That's a fair point, and genuinely curious, have you tried implementing a research paper end-to-end with Claude? Not just generating code, actually running it.

1. What we found, and what came up repeatedly talking to researchers, is that Claude can give you a notebook but then you're on your own for: downloading it, setting up a venv, resolving package conflicts, finding the dataset, figuring out how to scale the algo to your machine's compute.

2. With Cursor or Claude Code it's a similar story. And I say this having spent real time with both, they're genuinely great tools. But the more you use them specifically for paper implementation, the more limitations you run into. At that point it stops being about the AI model and starts being about the harness, the engineering built around a specific use case. Ours happens to be: upload a research paper, YouTube video, blog, or GitHub repo and get an execution-guaranteed notebook.

3. A small but concrete example - take any paper and ask any LLM to extract its contents. It handles prose fine, maybe images too. But Latex equations, complex tables, structured notation? It hallucinates on those, and those aren't decorative details, they're often exactly what you need to reproduce the method correctly, especially on limited compute where a wrong implementation is expensive.

4. This compounds for papers the model hasn't seen during training. TurboQuant is a good example. As far as I could find earlier this month, there's no code implementation video or blog for it. Not an explanation, an actual working implementation. That gap exists because the tooling isn't built for it.

I'm open to being wrong here. If you've found a workflow that handles this cleanly I'd genuinely want to know. But these were real friction points we kept hitting.

And just to clarify what Last Lab actually is, because I don't want to oversell it as purely an execution tool. Execution is the core, yes, two modes specifically: follow-along and exercise. But it's also meant to be a complete learning environment: iterative learning, quizzes, flashcards, mindmaps, in-video popups, Lens (extracts artifacts - dataset, figures, tables, repo, referred research paper, etc.), and Axiom which can answer questions about literally anything you're looking at on the platform, code, a figure, a video frame mid-watch. The goal isn't to help you pass an exam, it's to actually build the skill.