The cost of writing code collapsed, and the cost of reviewing, fixing and operating it is following, and I'm assuming it gets there. What's left of making software is finding out what people actually want, defining it precisely, and making it pleasant to use. That cost is per piece of software and doesn't transfer, so as the amount of software goes to infinity, which it will because there's no ceiling on demand, that cost becomes the whole job.

— Laurie Voss, We are all Product Engineers now

Tags: laurie-voss, generative-ai, agentic-engineering, ai, llms, deep-blue, careers

Production code written by Claude should have a higher bar than if it was written by a human. At Anthropic, we have many guardrails in place to make sure this is happening: lots of lint rules, lots of tests, Claude-driven end to end tests, Claude-powered fuzzers running daily, automated code reviews and security reviews, automated code refactoring, and so on. Without these, you can end up with a mess that is hard to maintain down the line.

— Boris Cherny

Tags: claude, ai, claude-code, llms, coding-agents, ai-assisted-programming, generative-ai, agentic-engineering, boris-cherny, anthropic

Datasette 1.0a39 and 0.65.4 security releases

Today we're releasing two new security patch versions of Datasette: 1.0a39 and 0.65.4 - one for the current alpha series and one for the stable 0.65.x family.

These are security fixes which you should apply if you are running a Datasette instance on the public web - in particular if that instance mixes both public and private tables.

Following issues reported by Sevban Dönmez, Alex Garcia and I ran an extensive audit of Datasette using Claude Fable 5.1, GPT-5.6, and GPT-6 Astra. We then spent almost a week collaborating on and reviewing the fixes.

They helped find some very subtle bugs. We'll be incorporating security audits by frontier models into all of our development work going forward.

Alex came up with a way of splitting the work which I found extremely productive:

Alex Garcia and I worked together running and then responding to the audit, working in a shared private repository. For most of the issues we split the work: one of us would create the automated tests highlighting the issue, then the other would implement the fix. This ensured that two separate humans had eyes on each of the issues, in addition to our coding agents running different models.

Tags: releases, security, ai, datasette, generative-ai, llms, agentic-engineering, ai-security-research