Around that time I built a CLI to access and manage monitoring cameras that my company is selling. After giving a demo to my leadership I strongly adviced against releasing it to public. Giving agents access to some stuff is bad for customers.
Exactly, for long running vibe coded stuff that I don't care about quality getting big and smart model is the only option. But for high quality changes where I need to have control and understand everything, where I do everything in small chunks - I can use basic model like Sonnet.
Even with examples it's still not convincing. I'm working on real products so I don't have time to waste comparing models that won't be relevant next month.
Maybe it depends on the task, but the biggest productivity gains are from boiler plate generation, and there it's as easy as "generate me the boiler plate". Even if you can learn some very specific workflows today they would be model dependent and mostly obsolete within a month or two.
I work with AI everyday, despite what many people suggest there is so little to learn. After a couple of hours you are good to go. You don't even need gstack.
Sure, but for many folks the distraction is irresistible. It was difficult already to put care and craft into a product, having a slot machine for your attention makes it damn impossible.
In the old days, producing all those things would be tremendous learning opportunity. Today it's a pure waste, not producing income is not a problem, not producing anything is.
AI make easy work even easier, at the same time it shortens the attention span making it more difficult to do any difficult work. That's why there is so little real progress despite huge productivity gains.
Any idea what makes for such a diff between your and theirs numbers? Batching? Or could they do a crazy prefix caching across all nodes to reduce the actual processing.