Plus: A nimble, composable example with Github Actions, Hamilton, and Runhouse
Trusting AI with Your Data: Safe Automation from Branch to Production
Zach Lloyd on Twitter / X
The software factory approach is getting pretty popular, but it can be daunting to adopt all at once.Let's go through “crawl, walk, run” steps for making the transition from local, interactive agents to automated cloud development. https://t.co/RugDAn4Vqg— Zach Lloyd (@zachlloydtweets) September 15, 2026
Managing Changes from Multiple AI Agents
In this video, I walk you through managing changes from multiple AI agents in our to-do app, specifically focusing on the Expert to CSV button. We explore design options using ground agents, where one agent proposes a green design and the other a blue one. After some time, both agents complete their tasks, and their changes are synchronized to GitHub and our local machine. I demonstrate the final designs and decide to ship the green version as the main one. I encourage you to apply this approach in your own projects and consider using rebase in GitHub for similar tasks.
Karpathy shares 'LLM Knowledge Base' architecture that bypasses RAG with an evolving markdown library maintained by AI
Karpathy proposes something simpler and more loosely, messily elegant than the typical enterprise solution of a vector database and RAG pipeline.
Peter van Hardenberg - Ink and Switch, Automerge
AI native industrial data platform for manufacturing | UMH
Standardize industrial data across sites and systems to reduce costs, improve efficiency and accelerate execution. Open-source, deployed at production sites across Europe, live in weeks.
Getting Started with ML and AI in Research Software | Software Sustainability Institute
Getting started with ML in research software means embracing a shift in how results are produced and reproduced. Instead of a fixed execution path, research software teams work with systems whose behaviour emerges from data, configuration, and training dynamics. Reproducibility becomes a matter of capturing the process rather than relying solely on the code. The tools and techniques outlined here can be adopted incrementally into existing projects, and together they provide a practical foundation for reproducible ML research.
Official Lean AI Native Companies Leaderboard
Tracking the rise of lean AI native companies revolutionizing the startup world. Discover companies generating billions with minimal teams.
Build Bigger With Small Ai: Running Small Models Locally
Databricks: Leading Data and AI Platform for Enterprises
Databricks offers a unified platform for data, analytics and AI. Build better AI with a data-centric approach. Simplify ETL, data warehousing, governance and AI on the Data Intelligence Platform.
GitHub - Responsible-Dataset-Sharing/easy-dataset-share: A CLI tool that helps AI researchers share datasets responsibly.
A CLI tool that helps AI researchers share datasets responsibly. - Responsible-Dataset-Sharing/easy-dataset-share
EnterpriseReady | Ep. #2, The Early Days of GitHub with Tom Preston-Werner | Heavybit
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Electric | Agents on sync
Electric provides the data primitives and infra to build collaborative, multi-agent systems. Including Postgres Sync, Durable Streams, TanStack DB and PGlite.
