So you believe that your work will be done by AI and you will enjoy life more? This is not a loaded question, just trying to understand what your future ideal day / week would look like as an "ai optimist".

What is an ai enterprise tool?

An ai tool that is priced out of the hands of the average person.

Fwiw, I think the genie is out the bottle. We are waiting on hardware to catch up, which it will.

I also notice these things. Otoh i spend definitely less than 50% of my time typing in code so it is impossible that it gives more than 2x speedup. And sometimes i lose time babysitting and rewriting stuff so all in all it is kinda no productivity gain.

> If it was just programming being automated, then whatever.

There is nothing on horizon which automates a programmer’s work. Typing in code is faster now, and some things “only need pointing out” like an existence of a “bug” which an llm + harness might be able to mitigate. Automated tests might capture regressions and possibly written by llm + harness. If you replicate this in other professions what will you get?

> My time as an experienced software engineer is worth a lot of money - a whole lot more than $12,000 for the past six months

From this I assume you think that what the llm has generated is as valuable as your own work generally is. How do you even calculate this?

So you don’t understand what you generate with ai and think that it will be a solution for a problem you can only solve using sql.

No, it's easy enough to understand the query once the AI has generated it. I have looked up how to do it many times after all.

Yes

If the AI's query pulled what I intended to pull, why should I care to understand the SQL any more than I should understand the Query Plan or the Machine Code?

There's nothing wrong with using SQL only when you know in advance exactly which records you wish to query.

But if you ever need to query unknown data, then probably you should learn SQL a bit deeper.

As with regex, querying is about not getting what you don't want as much as it is about getting what you want. And the former of the two is much more difficult to verify.

If someone just said "it's cold" then yeah that's kinda toxic.

If they said "turn on the heater" then you have no ambiguity

What do you base this on? For me it is almost impossible to guess what fits into the context of an llm. Sometimes trivial tasks fail, sometimes quite complex things get one shotted.

Poe’s law is strong with this one

I think that's an incredibly reductionist and sarcastic take. I'm also in Product, but was an engineer for over a decade prior. I find that having strong structured functional specifications and a good holistic understanding of the solution you're trying to build goes a long way with AI tooling. Just like any software project, eliminating false starts and getting a clear set of requirements up front can minimize engineering time required to complete something, as long as things don't change in the middle. When your cycle time is an afternoon instead of two quarters, that type of up front investment pays off much better.

I still think AI tooling is lacking, but you can get significantly better results by structuring your plans appropriately.

Tell me more! I'm trying to figure out how you got that.

I understand that you are serious. I am also serious here.

Have you built anything purely with LLM which is novel and is used by people who expect that their data is managed securely, and the application is well maintained so they can trust it?

I have been writing specifications, rfcs, adrs, conducting architecture reviews, code reviews and what not for quite a bit of time now. Also I’ve driven cross organisational product initiatives etc. I’m experimenting with openspec with my team now on a brownfield project and have some good results.

Having said all that I seriously doubt that if you treat the english language spec and your pm oversight as the sole QA pillars of a stochastic model transformer you are making a mistake.

Well, I was a QA engineer and an SDET before being a product manager, how does that change your opinion?

I think it is great!

The issue is that validation needs presence and it is the limiting factor - common knowledge, but is part of the “physics”. Also maintenance gets really tricky if the codebase has warts in it - which it will have. I get much more easy to understand architecture out of an LLM driven code generation process if I follow it and course correct / update the spec process based on learnings.

Example: yesterday I’ve introduced a batch job and realized during the implementation phase that some refactoring is needed so the error boundary can be reused in the batch application from the main backend. This was unplanned and definitely not a functional requirement - could be documented as non-functional. There was a gap between the agent’s knowledge and mine even though the error handling pattern is well documented in the repository. Of course this can be documented better next time if we update the process of openspec writing but having these gaps is inevitable unless formal and half-formal definitions are introduced - but still there needs to be someone with “fresh eyes” in the loop.

I think it's just sarcasm coming from the stereotypical HN attitude that Product Managers only get in the way of the real work of engineering. Ignore it; they're basically proving your point.

I'd suggest you to work on your general mood - drugs can help, but nature is also wonderful.

I think I have a relatively good life, but I still have hard times. I had circa 6 months long depression streak after my child was born (I'm male).

For me the best mood fixer is a walk still. Super small commitment, great with a dog too. For a weekend the best is a longer hike. I practice yoga and train my body - great mood boosters. I've trained my body to be able to sit comfortably on the ground so I can work from anywhere - sunshine in park hellooo.

Hope you find your rhythm soon!