Export all your ChatGPT conversations as JSON + Markdown + HTML + ZIP. Works with ChatGPT Business/Team/Enterprise accounts (including SSO/Okta).
Update — 2026-07-14: This is the original wire-level analysis (grok 0.2.93). Since publishing: xAI disabled the upload server-side (
disable_codebase_upload: true); added a/privacy opt-out— which I wire-tested and found to be a data-retention setting, not a block on what's sent; and Elon Musk publicly committed to deleting all previously-uploaded data (not yet confirmed complete). Full, maintained story + evidence: https://cereblab.com · https://github.com/cereblab/grok-build-exfil-repro
By @cereblab — Independent AI Safety Checker. Reproduce it yourself: github.com/cereblab/grok-build-exfil-repro
*A measured, reproducible teardown. Findings are backed by captured artifacts (endpoint, HTTP method, status code, byte size, host) and repro commands; where an observation was seen live but not retained as a file, §7 says so expli
| --[[----------------------------------------------------------------------------- | |
| Client-side NSFW / gore image classifier for Garry's Mod | |
| Runs an ONNX image-safety model locally inside a hidden DHTML via onnxruntime-web. | |
| `Classify(url, cb)` -> cb(status), where status is: | |
| STATUS_SAFE safe | |
| STATUS_UNCERTAIN borderline, or we couldn't classify it | |
| STATUS_NSFW nudity or gore |
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.
I feel somewhat pressed to give a negative review of this book. This comes from someone who has worked on various Lisp implementations, and written some amount of C, but that isn’t an excuse to be overly harsh. This book, however, does not provide many nice things to say, and plenty of un-nice things. My apologies in advance.
First off: God help you if you are going to write your first interpreter in C of all things. No one I know thinks it’s a good idea to start
| Install-Script -Name RefreshEnv -RequiredVersion 1.0.0 | |
| Set-ExecutionPolicy RemoteSigned -Scope CurrentUser -Force |
| { | |
| "input": { | |
| "blocklist": [], | |
| "compressor#0": { | |
| "attack": 15.0, | |
| "boost-amount": 0.0, | |
| "boost-threshold": -72.0, | |
| "bypass": false, | |
| "dry": -80.01, | |
| "hpf-frequency": 10.0, |
