First of all, you need to understand you are not writing documentation for you, nor for your current team. You are writing documentation for the future developers that were not there when you first wrote this code. Worst of it, they might think it is bad code for many reasons and won’t understand why you wrote this way.
Taken from: How to write good software technical documentation
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| // the cursor itself glides between cells instead of jumping. | |
| // Unlike a trail shader, this one is the focused cursor, so it | |
| // requires ghostty's own cursor to be hidden: | |
| // | |
| // cursor-opacity = 0 | |
| // custom-shader = "./shaders/cursor_glide.glsl" | |
| // --- CONFIGURATION --- | |
| const float DURATION = 0.14; // seconds for one glide |
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/ active-learning-prompt.md
Last active
September 15, 2026 07:44
Efektywna nauka z AI - prompty
Uczę się technologii X. Mam przed sobą pliki z prawdziwego projektu, który zbudowałem z AI: @y.
Wyjaśniaj krok po kroku, skupiając się na jednym kluczowym koncepcie.
Zacznij od tego, co się dzieje na serwerze gdy użytkownik wchodzi na stronę, a potem co dzieje się w przeglądarce.
Po każdym kroku zadaj mi pytanie sprawdzające - nie kontynuuj, dopóki nie odpowiem poprawnie.
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| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
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.
MaskRay
/ startup-files-and-sequences.md
Last active
September 15, 2026 07:29
If you link a program with a compiler driver (clang/gcc) in a standard way (not -nostdlib), the following components are usually on the linker command line.
- crt1.o (glibc/musl):
-no-pie/-pie/-static-pie- crt1.o:
-no-pie - Scrt1.o:
-pie,-shared - rcrt1.o:
-static-pie - gcrt1.o:
- crt1.o:
- crti.o (glibc/musl)
- crtbegin.o
- crtbegin.o:
-no-pie
alice-i-cecile
/ Bevy Merge Train for 2026-09-14.md
Created
September 15, 2026 04:09
Bevy Merge Train for 2026-09-14
+20/-21 for a 40% speed-up in a shadows system? Sign me up! Oh, it's smarter caching. Of course it's smarter caching. One of these days we're going to find a new optimization I promise.
Benchmarks look great (don't merge performance PRs without at least some attempt please...); code is sane, merging.
This is only one small bit of rendering, but we'll chop up this elephant 1000 cuts at a time.
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