TokenZip v2 transforms Karpathy's llm wiki concept into a gzip like token compression engine on top of entire codebase, which can reduce the LLM input token cost upto by 95% when using with Coding Copilots like Claude Code, Codex etc. Instead of generating a flat text summary, it builds a multi-level, queryable, chainable knowledge graph β from repo β modules β files β symbols β stored locally in .tokenzip/db, exposed as an MCP server for any AI copilot, and kept fresh via git hooks
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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.
| Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36 | |
| Mozilla/5.0 (Windows NT 6.3; WOW64; Trident/7.0; MDDCJS; rv:11.0) like Gecko | |
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| Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; ASU2JS; rv:11.0) like Gecko | |
| Mozilla/5.0 (X11; Linux i686) | |
| Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; MAARJS; rv:11.0) like Gecko | |
| Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36 OPR/42.0.2393.517 | |
| Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36 | |
| Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36 | |
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| # safetensors_convert_fp16_to_8bit.py | |
| # Python 3.11.7 | |
| import safetensors.torch | |
| import safetensors | |
| import torch | |
| import os | |
| # blacklist takes priority over whitelist | |
| # a tensor will only be cast if it matches the whitelist but not the blacklist |
This is a detailed runbook for setting up a production server on an Ubuntu 16.04 cloud VPS for automated deployment of static web content to be served by an NGINX web server and Node.js web application behind an NGINX reverse-proxy, both with SSL/TLS (https) support.
The steps are as follows: