On AI and building your own GIS software

The Git-ification of the World — Tim Morrissey

Why AI conquered software first, and what that tells us about which industries are next.

Is there something it is like to be an AI?

Posted on Wednesday 2 Jul 2025. 1,593 words, 6 links. By Matt Webb.

AI coding wisdom from the people who would know

Essays and threads from experienced developers who've gone deep on AI-assisted coding.

Request for Proposals: The Launch Sequence | IFP

Apply to our rolling effort to find, scope, and build the most important projects to prepare the world for advanced AI

The Scaling Era: An Oral History of AI, 2019–2025

An inside view of the AI revolution, from the people an…

Git AI - Track AI Code all the way to production

Cross-agent observability from prompt to production. Track AI-generated code from Cursor, Claude Code, GitHub Copilot, Gemini, and more through the entire SDLC.

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 ...

How we use AI at Stalwart

It is difficult to have a conversation about software in 2026 without AI showing up in it. Two years ago the interesting question was…

Where does the rigor go? Research software and the future of trustworthy science.

Generative AI now makes it dramatically easier to produce something that looks like research: analysis code, figures, literature reviews, even whole pap…

Gitpod is now Ona: your AI software engineer

Launching the mission control for your personal team of autonomous SWE agents.

Gitpod is now Ona: your AI software engineer

Launching the mission control for your personal team of autonomous SWE agents.

Writing documentation for AI: best practices | kapa.ai docs

Retrieval-Augmented Generation (RAG) systems like Kapa rely on your

Large AI models are cultural and social technologies

Implications draw on the history of transformative information systems from the past , Debates about artificial intelligence (AI) tend to revolve around whether large models are intelligent, autonomous agents. Some AI researchers and commentators speculate that we are on the cusp of creating agents with artificial general intelligence (AGI), a prospect anticipated with both elation and anxiety. There have also been extensive conversations about cultural and social consequences of large models, orbiting around two foci: immediate effects of these systems as they are currently used, and hypothetical futures when these systems turn into AGI agents—perhaps even superintelligent AGI agents. But this discourse about large models as intelligent agents is fundamentally misconceived. Combining ideas from social and behavioral sciences with computer science can help us to understand AI systems more accurately. Large models should not be viewed primarily as intelligent agents but as a new kind of cultural and social technology, allowing humans to take advantage of information other humans have accumulated.