Persistent compute
CPU and a generous GPU free tier. Your environment, your files, and your state are all still there when you come back in six months and hit Run.
Last Lab turns any existing into running code in 10 minutes.
Drop in a research paper, YouTube video, repo, blog post, or your own slides. Get back a notebook that runs, with the code explained as it goes and the compute to run it on.Including papers nobody has implemented yet.
Works with
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The guarantee
100%
execution guarantee
Every notebook runs, or it doesn't ship. Nothing reaches you until it has been run, so you hit Run All and it works.
Dependencies resolved
Datasets fetched
Environment matched to your compute
Every other tool hands you generated code and wishes you luck. You never find out whether the implementation was correct or just confident. We close that gap. The proof that it runs is that we ran it.
The problem
Knowing how something works isn't the same as running it
Between the thing you're reading or watching and code you can execute, four things break.
The code isn't there
Most technical work ships without an implementation. The paper has no repo, the lecture has no notebook, and the blog post has a snippet that assumes the other nine files. What does ship often no longer runs.
The source is hard to read correctly
Equations, architecture tables, and hyperparameters live in dense two-column PDFs and half-visible slides. The naming never lines up either: the paper's d_k is the repo's head_dim is the lecture's "channel size." Map one variable wrong and the implementation is silently incorrect.
The environment is the real work
Dependencies, driver versions, and dataset access. Reproduction usually dies here, not at the algorithm.
Generated code is unverified code
An agent that writes an implementation and hands it over hasn't proven anything. You inherit the debugging, and you never learn whether it matched the source.
What you get
A notebook that runs, explained as it runs
Not a summary of the source. Not a chat about the source. The implementation itself, executing on real compute.
Explanations you can check
Every explanation is built from what's actually in your resource. Equations keep the paper's notation. Diagrams are the originals, not redrawings.
Nothing in the explanation is there because a model thought it sounded right. If you don't believe a line, click it and go to the source.
Three ways to work through it
Follow along
The implementation, sized to the compute you have. Hit run all and watch it work end to end.
Full scale
The configuration and architecture as published, unmodified at its real size.
Exercises
Cells left blank with test cases attached, Coursera-style. Write the implementation yourself, run the grader, and find out whether it actually stuck.
Where it matters most
The paper-to-explainer gap is real. We close it.
GPT-2 published in 2019. Karpathy's from-scratch dropped in 2023. Four years apart. Last Lab runs the idea the day it's published, in 10 minutes. Implementation, explanation, and compute, done.
A paper with no implementation
TurboQuant, running, with each step traced back to the paper.
A lecture series with no notebook
3Blue1Brown's 10-video Neural Network playlist becomes cells you execute, in the order it was taught.
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Around the notebook
Everything the work depends on, already here
Lens: extracted and beyond
The real time sink wasn't the idea. It was resolving datasets, cited papers, and formulas hidden behind rabbit holes. We pull them out and place them next to the source.
Extracted
Everything your resource actually contains: figures, tables, equations, datasets, referenced repos, and cited papers, pulled out and collected, each one linked to where it appeared. Open any artifact in quick view, keep it in your lab storage, or jump straight back to the source it came from.
Beyond
Most resources only cover part of the topic. You finish decision trees and nothing tells you the next moves are random forests, then XGBoost. Beyond finds what your resource is missing and builds a path through it.
Axiom: the assistant that already read your resource
Hyper-contextualized to the session you're in. Anything you can see on the platform, whether that's a video frame, a lab cell, a quiz answer, a note, or an extracted artifact, you can reference and ask about directly. No pasting, no re-explaining your own context.
Marquee select: snip anything, anywhere
An architecture diagram flashes past at 14:32 and doesn't make sense. Snip the frame, ask Axiom.
Ask Axiom
A contextual question about precisely what you selected, answered from your resource.
Add to Lens
Send the selection into your extracted artifact library, stored with where it came from.
Add to Notes
Add the selected content to your notes without breaking your flow.
And the rest of it
Quizzes
Grounded in the source, each answer cited back to where it's justified.
Flashcards
Resource-grounded flashcards for active recall, with optional scoring built in.
Personalization
Last Lab learns from your preferences, interactions, and learning habits, so you get a learning experience tailored just for you.
Notes
Generated reference notes plus a Notion-style rich-text editor of your own. LaTeX supported.
Mind maps
Visualize how a concept breaks down and how the pieces connect to each other, editable the moment you disagree.
Search
Across every lab, note, and conversation you've made, down to the timestamp.
Real-time collaboration
Shared sessions with access control, so several authorized people can be in the same lab in real time: teams, classrooms, study groups.
Who it's for
Four ways people use it
Researchers
Reproduce a paper, compare leading approaches, find research gaps, or run ablations. Bring papers, videos, repositories, and blogs into one executable research workspace.
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Students
Turn a tutorial, lecture, playlist, PDF, or linked repository into a verified lab in 10 minutes, then learn through explanations, exercises, notes, quizzes, and recall tools.
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Institutes
Bootcamps, colleges, or research labs. Hand out assignments as labs that run identically for every learner, on compute you don't provision or support. Instructor dashboards to assign and grade; student dashboards to track progress.
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Enterprises
Get Last Lab deployed in your own premises. Point it at what your teams already learn from (docs, talks, papers, repos) and get it back runnable, with isolated, auditable execution.
Learn more
Institute pilots are open. If you run a bootcamp, a training program, or a lab, we'll set up a cohort with you.
Built for speed, built for execution
Go from existing technical knowledge to verified execution without rebuilding the environment yourself.
↓99%
Less time on setup
10 min
From source to verified lab
100%
Browser-based
↑3x
Faster understanding
Frequently Asked Questions
Beyond Artificial
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