Ensure our operating system is entirely up to date:
sudo apt-get update
sudo apt-get upgrade| use_debug false | |
| use_bpm 130 | |
| # Our mixer! | |
| master = (ramp *range(0, 1, 0.01)) | |
| kick_volume = 1 | |
| bass_volume = 1 | |
| revbass_volume = 1 | |
| snare_volume = 0.5 | |
| hats_volume = 0.5 |
| use_debug false | |
| use_bpm 130 | |
| # Our mixer! | |
| master = (ramp *range(0, 1, 0.01)) | |
| kick_volume = 1 | |
| bass_volume = 1 | |
| revbass_volume = 1 | |
| snare_volume = 0.5 | |
| hats_volume = 0.5 |
| [ | |
| { | |
| "where": "Layer", | |
| "if": { | |
| "Datasource.type": "postgis" | |
| }, | |
| "then": { | |
| "Datasource.dbname": "hdmht", | |
| "Datasource.user": "skors", | |
| "Datasource.host": "localhost" |
| Station Name | Strem link |
|---|---|
| Радио Мелодия (СПБ) | http://stream128.melodiafm.spb.ru:8000/melodia128 |
| French Jazz | http://icepe6.infomaniak.ch:80/jazz-wr01-128.mp3 |
| Swing Radio | http://icepe2.infomaniak.ch:80/swingfm-128.mp3 |
| Радио Золотой Век | http://setmedia.ru:8000/high |
| Радио Подмосковные вечера | http://setmedia.ru:8000/high5 |
| Ностальжи | http://85.143.216.212:8025/stream |
Never ask the user to copy-paste code. Instead, use Slicer web server that allows execution of Python code via a web API.
You can connect to a running instance of 3D Slicer by giving these instructions to the user: Go to Web Server module, enable "Slicer API exec" in Advanced section, and click "Start server" button.
Alternatively, find a free port (2016, 2017, 2018, ...) and start 3D Slicer with the Web Server "Slicer API exec" endpoint enabled:
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.