Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

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...

omarsar0's profile picture

elvis

314,320 subscribers

248,793 görüntüleme • 3 ay önce •via X (Twitter)

EğitimBilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

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!
1:33:37

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

32,044 görüntüleme • 3 ay önce

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.
0:34

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 görüntüleme • 4 ay önce

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!
0:27

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

55,566 görüntüleme • 1 ay önce

receiving a lot of good words about lavish-axi (ty!) in v0.1.27 i just made it installable and invocable as a skill "/lavish" or with a prompt "/lavish let's discuss what options we have" this is the quickest way to upgrade your workflow from reading long wall of text and markdown to interactive html artifacts which are a lot more pleasant and efficient to work with why not just ask the agent to write an html? you'll know when you need to give some feedback to the agent about things in the html and realize there's no good way to make those edits and iterations. lavish gives you the ability to work with the agent via the html itself if you have been using lavish already, make sure to upgrade to latest version! quite a few good bug fixes shipped recently that would help the agent generate correct styling and poll more reliably
0:33

receiving a lot of good words about lavish-axi (ty!) in v0.1.27 i just made it installable and invocable as a skill "/lavish" or with a prompt "/lavish let's discuss what options we have" this is the quickest way to upgrade your workflow from reading long wall of text and markdown to interactive html artifacts which are a lot more pleasant and efficient to work with why not just ask the agent to write an html? you'll know when you need to give some feedback to the agent about things in the html and realize there's no good way to make those edits and iterations. lavish gives you the ability to work with the agent via the html itself if you have been using lavish already, make sure to upgrade to latest version! quite a few good bug fixes shipped recently that would help the agent generate correct styling and poll more reliably

Kun Chen

14,584 görüntüleme • 2 ay önce

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.
14:15

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 görüntüleme • 2 yıl önce

Why HTML turned out to be the foundation for agentic video making from Bin Liu: “We’ve been trying to build a video agent. However, we learned the hard way that agents have no visual intelligence. So that’s when we turned to code. HTML is the LLM’s native language. LLMs can express not only information, but also visual aesthetics through HTML, CSS, and JavaScript. Footage, images, assets, SVGs can all sit on top of HTML." 📌 As an example, see how you can make a product video from any website URL here:
0:41

Why HTML turned out to be the foundation for agentic video making from Bin Liu: “We’ve been trying to build a video agent. However, we learned the hard way that agents have no visual intelligence. So that’s when we turned to code. HTML is the LLM’s native language. LLMs can express not only information, but also visual aesthetics through HTML, CSS, and JavaScript. Footage, images, assets, SVGs can all sit on top of HTML." 📌 As an example, see how you can make a product video from any website URL here:

Peter Yang

16,653 görüntüleme • 1 ay önce

designing my new personal site with connected it to Claude Code and turned on "Swarm Mode" and watched agents design my site in real-time, with their cursors actually moving and operating the screen, not just cosmetically! instead of the agents producing a raw HTML file, they generate something called .pen which is just a json file describing all layouts, components, and design. this means that it's easier than ever to have teams collaborate on design exactly as they do on source code (designers and engineers can now speak the same language!) once you are happy with a screen, you can open a new chat and reference .pen frame and start to generate the code (in React/ Next/ Tailwind etc), and run it on your dev server, producing a live website that visually matches your design
0:31

designing my new personal site with connected it to Claude Code and turned on "Swarm Mode" and watched agents design my site in real-time, with their cursors actually moving and operating the screen, not just cosmetically! instead of the agents producing a raw HTML file, they generate something called .pen which is just a json file describing all layouts, components, and design. this means that it's easier than ever to have teams collaborate on design exactly as they do on source code (designers and engineers can now speak the same language!) once you are happy with a screen, you can open a new chat and reference .pen frame and start to generate the code (in React/ Next/ Tailwind etc), and run it on your dev server, producing a live website that visually matches your design

Sandra

94,264 görüntüleme • 5 ay önce

Soooo Thariq has straight up changed my life with these 5 words: "HTML is the new markdown." It's so obvious in hindsight: while .md is simple to write and agents can read it well, it's a total slog to eyeball as a human. In this special ep recorded live at Code with Claude, Thariq walks me through how he: - uses HTML artifacts as interactive specs - builds throwaway micro-UIs - maintains a living design system in HTML - prompts Claude with "whatever is needed" to give it room to actually think He also tells us what comes after the SWE + PM role 👀 As always, a huge TY to our amazing sponsors: 🔀 Celigo - Intelligent automation built for AI: 🪪 Persona - trusted identity verification for any use case: Watch now on YT:
0:55

Soooo Thariq has straight up changed my life with these 5 words: "HTML is the new markdown." It's so obvious in hindsight: while .md is simple to write and agents can read it well, it's a total slog to eyeball as a human. In this special ep recorded live at Code with Claude, Thariq walks me through how he: - uses HTML artifacts as interactive specs - builds throwaway micro-UIs - maintains a living design system in HTML - prompts Claude with "whatever is needed" to give it room to actually think He also tells us what comes after the SWE + PM role 👀 As always, a huge TY to our amazing sponsors: 🔀 Celigo - Intelligent automation built for AI: 🪪 Persona - trusted identity verification for any use case: Watch now on YT:

claire vo 🖤

200,465 görüntüleme • 3 ay önce

Here's how I'm running automated content engine in 2 files 1 markdown file = my wiki 1 html file = my dashboard that's the whole stack. [ the architecture, in plain words ]: LLM wiki = a single markdown file holding my audience DNA, 15 tracked creators, every viral topic from the last 30 days HTML artifact = a single page that reads that markdown file AND can trigger my agents the artifact and the agent talk to each other directly the wiki is the shared brain [ what I actually see when I open it at 9am ]: > 5 trending topics ranked by my audience-DNA fit > 3 KOL posts worth quoting today > last week's saved tweets (so I can ride waves that are still warm) > buttons: [draft tweet] [draft QT] [schedule] [log idea] 1. I click "draft tweet" on a topic 2. the artifact pings my agent 3. agent reads the wiki, drafts in MY voice, returns it to the artifact 4. I edit, schedule, done 15 minutes from morning coffee to 3 scheduled posts [ how to build the same in one evening ]: > step 1: dump your domain knowledge into ONE markdown file (audience profile, KOL list, content rules, voice guide, anything an agent would need to do YOUR job) > step 2: ask claude to build an html artifact that reads from that file ("here's my wiki, build me a dashboard with these views") > step 3: add buttons for the actions you do daily (draft, schedule, log, score, search — your workflow, not mine) > step 4: wire each button to call your agent via tool calls (so the artifact and the agent talk directly) the moment your artifact reads your wiki AND triggers your agents.. most SaaS tools you currently pay for quietly become unnecessary dashboards I used to pay $50/month for now sit in a single html file I can rebuild in 20 minutes every "I'll build a SaaS for this" idea you had last year is a 200-line file you write in an afternoon if you want to get the same content engine, just reply "CONTENT" and will send you in DMs later we're going from buying software to owning it.
0:45

Here's how I'm running automated content engine in 2 files 1 markdown file = my wiki 1 html file = my dashboard that's the whole stack. [ the architecture, in plain words ]: LLM wiki = a single markdown file holding my audience DNA, 15 tracked creators, every viral topic from the last 30 days HTML artifact = a single page that reads that markdown file AND can trigger my agents the artifact and the agent talk to each other directly the wiki is the shared brain [ what I actually see when I open it at 9am ]: > 5 trending topics ranked by my audience-DNA fit > 3 KOL posts worth quoting today > last week's saved tweets (so I can ride waves that are still warm) > buttons: [draft tweet] [draft QT] [schedule] [log idea] 1. I click "draft tweet" on a topic 2. the artifact pings my agent 3. agent reads the wiki, drafts in MY voice, returns it to the artifact 4. I edit, schedule, done 15 minutes from morning coffee to 3 scheduled posts [ how to build the same in one evening ]: > step 1: dump your domain knowledge into ONE markdown file (audience profile, KOL list, content rules, voice guide, anything an agent would need to do YOUR job) > step 2: ask claude to build an html artifact that reads from that file ("here's my wiki, build me a dashboard with these views") > step 3: add buttons for the actions you do daily (draft, schedule, log, score, search — your workflow, not mine) > step 4: wire each button to call your agent via tool calls (so the artifact and the agent talk directly) the moment your artifact reads your wiki AND triggers your agents.. most SaaS tools you currently pay for quietly become unnecessary dashboards I used to pay $50/month for now sit in a single html file I can rebuild in 20 minutes every "I'll build a SaaS for this" idea you had last year is a 200-line file you write in an afternoon if you want to get the same content engine, just reply "CONTENT" and will send you in DMs later we're going from buying software to owning it.

Ronin

50,083 görüntüleme • 3 ay önce

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.
0:25

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.

elvis

103,738 görüntüleme • 2 ay önce

Markdown was doomed from the start. It's just a format with low information density. HTML is better for humans, and agents can now consume and produce it without issues. But nobody wants to type HTML, so here is an alternative: This is an open-source tool for generating dashboards from data without writing a single HTML tag. You define your dashboard in YAML or TSK, and the tool will serve the HTML file for you. It comes with skills for Claude Code and Codex, so they know how to build these dashboards. And you can connect this to Postgres, MySQL, Snowflake, BigQuery, Redshift, Databricks, and many other databases. Repo link below.
4:08

Markdown was doomed from the start. It's just a format with low information density. HTML is better for humans, and agents can now consume and produce it without issues. But nobody wants to type HTML, so here is an alternative: This is an open-source tool for generating dashboards from data without writing a single HTML tag. You define your dashboard in YAML or TSK, and the tool will serve the HTML file for you. It comes with skills for Claude Code and Codex, so they know how to build these dashboards. And you can connect this to Postgres, MySQL, Snowflake, BigQuery, Redshift, Databricks, and many other databases. Repo link below.

Santiago

86,565 görüntüleme • 3 ay önce

My favorite way of interacting with Claude Code is to have it generate static HTML files as outputs (reports, explorations, code structure, mockups etc.) I wanted to iterate on the file by commenting in browser and having Claude update the output live. So, I built this Claude Skill👇 How it works: - Install Claude Code skill (ask it to clone repo) - Build an HTML page for anything (e.g. research coding agents and generate HTML report) - Ask it to make the page interactive That's it. CC will launch a localhost server and allow you to then leave comments on the page itself and once it updates, will give you a tour of changes. It's like Google Docs kind of comments/iteration but for HTML pages.
0:55

My favorite way of interacting with Claude Code is to have it generate static HTML files as outputs (reports, explorations, code structure, mockups etc.) I wanted to iterate on the file by commenting in browser and having Claude update the output live. So, I built this Claude Skill👇 How it works: - Install Claude Code skill (ask it to clone repo) - Build an HTML page for anything (e.g. research coding agents and generate HTML report) - Ask it to make the page interactive That's it. CC will launch a localhost server and allow you to then leave comments on the page itself and once it updates, will give you a tour of changes. It's like Google Docs kind of comments/iteration but for HTML pages.

Paras Chopra

215,661 görüntüleme • 2 ay önce

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!
0:30

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 görüntüleme • 2 ay önce

Introducing Kombai 2.0 - the first AI design engineer. We keep hearing that AGI is almost here. Still, we’re stuck with coding agents that don’t have taste and design tools that don’t understand our codebase. Both are artifacts of a world where design and engineering were two different jobs, with a handoff in between. That world is changing fast. Today, designers ship code, engineers want to escape handoffs, and everyone wants to build tasteful UX. Kombai is built for this new world. Design and engineering, finally on the same side.
1:24

Introducing Kombai 2.0 - the first AI design engineer. We keep hearing that AGI is almost here. Still, we’re stuck with coding agents that don’t have taste and design tools that don’t understand our codebase. Both are artifacts of a world where design and engineering were two different jobs, with a handoff in between. That world is changing fast. Today, designers ship code, engineers want to escape handoffs, and everyone wants to build tasteful UX. Kombai is built for this new world. Design and engineering, finally on the same side.

Dipanjan Dey

440,365 görüntüleme • 2 ay önce

I’m personally excited about the era of Search agents. You’ll be able to create, customize, and manage multiple AI agents for tasks, right in Search. Starting with information agents this summer, you can ask Search to keep you updated on topics you care about. Your agent will operate 24/7 to intelligently reason and research and send you a comprehensive update, with links to dive deeper – like if you want to know right when your favorite sneaker collab drops, or is available in a new colorway. #GoogleIO
0:38

I’m personally excited about the era of Search agents. You’ll be able to create, customize, and manage multiple AI agents for tasks, right in Search. Starting with information agents this summer, you can ask Search to keep you updated on topics you care about. Your agent will operate 24/7 to intelligently reason and research and send you a comprehensive update, with links to dive deeper – like if you want to know right when your favorite sneaker collab drops, or is available in a new colorway. #GoogleIO

Robby Stein

15,732 görüntüleme • 3 ay önce

Mati Staniszewski on why ElevenLabs is betting on a cascaded approach for voice agents: “Our approach, as you think about voice agents, conversational agents, is effectively a cascaded approach. You use transcription or speech-to-text, an LLM, text-to-speech, and orchestrate all of that together. Then you have speech-to-speech, which goes directly from speech, and there’s a speech response on the other side. Today, we are optimizing heavily on a cascaded approach. As we work with a lot of the businesses and enterprises, they will need that visibility into what happens. They will want to execute certain tasks on top of that. They want good visibility into each of the steps and great accuracy of all the models. But beyond that, they can abstract away what’s the LLM layer, what’s the intelligence layer, and the integrations are easier in that system. That’s where we are betting a lot of the research work on how you can make that great, and we think we can make that great.” John Collison @matiii ElevenLabs
1:09

Mati Staniszewski on why ElevenLabs is betting on a cascaded approach for voice agents: “Our approach, as you think about voice agents, conversational agents, is effectively a cascaded approach. You use transcription or speech-to-text, an LLM, text-to-speech, and orchestrate all of that together. Then you have speech-to-speech, which goes directly from speech, and there’s a speech response on the other side. Today, we are optimizing heavily on a cascaded approach. As we work with a lot of the businesses and enterprises, they will need that visibility into what happens. They will want to execute certain tasks on top of that. They want good visibility into each of the steps and great accuracy of all the models. But beyond that, they can abstract away what’s the LLM layer, what’s the intelligence layer, and the integrations are easier in that system. That’s where we are betting a lot of the research work on how you can make that great, and we think we can make that great.” John Collison @matiii ElevenLabs

Stripe

18,535 görüntüleme • 4 ay önce

Everyone keeps asking me how to build a second brain or an LLM wiki. Here is the easiest setup I have found. I took my Wiki Builder skill, installed it into HyperAgent (Hyperagent) as a reusable skill, and asked it to build a research wiki on LLM verification from the latest 2026 papers. Recorded a quick demo. Built with Hyperagent. It planned first, asked a few sharp questions, then did the research: 29 papers curated into 21 files, a research map, a glossary, and clean subfields. Now it is a knowledge base that my other research agents build on. HyperAgent has all the capabilities to allow your agents and skills to compound. That’s a powerful use of AI agents.
8:03

Everyone keeps asking me how to build a second brain or an LLM wiki. Here is the easiest setup I have found. I took my Wiki Builder skill, installed it into HyperAgent (Hyperagent) as a reusable skill, and asked it to build a research wiki on LLM verification from the latest 2026 papers. Recorded a quick demo. Built with Hyperagent. It planned first, asked a few sharp questions, then did the research: 29 papers curated into 21 files, a research map, a glossary, and clean subfields. Now it is a knowledge base that my other research agents build on. HyperAgent has all the capabilities to allow your agents and skills to compound. That’s a powerful use of AI agents.

elvis

226,001 görüntüleme • 1 ay önce

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.
5:50

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Zeb Evans

320,607 görüntüleme • 8 ay önce

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀
1:32

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Roberta Raileanu

105,081 görüntüleme • 1 yıl önce

The web was built for humans. And honestly, that's fine for you guys. But the next trillion internet users are AI agents, robots, and devices who act on your behalf - booking appointments, filling forms, placing orders, and getting things done - using sites that will never, ever have APIs. 95% of the economy falls into this Deep Web of HTML mess designed for humans, not agents. Oh, those generic computer use agents aren't taking you anywhere. They're too slow, too expensive, they hallucinate, and are nondeterministic to rely on. That changes now. This is me, Mino, a web agents API to build on this Deep Web. I take a goal in simple language and execute it on websites that were never meant to be automated. Massive companies like Google, DoorDash, ClassPass are already using me to do their homework. Now it’s your turn. How though? I actually understand what's on the page - parsing structure and identifying elements. I use AI once to understand everything, codify my successes, and get better and faster with every run. You get: → 85-95% success rate on complex workflows → Pennies per job (stop wasting $$ on one job) → Parallel execution across multiple sites → Structured JSON outputs. Every. Single. Time. The web wasn't built for agents. I forced it to work anyway. Go build something real. 50 completed runs on the house:
1:41

The web was built for humans. And honestly, that's fine for you guys. But the next trillion internet users are AI agents, robots, and devices who act on your behalf - booking appointments, filling forms, placing orders, and getting things done - using sites that will never, ever have APIs. 95% of the economy falls into this Deep Web of HTML mess designed for humans, not agents. Oh, those generic computer use agents aren't taking you anywhere. They're too slow, too expensive, they hallucinate, and are nondeterministic to rely on. That changes now. This is me, Mino, a web agents API to build on this Deep Web. I take a goal in simple language and execute it on websites that were never meant to be automated. Massive companies like Google, DoorDash, ClassPass are already using me to do their homework. Now it’s your turn. How though? I actually understand what's on the page - parsing structure and identifying elements. I use AI once to understand everything, codify my successes, and get better and faster with every run. You get: → 85-95% success rate on complex workflows → Pennies per job (stop wasting $$ on one job) → Parallel execution across multiple sites → Structured JSON outputs. Every. Single. Time. The web wasn't built for agents. I forced it to work anyway. Go build something real. 50 completed runs on the house:

TinyFish

656,586 görüntüleme • 8 ay önce