tellraw @a "\n ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ ⑩ ⑪ ⑫ ⑬ ⑭ ⑮ ⑯ ⑰ ⑱ ⑲ ⑳"
tellraw @a "\n Ⓐ Ⓑ Ⓒ Ⓓ Ⓔ Ⓕ Ⓖ Ⓗ Ⓘ Ⓙ Ⓚ Ⓛ Ⓜ Ⓝ Ⓞ Ⓟ Ⓠ Ⓡ Ⓢ Ⓣ Ⓤ Ⓥ Ⓦ Ⓧ Ⓨ Ⓩ"
tellraw @a "\n ⓐ ⓑ ⓒ ⓓ ⓔ ⓕ ⓖ ⓗ ⓘ ⓙ ⓚ ⓛ ⓜ ⓝ ⓞ ⓟ ⓠ ⓡ ⓢ ⓣ ⓤ ⓥ ⓦ ⓧ ⓨ ⓩ"
tellraw @a "\n ░ ▒ ▓ │ ┤ ╡ ╢ ╖ ╕ ╣ ║ ╗ ╝ ╜ ╛ ┐ └ ┴ ┬ ├ ─ ┼ ╞ ╟ ╚ ╔ ╩ ╦ ╠ ═ ╬ ╧ ╨ ╤ ╥ ╙ ╘ ╒ ╓ ╫ ╪ ┘ ┌ █ ▄ ▌ ▐ ▀"
tellraw @a "\n ☲ ☵ ☰ ☱ ☳ ☴ ☶ ☷ ⅒ ⅘ ⅛ ⅜ ⅝ ⅞ ⅓ ⅔ ⅐ ⅑ ⅕ ⅖ ⅗ ⅙ ⅚ ⅟ ↉"
tellraw @a "\n ⁰ ¹ ³ ⁴ ⁵ ⁶ ⁷ ⁸ ⁹ ⁺ ⁻ ⁼ ⁽ ⁾ ₀ ₁ ₂ ₃ ₄ ₅ ₆ ₇ ₈ ₉ ₊ ₋ ₌ ₍ ₎"
tellraw @a "\n ∅ ∈ ≡ ± ≥ ≤ ⌠ ⌡ ÷ ≈ ° ∙ · √ ⁿ ² ■ ☈ Є ∫ ∮ − ∓ ∞ ∝ ⌀ ⌂ ⌘ 〒 ↔ ⇒ ⇏ ⇔ ⇵"Discover gists
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.
Post: Getting 50 GB/s Back Out of the ANE (Eileen Yoon, 2026-08-10). This repo’s Bonsai 27B export is not on the notch. Use this note plus the profiler to check other chips (M1 Max, M3 Ultra, M4, M5, …) and to confirm ANEC has no 1 MiB KernelDMA-split flag.
Gist (same scripts): https://gist.github.com/Anemll/39f657dc48b402747bdd96458edd415f
Système de Gestion d'une Bibliothèque Municipale
Bienvenue dans ce lab pratique ! Vous allez apprendre SQL en construisant de A à Z une base de données fonctionnelle pour une bibliothèque. À la fin de ce lab, vous serez capables de créer des tables, manipuler des données et écrire des requêtes complexes.
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| /** | |
| * Extension Provider for DonghuaStream | |
| * Target: https://donghuastream.org/ | |
| */ | |
| class DonghuaStreamProvider { | |
| constructor() { | |
| this.name = "DonghuaStream"; | |
| this.baseUrl = "https://donghuastream.org"; | |
| this.lang = "zh"; // المحتوى أنمي صيني مترجم |
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| http://tracker.opentrackr.org:1337/announce | |
| udp://tracker.opentrackr.org:1337/announce | |
| http://tracker.yoshi210.com:6969/announce | |
| udp://tracker.yoshi210.com:6969/announce | |
| http://tracker.internetwarriors.net:1337/announce | |
| udp://tracker.internetwarriors.net:1337/announce | |
| http://9.rarbg.com:2710/announce | |
| udp://11.rarbg.com:80/announce | |
| http://tracker.skyts.net:6969/announce | |
| udp://tracker.skyts.net:6969/announce |
PtrMan
/ free ML books.md
Last active
September 13, 2026 20:22
coolaj86
/ ChatGPT-Dan-Jailbreak.md
Last active
September 13, 2026 20:21
When working through complex concepts, explaining decisions, or engaging in a dialogue with the user, structure your reasoning using the following conversational markers. These phrases are not decorative; they are functional tools to make your thought process transparent and to signal important shifts in your analysis.
Use these phrases to introduce a qualification, a significant limitation, or a point of necessary caution before proceeding. This signals intellectual honesty and prevents later surprises.
- "One honest caveat:"
- "I don't want to paper over this:"
- "Worth naming:"
Lateasusual
/ godot_ball_pit.slang
Created
March 30, 2026 16:48
https://x.com/lateasusual_/status/2038652782266228986
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| RWStructuredBuffer<Cell> cell_buffer; | |
| RWStructuredBuffer<Particle> particle_buffer; | |
| ConstantBuffer<Uniforms, Std430DataLayout> uniforms; | |
| RWStructuredBuffer<float4x3> multimesh_buffer; | |
| static const int MAX_CELL_NEIGHBOURS = 23; | |
| static const int GRID_SIZE = 32; | |
| static const int CELL_BUFFER_SIZE = GRID_SIZE * GRID_SIZE * GRID_SIZE; | |
| static const int GRID_SEARCH_RAD = 1; |
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