These are notes to the stream: https://youtu.be/S9V-pcTrdL8
- We are not aware of a lot of GNU software available to us.
- Seems that Guix more hacker-friendly/explorable.
| Description | Nix | Guix | Comment |
Andrey Shmelev maestrow
dopey
/ main.go
Last active
April 28, 2026 19:04
— forked from denisbrodbeck/main.go
How to generate secure random strings in golang with crypto/rand.
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| package main | |
| import ( | |
| "crypto/rand" | |
| "encoding/base64" | |
| "fmt" | |
| "io" | |
| "math/big" | |
| ) |
tinoji
/ proxmox_lxc_pct_provisioner.sh
Created
February 7, 2018 01:02
Create and provision Proxmox LXC by pct command
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| pct create <id> /var/lib/vz/template/cache/centos-7-default_20170504_amd64.tar.xz \ | |
| -arch amd64 \ | |
| -ostype <centos|ubuntu|etc> \ | |
| -hostname <hostname> \ | |
| -cores <cores> \ | |
| -memory <memory(MB)> \ | |
| -swap <swap(MB)> \ | |
| -storage local-lvm \ | |
| -password \ | |
| -net0 name=eth0,bridge=<bridge>,gw=<gateway>,ip=<cidr>,type=veth &&\ |
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| https://medium.com/@clem.boin/creating-a-minimal-kernel-development-setup-using-qemu-and-archlinux-987896954d84 | |
| # Install Arch system | |
| qemu-image -f qcow2 kernel-dev-archlinux.img 4G | |
| wget http://mirrors.edge.kernel.org/archlinux/iso/2018.12.01/archlinux-2018.12.01-x86_64.iso | |
| # Note that ping does not work here | |
| qemu-system-x86_64 -cdrom archlinux-2018.12.01-x86_64.iso -boot order=d -drive file=kernel-dev-archlinux.img,format=qcow2 -m 2G -enable-kvm -cpu host -smp 8 -net user,hostfwd=tcp::10022-:22 -net nic | |
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.
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 — from repo → modules → files → symbols — stored locally in .tokenzip/db, exposed as an MCP server for any AI copilot, and kept fresh via git hooks
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