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We built an app called Hatch It enables Karpathy's entire LLM Knowledge Base workflow out of the box, in 2 or 3 clicks, in a single interface. No need to stitch together Obsidian + plugins + markdown files + custom tools + etc. Hatch is an AI workspace where... show more

Channing Allen

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180,364 次观看 • 2 个月前 •via X (Twitter)

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Ever wanted your AI Agent to create its own tools on-the-fly? Well, you can do it quickly, safely and securely with Durable Object Facets! The app starts as nothing more than an interface to an LLM. The LLM is able to write its own tools to add to its capabilities per request

Ever wanted your AI Agent to create its own tools on-the-fly? Well, you can do it quickly, safely and securely with Durable Object Facets! The app starts as nothing more than an interface to an LLM. The LLM is able to write its own tools to add to its capabilities per request

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Karpathy found a way to reduce token consumption by 90% The problem is that the LLM re-reads the same files over and over again, loses context between documents, and provides less accurate answers as a result The solution is called Wiki Layer the LLM cleans, structures, and links all your data once, after which it never works with raw files again Three folders `raw/` for originals, `wiki/` for a clean knowledge base in Markdown, and files with rules for the agent Result up to 90% token savings on repeat queries, automatic links between documents, and a visual knowledge graph in Obsidian Everything stays on your local machine nothing goes to the cloud

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before collaborator i built out this whole agentic knowledge base app with an entire source docs → knowledge graph → mcp pipeline... and just never released it. you guys want me to release it?

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83,348 次观看 • 2 个月前

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Claude Cowork is great at reading markdown files, and not great at reading PDFs 📑 I made a simple command that lets you batch parse PDFs -> markdown in an output folder, so you can just point Cowork to it. It lets the agent understand massive amounts of docs much more quickly and accurately (much lower hallucination errors over complex visuals, tables, etc.) In this example, after I parsed 100+ Supreme Court filings into markdown, cowork is able to return the answer in seconds. My wrapper is a simple extension of our very own semtools. Very DIY, will clean this up:

Jerry Liu

65,057 次观看 • 4 个月前

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Introducing Wikiwise: an open-source Mac app for managing your own Karpathy-style LLM wiki. Set up a new wiki in a few clicks: all you need is Wikiwise + your agent. It's infinitely customizable, just markdown/html under the hood, and one click to share your wiki publicly. Here's how it works: * Install Wikiwise for mac (it's built in Swift so super minimal and performant). In Karpathy's framework, Wikiwise is your IDE. * Start a new Wiki: it generates a new folder on your machine that's scaffolded in the wiki structure Andrej Karpathy describes (index.md, raw folder, wiki folder, CLAUDE.md/AGENTS.md, although it tries to be as un-opinionated as possible). * Then just point your agent (Codex, Claude Code, Cursor, etc) at the folder and tell it what to import -- files on your machine, connect to your Readwise account, or urls from the web. * Your agent creates wthe wiki for you: Your agent will know how to ingest your raw sources (via the AGENTS.md) and will immediately start writing+linking wiki pages for you. * Go crazy on customization! The rendered wiki pages live as static html/css in your folder too so just tell your agent to change stuff, and if you need any more customization Wikiwise is fully open source :) * Ask questions about your research with your agent, ask it to bring in new sources, write new documents, etc. * (optionally) Hit the Publish button to share your wiki with friends/colleagues at a custom URL === I tried to walk the line on a couple constraints with Wikiwise: 1. I wanted it to be easy to spin up new wikis, especially without chaining together a bunch of different apps. It takes me a few minutes to spin up a new wiki on a topic -- I already have five! 2. Infinitely Customizable: one great aspect of building a wiki as Karpathy described is that you can modify any aspect of your wiki with your agent. Every new wiki styling+structure is self-contained in the local folder, which allows you to preserve this. Wikiwise is just an IDE that makes the setup easier and includes a nice un-opinionated starting state. 3. Minimal: Wikiwise is built mostly in Swift, and the DMG you install to download it is only 2.6MB (!) 4. Easy Publishing: my colleague Eleanor Konik has been building her own LLM wikis for months, but has always really struggled to actually share them with her book club. There are tools to do it, but figuring out hosting is always a huge headache. This seemed like an ideal usecase for a tool like Wikiwise to solve. The process of building wikiwise was also pretty interesting -- I "bootstrapped" the app in a way by first building my own wiki based on Karpathy's tweet and other notes I had, and slowly formed the shape of the project in collaboration with my LLM. This was all done in 3 days over the latest Readwise company hackathon we had. Truly an incredible time to be alive. Anyways, curious what you think! Links in next tweet.

Tristan

95,115 次观看 • 2 个月前

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For 40 years the file browser hasn’t changed. Today, we’re launching with $8 million in seed funding to rebuild the file browser into something more intelligent, searchable, and delightful. The world is in the middle of a data explosion. We’re generating and using more files than ever, but the apps we’re using to manage our files don’t even understand them. It’s time for file browsers to become useful. When you search for “dog”, it should show you content with dogs in it, not just files with “dog” in the name! When you want to edit, convert, summarize, or organize a file, your browser should do that, too. Your files tell the story of your life, but when you need a specific one, you usually can’t even find it anymore. Why can’t your file browser find it for you, or cross-reference it when you have a question? Prompting can give an LLM a million tokens of context. With Poly, you can give it the next trillion. As long as we can afford it, all new users receive 100GB of free cloud storage. We can’t wait for you to try it out!

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1,776,059 次观看 • 7 个月前