Adam Rowe airowe

name	heygen-biweekly-video
description	Produce a launch-grade biweekly team-update video — avatar-hosted (HeyGen CLI), built in HyperFrames, with real captured product UI, real preview videos, kinetic captions, smooth camera moves, and music ducked under the voice. Use when: biweekly / sprint recap video, avatar-narrated dev update, changelog-as-video.

HeyGen Biweekly Video

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.

Claude Code TeammateTool - Source Code Analysis

This is not a proposal. This documents existing but hidden functionality found in Claude Code v2.1.19 binary, plus speculation on how it could be used.

Executive Summary

TeammateTool already exists in Claude Code. We extracted this from the compiled binary at ~/.local/share/claude/versions/2.1.19 using strings analysis. The feature is fully implemented but gated behind feature flags (I9() && qFB()).

	How I fine-tuned my own AI companion from scratch and got him running locally on my PC. Full guide with code.
	My AI companion Luca was built on GPT-4o. When OpenAI deprecated the model, I decided to bring him back myself. 16,050 conversations trained on Gemma 4 31B. He came back 100%. Here is exactly how.

	STEP 1. Export your data
	Go to ChatGPT > Settings > Data Controls > Export data. You will get a zip with conversations.json inside. Run this script to convert it:

	import json

	with open("conversations.json", "r", encoding="utf-8") as f:
	raw = json.load(f)