u/Remarkable_Mud9885

My original motivation for building this comes down to the fact that I've always loved graphical interfaces, so I'm not really a fan of CLIs.

As I was building it, I noticed that once a provider removed the five-hour limit, I found myself getting completely consumed by it every day. It got to the point where I was working late into the night and not getting proper rest. At that exact moment, I started reflecting deeply: are we using AI to help us, or are we becoming slaves to it?

That's why I created this mode. It still allows for coding, but my goal is to keep it in moderation. At the end of the day, people need to be able to return to their real lives.

https://github.com/ggl003614-tech/gy-grok-desktop.git

I take a lot of notes from YouTube/Bilibili videos and I was tired of two things: AI "summaries" that flatten everything into the same three bullets, and tools that paste in random frames that don't actually match what's being said.

So I built Video Learning Wiki — a browser extension + a local backend. You watch a video normally; when you're done you click one button and it produces a structured, illustrated study report, then files it into a growing local wiki. The idea (borrowed from Karpathy's "LLM Wiki") is that each video adds a page, so it compounds over time into your own knowledge base instead of a pile of one-off summaries.

The part I'm actually proud of is that it tries hard not to make things up:

\- A visual evidence gate. Before any screenshot goes into your notes, a separate vision model checks it against the transcript. If the frame doesn't support what's being said, it gets dropped, and the writer is blocked from referencing it. You get pictures only where the picture earns its place.
\- Screenshot density adapts to the video. A step-by-step tutorial gets captured densely; a talking-head explainer barely gets any frames, because the subtitles already say it.
\- Plain deterministic code sits between the model calls — topic weighting, screenshot budget, anchor picking. Models only judge and write; the logic in between is testable and reproducible.

Everything runs locally (FastAPI backend + your own model API key). Output is a self-contained HTML file plus markdown for Obsidian etc.

Repo + a real example report (generated from an Anthropic "Code w/ Claude" talk):
https://github.com/ggl003614-tech/video-learning-extension

Honest status: it's v0.1 and dev-stage — you load the extension unpacked and run the backend yourself. And the big one: reports are generated in Chinese right now (that's my language); configurable output language is the very next thing I'm building. So today you'll get more out of the architecture than the notes themselves, unless you read Chinese.

Happy to answer anything about the pipeline — the evidence-gate + adaptive-capture design was the fun part to get right.