updated 2024-09-20
Version 2409? (Build 16.0.17928.20148)
| blueprint: | |
| name: IKEA E2490 BILRESA Scroll Wheel (Zigbee2MQTT + ZHA) | |
| description: | | |
| Unified controller blueprint for IKEA E2490 BILRESA scroll wheel working with Zigbee2MQTT and ZHA. | |
| - Buttons: on, off, on_double, off_double | |
| - Scroll: brightness_move_to_level with action_level from Zigbee2MQTT main topic, or ZHA move_to_level args | |
| Supports light brightness, media_player volume, light color_temp, light hue. | |
| Version: 2026-01-18 | |
| domain: automation |
This guide has moved. It is now maintained at github.com/kaczmar2/pihole-ssl-guide, which now fully automates this process with scripts. The manual steps below remain for reference, but are no longer updated here.
See my other guides for SSL certificates on Pi-hole v6:
The TrueNAS installer doesn't have a way to use anything less than the full device. This is usually a waste of resources when installing to a modern NVMe which is usually several hundred of GB. TrueNAS SCALE will use only a few GB for its system files so installing to a 16GB partition would be helpful.
The easiest way to solve this is to modify the installer script before starting the installation process.
| # LLM Wiki — [YOUR FIELD] | |
| A personal knowledge base of [YOUR FIELD] papers, following [Karpathy's LLM Wiki pattern](https://gist.github.com/karpathy/1dd0294ef9567971c1e4348a90d69285): | |
| ``` | |
| Original PDF → sources/*.md (LLM summary) → wiki/{category}/*.md (final page) | |
| ``` | |
| **Language policy**: All wiki content is in English. Conversation can be in any language. |
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.
Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[