The problem with the "if it works who cares what the code looks like" mindset for agentic work is that it assumes the agent has a perfect understanding of "works." Realistically, things are underspecified, agents make bad assumptions, etc. To be fair, agents are pretty good at unit test coverage. They're pretty bad at designing human experiences (API, CLI flags, etc.), especially cohesive ones for future roadmap plans they may not have visibility into (unless your backlog is perfect and vision fully laid out, which I doubt). They're bad at knowing where performance matters and what type (CPU vs memory tradeoffs). They're bad at where compatibility matters and where it doesn't (and tend to err on the side of preserving it without further guidance). Etc. Unless you have this ALL specified, you can't possibly claim "it works" without taking a look and thinking about it.

I'm an old programmer (by programmer standards). I use agents daily, extensively. Here's a view from the other side of "wooooow look how many lines it wrote!", taken from my work today.

there's a new word i'm hearing a lot in the most frontier-pushingest coding-agent builders: _program design_ for even the best agentic coders trying to maintain code quality, we've all seen it - you come up with something to build - you research the codebase, riff with the agent, align on what the end state looks like - you (or the agent) breaks it down into tasks for individual agents / context windows - you rip the implementation - the code works or is close to working - and it follows your spec to the letter but the code itself is still trash - poorly factored methods - leaky abstractions - tramp data - overloaded interfaces - try catch, useEffect, global variables everywhere I thought models would catch up, or that this wouldn't matter - that if we stayed in spec-land, understood the high-level architecture, and tokenmaxxed hard enough, we would be able to skip code review and just stay shipping doesn't seem to be working out that way I have seen agent-owned codebases spin up out of nothing... ...and I have seen them collapse into rubble within 6 months now there's something to be said about "skate where the puck is going"... ...and I can't tell you what tomorrows models will be capable of but I *can* tell you that *today*, models are mid-to-bad at program design you can solve some of this with memory / agents.md, but the scope of program design is massive. - entire companies have been built to help you implement it - books, classes, and professions have spun up around it are you building something to last? Or are you slinging more slop on the pile? anyways, thats the post, stay tuned for a fun announce tomorrow y'all 🙂

i think the models are great and do amazing things day to day and then i go to use them as a brainstorm partner for creative work and they are just horrible, no amount of steering gets them to be even slightly better makes you think about what these things actually are

More AI-generated code doesn't make your team faster. It might actually slow you down.

apparently there's no zero data retention for Claude Fable 5

I have a new kind of big button that I can press for Codex. Over the next 100 days, we will select one person per day who does impressive or incredibly useful work with Codex and give them 10X usage limits for a month to see what they can do with it. First one tomorrow.