Stop Waiting. Start Modeling.

The first development environment that catches typos, intelligently caches runs, and actually respects your time. Cut your iteration cycles in half.

Closed Beta Launches 26th May.

Find Out How
Find Out How
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Taking the versatility of Jupyter notebooks and turning them into something with which you can iterate quickly, explore actual big data and productionize without the mess.

Screenshot of a computer screen displaying a data table and code related to a Valentine's Day couples dataset, with file names on the side menu.

Fed up with this?

Screenshot of an error message from a Python script indicating that the function 'train_multilinear_regression_final' was called, but the variable 'training_coefficients' is not defined.

How about this?

Screenshot of a Reddit post from r/datascience titled 'Nobody talks about all of the waiting in Data Science,' discussing the long waiting times when executing queries or training models on large datasets, and the author's current experience with waiting for a query to finish on a table with billions of rows.
Sign Up for the Closed Beta
Sign Up for the Closed Beta

Where does your day actually go?

50-60% of the job is iteration, not insight

(a conservative estimate)

  • 45%-60% of Data Scientist time is spent on data prep (BigDatawire, Forbes/Trifecta)

  • 10-20% of developer time is lost to slow builds, slow runs, environment waiting or blocked execution (GitHub, McKinsey)

  • Only 20-30% of DS time is spent “modelling” (Kaggle/BurtchWorks) and modelling includes heavy iteration.

All that time waiting is pretty boring… and pretty frustrating…

Sign Up for the Closed Beta
Sign Up for the Closed Beta

Built by Data Scientists. Engineered for Sanity.

We’re not a "magic AI box." We’re a better toolkit that reduces dead time through:

We reduce dead time through:

  • Reduction of iterations via detecting typos and bugs earlier in the process

  • Incremental Computing to reduce re-calculating slow running functions unnecessarily

  • Multithreading. How is this not a universal thing in Python already?

  • Standardised formats for painless pipeline handoffs

  • Unit & measure typing (no more adding Celsius to metres)

  • Full meta data lineage, licensing and encryption handled automatically

  • Many more things we’re not allowed to talk about yet….

We spent years as quants and data scientists waiting for kernels to restart and models to converge. We got tired of waiting and built the tool we wish we had. We’re launching the closed beta in Feb and would love to see you there.

(Cross compatible with your current Pandas/Polars/Pytorch stack)

Closed Beta Launches 26th May!

Sign Up for the Closed Beta
Sign Up for the Closed Beta
Screenshot of a coding tutorial titled 'PUFFBOOK DEMO' demonstrating Python code for mean and variance calculations, including error messages and variable outputs, and an interactive table showing character relationships and attributes.