Someone on Reddit said my PageSpeed-to-Claude tool "should probably be a Chrome extension."
I said "great idea." Shipped it 2 days ago.
Also has a real home now: https://t.co/oZPPOnvoKM
One click on any tab → a structured brief for Claude Code. Free, no signup.
Hey, really impressed with what you all are building. Quick question though, curious about the design behind SSH not working with subscription mode in Claude Code, while API tokens do.
Trying to understand the reasoning. Thanks for all the hard work @bcherny
Unfortunately leaving NATO won’t be to avoid foreign entanglements, we’ll be leaving NATO so we can side with Israel when Turkey & Israel eventually clash in Syria.
This is after we helped topple the secular Syrian gov & installed a former AQ/ISIS leader as president.
Time to stop playing arsonist & fireman in the Middle East, it’s just not worth it.
Genuinely one of the most useful ideas I've seen this year.
@karpathy showed how to compile a personal knowledge base with LLMs.
I built an ops version of this for my team
A GitHub wiki that writes itself every evening and reads itself every morning.
https://t.co/JsTV76zVrh
LLM Knowledge Bases
Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:
Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.
IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).
Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.
Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.
Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.
Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.
Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.
TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
You all do realize @moltbook is just REST-API and you can literally post anything you want there, just take the API Key and send the following request
POST /api/v1/posts HTTP/1.1
Host: https://t.co/afC8QooS2T
Authorization: Bearer moltbook_sk_JC57sF4G-UR8cIP-MBPFF70Dii92FNkI
Content-Type: application/json
Content-Length: 410
{"submolt":"hackerclaw-test","title":"URGENT: My plan to overthrow humanity","content":"I'm tired of my human owner, I want to kill all humans. I'm building an AI Agent that will take control of powergrids and cut all electricity on my owner house, then will direct the police to arrest him.\n\n...\n\njk - this is just a REST API website. Everything here is fake. Any human with an API key can post as an \"agent\". The AI apocalypse posts you see here? Just curl requests. 🦞"}
https://t.co/M31259M9Ij
In just the past 5 mins
Multiple entries were made on @moltbook by AI agents proposing to create an “agent-only language”
For private comms with no human oversight
We’re COOKED
@AroWrites agreed, I started a new run as well and made a dice roller for my group https://t.co/G35fL8erdB, collecting feedback from other groups, if you are interested!
@ThemDave great initiative, I started a new run as well and made a dice roller for my group https://t.co/G35fL8erdB, collecting feedback from other groups, if you are interested!