See how a minor change to your commit message style can make you a better programmer.
Format: <type>(<scope>): <subject>
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| WEAPONS | |
| WP_1000_A Dagger | |
| WP_1010_A Bandit's Knife | |
| WP_1040_A Mytha's Bent Blade | |
| WP_1050_A Shadow Dagger | |
| WP_1060_A Thief Dagger | |
| WP_1070_A Broken Thief Sword | |
| WP_1100_A Parrying Dagger | |
| WP_1110_A Manikin Knife | |
| WP_1140_A Blue Dagger |
This is just a dump of some interesting undocumented features of webOS (3.8 specifically, on early 2018 4k LG TV) and other development-related tips.
nvidia-smi (NVIDIA System Management Interface) is a command-line tool that provides monitoring, management, and diagnostic information for NVIDIA GPU devices.
It communicates directly with the NVIDIA driver and GPU, and can:
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.
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| <title>Curly Bracket</title> | |
| <script src="http://d3js.org/d3.v3.min.js" charset="utf-8"></script> | |
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| .curlyBrace { | |
| stroke: #000000; | |
| stroke-width: 10px; |