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Newsletter #303: Autoresearch: Run ML Experiments on Autopilot with Git-Backed Rollback

Newsletter #303: Autoresearch: Run ML Experiments on Autopilot with Git-Backed Rollback

Grab your coffee. Here are this week’s highlights.


📅 Today’s Picks

gws: One CLI for Drive, Gmail, Calendar, and Sheets

Code example: gws: One CLI for Drive, Gmail, Calendar, and Sheets

Problem

Managing Workspace through the browser means clicking through multiple apps just to pull a spreadsheet, check your calendar, and send a follow-up email.

That manual loop adds up fast when you repeat it daily or weekly.

Solution

gws is a CLI that unifies every Workspace service behind simple terminal commands with structured JSON output ready for scripting.

Key capabilities:

  • Single interface for Drive, Gmail, Calendar, Sheets, Docs, and more
  • JSON output that pipes directly into your existing scripts and workflows
  • 100+ AI agent skills that let LLMs orchestrate Workspace tasks programmatically

Autoresearch: Run ML Experiments on Autopilot with Git-Backed Rollback

Code example: Autoresearch: Run ML Experiments on Autopilot with Git-Backed Rollback

Problem

Running experiments manually means adjusting one hyperparameter, waiting for training to finish, checking results, and repeating for hours.

Progress stops the moment you step away, and you only explore the narrow set of ideas you thought of.

Solution

Autoresearch is an open-source framework that solves this with an autonomous loop. An AI agent commits each change to git, trains for 5 minutes, and checks whether the model actually improved.

If the metric improves, the change stays. If not, the agent reverts to the last good state automatically.

Key benefits:

  • Git-backed snapshots before every experiment for instant rollback
  • Structured results log that survives crashes and tracks every attempt
  • Continuous looping with no human confirmation needed

☕️ Weekly Finds

PyMC [ML] – Bayesian statistical modeling with advanced MCMC and variational inference algorithms

lifelines [ML] – Survival analysis in Python, including Kaplan-Meier, Nelson-Aalen, and regression

causal-learn [ML] – Causal discovery with constraint-based, score-based, and functional causal model methods

Looking for a specific tool? Explore 70+ Python tools →

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Work with Khuyen Tran

Work with Khuyen Tran