[h4ny]

Tangentially related, I feel that SWEs who claim that they are more productive with AI haven't actually demonstrated with real examples of how they are actually more productive.

Nobody I follow (including some prominent bloggers and YouTubers) claiming productivity increase is recording or detailing any workflow or showing real world, non-hobby (scalable, maintainable, readable, secure, etc.) workflows of how to do it. It's like everyone who "knows what they are doing" is hiding what the secret sauce for a competitive edge or that they are all just mediocre SWEs hyping AI up and lying because it makes them more money.

Even real SWEs in large companies I know can't really seem to tell me how their productivity is increasing, and when you dig deeper it always seem to be well-scoped and well-understood problems (which is great, but doesn't match the level of hype and productivity increase that everyone else is claiming) -- and they still have to be very careful with reviewing (for now).

It's almost like AI makes SWE brains go mush and forget about logic and data.

[simonw]

I wrote 4,800 words about how I'm using LLMs to help me code here, because I was frustrated at how little detailed information there was on that topic: https://simonwillison.net/2025/Mar/11/using-llms-for-code/

[MattSayar]

In fairness, it's also still a NEW technology in the scale of tech. For comparison, it takes years after a gaming console is released for teams to optimize and squeeze every last ounce of performance out of the hardware.

We're just getting started with AI, and we're still "stuck" in the chat interfaces because of the storming success of ChatGPT a few years ago. Cursor, GitHub Copilot etc. are cool but they're still "launch titles" to continue my analogy from above.

New models are still coming out (but slowing down) with increased capabilities, context windows, etc. and I'm sure the killer app is still waiting to be unearthed. In the meantime, I'm having a lot of fun building my hobby code. Collectively, we're going to morph that into something more scalable and enterprisey, it's just a matter of time.

[nerdponx]

I'm a "data scientist", but I have absolutely improved my productivity in the last year or so by conversing with LLM chatbots to work through tough problems, get ideas, figure out project plans, etc. I can see the effect in my list of completed projects, the overall speed isn't that much higher, but the quality has definitely gone up, because I'm able to work through things more quickly and get to good solutions faster, so I can spend the more time iterating on good ideas and less time trying figure out which ideas are even good.

For programming, meh, it helps when I'm really tired and don't want to read documentation. Can't imagine using it in a serious capacity for writing code except in a huge codebase, where I might want it to explain to me how things fit together in order to make some change or fix a bug.