The major important thing is the documentation has to be implementation independent and specification concise. Dependencies where ever necessary are allowed to be specified.
Also it is allows HTML tags to be used in between the documentation comments. Pretty much all tags are self explanatory.
Meta Annotations
@author Ex: @author Jane Doe
@version Ex: @version v1.0-alpha
This guide provides steps on how to install Tensorflow and Pytorch on Linux environment (including WSL2 - Windows Subsystem for Linux) with NVIDIA GPU support. Here I focus on Ubuntu 24.04 and WSL2 (Windows 11) but things should work on more/less recent/relevant versions. From what I've checked there are no full consistent guidelines, hopefully this one should clear things up (also serve as a reminder for me).
To install purely on Windows 10/11 (no WSL), I suggest to follow this tutorial.
This is the gist of how to setup a NixOS server on a Hetzner Cloud instance with an admin user, ssh access and configuration management via git.
ip --brief --color address. The address can also be optained from the Hetzner Cloud web interface.
| def write_midi_file_from_generated(generate, midi_file_name = "result.mid", start_index=49, fs=8, max_generated=1000): | |
| note_string = [note_tokenizer.index_to_notes[ind_note] for ind_note in generate] | |
| array_piano_roll = np.zeros((128,max_generated+1), dtype=np.int16) | |
| for index, note in enumerate(note_string[start_index:]): | |
| if note == 'e': | |
| pass | |
| else: | |
| splitted_note = note.split(',') | |
| for j in splitted_note: | |
| array_piano_roll[int(j),index] = 1 |
Add this file to your AI assistant's system prompt or context to help it avoid common AI writing patterns. Source: tropes.fyi by ossama.is
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.
Алгоритм Fiber — это сердце архитектуры React (начиная с 16 версии). Если говорить просто, это полная переработка механизма рендеринга, которая превратила процесс обновления интерфейса из «непрерывного потока» в «умный планировщик».
Вот как это устроено «под капотом».
