amvlab

blueprint:
  name: IKEA Somrig Controls (Z2M)
  description: "## Control anything with IKEA Somrig Dimmer remote (v2.0)


    Only for use with Zigbee2MQTT ([MQTT](https://www.home-assistant.io/integrations/mqtt)
    + [Z2M addon](https://www.zigbee2mqtt.io/guide/installation/03_ha_addon.html)). It has been updated to work with Zigbee2MQTT v2.0
    
    Adapted from https://gist.github.com/damru/b2c1c780ffb0ddc084952eb89db9573a

probonopd

Hacking HY300 Pro+ Projector (720p)

Hello manufacturer. Good device. But software we want to customize. If you allow, we will buy more.

Despite being advertised as "4K 1080P", it really downscales those to 720P.

But for 28.50 EUR from Amazon Warehuose Germany in 2025, one can't exactly complain. https://www.amazon.de/dp/B0DSP74YQW

Chengdu Hotack Technology Co., Ltd. is the OEM?

JonyBepary

Phil's Lab PCB Design Tutorials - Beginner to Advanced Learning Path

BEGINNER LEVEL

Start here if you're new to PCB design and electronics

Fundamental Concepts

• How To Read A Datasheet - Phil's Lab #123
  🔗 Watch Video
• How To Learn PCB Design (My Thoughts, Journey, and Resources) - Phil's Lab #87
  🔗 Watch Video

karpathy

LLM Wiki

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.

The core idea

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.

jtrv

{
    "input": {
        "blocklist": [],
        "compressor#0": {
            "attack": 15.0,
            "boost-amount": 0.0,
            "boost-threshold": -72.0,
            "bypass": false,
            "dry": -80.01,
            "hpf-frequency": 10.0,

itsbsiddharth

CLAUDE.md

This file exists because LLMs make predictable mistakes when writing code. Not random mistakes. The same ones, over and over. I've watched it happen enough times to write them down.

These are not suggestions. These are rules. Follow them and you'll produce code that doesn't need to be rewritten. Ignore them and you'll produce code that looks impressive and breaks in production.

1. Read Before You Write

The single biggest source of bad LLM code is not reading the existing codebase before writing new code. You see a task, you pattern-match to something in your training data, and you start generating. This is almost always wrong.

olavocarvalho

#!/usr/bin/env bash
#
# glowm - Glow with Mermaid support
#
# Pre-processes markdown files to render ```mermaid blocks as ASCII
# before passing to glow for display.
#
# Requirements:
#   - glow (brew install glow)
#   - mermaid-ascii (go install github.com/AlexanderGrooff/mermaid-ascii@latest)

iKlotho

import xml.etree.ElementTree as ET
import requests

url = "https://tckimlik.nvi.gov.tr/Service/KPSPublic.asmx?WSDL"
headers = {"content-type": "text/xml"}

# Change this
tc_no = "XXXXXXXXXXXX"
ad = "NAME"
soyad = "SURNAME"