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Indeed.

All this “productivity” has not resulted in one meaningful open source PR or one interesting indie app launch, and I can’t square my own experience with the hype machine.

If it’s not all hat and no cattle, someone should be able to show me some cows.

>I can’t square my own experience with the hype machine.

Me neither. My gut feeling is it's the inexperienced who gain the most from generative AI. That does seem to be confirmed by papers like this:

https://mitsloan.mit.edu/ideas-made-to-matter/workers-less-e...

At most I've found it helps with some of the routine work but saving a few minutes typing doesn't offset the problems it creates.

I find it hard to believe the inexperienced would benefit at all. Ai assisted coding requires serious general experience in all matters software to get good value out of it.

I find this hard to believe- how would you even know if someone used AI in producing a PR or indie product? Are you omniscient?

Further, there are articles here on HN all the time about people using AI for actual serious work. Heres a pretty significant example :

https://sean.heelan.io/2025/05/22/how-i-used-o3-to-find-cve-...

I dunno, as an engineer who likes to make side projects, I can say with high certainty that LLMs have helped me compensate for things I'm worse at when coding a product.

I'm good at the engineering side of things, I'm good at UI, I'm good at UX, I'm good at css, I'm just not good at design.

So I tell the LLM to do it for me. It works incredibly well.

I don't know if it's a net increase in productivity for me, but I am absolutely certain that it is a net increase in my ability to ship a more complete product.

That makes perfect sense to me. I’m finding real value in natural language search for code and docs, and “remind me how to do X.”

It’s the extraordinary claims of 10x speed and crazy autopilot that have me looking around for missing cows.

Why do that when they can ignore you and keep living in their bubble?

> Yesterday almost my whole day was wasted because I chose to attack a problem primarily by using Claude 4 Sonnet

I have been extremely cynical about LLMs up until Claude 4. For the specific project I've been using it on, it's done spectacularly well at specific asks - namely, performance and memory optimization in C code used as a Python library.

Honestly, its mind boggling. Am I the worst prompter ever?

I have three python files (~4k LOC total) that I wanted to refactor with help from Claude 4 (Opus and Sonnet) and I followed Reed Harper's LLM workflow...the results are shockingly bad. It produces an okay plan, albeit full of errors, but usable with heavy editing. In the next step though, most of the code it produced was pretty much unusable. It would've been far quicker for me to just do it myself. I've been trying to get LLMs on various tasks to help me be faster but I'm just not seeing it! There is definitely value in it in helping to straighten out ideas in my head and using it as StackOverflow on roids but that's where the utility starts to hit a wall for me.

Who are these people who are "blown away" by the results and declaring an end to programming as we know it? What are they making? Surely there ought to be more detailed demos of a technology that's purported to be this revolutionary!?

I'm going to write a blog post with what I started with, every prompt I wrote to get a task done and responses from LLMs. Its been challenging to find a detailed writeup of implementing a realistic programming project; all I'm finding is small one off scripts (Simon Willison's blog) and CRUD scaffolding so far.

I couldn't agree more. This has been my exact experience.

Like you I'll probably write a blog post and show, prompt by prompt, just how shockingly bad Claude frequently is. And it's supposed to be one of the best at AI assisted coding, which mean the others are even worse.

That'll either convince people, match their experiences, or show me up to be the worst prompter ever.

I think you're supposed to let the AI write the bad python code and then do the refactoring yourself. No way I'm letting the AI make changes to 150 files with tons of cross-concerns when I don't even fully understand it all myself unless I dig into the code.

That being said copilot and chatgpt have been a 40% productivity boost at least. I just write types that are as tightly fitting as possible, and segregate code based on what side effects are going to happen, stub a few function heads and let the LLM fill in the gaps. I'm so much faster at coding than I was 2-3 years ago. It's like I'm designing the codebase more than writing it.

I don’t think AI marks the end of software engineers, but it absolutely can grind out code for well specified, well scoped problem statements in quarter-minutes that would take a human an hour or so.

To me, this makes my exploration workflow vastly different. Instead of stopping at the first thing that isn’t obviously broken, I can now explore nearby “what if it was slightly different in this way?”

I think that gets to a better outcome faster in perhaps 10-25% of software engineering work. That’s huge and today is the least capable these AI assistants will ever be.

Even just the human/social/mind-meld aspects will be meaningful. If it can make a dev team of 7 capable of making the thing that used to take a dev team of 8, that's around 15% less human coordination needed overall to get the product out. (This might even turn out to be half the benefit of productivity enhancing tools.)

> Instead of stopping at the first thing that isn’t obviously broken, I can now explore nearby “what if it was slightly different in this way?”

What? Software engineering is about problem solving, not finding the first thing that works and called it a day. More often than not, you have too many solutions and the one that's implemented is the result of a list of decisions you've taken.

> If it can make a dev team of 7 capable of making the thing that used to take a dev team of 8, that's around 15% less human coordination needed overall to get the product out.

You should really read the mythical man month.

I credit my understanding of the incredible costs relating to the increased need for coordination and the sharply decreasing return on productivity for additional people to The Mythical Man Month.

I don't take credit for the value of being able to do with 7 what currently takes 8, but rather ascribe it to the ideas of Fred Brooks (and others).

> I have to be cynical and believe those shouting about LLMs taking over technical skill must have lots of stock in the AI companies.

I'm far from being a "vibe" LLM supporter/advocate (if anything I'm the opposite, despite using Copilot on a regular basis).

But, have you seen this? Seems to be the only example of someone actually putting their "proompts" where their mouth is, in a manner of speaking. https://news.ycombinator.com/item?id=44159166

It’s interesting that your point about wasting time makes a second point in your favor as well.

If you don’t have the knowledge that begets the skills to do this work then you would never have known you were wasting your time or at least how to stop wasting time.

LLM fanboys don’t want to hear this but you can’t successfully use these tools without also having the skills.

Edit for the parent comment:

> in the end it just could NOT solve the challenge I gave it.

Last week I was like, I might as well vibe code with free Gemini and steal his credit than researching something destined to be horrible as Android Camera2 API, and found out that at least me using this version of Gemini do better if I prompt it in a... casual language.

"ok now i want xyz for pqr using stu can you make code that do" rather than "I'm wondering if...", with lowercase I and zero softening languages. So as far as my experience goes, tiny details in prompting matter and said details can be unexpected ones.

I mean, please someone just downvote and tell me it's MY skill issue.

I totally just verbalize my inner monologue, swearing and everything. Sometimes I just type "weeeeeeeelllllll" and send it, to get more LLM output or to have it provide alternatives.

It might sound weird but I try to make the LLM comfortable. Because I find you get worse results when you point out mistake after mistake and it goes into apologetic mode. Also because being nice puts me in a better mood and it makes my own programming better.

vibe coding as it were :p