# Last Lab: Turn Technical Knowledge Into Running Code > Last Lab turns any paper, video, repository, or PDF into a verified, running Jupyter notebook in 10 minutes, with the compute to run it on. Source: https://www.lastlabai.com --- Last Lab turns any existing technical knowledge into a verified, running Jupyter notebook in about 10 minutes, with source-grounded explanations, learning tools, and the compute to run it on. **99% less time on setup | 10 min from source to verified lab | 100% browser-based | 3x faster understanding** ## The 100% execution guarantee Every notebook runs, or it does not ship. Nothing is delivered until it has been executed end to end, so when you hit Run All it works. Covered by the guarantee: - Dependencies resolved - Datasets fetched - Environment matched to the compute you have The contrast: other tools hand you generated code and wish you luck. You never learn whether the implementation was correct or merely confident. ## The problem Four things break between reading or watching something and running it. 1. **The code is not there.** Most technical work ships without an implementation, and what does ship often no longer runs. 2. **The source is hard to read correctly.** Dense two-column PDFs, half-visible slides, and notation mismatches (`d_k` vs `head_dim` vs "channel size") make silent incorrectness easy. 3. **The environment is the real work.** Reproduction dies at dependencies, drivers, and dataset access, not at the algorithm. 4. **Generated code is unverified code.** An agent that hands over an implementation has proven nothing. You inherit the debugging. ## What you get | Mode | What it is | | --- | --- | | Follow along | The implementation sized to the compute you have. Run all, watch it work end to end. | | Full scale | The configuration and architecture as published, unmodified, at real size. | | Exercises | Blank cells with attached test cases. Write it yourself and run the grader. | Plus explanations you can check: every explanation is built from what is actually in the resource. Equations keep the paper's notation, diagrams are the originals rather than redrawings, and any line can be clicked back to its source. ## Proof - **TurboQuant**: a paper with no public implementation, running, with each step traced back to the paper. - **3Blue1Brown's Neural Networks playlist**: ten videos with no notebook, turned into executable cells in the order it was taught. - **The gap**: GPT-2 published in 2019. Karpathy's from-scratch explainer landed in 2023. Four years. Last Lab targets the day of publication, in 10 minutes. ## Everything around the notebook Persistent compute (CPU plus a generous GPU free tier, with environment, files, and state still there six months later) | Quizzes grounded in the source with every answer cited | Flashcards with optional scoring | Notes, both generated reference notes and a rich-text editor with LaTeX | Editable mind maps | Search across every lab, note, and conversation, down to the timestamp | Real-time collaboration with access control | Iterative learning that tracks what you have demonstrated rather than what you were shown | Role-based access control with admin dashboards | Private deployment on custom infrastructure, VPC, private cloud, or on-premise. ## Named subsystems - **Axiom**: the assistant that already read your resource. Reference any video frame, lab cell, quiz answer, note, or extracted artifact directly. No pasting. - **Lens**: the extracted-artifact workspace. *Extracted* collects figures, tables, equations, datasets, referenced repos, and cited papers, each linked back to where it appeared. *Beyond* finds what the resource is missing and builds a path through it. - **Marquee select**: snip any region of a video frame, document, or cell, then ask Axiom, add to Lens, or add to notes. ## What Last Lab will not do It will not build an application from a requirements document. That is construction from a specification, not reproduction. ## Frequently asked ### What does the 100% execution guarantee cover? Every notebook we deliver has been executed before it reaches you: dependencies installed, datasets resolved, cells passing. If it can't be made to run, it doesn't ship and you're told why. ### What can I actually drop in? arXiv and other papers, GitHub repos, YouTube videos and playlists, documentation sites, blog posts, and your own PDFs and slides. Mixed sources in one lab work too. ### Can it implement something with no public code? Yes, that's the case we're built for. Working from an existing repo is the easy half. ### Do I need my own GPU? No. Compute is on the platform, with a generous GPU free tier. The scaled notebook is sized to your tier, and the full-scale configuration is there for when you bring bigger hardware. ### What is iterative learning? A personalization layer that runs across everything you do on Last Lab. It tracks what you've actually demonstrated, not just what you were shown, and keeps regenerating quizzes, exercises, and resources aimed at your current gap. Once a concept holds, it stops resurfacing it. ### Why not just paste it into ChatGPT or Cursor? They'll give you code, but they won't tell you whether it runs, whether it matches the source, or where a number came from. You'll spend the session fighting an environment instead of learning the thing. ### Can several people work in the same lab? Yes. Sessions can be shared with access control, so a team, a class, or a study group can work in the same lab at the same time. ### Can I take the notebook with me? Yes, export the .ipynb or the full environment. Nothing is locked in. ### Can it build an application from my requirements doc? No. That's construction from a spec, not reproduction. Different job, different tool. --- Last Lab AI. https://www.lastlabai.com