Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

New VIDEO: From LLM Wikis to LLM Artifacts Shared all my thoughts on why LLM wikis and HTML artifacts are a big deal. Plus, new tools to help you build wikis and artifacts with agents. Just getting started!

elvis

312,794 subscribers

32,044 views • 4 months ago •via X (Twitter)

Arts Education Science & Technology

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

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.

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.

elvis

249,069 views • 4 months ago

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.

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.

elvis

59,673 views • 4 months ago

Artifacts, but 100% Open Source, Private & Local. Open WebUI has released a game changer update---"Artifacts". Instead of proprietary LLMs, you can now use fully LOCAL and PRIVATE LLM via ollama to build apps! Here I use Qwen 2.5 32B to build a game of life app.

Artifacts, but 100% Open Source, Private & Local. Open WebUI has released a game changer update---"Artifacts". Instead of proprietary LLMs, you can now use fully LOCAL and PRIVATE LLM via ollama to build apps! Here I use Qwen 2.5 32B to build a game of life app.

cocktail peanut

60,233 views • 1 year ago

New video is out! You no longer build one thing at a time on Replit. Run parallel agents to ship a website, mobile app, video, and pitch deck from one project, all at once. And you can now add multiple artifacts to projects you already have.

New video is out! You no longer build one thing at a time on Replit. Run parallel agents to ship a website, mobile app, video, and pitch deck from one project, all at once. And you can now add multiple artifacts to projects you already have.

Replit ⠕

16,234 views • 3 months ago

time to reveal my HTML workflow with agents HTML is the new markdown. Lavish is the new editor for your HTML artifacts just tell your agent - discuss the technical plan with me using `npx lavish-axi` 100% open source and runs locally. details in thread below 👇

time to reveal my HTML workflow with agents HTML is the new markdown. Lavish is the new editor for your HTML artifacts just tell your agent - discuss the technical plan with me using `npx lavish-axi` 100% open source and runs locally. details in thread below 👇

Kun Chen

57,573 views • 4 months ago

Introducing Agent Wikis! I've been using llm wikis (or knowledge bases) for research and videos for a while to ensure the information I'm providing is always up-to-date and accurate. Now you can access them for free! Just point your agent at to access wikis on many topics, including Hermes Agent, Hyperframes, Google Ads, and llama.cpp. They can be queried through a simple MCP server. Everything has been designed to be agent-friendly and lightweight (no context bloat). This is a beta release, so please let me know of any bugs or issues (I'm sure there are one or two). You can also join the waitlist if you would like us to create and maintain your own private wiki, or if you are a project owner you can request one for your project to make sure your users (and their agents) don't run into any common issues (and also be featured on the site). Thank you!

Introducing Agent Wikis! I've been using llm wikis (or knowledge bases) for research and videos for a while to ensure the information I'm providing is always up-to-date and accurate. Now you can access them for free! Just point your agent at to access wikis on many topics, including Hermes Agent, Hyperframes, Google Ads, and llama.cpp. They can be queried through a simple MCP server. Everything has been designed to be agent-friendly and lightweight (no context bloat). This is a beta release, so please let me know of any bugs or issues (I'm sure there are one or two). You can also join the waitlist if you would like us to create and maintain your own private wiki, or if you are a project owner you can request one for your project to make sure your users (and their agents) don't run into any common issues (and also be featured on the site). Thank you!

tonbi

46,993 views • 3 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.

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.

elvis

31,855 views • 5 months ago

The new DGrid AI Arena is here🏆 We just dropped a quick video walkthrough to help you get started in minutes. Come join the arena — we’d love to see you battle, build, and explore with us!🕹️ #DGrid #AIArena #AI #LLM

The new DGrid AI Arena is here🏆 We just dropped a quick video walkthrough to help you get started in minutes. Come join the arena — we’d love to see you battle, build, and explore with us!🕹️ #DGrid #AIArena #AI #LLM

DGrid AI

74,720 views • 3 months ago

He is 17 sold an LLM system to Anthropic for $2.2M - and at Stanford shared how to build and sell one from scratch: 00:20 - how to build an LLM system that Anthropic will buy for millions 08:34 - one prompt in Opus 5 and the LLM system is ready to sell in 60 minutes 23:47 - how to sell an LLM to Anthropic at 17 with no experience and reach $2.2M a year after watching I built my first LLM system in 60 minutes - and sold it to Anthropic for $120k. save & watch - the article below is one file that builds an LLM system and sells it to Anthropic for $2.2M.

He is 17 sold an LLM system to Anthropic for $2.2M - and at Stanford shared how to build and sell one from scratch: 00:20 - how to build an LLM system that Anthropic will buy for millions 08:34 - one prompt in Opus 5 and the LLM system is ready to sell in 60 minutes 23:47 - how to sell an LLM to Anthropic at 17 with no experience and reach $2.2M a year after watching I built my first LLM system in 60 minutes - and sold it to Anthropic for $120k. save & watch - the article below is one file that builds an LLM system and sells it to Anthropic for $2.2M.

Noisy

24,825 views • 9 days ago

He is 18 built an LLM and sold it to Anthropic for $3.2M - and at Stanford shared how to repeat it from scratch: 00:30 - how to build an LLM worth $100k in one evening 21:34 - how one LLM controls 1000 processes at the same time 43:47 - from first prompt to $3.2M from Anthropic after watching I built my first LLM in 60 minutes- a week later Anthropic offered me $100k for the system. save & watch - the article below shows how to build an LLM and sell it to Anthropic for $3.2M from scratch

He is 18 built an LLM and sold it to Anthropic for $3.2M - and at Stanford shared how to repeat it from scratch: 00:30 - how to build an LLM worth $100k in one evening 21:34 - how one LLM controls 1000 processes at the same time 43:47 - from first prompt to $3.2M from Anthropic after watching I built my first LLM in 60 minutes- a week later Anthropic offered me $100k for the system. save & watch - the article below shows how to build an LLM and sell it to Anthropic for $3.2M from scratch

Noisy

45,280 views • 7 days ago

In Antigravity, we’ve reimagined how you collaborate with Agents using Artifacts. Artifacts go beyond text. They are dynamic intermediates to communicate agentic progress like screen recordings, diagrams, or markdown. And you can leave feedback right on the artifact itself.

In Antigravity, we’ve reimagined how you collaborate with Agents using Artifacts. Artifacts go beyond text. They are dynamic intermediates to communicate agentic progress like screen recordings, diagrams, or markdown. And you can leave feedback right on the artifact itself.

Google Antigravity

66,822 views • 9 months ago

The State of Claude Artifacts - August 2024 With Artifacts generally available across Claude dot ai and our mobile apps, I figured now is a good time to record a walkthrough of everything that you can do with Artifacts today. Artifacts has so much potential as a new UX paradigm - we're just scratching the surface of what it will eventually become. I have a feeling that when we look back at this video in the not-too-distant future, we'll be amazed at how quickly Artifacts evolved.

The State of Claude Artifacts - August 2024 With Artifacts generally available across Claude dot ai and our mobile apps, I figured now is a good time to record a walkthrough of everything that you can do with Artifacts today. Artifacts has so much potential as a new UX paradigm - we're just scratching the surface of what it will eventually become. I have a feeling that when we look back at this video in the not-too-distant future, we'll be amazed at how quickly Artifacts evolved.

Alex Albert

83,686 views • 2 years ago

I summarized Andrej Karpathy's 1 hour "Intro to LLM" video into 3 minutes :) Personally, trying to get better at summarizing and communicating through both notes and verbally! Covers: > what is an LLM > how to build an LLM > limitations > future of LLMs my notes summary: Thanks to Kudzo S Ahegbebu and Harper Carroll for their help :)

I summarized Andrej Karpathy's 1 hour "Intro to LLM" video into 3 minutes :) Personally, trying to get better at summarizing and communicating through both notes and verbally! Covers: > what is an LLM > how to build an LLM > limitations > future of LLMs my notes summary: Thanks to Kudzo S Ahegbebu and Harper Carroll for their help :)

Sarah Chieng

255,528 views • 2 years ago

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!

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!

elvis

55,747 views • 2 months ago

We're also launching a preview of Artifacts on You can ask Claude to generate docs, code, mermaid diagrams, vector graphics, or even simple games. Artifacts appear next to your chat, letting you see, iterate, and build on your creations in real-time.

We're also launching a preview of Artifacts on You can ask Claude to generate docs, code, mermaid diagrams, vector graphics, or even simple games. Artifacts appear next to your chat, letting you see, iterate, and build on your creations in real-time.

Anthropic

589,568 views • 2 years ago

"Toward General Virtual Agents" I recently gave a talk at MIT. I argued that we should use tools from reinforcement learning and search to improve the capability and alignment of LLM agents. Slides: Video:

"Toward General Virtual Agents" I recently gave a talk at MIT. I argued that we should use tools from reinforcement learning and search to improve the capability and alignment of LLM agents. Slides: Video:

Stephen McAleer

167,888 views • 2 years ago

My friend applied to 200 tech jobs in two years. No PhD. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me a course that was never supposed to get out. A 3-hour video to build a full LLM from scratch. A developer teaches you exactly how LLMs like ChatGPT and Claude are actually built. I watched it last night. Halfway through, I realized it's embarrassingly simple to break into an AI lab. Bookmark this and read the article below. • 00:00 - intro to LLMs • 05:43 - LLM transformer architecture • 40:24 - training the LLM • 1:30:27 - modernizing the LLM • 2:33:53 - scaling the LLM

My friend applied to 200 tech jobs in two years. No PhD. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me a course that was never supposed to get out. A 3-hour video to build a full LLM from scratch. A developer teaches you exactly how LLMs like ChatGPT and Claude are actually built. I watched it last night. Halfway through, I realized it's embarrassingly simple to break into an AI lab. Bookmark this and read the article below. • 00:00 - intro to LLMs • 05:43 - LLM transformer architecture • 40:24 - training the LLM • 1:30:27 - modernizing the LLM • 2:33:53 - scaling the LLM

Roan

279,295 views • 2 months ago

A new entry to my popular series "LLM tools for plebs": claude-trace - Injects itself into Claude Code - Logs all traffic - Reconstructs conversations and shows what's going on behind the scenes (system prompts, all tool inputs/outputs, and more) Some observations. 🧵

A new entry to my popular series "LLM tools for plebs": claude-trace - Injects itself into Claude Code - Logs all traffic - Reconstructs conversations and shows what's going on behind the scenes (system prompts, all tool inputs/outputs, and more) Some observations. 🧵

Mario Zechner

221,359 views • 1 year ago

Learn to carry out red teaming attacks against your own LLM-based applications to spot and patch vulnerabilities! In our new short course, Red Teaming LLM Applications, Matteo Dora & Luca Martial of LLM testing company Giskard teach how to simulate malicious actions to discover vulnerabilities, and improve security. We start with prompt injection, where you can trick an LLM into bypassing safeguards to reveal private information, or say something inappropriate. There is no one-size-fits-all approach to security, but this course will help you identify some scenarios to protect against. We believe having red teaming capabilities widely known will result in greater transparency and safer LLM-based systems. However, we ask you to use the skills you gain from this course ethically. Please sign up here:

Learn to carry out red teaming attacks against your own LLM-based applications to spot and patch vulnerabilities! In our new short course, Red Teaming LLM Applications, Matteo Dora & Luca Martial of LLM testing company Giskard teach how to simulate malicious actions to discover vulnerabilities, and improve security. We start with prompt injection, where you can trick an LLM into bypassing safeguards to reveal private information, or say something inappropriate. There is no one-size-fits-all approach to security, but this course will help you identify some scenarios to protect against. We believe having red teaming capabilities widely known will result in greater transparency and safer LLM-based systems. However, we ask you to use the skills you gain from this course ethically. Please sign up here:

Andrew Ng

109,794 views • 2 years ago