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.
Measuring the Impact of Early-2025 AI on Experienced Open-Source...
Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the...
From OSS to Open Source AI: an Exploratory Study of Collaborative...
AI development is embracing open-source paradigm, but the fundamental distinction between AI models and traditional software artifacts may lead to a divergent open-source development paradigm with...
How building software is changing at Anthropic
A deepdive on what’s changed in how the leading AI lab makes software. Ever more code review and testing is done by AI, two-pizza teams very much alive, and more. Details from inside of Anthropic
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
The New Software Lifecycle
I co-wrote a Google whitepaper about how AI is changing the software lifecycle. I'm not going to summarize the whole thing. Instead, here are the handful of ...
AI | 2025 Stack Overflow Developer Survey
84% of respondents are using or planning to use AI tools in their development process, an increase over last year (76%). This year we can see 51% of professional developers use AI tools daily.
The CEO’s Guide to Generative AI: Cost of compute
The IBM Institute for Business Value uses data-driven research and expert analysis to deliver thought-provoking insights to leaders on the emerging trends that will determine future success.'
Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
We conduct a randomized controlled trial to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.
Science that Compounds: The Need for A New Substrate for Research in the Age of AI
This paper is a perspective from Lightcone Research, an open-source initiative building tooling for scientific research in the age of agentic AI.
Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl
Notes from my Thoughtworks colleagues on AI-assisted software delivery
Continuous AI
Exploring LLM-powered automation in platform-based software collaboration
Models.dev — An open-source database of AI models
Models.dev is a comprehensive open-source database of AI model specifications, pricing, and features.
After automation: Software will work for you, not on you
Alex Komoroske, CEO and cofounder, Common Tools — AI promises “infinite software”—endless tools tailored to every need—but funneled through today's app stores, that abundance just means more silos, more trapped data, and more to orchestr...
LukeW | The Evolution of AI Products
At this point, the use of artificial intelligence and machine learning models in software has a long history. But the past three years really accelerated the ev...
Introduction to Agents
Discover what actually works in AI. Join millions of builders, researchers, and labs evaluating agents, models, and frontier technology through crowdsourced benchmarks, competitions, and hackathons.
The Open-Source Toolkit for Building AI Agents v2
An opinionated, developer-first guide to building AI agents with real-world impact
