Extract Claude Code session history into readable, queryable markdown files.

Reads ~/.claude/projects/<project>/*.jsonl session files and produces structured markdown in convos/ with:

• Your prompts (full text)
• Assistant responses (text only, tool calls compressed to one-liners)
• Insight blocks collected into a summary section

Target: Crack Google L3/L4/L5 by 2026 Duration: 24 weeks (6 months intensive preparation) Daily Time Commitment: 3-4 hours minimum

This guide shows how to install ClassPad Manager Subscription for ClassPad II on Linux using Wine.

• Go to: https://edu.casio.com/softwarelicense/index.php
• Download the ZIP file (it will look like cp2m_sub_2017001_2b.zip).

What is strict aliasing? First we will describe what is aliasing and then we can learn what being strict about it means.

In C and C++ aliasing has to do with what expression types we are allowed to access stored values through. In both C and C++ the standard specifies which expression types are allowed to alias which types. The compiler and optimizer are allowed to assume we follow the aliasing rules strictly, hence the term strict aliasing rule. If we attempt to access a value using a type not allowed it is classified as undefined behavior(UB). Once we have undefined behavior all bets are off, the results of our program are no longer reliable.

Unfortunately with strict aliasing violations, we will often obtain the results we expect, leaving the possibility the a future version of a compiler with a new optimization will break code we th

Simple overview of use/purpose.

An in-depth paragraph about your project and overview of use.

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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Resonance: A Plague Tale Legacy Trainer toolkit for Windows with Resonance Points Editor, Game Speed, Configurable Hotkeys, Saved Profiles, Trainer Dashboard, Quick Presets, modern dashboard, saved presets, and desktop-focused setup.