| title | LDAP Search Filter Cheatsheet |
|---|---|
| author | Jon LaBelle |
| date | January 4, 2021 |
| source | https://jonlabelle.com/snippets/view/markdown/ldap-search-filter-cheatsheet |
| notoc | true |
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Fully unlocked FL Studio 2026 with all native plugins included. Tested, stable, and working.
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by Flamerix AI · MIT License
Tested on Linux, Claude Code 2.1.257–2.1.312; macOS reported working by others (see Tested versions). The binary is not modified (
BUN_OPTIONS=--preload). An update can silently undo it, so see Verify.
Every ~30 seconds, Claude Code re-sends each background agent's full transcript to the model just
to fill that agent's status line in the agents panel. no-agent-summary.js is a Bun --preload
A curated list of awesome open source Android applications, tutorials and resources. Inspired by other awesome-* projects.
- CashApp: $CipherOps
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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.
