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llmslave2 3 days ago

> Even with refinement and back-and-forth prompting, I’m easily 10x more productive

Developers notoriously overestimate the productivity gains of AI, especially because it's akin to gambling every time you make a prompt, hoping for the AI's output to work.

I'd be shocked if the developer wasn't actually less productive.

BeetleB 3 days ago | parent | next [-]

For personal projects, 10x is a lower bound. This year alone I got several projects done that had been on my mind for years.

The baseline isn't what it would have taken had I set aside time to do it.[1] The baseline is reality. I'm easily getting 10x more projects done than in the past.

For work, I totally agree with you.

[1] Although it's often true even in this case. My first such project was done in 15 minutes. Conceptually it was an easy project. Had I known all the libraries, etc out would have taken about an hour. But I didn't, and the research alone would have taken hours.

And most of the knowledge acquired from that research would likely be useless.

dolebirchwood 3 days ago | parent | prev | next [-]

I accept there are productivity gains, but it's hard to take "10x" seriously. It's such a tired trope. Is no one humble enough to be a meager 2.5x engineer?

llmslave2 3 days ago | parent | next [-]

Even 2.5x is absurd. If they said 1.5x I might believe them.

OsrsNeedsf2P 3 days ago | parent | next [-]

I'm building an AI agent for Godot, and in paid user testing we found the median speed up time to complete a variety of tasks[0] was 2x. This number was closer to 10x for less experienced engineers

[0] tasks included making games from scratch and resolving bugs we put into template projects. There's no perfect tasks to test on, but this seemed sufficient

nicoburns 3 days ago | parent | next [-]

Have you evaluated the maintainability of the generated code? Becuause that could of course start to count in the negative direction over time.

Some of the AI generated I've seen has been decent quality, but almost all of it is much more verbose or just greater in quantity than hand written code is/would be. And that's almost always what you don't want for maintenance...

llmslave2 3 days ago | parent | prev | next [-]

That sounds reasonable to me. AI is best at generating super basic and common code, it will have plenty of training on game templates and simple games.

Obviously you cannot generalize that to all software development though.

brandensilva 3 days ago | parent | next [-]

As you get deeper beyond the starter and bootstrap code it definitely takes a different approach to get value.

This is in part because context limits of large code bases and because the knowledge becomes more specialized and the LLM has no training on that kind of code.

But people are making it work, it just isn't as black and white.

bonesss 3 days ago | parent [-]

That’s the issue, though, isn’t it? Why isn’t it black and white? Clear massive productivity gains at Google or MS and their dev armies should be visible from orbit.

Just today on HN I’ve seen claims of 25x and 10x and 2x productivity gains. But none of it starting with well calibrated estimations using quantifiable outcomes, consistent teams, whole lifecycle evaluation, and apples to apples work.

In my own extensive use of LLMs I’m reminded of mouse versus command line testing around file navigation. Objective facts and subjective reporting don’t always line up, people feel empowered and productive while ‘doing’ and don’t like ‘hunting’ while uncertain… but our sense of the activity and measurable output aren’t the same.

I’m left wondering why a 2x Microsoft of OpenAI would ever sell their competitive advantage to others. There’s infinite money to be made exploiting such a tech, but instead we see highschool homework, script gen, and demo ware that is already just a few searches away and downloadable.

LLMs are in essence copy and pasting existing work while hopping over uncomfortable copyright and attribution qualms so devs feel like ‘product managers’ and not charlatans. Is that fundamentally faster than a healthy stack overflow and non-enshittened Google? Over a product lifecycle? … ‘sometimes, kinda’ in the absence of clear obvious next-gen production feels like we’re expecting a horse with a wagon seat built in to win a Formula 1 race.

int_19h 3 days ago | parent | prev [-]

> That sounds reasonable to me. AI is best at generating super basic and common code

I'm currently using AI (Claude Code) to write a new Lojban parser in Haskell from scratch, which is hardly something "super basic and common". It works pretty well in practice, so I don't think that assertion is valid anymore. There are certainly differences between different tasks in terms of what works better with coding agents, but it's not as simple as "super basic".

llmslave2 3 days ago | parent [-]

I'm sure there is plenty of language parsers written in Haskell in the training data. Regardless, the question isn't if LLMs can generate code (they clearly can), it's if agentic workflows are superior to writing code by hand.

int_19h 3 days ago | parent [-]

There's no shortage of parsers in Haskell, but parsing a human language is very different from parsing a programming language. The grammar is much, much more complex, and this means that e.g. simple approaches that adequate error messages don't really work here because failures are non-actionable.

teaearlgraycold 3 days ago | parent | prev [-]

One concern is those less experienced engineers might never become experienced if they’re using AI from the start. Not that everyone needs to be good at coding. But I wonder what new grads are like these days. I suspect few people can fight the temptation to make their lives a little easier and skip learning some lessons.

thornewolf 3 days ago | parent | prev | next [-]

I estimated that i was 1.2x when we only had tab completion models. 1.5x would be too modest. I've done plenty of ~6-8 hour tasks in ~1-2 hours using llms.

enraged_camel 3 days ago | parent [-]

Indeed. I just did a 4-6 month refactor + migration project in less than 3 weeks.

kmoser 3 days ago | parent | prev [-]

I recently used AI to help build the majority of a small project (database-driven website with search and admin capabilities) and I'd confidently say I was able to build it 3 to 5 times faster with AI. For context, I'm an experienced developer and know how to tweak the AI code when it's wonky and the AI can't be coerced into fixing its mistakes.

llmslave2 3 days ago | parent | next [-]

What's the link?

kmoser 3 days ago | parent [-]

The site is password protected because it's intended for scholarly researchers, and ironically the client doesn't want LLMs scraping it.

kmoser 2 days ago | parent | prev [-]

Downvoted for...confidently saying how successful I was using an AI? I don't get it.

signatoremo 3 days ago | parent | prev | next [-]

10x probably means “substantial gain”. There is no universal unit of gain.

However if the difference is between doing a project vs not doing is, then the gain is much more than 10x.

bpodgursky 3 days ago | parent | prev | next [-]

I don't know what to tell you, it's just true. I have done what was previously days of BI/SQL dredging and visualizing in 20 minutes. You can be shocked and skeptical but it doesn't make it not true.

isodev 3 days ago | parent | prev [-]

There is no x is because LLM performance is non deterministic. You get slop out at varying degrees of quality and so your job shifts from writing to debugging.

ckw 3 days ago | parent | prev | next [-]

From one personal project,

Last month:

  128 files changed, 39663 insertions(+), 4439 deletions(-)
  Range: 8eb4f6a..HEAD
  Non-merge commits: 174
  Date range (non-merge): 2025-12-04 → 2026-01-04 (UTC)
  Active days (non-merge): 30
Last 7 days:

  59 files changed, 19412 insertions(+), 857 deletions(-)
  Range: c8df64e..HEAD
  Non-merge commits: 67
  Date range (non-merge): 2025-12-28 → 2026-01-04 (UTC)
  Active days (non-merge): 8
This has a lot of non-trivial stuff in it. In fact, I'm just about done with all of the difficult features that had built up over the past couple years.
croemer 3 days ago | parent | prev | next [-]

Don't worry, it's an LLM that wrote it based on the patterns in the text, e.g. "Starting a new project once felt insurmountable. Now, it feels realistic again."

NewsaHackO 3 days ago | parent [-]

That is a normal, run of the mill sentence.

friendzis 3 days ago | parent | next [-]

Yes, for an LLM. The good thing about LLMs is that they can infer patterns. The bad thing about LLMs is that they infer patterns. The patterns change a bit over time, but the overuse of certain language patterns remains a constant.

One could argue that some humans write that way, but ultimately it does not matter if the text was generated by an LLM, reworded by a human in a semi-closed loop or organically produced by human. The patterns indicate that the text is just a regurgitation of buzzwords and it's even worse if an LLM-like text was produced organically.

croemer 3 days ago | parent | prev [-]

I can't prove it of course but I stand by it.

int_19h 3 days ago | parent [-]

Claiming that use of more complicated words and sentences is evidence of LLM use is just paranoia. Plenty of folk write like OP does, myself included.

lowbloodsugar 3 days ago | parent | prev | next [-]

I think it depends what you are doing. I’ve had Claude right the front end of a rust/react app and it was 10x if not x (because I just wouldn’t have attempted it). I’ve also had it write the documentation for a low level crate - work that needs to be done for the crate to be used effectively - but which I would have half-arsed because who like writing documentation?

Recently I’ve been using it to write some async rust and it just shits the bed. It regularly codes the select! drop issue or otherwise completely fails to handle waiting on multiple things. My prompts have gotten quite sweary lately. It is probably 1x or worse. However, I am going to try formulating a pattern with examples to stuff in its context and we’ll see. I view the situation as a problem to be overcome, not an insurmountable failure. There may be places where an AI just can’t get it right: I wouldn’t trust it to write the clever bit tricks I’m doing elsewhere. But even there, it writes (most of) the tests and the docs.

On the whole, I’m having far more fun with AI, and I am at least 2x as productive, on average.

Consider that you might be stuck in a local (very bad) maximum. They certainly exist, as I’ve discovered. Try some side projects, something that has lots of existing examples in the training set. If you wanted to start a Formula 1 team, you’re going to need to know how to design a car, but there’s also a shit ton of logistics - like getting the car to the track - that an AI could just handle for you. Find boring but vital work the AI can do because, in my experience, that’s 90% of the work.

llmslave2 3 days ago | parent [-]

Mmm, I do a lot of frontend work but I find writing the frontend code myself is faster. That seems to be mostly what everyone says it's good for. I find it useful for other stuff like writing mini scripts, figuring out arguments for command line tools, reviewing code, generating dumb boilerplate code, etc. Just not for actually writing code.

lowbloodsugar 3 days ago | parent [-]

I’m better at it in the spaces where I deliver value. For me that’s the backend, and I’m building complex backends with simple frontends. Sounds like your expertise is the front end, so you’re gonna be doing stuff that’s beyond me, and beyond what the AI was trained on. I found ways to make the AI solve backend pain points (documentation, tests, boiler plate like integrations). There’s probably spaces where the AI can make your work more productive, or, like my move into the front end, do work that you didn’t do before.

turtlebits 3 days ago | parent | prev | next [-]

Numbers don't matter if it makes you "feel" more productive.

I've started and finished way more small projects i was too lazy to start without AI. So infinitely more productive?

Though I've definitely wasted some time not liking what AI generated and started a new chat.

llmslave2 3 days ago | parent | next [-]

> Numbers don't matter

Yes that's already been well established.

globular-toast 3 days ago | parent | prev [-]

It does matter because you're still using up your life on this stuff.

stavros 3 days ago | parent | prev | next [-]

Just as a personal data point, are you a developer? Do you use AI?

llmslave2 3 days ago | parent [-]

Yes and yes.

stavros 3 days ago | parent [-]

And you find yourself less productive?

llmslave2 3 days ago | parent [-]

No but I don't use it to generate code usually.

I gave agents a solid go and I didn't feel more productive, just became more stupid.

kmoser 3 days ago | parent [-]

A year or so ago I was seriously thinking of making a series of videos showing how coding agents were just plain bad at producing code. This was based on my experience trying to get them to do very simple things (e.g. a five-pointed star, or text flowing around the edge of circle, in HTML/CSS). They still tend to fail at things like this, but I've come to realize that there are whole classes of adjacent problems they're good at, and I'm starting to leverage their strengths rather than get hung up on their weaknesses.

Perhaps you're not playing to their strengths, or just haven't cracked the code for how to prompt them effectively? Prompt engineering is an art, and slight changes to prompts can make a big difference in the resulting code.

llmslave2 3 days ago | parent | next [-]

Perhaps it is a skill issue. But I don't really see the point of trying when it seems like the gains are marginal. If agent workflows really do start offering 2x+ level improvements then perhaps I'll switch over, in the meantime I won't have to suffer mental degradation from constant LLM usage.

anhner 3 days ago | parent | prev [-]

and what are those strengths, if you don't mind me asking?

kmoser 3 days ago | parent [-]

  - Providing boilerplate/template code for common use cases
  - Explaining what code is doing and how it works
  - Refactoring/updating code when given specific requirements
  - Providing alternative ways of doing things that you might not have thought of yourself
YMMV; every project is different so you might not have occasion to use all of these at the same time.
anhner 3 days ago | parent [-]

I appreciate your reply. A lot of people just say how wonderful and revolutionary LLMs are, but when asked for more concrete stuff they give vague answers or even worse, berate you for being skeptical/accuse you of being a luddite.

Your list gives me a starting point and I'm sure it can even be expanded. I do use LLMs the way you suggested and find them pretty useful most of the time - in chat mode. However, when using them in "agent mode" I find them far less useful.

kmoser 4 hours ago | parent | next [-]

Here's a concrete example of what can be done with Opus 4.5, and how to do it: https://burkeholland.github.io/posts/opus-4-5-change-everyth...

kmoser 2 days ago | parent | prev [-]

"Agent mode" is vastly different in many ways. I suggest you read up on how people write things like CLAUDE.md files. But as I said earlier, every project is different, and one what one person was successful with won't necessarily make you successful, so you may find it more helpful to get somebody to coach you through prompting agents for your particular projects.

bsimpson 3 days ago | parent | prev | next [-]

One of my favorite engineers calls AI a "wish fulfillment slot machine."

geoffbp 3 days ago | parent | prev | next [-]

Username checks out

marcellus23 3 days ago | parent | prev [-]

> I'd be shocked if the developer wasn't actually less productive

I agree 10x is a very large number and it's almost certainly smaller—maybe 1.5x would be reasonable. But really? You would be shocked if it was above 1.0x? This kind of comment always strikes me as so infantilizing and rude, to suggest that all these developers are actually slower with AI, but apparently completely oblivious to it and only you know better.

llmslave2 3 days ago | parent [-]

I would never suggest that only I know better. Plenty of other people are observing the same thing, and there is also research backing it up.

Maybe shocked is the wrong term. Surprised, perhaps.

marcellus23 3 days ago | parent | next [-]

There are simply so many counterexamples out there of people who have developed projects in a small fraction of the time it would take manually. Whether or not AI is having a positive effect on productivity on average in the industry is a valid question, but it's a statistical one. It's ridiculous to argue that AI has a negative effect on productivity in every single individual case.

llmslave2 3 days ago | parent [-]

It's all talk and no evidence.

sarchertech 3 days ago | parent | prev [-]

We’re seeing no external indicators of large productivity gains. Even assuming that productivity gains in large corporations are swallowed up by inefficiencies, you’d expect externally verifiable metrics to show a 2x or more increase in productivity among indie developers and small companies.

So far it’s just crickets.