danenania

Outside of mathematics, I think it’s very rare for a single human to understand 100% of any complex undertaking, and this was true long before AI.

Another suggestion to get the most bang for your buck: use the best model you have access to with max reasoning for planning, implement with a smaller model/lower reasoning, then review with the big model. Repeat as needed.

Input tokens are much cheaper than output tokens. Not only because of baseline price—caching makes a huge difference too. There are many ways to take advantage of this asymmetry to get similar quality for a fraction of the cost!

Super cool! Can you talk a bit about how you built it?

Being one of the people on this list... yes I would personally say it's mainly about agency and determination, and being obsessive/perfectionist about details. I think YC selects for these traits much more than domain expertise or raw intelligence.

Now that I'm working at a large org (before this my career was purely in startups), I see the importance of this all the time. When you work on a significant project in a bigger org, you get blocked by all kinds of things. Most have very reasonable explanations, but you are blocked nonetheless. Many people just accept this and allow things to proceed pretty slowly, or they accept tradeoffs that make the product worse because it's the path of least resistance. If you want to go faster and not compromise on details, you often have to be persistent and willing to follow up on things to the point of being annoying, which feels similar to being a founder.

> publicly reported numbers

Anyone can google it /shrug

I’d also point out that LLM inference revenue already totals more than 100B annually based on publicly reported numbers. Almost none of that is replacing knowledge workers. Almost all is increasing their productivity. So empirically what you describe is already happening to a nontrivial degree.

Cool, good to hear. I think it’s often the case even within an individual file or change that it’s 90% routine and 10% critical to review. That’s a big part of the problem in my mind.

Interested to try this! Have you thought about separating the parts of a PR that are routine/uninteresting from the parts that are load-bearing and need more careful review?

I mean it’s quite literally a command line interface to their tool… what else should it be called that differentiates it from a pure browser flow?

What you are describing sounds more like “TUI” than “CLI” imo. A CLI is an interface—it’s about the input step. It makes no promise about what happens after that.

You can’t get every detail right up front, but you can build a robust foundation from the beginning.

The argument seems to be that AI is causing managers to demand faster results, and so everything has to be a one-shotted mess of slop that just barely works. My point is that it doesn’t take much longer to build something solid instead. Implementation time and quality/robustness are not tightly coupled in the way they used to be.

You’re assuming that building something robustly is significantly more time consuming than the “quick and dirty” version. But that’s not really true anymore. You might need to spend another hour or two thinking through the task up front, but the implementation takes roughly the same amount of time either way.

Getting the model to do it is the skill.

Line by line is no longer what I need to think about. I think about types/schemas, architectural division, contracts between services and components, how to test thoroughly, scaling properties, security properties, and these kinds of things.

It takes very little time to polish now.