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LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today. It's about being intentional in building and scaling your intelligence stack. To showcase this, I wanted to share an LLM Wiki I... have built over the last couple of months. It's called PaperWiki, and I use it across all my research workflows, along with my research agents. In fact, I also use it to curate papers I share with my communities, newsletter, and on X. The PaperWiki is updated regularly with automations, so I basically have agents on a loop maintaining it. All the entries are ingested from different sources and stored in a vault (Obsidian) and further indexed using qmd. And then further presented via an HTML artifact. So all of it is easily accessible to all my agents and easily searchable through full-text search and rich semantic search. The structure of the wiki has proven significantly useful to start interesting and exciting cutting-edge research projects with my research agents (from building tiny and more efficient gpt/difussion llms to building out SoTA harnesses and memory systems). It turns out that agents love markdown files and can more easily navigate the papers given the rich metadata structure of the wiki. I am just getting started on this, but it's clear to me that we should all be experimenting with LLM Wikis. Here's why: Building LLM knowledge bases gets you into the habit of leveraging AI outputs in all kinds of creative ways. It's the good kind of tokenmaxxing we should all be pushing for. LLM Wikis can be maintained automatically in a loop. I use an automation that updates the wiki every day based on papers I curate. The curation is another automation I run in a loop (with a bit of human in the loop), so I get to build on all my previous knowledge and expertise, and all of it compounds the deeper the integration/layers. One interesting result of this process is that I feel like I can better spot high-quality papers and remove noise more easily. Social media could never solve that. And most paper aggregators use metrics I simply don't trust. I like that agents can help with the noise vs. signal problem. This is important for research. Lots of people consider agents to produce mostly slop. But it doesn't have to be that way. Careful curations, prompts, automations, verifiers, and human-in-the-loop can produce some astonishing results. And you really don't need frontier models for this. I use a combination of frontier models (opus-4.8) and open-weight models (deepseek-v4-flash) to maintain this. An exciting future work (we are working on this DAIR.AI) is to tune specialized models on top of this to allow LLMs to quickly understand cutting-edge research ideas and can better conceptualize research strategies that further accelerate scientific research agents. I plan to open-source a bunch of this work, including the artifact, but this is currently work in progress, and I was excited to share some thoughts as I continue working on it. Sharing more as I go. Stay tuned!show more

elvis

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51,419 views • 3 days ago •via X (Twitter)

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arXiv Papers → LLM Artifacts This is how I keep up with AI research now. It's like having access to the most personalized arXiv feed. Automations run everyday to curate papers based a set of rules and insights. Curated papers are indexed and power the artifacts. Agent convert papers to LLM wikis (based on Andrej Karpathy idea), which means insights are indexed and easily searchable and reusable. I feel like LLM Artifacts is the natural evolution to LLM Wikis. It's about making that knowledge actionable. Artifacts are customizable via agents. Artifacts can interact with agents and are dynamic in nature. Anything can be injected into the artifact as needed (insights, components, suggested experiments, action items, etc). I can take action on Artifact items with my agent orchestrator (Electron app). So I can ask questions about any paper and automate experiments in the background right from within the artifact. This is more than a visual. It's not a single prompt. It's several proactive agents coordinating to surface interesting facts, knowledge, and insights that I can act on a researcher. Agents are not just for generating useful artifacts, they are useful to keep learning and staying on the cutting edge of knowledge. Stay tuned for more.

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elvis

58,750 views • 2 months ago

YT Podcast → LLM Artifact This is now my favorite way to consume podcasts. Knowledge artifacts generated by agents. The agent (Opus 4.7) spots important insights, does deep analysis, and generates thought-provoking observations that really get me curious to research further. All the research goes into a self-improving wiki for later use by any of my agents. I am using Elevenlabs Scribe for diarization. Skills and scripts to guide the artifact generation. Artifacts are just plain HTML + JS (e.g., chart.js). You can go further on anything by selecting text and components (charts) and doing deeper research, as I show in the clip. I expect more people to use agents in this way.

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31,855 views • 2 months ago

LLM Wikis + HTML Artifacts are insanely powerful. You should seriously consider this in your workflows. LLM Wikis captures all the important information that lets you and your agents do meaningful work. HTML artifacts present that information in interesting ways that allow you to take important actions along with your agents. My HTML artifacts sit on top of my LLM wikis. They are dynamic and are easily extended as needs arise. I have hooked my Artifacts to talk to my agents, and similarly, the agents can talk to artifacts. This has allowed me to build powerful artifacts that reduce my inbox to zero, keep me updated on any topic of interest, fast prototyping, do deep research, design/trigger new experiments, generate figures to improve understanding, schedule research, search relevant information, discover topics, and so much more. What you see in the clip is not a website. It's a simple interactive HTML artifact. HTML artifacts are useful for designers, engineers, researchers, students, and anyone working with agents. Lastly, HTML doesn't replace Markdown. They are a much better combination working together.

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246,267 views • 1 month ago

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

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elvis

102,035 views • 1 month ago

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when I look at my old simulations, a lot of them, most of them, can be easily vibe setup with agents, or even one shot by the agents. it makes me wonder how many of the ideas and devices that were results of branching off of originally intended directions and goals would I miss if I attempt them now with the agents? How many new ones would the agents be able to attempt and identify? Is inverse design just the most stupid and bruteforce version of auto research? i should get a workflow with MOOSE working so I could set the agents free in the world of multiphysics simulations.

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62,983 views • 3 months ago

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The Peel

51,632 views • 4 months ago

Today, I am launching Paper Breakdown. - PBD gets you academic paper recommendations and lets you study CS/ML/AI research with LLM agents. - It highlights relevant sections directly in the actual PDF - generates flowcharts/illustrations too - we provide an in-build screenshot tool to send images to the agent directly from the paper. - we also got agentic paper search that allows you to search our database of 70,000+ CS and ML Arxiv papers in seconds using natural language. I have been building PBD for almost half a year - it all started as a means for me to keep up with research and use AI to produce visuals and scripts for my own YouTube videos. I have developed it enough to confidently recommend it to you. Visit our landing page to learn more.

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ChatGPT Deep Research is one of the most useful AI agents out there. I use it for complex tasks, so it's one of the top AI products I get value from. I finally got to record how I use it and how to best leverage OpenAI's Deep Research. Enjoy! (bookmark it)

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65,442 views • 1 year ago

Deep Research is the most fun I've had with an AI tool since NotebookLM was launched! You can't imagine how incredible this agent feels for someone like me that loves doing research. I took it for a spin here and added my thoughts: I am impressed by the reasoning capabilities of the o3 model which operated under the hood. This is RL at its best once again! Synthesizing large amounts of information from different sources is no easy feat. I can tell you all about it from my experience building and scaling RAG systems. I have so many ways I plan to use this. More on this soon.

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26,459 views • 1 year ago

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The All-In Podcast

229,185 views • 1 month ago

Google DeepMind CEO Demis Hassabis on "leaving AI in the lab for longer” (full question + answer in the video as I've seen him misquoted). Here's what he said: "For me, the best use case of AI was to improve human health and accelerate scientific discovery..." "Given how important AGI is and how transformative a technology is, maybe the most transformative one in human history, I thought it would be best to approach the sort of latter stages of building it, which we're in now, using the scientific method, very carefully, very precisely, very thoughtfully, and rigorously with all the best scientists, in my ideal world, collaborating on in CERN-like effort, on making sure each step we understood each step each as we got to the final goal of, of building AGI.... "While we're building AGI in this careful scientific way, humanity could benefit from the proceeds of that, like cures for cancer, or maybe new energy sources or new materials… “Looking at this from 20, 30 years ago when I started out on all of this, that would have been the ideal way for it to play out, in my opinion. “Now, it didn't happen like that because technology's unpredictable and in fact, it turns out that things like language were a lot easier than we were all expecting… “We were sort of playing around with that, so were the other leading labs, but of course with ChatGPT and fair play to OpenAI, they scaled it and then they put it out there. “And I think even they say it was kind of a research experiment. They didn't realize it would go so viral. And I think none of us did and we had sort of fairly equivalent systems at the time… “Now, the downside of it is, we're in this sort of ferocious commercial pressure race that everyone's sort of locked into currently. “And then on top of that, there's geopolitical issues like the US-China race and so on. So there's sort of multiple levels of pressure to sort of move fast. So the benefit of that, of course, you get faster progress, obviously. The progress is just at lightning speed these days. So that's good for all the good use cases. The second benefit is that everybody, all of the viewers out there, everyone, you're all getting to use the most cutting edge AI technology, perhaps only three to six months behind what is actually in the labs. So that's kind of mind blowing. “It's also great because I think it gives everyone a feeling for, it's democratizing AI. It's giving everyone a feeling for what it's like to interact with cutting edge AI and what it can do and what it can't do… “So I think there's positives and negatives about the way it's gone. It's not the way I dreamed about years ago where we would be sort of contemplating this philosophically and carefully considering each next step. We're not in that world. And I'm, although I'm a scientist first and foremost, I'm also a pragmatic engineer. So, we have to deal with the world as we find it and make the best of that. And we try to do that by advancing the frontier, but also trying to be as responsible as we can with doing that as we deploy these, you know, very powerful technologies, like Gemini and Alphafold.”

Google DeepMind CEO Demis Hassabis on "leaving AI in the lab for longer” (full question + answer in the video as I've seen him misquoted). Here's what he said: "For me, the best use case of AI was to improve human health and accelerate scientific discovery..." "Given how important AGI is and how transformative a technology is, maybe the most transformative one in human history, I thought it would be best to approach the sort of latter stages of building it, which we're in now, using the scientific method, very carefully, very precisely, very thoughtfully, and rigorously with all the best scientists, in my ideal world, collaborating on in CERN-like effort, on making sure each step we understood each step each as we got to the final goal of, of building AGI.... "While we're building AGI in this careful scientific way, humanity could benefit from the proceeds of that, like cures for cancer, or maybe new energy sources or new materials… “Looking at this from 20, 30 years ago when I started out on all of this, that would have been the ideal way for it to play out, in my opinion. “Now, it didn't happen like that because technology's unpredictable and in fact, it turns out that things like language were a lot easier than we were all expecting… “We were sort of playing around with that, so were the other leading labs, but of course with ChatGPT and fair play to OpenAI, they scaled it and then they put it out there. “And I think even they say it was kind of a research experiment. They didn't realize it would go so viral. And I think none of us did and we had sort of fairly equivalent systems at the time… “Now, the downside of it is, we're in this sort of ferocious commercial pressure race that everyone's sort of locked into currently. “And then on top of that, there's geopolitical issues like the US-China race and so on. So there's sort of multiple levels of pressure to sort of move fast. So the benefit of that, of course, you get faster progress, obviously. The progress is just at lightning speed these days. So that's good for all the good use cases. The second benefit is that everybody, all of the viewers out there, everyone, you're all getting to use the most cutting edge AI technology, perhaps only three to six months behind what is actually in the labs. So that's kind of mind blowing. “It's also great because I think it gives everyone a feeling for, it's democratizing AI. It's giving everyone a feeling for what it's like to interact with cutting edge AI and what it can do and what it can't do… “So I think there's positives and negatives about the way it's gone. It's not the way I dreamed about years ago where we would be sort of contemplating this philosophically and carefully considering each next step. We're not in that world. And I'm, although I'm a scientist first and foremost, I'm also a pragmatic engineer. So, we have to deal with the world as we find it and make the best of that. And we try to do that by advancing the frontier, but also trying to be as responsible as we can with doing that as we deploy these, you know, very powerful technologies, like Gemini and Alphafold.”

Cleo Abram

64,840 views • 2 months ago

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)

Lovis Odin

58,571 views • 9 months ago

This is not Ai. This is from years ago in Ireland. I saved it on my phone and whenever I feel down and the World is getting to me I watch this on a loop for a mind reset. Works every time. I love the fact there are people out there doing this kind of thing to give others joy. JOY! That is the word. We need more JOY. JOY up your day and watch this on a loop or download it to your phone and do what I do for that JOY refix. Or share this with your friends and give them some JOY. x #joy #bigbird #sesamestreet #love

This is not Ai. This is from years ago in Ireland. I saved it on my phone and whenever I feel down and the World is getting to me I watch this on a loop for a mind reset. Works every time. I love the fact there are people out there doing this kind of thing to give others joy. JOY! That is the word. We need more JOY. JOY up your day and watch this on a loop or download it to your phone and do what I do for that JOY refix. Or share this with your friends and give them some JOY. x #joy #bigbird #sesamestreet #love

Hifi Sean

64,720 views • 2 months ago

The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.

The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.

elvis

140,600 views • 1 year ago