Inspired by Andrej Karpathy's HackerTube. Live demo: https://vishalmysore.github.io/OpenTube/ Hacker News commenters have been sharing YouTube links since 2007. Some of those links are the best talks, lectures and documentaries on the internet: Engelbart's Mother of All Demos, Robert Lustig on sugar, Joel Spolsky on Excel. Most of them are not recommendations at all. They are a music track, a news clip, a joke, a link dropped as evidence in an argument.
Karpathy's HackerTube showed that an LLM can sort the two and turn twenty years of comments into a watch list. OpenTube does the same thing and also publishes three numbers that matter if you want to trust the list, or build something like it yourself:
Take two comments that both contain a YouTube link:
People disagree on the cases in between. Out of 300 random comments, 32 were hard enough to be flagged as borderline. If you hand the whole archive to an LLM and publish what comes out, you don't know how good the list is. You also pay for an LLM call on every one of 257,038 comments.
The filter is a cascade of two models.
Both run on a laptop CPU with ONNX Runtime for the batch run, and in the browser for the demo. No API key is needed.
Every Hacker News item from 2006 to 2026 (49 million of them) was scanned for YouTube links. That gives 185,909 distinct videos. 1,345 of them were linked by ten or more different people, and that set is the canon. Each card shows how many people linked the video, the thread it came from, the comment that recommended it, and which model made the call.
The Receipts page shows the funnel from 49 million items down to the kept videos, the accuracy table, and the cost of each stage at prices you can edit. The thresholds are editable too, so you can see how the trade-off between precision and recall moves.
| Filter | Accuracy (cross-validated) | Precision | Recall | Time per comment | Sent to LLM |
|---|---|---|---|---|---|
| Laya only | 70.8% | 76.9% | 29.1% | 1.28 s | 0% |
| Qwen2.5-1.5B only | 67.7% | 59.1% | 50.5% | 4.11 s | 100% |
| Cascade | 71.2% | 67.5% | 50.5% | 2.46 s | about 30% |
Two results stand out:
For a sense of scale, answering "not a recommendation" every time already scores 65.7%. This is a hard call, and a few points of accuracy matter. Projected over all 257,038 comments on this CPU: about 12 days with the LLM on everything, about 6 with the cascade, about 4 with Laya alone.
All 300 labelled comments are on the Labels page next to the rubric, with each model's probability shown under every comment. If you disagree with a label, change it. The Receipts page re-scores everything with your version, and you can export your labels as JSON.
The videos are linked in a graph: two videos are connected when they were shared in the same HN thread, by the same person, or with similar recommendation text. When you ask a question such as "documentaries about the history of computing":
Without WebGPU you still get the retrieved videos and the graph, as in the screenshot above.
Built on the open-index/hacker-news dataset (ODC-By), Laya typed-decisions, Qwen2.5-1.5B-Instruct via WebLLM, and all-MiniLM-L6-v2. Inspired by Andrej Karpathy's HackerTube; not affiliated with him, Hacker News or YouTube.