Hi HN! We've been building a tool to simplify running ML, data, and simulation workloads in the cloud. The goal is to remove most of the setup (instances, environments, IAM, scaling rules, cleanup, etc.) and let you focus on the actual job.

The basic workflow is simple: take whatever command you already use and prepend "adviser run".

Examples:

adviser run python train.py

adviser run Rscript analysis.R

adviser run ./simulate

Adviser handles provisioning, environment setup, cost/performance optimization, and teardown automatically. No Terraform, Docker, or cluster config required.

We built this after seeing ML and research teams lose a lot of time to infra glue code. We're opening up a self-serve version now and would really appreciate feedback from this community. Whether that's around design, CLI ergonomics, missing features, or reasons this shouldn’t exist.

To make testing easier, the first $100 of compute is free. Link: https://github.com/adviserlabs/docs Slack: https://www.adviser.sh/ Demo: https://www.youtube.com/shorts/F9feaOr3TbA

Happy to answer questions or take blunt feedback.