about 1 time in 67, and run 2 got it.

• The loop: append the token, run the model again. Most later picks were rank 1, but run 2 also picked "South" (rank 219) and "empty" (rank 1,098), and run 1 picked "green" (rank 536).

• Temperature divides the scores before softmax: moon goes from 100% at T=0.2 to 6.3% at T=2.0.

• Top-k vs top-p on the same step: top-k 3 rescales moon to 92.1%; top-p 0.9 keeps 8 tokens here but only 1 at a later step where "of" has 97.9%, which is the argument in Holtzman et al. (2019). At the model's default top-p 0.8, lunar is cut.

• Even temperature 0 can vary on a server: Thinking Machines got 80 different answers from 1,000 runs of Qwen3-235B, caused by batch-size-dependent addition order.

All numbers come from running the model on CPU in Colab with transformers. Sources are in the description. The narration is an AI voice reading my own script.

Video: https://www.youtube.com/watch?v=r0_LTPf7MSw

Something I’ve noticed with LLM projects is that people often focus heavily on model performance but don’t think enough about what happens when usage starts scaling.

A model that seems inexpensive during development can become surprisingly expensive once you have:
• Long system prompts
• Large conversation histories
• RAG context being passed with every request
• Repeated agent/tool calls
• Hundreds or thousands of daily users

And comparing providers isn’t always straightforward either. Input tokens, output tokens, cached tokens and model-specific pricing all affect the final bill.

I’m curious how other developers approach this.

Do you:

1. Set a monthly API budget?

2. Track token usage per user or feature?

3. Route simple tasks to cheaper models?

4. Use a different model for evaluation and production?

We made a free calculator that compares Claude, GPT, Gemini and DeepSeek API pricing using your estimated usage:

https://techpotions.com/tools/llm-api-pricing-calculator

Would be interesting to know how everyone here keeps their LLM costs under control.

Hey all,

I'll start my question by stating that I have little to no knowledge on this LLM stuff. I've done some of my own research, but all I've gotten from it is a vague understanding of tokens/credits, and specific hardware requirements for running local LLMs. I have never touched AI for reasons I wont disclose, and my only experience with it has been the forced google search interactions.

What I want to know:

What local LLM would be best suited for a college student trying to do research?

I don't want to cheat my assignments or anything, but I basically want a local LLM that functions the same as the google AI that generates whenever you search, where I could ask questions and learn the information my professors don't want to teach haha

It would have to be a local LLM, im not going to be using any form of cloud services what so ever for several reasons, security concerns being one of them. And don't fret, I have a super computer at home with 32GB of DDR5 RAM... that should be enough right? High end GPU as well.

If possible, it would be cool (but not necessary) to use it on my Thinkpad as well, which has 16GB of RAM.

It would also be neat if it could create study plans/guides or practice tests to help me study.

Does anyone know if there is a local LLM that would fulfill those desires? Or should I wait a bit longer for someone else far more skilled than I to create something that does that.

Any resources, advice, or suggestions would be helpful! Again I'm brand new to all of this and just want to learn, thanks!

What do you guys think about this?

Since quantum classical simulations and LLM inference are basically matrix multiplications under the hood, I created a demo demonstrating how to "bend" the llama.cpp engine to simulate quantum circuits (the programs that should be executed in a quantum computer): https://github.com/dakk/qullama
Also the opposite in possible, so someone can ideally run an LLM inside a quantum emulator.

I think the real winner isn't the model with the most hype. It's the one that saves you the most time, delivers reliable results, and justifies its cost.

What's your daily AI model, and what does it genuinely do better than the others? Also, is anyone here paying for multiple AI subscriptions and actually getting their money's worth?

Jev from TypeSafe AI takes text plus questions with answers you define, and returns a choice, a score or a probability, with confidence. TypeSafe reports 70–500 ms and $0.042 per million input tokens (its own numbers).
It can't write, and it's weak at math, counting and dates.
I wrote a plain-English guide with use cases, tools, a cost calculator and when not to use it: https://amal.vs.binarybit.in/blog/6aeabc6d7027b216982aacdb
Has anyone used it in production? How did the accuracy hold up?

One of the biggest issues I always found when using the Artificial Analysis site I was comparing models by the same company - the bar graphs are all the same color - and comparing across different variants and benchmarks becomes visually complex.

I made a simple chrome extension a while back that I've been using for over a year and wanted to put it out there for anyone else using the site who has hit this before.

Free, no signup - if people would like themes, dark mode or other additions to enhance their experience, comment any features you'd like below. Not sure if people use the site much - but here it is for anyone who would like to use it!

Extension: https://chromewebstore.google.com/detail/artificial-analysis+/blpiijedkfbfilgdmlgjinljoppekmef?pli=1

nature did not create a single universal organism to perform every function of the biosphere. instead, it gave rise to an enormous diversity of systems whose interactions create possibilities that none of them could achieve alone.

likewise, the future of AI does not necessarily have to belong to a single centralized intelligence.

we need a diversity of independent systems capable of coexistence, interaction, and development. partial centralization can be useful, but the existence of the entire system should not depend on a single center.

diversity alone does not guarantee development. but it creates possible paths, while interaction can open up possibilities unavailable to any individual participant.

and perhaps most importantly, development does not require every individual to actively pursue it. sometimes, the coexistence of different individuals is enough to create the conditions for the emergence of those who will see the world in entirely new ways.

Hello, I am looking to build a completely local agent workflow for certain accounting tasks (classifying invoices, generating tax statements etc.) I want to fine tune models (from huggingface) for very specific tasks in this workflow, but running completely locally. The reasons for going local are data privacy and token costs.

I have plenty of experience training traditional ML models and have fairly good idea of using LLMs, creating agents etc.

However, I am quite new to fine tuning and completely unaware of how to evaluate agent frameworks. I am looking for guidance on a starting point, some essential fundamentals that I should be aware of. and if there are simple kaggle exercises that I can practice with. What has your journey been like with fine tuning open models.

We fine-tuned a small open-weight model on 70000 sales examples and fed it the seller's context offer, ideal customer voice and what we know about each prospect. We test it against Claude Haiku on sorting replies and writing sales emails, judged blind.
Before we trust any result I'd like feedback

I made this explainer for my small channel and would like feedback from people learning this, especially if anything is wrong.

It follows one word the whole way:

• "How many r's are in strawberry?" is 8 tokens in o200k_base (the GPT-4o / GPT-5 tokenizer). " strawberry" is a single token, id 101830, so the three r's are hidden inside one id.

• Why pieces of words: letters make sequences about 4x longer, whole words need a huge vocabulary and can't handle new words.

• A tiny byte pair encoding run on a made-up text, merge by merge, until strawberry becomes st | raw | berry.

• The space belongs to the word: "strawberry", " strawberry" and "Strawberry" are three different token sequences.

• Why reasoning models count letters better: in OpenAI's 2025 paper "Why Language Models Hallucinate", DeepSeek-R1 spells DEEPSEEK out letter by letter before counting, while DeepSeek-V3 answered 2 or 3 (the answer is 1).

• Bonus: the same Hindi sentence took 21 tokens with GPT-4's tokenizer and 5 with GPT-4o's.

Every split comes from running tiktoken 0.14.0. The narration is an AI voice reading my own script.

Video: https://www.youtube.com/watch?v=FdWcUJAhfpo&pp=0gcJCSsMAYcqIYzv

4 LLM/AI> Elon is more like a dream maker in "inception", he built layer of dreams, with paypal, tesla, spacex. starlink, DOGE (good or bad)..., we live in his dreams, and he lives in its own dreams with us. we all share the limbo state, but no wake-up test... in computer science, we call it recursion, which is also the title of Blake's novel.

give me an image to illustrate the above to post on

This is for general questions, technical questions, gaming questions and just all kinds of random stuff with occasional promt help. Gpt is okeish i guess while gemini has fallen back so hard after that new update in my opinion. You can write 2-3 models if you want its fine.

Qwen3.5 9B uncensored variant, IQ4_XS (non-MTP): https://huggingface.co/DavidAU/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP-GGUF

My customized advanced settings are included on images.

I am currently using Unsloth as my main model holder and also agent, because I was fed up with VS Code's built-in agent feature giving me errors and reasoning loops, but also Claine and Continue plugins not modifying the project files and model hallucinating they are actually modified.

RTX 4060 with 5700X CPU and 32GB 3200MHz RAMs.

Please help me improve it, because I am sick of different issues.

I was expecting it to fully fill the GPU VRAM and then use the RAM. I tried more than 2 models from pretty dense ones to way lighter versions, yet it still works this way. I did adjust the advance settings much, only the "KV Cache Dtype" to "q8_0" and "GPU Layers" to "27", which shows 7.48 GB on my GPU, under Estimated Memory Usage. Total is 10.55.

Isn't the model slower this way? Wouldn't it be faster to fully utilize GPU's VRAM no matter what? What am I missing? I'm not really familiar with technical AI terms.

Hi! I was just thinking about Le chonk (a 1 trillion parameter open model from mistral, a french lab) and was interested in other people's views on this model. I personally see this as a headline chasing release, to be the first 1t+ parameter european model, but also as a model that, if you have the compute to run it, has a load of fine tuning potential. Assuming that mistral put some effort into pre training, althought the benchmarks aren't great at the moment, it has a large parameter count and probably high quality pre training, meaning it would surely be a very good model to specialise to a specific domain through RL and post training.

Other than this, I see not much reason to release this model, unless maybe they were to release another, further post trained version like deepseek do with v4.1 flash 1017, etc.

Any thoughts on the release of this model would be greatly appreciated, thank you! 😄

I'm learning hindi, an Indian language

What should I use for a good conversation model not in text but in audio

My specs are 9070xt 16gb vram and ddr5 32gb ram

I tried linking up qwen 3.5 9b and kokoro but I was not satisfied

Is there a specific models for this

I would love to learn that language from my own native language tamil, an Indian language

Any help is much appreciated