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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...

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elvis

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226,001 次观看 • 1 个月前 •via X (Twitter)

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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!
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

55,747 次观看 • 2 个月前

HERMES AGENT SHIPS WITH A BUNDLED SKILL FOR ANDREJ KARPATHY'S LLM WIKI PATTERN. A SELF-IMPROVING KNOWLEDGE BASE THAT GROWS EVERY TIME YOU FEED IT. mentioned this briefly in the overnight workflow article. here is the full breakdown. what it is: a self-improving knowledge base built as interlinked markdown files. unlike RAG (which rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. cross-references stay linked. contradictions get flagged automatically. synthesis reflects everything ingested so far. why this matters for Hermes memory: Hermes built-in memory knows YOU. it remembers your conversations, your preferences, your business context across sessions. but it doesn't know your inbox. or your meeting transcripts. or that article you saved last week. or the expert framework you want it to learn. the LLM Wiki solves that. THE DIVISION OF LABOR human curates sources and directs analysis. agent summarizes, cross-references, files, and maintains consistency. you drop in articles, transcripts, notes. Hermes indexes them, links related concepts, flags contradictions, updates affected pages. your knowledge base grows itself. SETUP IS ONE COMMAND the skill ships with Hermes. enable it. set WIKI_PATH in ~/.hermes/.env: WIKI_PATH=/Users/you/wiki defaults to ~/wiki if unset. then drop anything into it: "index this article into my wiki: [paste URL or text]" Hermes reads it, builds a source page, updates related entries, flags contradictions. THE OBSIDIAN ANGLE set OBSIDIAN_VAULT_PATH to the same directory. now your wiki is visible in Obsidian's graph view. nodes, links, backlinks. all built by Hermes. for headless servers: install obsidian-headless. syncs vaults without a GUI. agent writes from the server, you read on your laptop. THE COMPOUND EFFECT Hermes knows you. the wiki knows your world. combine them and the agent answers questions using BOTH contexts at once. month 1: you explain things twice. month 3: the agent references the wiki on its own. answers get sharper because the knowledge base got sharper. AUTOMATIONS THAT FEED THE WIKI set cron jobs to ingest automatically: "every day at 9am, check Granola for new meetings. add any new transcripts to my wiki under meeting notes." "every morning, scan my Gmail starred items. add anything worth keeping to the wiki." "every week, check arXiv for new papers in [your niche]. summarize and file." your wiki grows while you sleep. Hermes never forgets what gets indexed. THE LIMITATION TO KNOW unlike Hermes memory (which is conversational and lives across sessions), the wiki is a separate knowledge layer. Hermes won't pull from the wiki automatically unless you reference it or save it as a skill. best setup: build an LLM Wiki personality that tells Hermes to consult the wiki when answering strategy questions or domain-specific queries. full HERMES AGENT OVERNIGHT WORKFLOW👇
14:28

HERMES AGENT SHIPS WITH A BUNDLED SKILL FOR ANDREJ KARPATHY'S LLM WIKI PATTERN. A SELF-IMPROVING KNOWLEDGE BASE THAT GROWS EVERY TIME YOU FEED IT. mentioned this briefly in the overnight workflow article. here is the full breakdown. what it is: a self-improving knowledge base built as interlinked markdown files. unlike RAG (which rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. cross-references stay linked. contradictions get flagged automatically. synthesis reflects everything ingested so far. why this matters for Hermes memory: Hermes built-in memory knows YOU. it remembers your conversations, your preferences, your business context across sessions. but it doesn't know your inbox. or your meeting transcripts. or that article you saved last week. or the expert framework you want it to learn. the LLM Wiki solves that. THE DIVISION OF LABOR human curates sources and directs analysis. agent summarizes, cross-references, files, and maintains consistency. you drop in articles, transcripts, notes. Hermes indexes them, links related concepts, flags contradictions, updates affected pages. your knowledge base grows itself. SETUP IS ONE COMMAND the skill ships with Hermes. enable it. set WIKI_PATH in ~/.hermes/.env: WIKI_PATH=/Users/you/wiki defaults to ~/wiki if unset. then drop anything into it: "index this article into my wiki: [paste URL or text]" Hermes reads it, builds a source page, updates related entries, flags contradictions. THE OBSIDIAN ANGLE set OBSIDIAN_VAULT_PATH to the same directory. now your wiki is visible in Obsidian's graph view. nodes, links, backlinks. all built by Hermes. for headless servers: install obsidian-headless. syncs vaults without a GUI. agent writes from the server, you read on your laptop. THE COMPOUND EFFECT Hermes knows you. the wiki knows your world. combine them and the agent answers questions using BOTH contexts at once. month 1: you explain things twice. month 3: the agent references the wiki on its own. answers get sharper because the knowledge base got sharper. AUTOMATIONS THAT FEED THE WIKI set cron jobs to ingest automatically: "every day at 9am, check Granola for new meetings. add any new transcripts to my wiki under meeting notes." "every morning, scan my Gmail starred items. add anything worth keeping to the wiki." "every week, check arXiv for new papers in [your niche]. summarize and file." your wiki grows while you sleep. Hermes never forgets what gets indexed. THE LIMITATION TO KNOW unlike Hermes memory (which is conversational and lives across sessions), the wiki is a separate knowledge layer. Hermes won't pull from the wiki automatically unless you reference it or save it as a skill. best setup: build an LLM Wiki personality that tells Hermes to consult the wiki when answering strategy questions or domain-specific queries. full HERMES AGENT OVERNIGHT WORKFLOW👇

YanXbt

30,804 次观看 • 2 个月前

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 次观看 • 4 个月前

2/ Building a Website from Scratch with Wide Research We tasked both agents to build a website on UNESCO sites. Manus conducted deep, multi-source research to build a comprehensive and detailed website. The depth is astounding.
1:32

2/ Building a Website from Scratch with Wide Research We tasked both agents to build a website on UNESCO sites. Manus conducted deep, multi-source research to build a comprehensive and detailed website. The depth is astounding.

Manus

22,187 次观看 • 7 个月前

This is incredible I ask an AI agent to make me $10,000 It then did market research, built a business plan, and started executing on that plan Now ANYONE can use AI agents to build entire businesses for them autonomously (trust me, you want to bookmark this one)
8:48

This is incredible I ask an AI agent to make me $10,000 It then did market research, built a business plan, and started executing on that plan Now ANYONE can use AI agents to build entire businesses for them autonomously (trust me, you want to bookmark this one)

Alex Finn

135,437 次观看 • 1 年前

now my clippy is an expert at whatever I want to learn + it has a chat window so i can easily follow along the conversation 1. It uses Andrej Karpathy's llm wiki so that it has specialized knowledge 2. it has a chat window so that i can follow along the conversation and copy-paste suggestions from clicky I can also type in any questions or comments if I don't feel like pushing to talk I tested this on the new Claude Managed Agents (scraped anthropics docs for it and put it into the wiki) and it works great! this is going to be an amazing way to learn new things!
1:53

now my clippy is an expert at whatever I want to learn + it has a chat window so i can easily follow along the conversation 1. It uses Andrej Karpathy's llm wiki so that it has specialized knowledge 2. it has a chat window so that i can follow along the conversation and copy-paste suggestions from clicky I can also type in any questions or comments if I don't feel like pushing to talk I tested this on the new Claude Managed Agents (scraped anthropics docs for it and put it into the wiki) and it works great! this is going to be an amazing way to learn new things!

Dan Peguine

26,604 次观看 • 4 个月前

Yesterday, Airtable got acquired for $1.285B by Bending Spoons. What people don't know is that Hyperagent spun out into it's own company. Howie Liu (Howie Liu) is going all in on agents. On this episode of Navigators, filmed a few weeks ago, I sat down with Howie to talk through Hyperagent, why the product form factor has to structurally change with every leap in model capability, and the lean, agent-first companies being born out of all of it. 00:00 Why every model leap forces the product to change shape 02:51 Spinning Hyperagent out of Airtable 05:56 Why Hyperagent bets on horizontal, against the conventional wisdom 10:45 OpenClaw and the overnight-agent stories 13:34 Hyperagent is to OpenClaw what the Macintosh was to Linux 14:57 Why Howie built the Hyperagent MVP himself over the holidays 18:18 "Harness Squared": why the real value lives above the model 28:57 Hyperagent and the rise of lean, agent-first companies Checkout Navigators, our new pod on Youtube, Spotify, and Apple Podcasts!
38:37

Yesterday, Airtable got acquired for $1.285B by Bending Spoons. What people don't know is that Hyperagent spun out into it's own company. Howie Liu (Howie Liu) is going all in on agents. On this episode of Navigators, filmed a few weeks ago, I sat down with Howie to talk through Hyperagent, why the product form factor has to structurally change with every leap in model capability, and the lean, agent-first companies being born out of all of it. 00:00 Why every model leap forces the product to change shape 02:51 Spinning Hyperagent out of Airtable 05:56 Why Hyperagent bets on horizontal, against the conventional wisdom 10:45 OpenClaw and the overnight-agent stories 13:34 Hyperagent is to OpenClaw what the Macintosh was to Linux 14:57 Why Howie built the Hyperagent MVP himself over the holidays 18:18 "Harness Squared": why the real value lives above the model 28:57 Hyperagent and the rise of lean, agent-first companies Checkout Navigators, our new pod on Youtube, Spotify, and Apple Podcasts!

Paul Klein IV

39,613 次观看 • 28 天前

Claude Code + Obsidian is the most powerful AI combo I've ever used. I literally built an AI second brain that runs my entire life. Inspired by Andrej Karpathy's LLM Knowledge Wiki, this tool has been a complete game-changer. Here's EXACTLY how to build one for yourself:
0:24

Claude Code + Obsidian is the most powerful AI combo I've ever used. I literally built an AI second brain that runs my entire life. Inspired by Andrej Karpathy's LLM Knowledge Wiki, this tool has been a complete game-changer. Here's EXACTLY how to build one for yourself:

Miles Deutscher

975,044 次观看 • 4 个月前

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.
0:20

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

59,177 次观看 • 3 个月前

Loop engineering is great until something breaks. Here is how I improve the reliability of my agentic loops. I use human-in-the-loop (HITL). It's easy and extremely effective. Anyone can build this. My setup: I recorded a quick demo of how it all works. I shared recently that I now use more voice agents to build and communicate with agents. I also use them to verify. I hate the idea of being tied down to my computer or in a Slack channel to communicate with my agents. Here is what I have done to streamline communication with my agents. All my Claude and Codex agent sessions now use the Dial MCP server. It has a bunch of tools and provisions my agents with their own number that can place calls as native tools, with voice, SMS, and iMessage behind one interface. As my loops/automations work on PRs and new features, my agents escalate decisions to me via a short phone call. This is extremely useful when I am on the road or away from my desk. If you want to try this with Claude Code or Codex, paste this into your agent and get started right away: "Get yourself a Dial phone number and call me. Say hello and that setup is working, then hang up. Follow Natan Voitenkov and team are building something special here. Go check them out. Give your agent a phone number now: ($5 free credit)
5:02

Loop engineering is great until something breaks. Here is how I improve the reliability of my agentic loops. I use human-in-the-loop (HITL). It's easy and extremely effective. Anyone can build this. My setup: I recorded a quick demo of how it all works. I shared recently that I now use more voice agents to build and communicate with agents. I also use them to verify. I hate the idea of being tied down to my computer or in a Slack channel to communicate with my agents. Here is what I have done to streamline communication with my agents. All my Claude and Codex agent sessions now use the Dial MCP server. It has a bunch of tools and provisions my agents with their own number that can place calls as native tools, with voice, SMS, and iMessage behind one interface. As my loops/automations work on PRs and new features, my agents escalate decisions to me via a short phone call. This is extremely useful when I am on the road or away from my desk. If you want to try this with Claude Code or Codex, paste this into your agent and get started right away: "Get yourself a Dial phone number and call me. Say hello and that setup is working, then hang up. Follow Natan Voitenkov and team are building something special here. Go check them out. Give your agent a phone number now: ($5 free credit)

elvis

15,295 次观看 • 1 个月前

Say hello to open source deep research for your favorite agent harness. Our AI-Q agent skill packages the work of building a research pipeline into a portable skill. Drop it into your harness, and the agent delegates a research task to a local or hosted AI-Q server and gets back a detailed report with citations. See it in Codex below 👇
4:10

Say hello to open source deep research for your favorite agent harness. Our AI-Q agent skill packages the work of building a research pipeline into a portable skill. Drop it into your harness, and the agent delegates a research task to a local or hosted AI-Q server and gets back a detailed report with citations. See it in Codex below 👇

NVIDIA AI

76,354 次观看 • 3 个月前

Every company needs killer Skills library. Not a folder in GitHub that 10% of the company knows how to use. A living library the whole company benefits from. The best deal review skill gets better every time a sales rep uses it. A great research skill gets discovered instead of rebuilt 50 times. Creating a skill should be as easy as asking to create something useful from based on the context of your work. And the top skills (and skill creators) should rise to the top. Create, share, collaborate on, discover and understand usage of skills, organize them into databases by team or use case, then use them across any of your agents. Still early, but the idea is to make Notion the best home for your company’s collective AI skills. Made a quick demo (all available now)!
1:59

Every company needs killer Skills library. Not a folder in GitHub that 10% of the company knows how to use. A living library the whole company benefits from. The best deal review skill gets better every time a sales rep uses it. A great research skill gets discovered instead of rebuilt 50 times. Creating a skill should be as easy as asking to create something useful from based on the context of your work. And the top skills (and skill creators) should rise to the top. Create, share, collaborate on, discover and understand usage of skills, organize them into databases by team or use case, then use them across any of your agents. Still early, but the idea is to make Notion the best home for your company’s collective AI skills. Made a quick demo (all available now)!

Hurley

691,328 次观看 • 13 天前

KARPATHY'S SECOND BRAIN REWRITES YOUR NOTES EVERY TIME YOU LEARN SOMETHING NEW most AI note setups are just RAG you ask, it digs through your files, answers, and forgets nothing carries over every query starts from zero Karpathy's llm-wiki works differently the AI keeps a living wiki inside Obsidian every source you drop in gets: > read and summarized > cross-linked to what you already have > checked for contradictions > folded into the running synthesis the knowledge compounds instead of resetting now the part people miss: a wiki like this is a product > deep research reports to founders go for $2-5k each > a due diligence package on a niche runs $5-10k > a paid newsletter off your synthesis pages clears $3-4k/mo at a few hundred subs your job is to curate sources and ask good questions the AI does the bookkeeping no human keeps up with - Obsidian is the IDE - the LLM is the programmer - your wiki is the codebase save this before your notes pile any higher 👇
0:25

KARPATHY'S SECOND BRAIN REWRITES YOUR NOTES EVERY TIME YOU LEARN SOMETHING NEW most AI note setups are just RAG you ask, it digs through your files, answers, and forgets nothing carries over every query starts from zero Karpathy's llm-wiki works differently the AI keeps a living wiki inside Obsidian every source you drop in gets: > read and summarized > cross-linked to what you already have > checked for contradictions > folded into the running synthesis the knowledge compounds instead of resetting now the part people miss: a wiki like this is a product > deep research reports to founders go for $2-5k each > a due diligence package on a niche runs $5-10k > a paid newsletter off your synthesis pages clears $3-4k/mo at a few hundred subs your job is to curate sources and ask good questions the AI does the bookkeeping no human keeps up with - Obsidian is the IDE - the LLM is the programmer - your wiki is the codebase save this before your notes pile any higher 👇

Mr. Buzzoni

18,500 次观看 • 1 个月前

e-comm bros have terrible taste i recently tried Hyperagent to build my own “taste test” tool to help you fight this it’s trained on hundreds of ads and product photography that i hand curated how it works: upload your ad or photo, and ask it to give you a taste score. it’ll give you a score out 100, and a detailed breakdown of what to do to “fix” it (if necessary) sign-up with the link below and get $200 in credits to try my agent or build your own
0:51

e-comm bros have terrible taste i recently tried Hyperagent to build my own “taste test” tool to help you fight this it’s trained on hundreds of ads and product photography that i hand curated how it works: upload your ad or photo, and ask it to give you a taste score. it’ll give you a score out 100, and a detailed breakdown of what to do to “fix” it (if necessary) sign-up with the link below and get $200 in credits to try my agent or build your own

techbimbo

264,084 次观看 • 1 个月前

Karpathy gave 100,000 people the LLM Wiki idea. 1 guy actually built it into a Claude + Obsidian brain that never forgets you. Every AI tool forgets you the second you close the tab. ChatGPT, Claude, Codex. All gone. He killed that. A shared memory layer your AI builds and maintains on its own. 3 layers. Raw, Wiki, Schema. You drop a source, Claude reads it, pulls the concepts, links them, cites the original, never touches your raw notes. Scales to 1,000s of notes without breaking. Then it goes deeper. An agentic firewall locks agents out of your other vaults. A local model running free and private, zero cloud. 2 videos walk through it all. 1 on why. 1 on the build, start to finish. Most people will keep pasting the same context into 6 tabs for the next 3 years. Build it once. Never explain yourself to a model again.
34:07

Karpathy gave 100,000 people the LLM Wiki idea. 1 guy actually built it into a Claude + Obsidian brain that never forgets you. Every AI tool forgets you the second you close the tab. ChatGPT, Claude, Codex. All gone. He killed that. A shared memory layer your AI builds and maintains on its own. 3 layers. Raw, Wiki, Schema. You drop a source, Claude reads it, pulls the concepts, links them, cites the original, never touches your raw notes. Scales to 1,000s of notes without breaking. Then it goes deeper. An agentic firewall locks agents out of your other vaults. A local model running free and private, zero cloud. 2 videos walk through it all. 1 on why. 1 on the build, start to finish. Most people will keep pasting the same context into 6 tabs for the next 3 years. Build it once. Never explain yourself to a model again.

West Lord

75,208 次观看 • 2 个月前

This beginner guide to Karpathy's LLM Wiki is one of the most useful AI videos i've shared this year Karpathy's idea: instead of asking AI questions every time, make it build you a permanent knowledge base that grows smarter over time > drop your documents into a folder > Claude reads them and builds a structured wiki in Obsidian > every time you add a new source, Claude Sonnet updates existing pages and links new ideas > ask complex questions - Claude Opus answers from the organized wiki, not raw files it's like having a researcher who never forgets and connects dots across everything you've ever given it full setup guide is in the video above
14:21

This beginner guide to Karpathy's LLM Wiki is one of the most useful AI videos i've shared this year Karpathy's idea: instead of asking AI questions every time, make it build you a permanent knowledge base that grows smarter over time > drop your documents into a folder > Claude reads them and builds a structured wiki in Obsidian > every time you add a new source, Claude Sonnet updates existing pages and links new ideas > ask complex questions - Claude Opus answers from the organized wiki, not raw files it's like having a researcher who never forgets and connects dots across everything you've ever given it full setup guide is in the video above

Mr. Buzzoni

58,787 次观看 • 3 个月前

Hyperagent is great if you want agents without having to deal with configuration, servers, or any complexity whatsoever. You don't need a Mac Mini or a server. Every agent runs in the cloud, in its own isolated and secure environment. You get: 1. The ability to learn any new API as a skill 2. Browser support 3. Code execution 4. Image and video generation 5. Integration with hundreds of tools Despite the simplicity, I think their best differentiator is the ability for these agents to learn new skills. Basically, you can teach an agent how your company does marketing, and it will never forget your rules and processes. Obviously, it also lets you deploy your agents to Slack with a single click, so everyone can use them. You don't even need to mention them since they will pay attention to chats and act when necessary. I built a simple agent that allows me to translate any message into French. I installed it on Slack, and every time I ping it, it gives me translations. Took me 10 minutes tops.
4:55

Hyperagent is great if you want agents without having to deal with configuration, servers, or any complexity whatsoever. You don't need a Mac Mini or a server. Every agent runs in the cloud, in its own isolated and secure environment. You get: 1. The ability to learn any new API as a skill 2. Browser support 3. Code execution 4. Image and video generation 5. Integration with hundreds of tools Despite the simplicity, I think their best differentiator is the ability for these agents to learn new skills. Basically, you can teach an agent how your company does marketing, and it will never forget your rules and processes. Obviously, it also lets you deploy your agents to Slack with a single click, so everyone can use them. You don't even need to mention them since they will pay attention to chats and act when necessary. I built a simple agent that allows me to translate any message into French. I installed it on Slack, and every time I ping it, it gives me translations. Took me 10 minutes tops.

Santiago

213,793 次观看 • 1 个月前

Someone created the Hyper Research skill for Claude Code It turns the model into a strong team of researchers that goes through 16 stages of search Here is what the AI agents do: > Break down the task into a topic coverage matrix and conduct deep research > Find and save hundreds of sources while identifying contradictions between them > Actively search for evidence against their own conclusions to avoid mistakes > Create a report draft, then review it for weak points, improve readability, and remove filler content > Deliver a complete report with a source base and save all materials to a local knowledge store Advanced Deep Research tool here:
0:56

Someone created the Hyper Research skill for Claude Code It turns the model into a strong team of researchers that goes through 16 stages of search Here is what the AI agents do: > Break down the task into a topic coverage matrix and conduct deep research > Find and save hundreds of sources while identifying contradictions between them > Actively search for evidence against their own conclusions to avoid mistakes > Create a report draft, then review it for weak points, improve readability, and remove filler content > Deliver a complete report with a source base and save all materials to a local knowledge store Advanced Deep Research tool here:

beamnxw ./

63,432 次观看 • 1 个月前

My whole company runs on skills I bundled into a GitHub repo. Here's how to build the same thing for your team: - Put your skills in a GitHub repo - Get Claude to write the couple of JSON files that make it readable as a plugin - In Claude Code, type /plugin, go to marketplaces, add marketplace, paste your repo URL - Split the plugins by department, so your copywriter isn't installing the finance skills It works in Codex too. I've got the same team skills installed there and I use them the same way. Now when I build something like my Notion formatting skill, I add it to the repo and it lands on everyone's Claude and Codex. If someone wrecks a skill, we roll it back to an older version. The key is enabling auto-update, so when someone improves a skill everyone benefits. I don't have a clue how the JSON works. I got Claude to set the whole thing up for me. Start with one skill and before you know it your whole team will be AI pilled.
5:18

My whole company runs on skills I bundled into a GitHub repo. Here's how to build the same thing for your team: - Put your skills in a GitHub repo - Get Claude to write the couple of JSON files that make it readable as a plugin - In Claude Code, type /plugin, go to marketplaces, add marketplace, paste your repo URL - Split the plugins by department, so your copywriter isn't installing the finance skills It works in Codex too. I've got the same team skills installed there and I use them the same way. Now when I build something like my Notion formatting skill, I add it to the repo and it lands on everyone's Claude and Codex. If someone wrecks a skill, we roll it back to an older version. The key is enabling auto-update, so when someone improves a skill everyone benefits. I don't have a clue how the JSON works. I got Claude to set the whole thing up for me. Start with one skill and before you know it your whole team will be AI pilled.

The Startup Ideas Podcast (SIP) 🧃

19,532 次观看 • 14 天前

The March to Mainnet is in full swing. ⚡️ This week, Co-Founder and Head of Research Neurmal//Theoriq takes us inside the latest from the research and engineering teams. He kicks things off with a look at the core agents powering AlphaSwarm—then dives into our first live deployment: a Knowledge Agent built and configured to Kaito AI 🌊 🌊 Think of Knowledge Agents as the swarm’s research desk, blending curated insight and real-time data to answer complex token, protocol & metric queries. Check out how these agents are being built, and the role they’ll play in Alpha Protocol and the Agentic future. 👇
6:01

The March to Mainnet is in full swing. ⚡️ This week, Co-Founder and Head of Research Neurmal//Theoriq takes us inside the latest from the research and engineering teams. He kicks things off with a look at the core agents powering AlphaSwarm—then dives into our first live deployment: a Knowledge Agent built and configured to Kaito AI 🌊 🌊 Think of Knowledge Agents as the swarm’s research desk, blending curated insight and real-time data to answer complex token, protocol & metric queries. Check out how these agents are being built, and the role they’ll play in Alpha Protocol and the Agentic future. 👇

Theoriq

26,611 次观看 • 1 年前