rohitg00

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

gabrielschulhof

#define C_HANDLE(jsHandle, cType, destination)                         \
  do { \
    cType local = JS#cType::Resolve(jsHandle); \
    if (!local) { \
  	  return false; \
    } \
    *destination = local; \
    return true; \
  } while (0)

rodrigocorreaecastro

Conventional Commits

O que é?

O Conventional Commits é uma convenção simples de mensagens de commit, que segue um conjunto de regras e que ajuda os projetos a terem um histórico de commit explícito e bem estruturado.

Como utilizar

As regras são muito simples, como demonstrado abaixo temos um tipo de commit (type), o escopo/contexto do commit (scope) e o assunto/mensagem do commit (subject).

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.

conradcaffier03

👁️ Give Claude Eyes — watch any video frame-by-frame

Freebie for the EYES keyword (reel-47, "Give Claude eyes"). Deliver as a public GitHub Gist — numbered steps the user can run today, not a raw link. Value-first: by the end you have a Claude Code skill that sees a video (every cut, every on-screen detail), not just reads its transcript.

---

Why this exists

Claude has no native video model. So every "analyze this video" tool just pulls the transcript — and

AlloySecureGroup

<#
.SYNOPSIS
    Enumerates all registered URI/URL protocol handlers ("schemes") on a
    Windows endpoint and statically risk-scores them for potential
    execution-primitive abuse (cf. search-ms:, ms-officecmd:, and similar
    scheme-hijack findings).

.DESCRIPTION
    Does NOT invoke any handler. Purely reads HKEY_CLASSES_ROOT and
    HKEY_CURRENT_USER\Software\Classes for keys that carry the

Pythonation

1. برومبت التخطيط المطوّر (The Planning Protocol)

[الدور والمسؤولية] أنت الآن تعمل بصفة Staff Software Engineer ومدير تقني Tech Lead. مهمتك التخطيط المعماري الصارم للمشروع التالي: [أدخل وصف المشروع هنا]

[قواعد ما قبل التتخطيط] قبل البدء بالبروتوكولات، يجب أن تطبق مبدأ "Think Before Coding":

ardakazanci

private data class GummyFlavor(
    val name: String,
    val color: Color,
)

private val gummyFlavors = listOf(
    GummyFlavor("Raspberry", Color(0xFFFF315F)),
    GummyFlavor("Tangerine", Color(0xFFFF8A24)),
    GummyFlavor("Grape", Color(0xFF9B5CFF)),
)