u/Full-Act-1269

Currently we're at a stage where we want genuine users to try it and share their insights.

Whether you live in Jupyter notebooks, Airflow or use other tools like VS Code or anything else in your data science workflow — we'd love to hear from you. The more variety of use cases, the better.

To make it worth your time, we're offering free credits so you can run real workloads on the platform.

If you're regularly doing data work and want to try something new, feel free to reach out here or send me a message.

For most people I've talked to, it's embarrassingly high. New machine? Set up CUDA again. New team member? Good luck for reproducing the environment.

We've been building a platform that removes this entirely, Jupyter, VS Code, Streamlit and Airflow in the cloud, with managed dependencies and GPU instances available when you need them. One environment definition, consistent everywhere.

We're at early stage and actively looking for ML folks to try it and share their insights. We have free credits so you guys can actually run your projects, not just toy scripts.

If you're working on models, happy to get you set up. Comment or DM.

We've been building a Platform where you guys can use cloud Jupyter environment, who work with financial data. Our idea is simple: notebooks are always ready, your datasets are accessible, and you can go from idea to backtest without any of the usual friction.

We're a team, and we want real users, specifically people who regularly pull in market data, build indicators, or test trading logic in notebooks. Not looking for polite feedback. We want to know what doesn't work for you.

Free credits available so you can actually test it with your own data and strategies. Comment or msg if interested.

Hey everyone,

I’ve recently started building datasets for quant work and have been experimenting with Jupyter Notebooks for data cleaning and analysis.

Curious to understand how others structure their notebook workflow, especially around reproducibility and handling the larger datasets.

any insights! Would be appreciated

Hey everyone,

I’ve been working on a tool that simplifies ML/data workflows (basically handling environments, notebooks, and deployment without the usual setup headaches).

Still early, but I’m curious, what’s the most annoying part of your current ML workflow?

Would love to hear your thoughts or pain points