| <!doctype html> | |
| <title>Site Maintenance</title> | |
| <style> | |
| body { text-align: center; padding: 150px; } | |
| h1 { font-size: 50px; } | |
| body { font: 20px Helvetica, sans-serif; color: #333; } | |
| article { display: block; text-align: left; width: 650px; margin: 0 auto; } | |
| a { color: #dc8100; text-decoration: none; } | |
| a:hover { color: #333; text-decoration: none; } | |
| </style> |
| import io | |
| class StringIteratorIO(io.TextIOBase): | |
| def __init__(self, iter): | |
| self._iter = iter | |
| self._left = '' | |
| def readable(self): | |
| return True |
| git checkout --orphan temp_master | |
| git rm -rf . | |
| git commit --allow-empty -m 'Make initial root commit' | |
| git rebase --onto temp_master --root master | |
| git branch -D temp_master |
| { pkgs ? import <nixpkgs> {} }: | |
| let | |
| # To use this shell.nix on NixOS your user needs to be configured as such: | |
| # users.extraUsers.adisbladis = { | |
| # subUidRanges = [{ startUid = 100000; count = 65536; }]; | |
| # subGidRanges = [{ startGid = 100000; count = 65536; }]; | |
| # }; |
| FROM clojure:openjdk-15-tools-deps AS builder | |
| WORKDIR /opt | |
| ADD deps.edn deps.edn | |
| RUN clj -Sdeps '{:mvn/local-repo "./.m2/repository"}' -e "(prn \"Downloading deps\")" | |
| RUN clj -e :ok | |
| COPY . . | |
| RUN clj -e :ok |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.