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Comment by sergiomattei | Hacker News Reader

I feel the same way.

> That includes code outside of the happy path, like error handling and input validation. But also other typing exercises like processing an entity with 10 different types, where each type must be handled separately. Or propagating one property through the system on 5 different types in multiple layers.

With AI, I feel I'm less caught up in the minutia of programming and have more cognitive space for the fun parts: engineering systems, designing interfaces and improving parts of a codebase.

I don't mind this new world. I was never too attached to my ability to pump out boilerplate at a rapid pace. What I like is engineering and this new AI world allows me to explore new approaches and connect ideas faster than I've ever been able to before.

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> explore new approaches and connect ideas faster

This is the hidden super power of LLM - prototyping without attachment to the outcome.

Ten years ago, if you wanted to explore a major architectural decision, you would be bogged down for weeks in meetings convincing others, then a few more weeks making it happen. Then if it didn't work out, it feels like failure and everyone gets frustrated.

Now it's assumed you can make it work fast - so do it four different ways and test it empirically. LLMs bring us closer to doing actual science, so we can do away with all the voodoo agile rituals and high emotional attachment that used to dominate the decision process.

That's only because no one understood agile or XP and they've become a "no one actually does that stuff" joke to many. I have first hand experience with prototyping full features in a day or two and throwing the result away. It comes with the added benefit of getting your hands dirty and being able to make more informed decisions when doing the actual implementation. It has always been possible, just most people didn't want to do it.

I basically just _accidentally_ added a major new feature to one of my projects this week.

In the sense that, I was trying to explain what I wanted to do to a coworker and my manager, and we kept going back and forth trying to understand the shape of it and what value it would add and how much time it would be worth spending and what priority we should put on it.

And I was like -- let me just spend like an hour putting together a partially working prototype for you, and claude got _so close_ to just completely one-shotting the entire feature in my first prompt, that I ended up spending 3 hours just putting the finishing touches on it and we shipped it before we even wrote a user story. We did all that work after it was already done. Claude even mocked up a fully interactive UI for our UI designer to work from.

It's literally easier and faster to just tell claude to do something than to explain why you want to do it to a coworker.

Are you not concerned that this world is deeply tied to you having an internet connection to one of a couple companies' servers? They can jack up the price, cut you off, etc.

Seeing how things are moving, I'm expecting for compute requirements to go down over a longer time horizon, as most technologies do.

I'd rather spend my time preparing for this new world now.

Not going to last long though, at least not professionally. AI will do the spec and architecture too. The LLM will do the entire pipeline between customer or market research to deployment. This is already possible with bug fixes pretty much. And many features too depending on the business.

It AI gets to that level generally, there won’t be a customer, a market research department, or a software company at all.

But if AI is capable of that it’s not a big step to being capable of doing any white collar job, and we’ll either reorganize our economy completely or collapse.

I don't know. LLMs are great at writing code; but you have to have the right ideas to get decent output.

I spend tons of time handholding LLMs--they're not a replacement for thinking. If you give them a closed-loop problem where it's easy to experiment and check for correctness, then sure. But many problems are open-loop where there's no clear benchmark.

LLMs are powerful if you have the right ideas. Input = output. Otherwise you get slop that breaks often and barely gets the job done, full of hallucinations and incorrect reasoning. Because they can't think for you.

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