Plus: A nimble, composable example with Github Actions, Hamilton, and Runhouse
Trusting AI with Your Data: Safe Automation from Branch to Production
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.
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
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.
Designing machine learning systems: an iterative process for production-ready applications
"Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references."--Amazon.com
Datacurve | The data engine for frontier AI
Custom data for long-horizon reasoning, software engineering, and data science.
Track your Data Pipelines
Track and document dplyr data pipelines. As you filter, mutate, and join your way through a data set, dtrackr seamlessly keeps track of your data flow and makes publication ready documentation of a data pipeline simple.
Lee Byron (OpenAI) - Teaching Models to Collaborate