To: Dario Amodei (CEO), Daniela Amodei (President), Jack Clark (Head of Policy)
Company: Anthropic PBC / Anthropic Ireland, Limited
From: Konstantin (Software Developer & Open Source Contributor)
Date: October 30, 2026
Reference ID: TS-01a18cb2-0119-7712-b3bd-a7717134a6d4
Invoice / Receipt: 9BF0758D-9606630 / #2711-4150

I've been running Andrej Karpathy's LLM Wiki pattern for several months — reading sources, compiling them into a compounding, git-diffable wiki instead of re-deriving everything from scratch every session — and I'm a genuine convert. But two things kept nagging at me: query salience (finding the right page reliably, not just something plausible) and token economy (not re-reading half the wiki to answer one question).

by Flamerix AI · MIT License

Platforms: Linux and Windows 11 tested by us; macOS reported by others. Tested on Claude Code 2.1.257–2.1.317. The binary is not modified (BUN_OPTIONS=--preload). An update can silently undo it, so see Verify.

Every ~30 seconds, Claude Code re-sends each background agent's full transcript to the model to

A hands-on one semester course where students build their own compiler from scratch, starting from elementwise programs and ending with training SOTA LLMs on GPUs. This course aggressively builds on the previous week, and is an exercise in slop management. If you let any slop in early, it will compound and you will not finish the class.

Course description: This course covers the design and implementation of a modern machine learning compiler, and examines the interaction between IR design, hardware capabilities, and the structure of machine learning programs. Topics covered include term rewriting, code generation, movement operators, kernel fusion, memory hierarchies, GPU architecture, automatic differentiation, and flash attention. It is a project course, providing experience with performance-oriented programming, managing a codebase that grows all semester, and working in 1 or 2 person teams, culminating in a compiler capable of training modern LLMs.

Prerequisites: This c

Keep in mind that getting your hands on a premium font does not mean you have the legal right to use it. It's probably really hard to check, but I personally wouldn't use a pirated font for professional use. It should, however, be fine for personal use. I am not responsible for any legal action taken against you.

Any search engine should work with this method.

1. Choose the font you want to download. Googling for specific types of fonts may help.
   • You might even find that the font is free to download!
2. Search the font like this: [Font name].ttf or [Font name].otf
   • You can add site:vk.com, site:github.com, or site:onlinewebfonts.com in your search in order to broaden your search results.
3. Click on one of the links and click Download/View Raw (GitHub).

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.

State: Dec. 2021

Description: Official F-Droid repository, serving apps build directly from its source code & signed by F-Droid.

Note: I didn't use AI to write this. I originally had it try to analyze the profiler GC logs, but there wasn't enough information for it to be conclusive (see below).