Discover gists
Note: I have moved this list to a proper repository. I'll leave this gist up, but it won't be updated. To submit an idea, open a PR on the repo.
Note that I have not tried all of these personally, and cannot and do not vouch for all of the tools listed here. In most cases, the descriptions here are copied directly from their code repos. Some may have been abandoned. Investigate before installing/using.
The ones I use regularly include: bat, dust, fd, fend, hyperfine, miniserve, ripgrep, just, cargo-audit and cargo-wipe.
| #ifndef PI | |
| #define PI 3.14159265359 | |
| #endif | |
| // adapted from https://www.shadertoy.com/view/WstfzH | |
| float2 SpherizeOriginal(float2 uv, float2 center, float radius) { | |
| float2 delta = uv - center; | |
| if (delta.x == 0 && delta.y == 0) return uv; | |
| float dist = length(delta); |
These are design constraints, not a checklist.
The GNUS C++ Coding Standards are authoritative for C++ syntax, naming, layout, language use, class design, error handling, file layout, includes, platform abstraction, and tooling. Do not override them with a general design principle or a local preference.
When two design principles conflict, choose the option that creates the lowest future cost in this repository while preserving correctness, clarity, and the existing architecture.
Sometimes you need text, rather than voice, output from screen readers. Why? It's really useful for bug reports ("this disclosure icon is announced as 'black dash triangle dash filled dash x2 underscore final dot png' and needs alt text"). Luckily, getting this text is easy to do.
In VoiceOver you press Option + Control + Shift + C to have the last item that was announced copied to the clipboard. Bonus feature: pressing Option + Control + Shift + Z to save the last phrase to the desktop as an audio file.
A three-finger quadruple tap copies the last announcement to the clipboard.
Export all your ChatGPT conversations as JSON + Markdown + HTML + ZIP. Works with ChatGPT Business/Team/Enterprise accounts (including SSO/Okta).
- JSON — Raw conversation data from the API
- Markdown — Clean text with headers per message, relative links to downloaded files
- HTML — ChatGPT-style conversation viewer with sidebar navigation, syntax-highlighted code blocks, and embedded images
Некоторым командам требуется второй аргумент, например для создания тега на объекте - второй аргумент это тип создаваемого тега. Пример:
# первый аргумент - создать тег, второй - тег композитинга
c4d.CallCommand(100004788, 50044)| ID | Название команды | Описание команды |
|---|
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.
