5 ms·

While I agree with the premise, I think this angle only applies on work one was going to do no matter what. The real power of these tools is that there are so m

While I agree with the premise, I think this angle only applies on work one was going to do no matter what. The real power of these tools is that there are so many ideas people would like to try, but never have the time or motivation to pursue. So the comparison is not only "built with and without LLM" but "would you even build this if you didn't have the LLM?". The gap in productivity in this case is much more wide.

keeganpoppen 2mo ago
that and 2x is "i'm 5'10" and i round up to 6'" low.
infecto 2mo ago
Agree so much. So many small bugs, nits, tweaks I just send off to an LLM agent to figure it out.
SkepticalWhale 2mo ago
Yes but I’ve seen some devs waste a lot of time using AI to build something that was a bad idea to begin with. Without AI they might have first spent more time validating the idea was worth it.
- petesergeant 2mo ago
  > Without AI they might have first spent more time validating the idea was worth it. Seems optimistic
- marssaxman 2mo ago
  For me it is exactly the opposite. AI makes it so easy to create test fixtures and run experiments that I now spend much more time validating ideas than I could ever afford to do before. When I write code for production, it's not "this ought to work", it's "here are the figures showing how well this works, on this dataset", where the dataset is also much larger than anything I would have used before, because I used AI to generate the tools which collected and organized it.
  - skydhash 2mo ago
    > it's not "this ought to work", it's "here are the figures showing how well this works, on this dataset" The thing with constraints is that you focus of the thing with high value first. So you focus on the most promising ideas first or choose experiments that can get rid of most ideas. Instead of trying to validate each ideas and generate what is most likely noise to the decision process. Like if I ever hire an assistant, I want like one to three options that are closely aligned to my needs, not a bible size report on 42 choices.
- rayiner 2mo ago
  Isn’t it better to be able to quickly test ideas and see what works and what doesn’t?
tails4e 2mo ago
Yes, I work in hardware design ans the cost to code up the RTL for an idea and compare it to the existing code in terms of PPA, its very expensive in terms of engineering time. This week I had 5 different architecites coded, pushed through a functional test bench, bit accurate models created and then run through ppa analysis all done via llms. So its a task tbis project could not afford to do without llms, but can save us 10% in ppa
nathell 2mo ago
I think the ideas worth pursuing still require substantial time and motivation, even with LLMs. Otherwise you just end up with a half-baked sloppy artifact that nobody will ever use or study, for a net negative benefit to humanity.
- supersour 2mo ago
  Yes this is true to some extent. I've been using LLMs to run some computer vision tests, and I've certainly noticed myself running into the trap of "just one more AI experiment", or "just one more change" while neglecting to actually properly integrate the learnings into my mental model. However, without an agent running its own experiments on a cloud GPU, would I realistically have invested my limited work hours and tried evaluating 10 different models, each with 10 different tuned parameters, to solve my specific use case? Or would I have tried 1-2 models and spent my time trying to optimize those models? I think there is some merit to the spray and pray approach when one is in the exploration phase of the solution space. Also, on more than one occasion now, I have had fable halve the inference latency of a model simply because the original implementation from an academic included unnecessary GPU-to-CPU-to-GPU transfers or similarly inefficient operations. Those optimizations came at essentially 0 time cost to me and I can verify that the outputs are byte-identical. Pretty sweet!
- arjie 2mo ago
  Well, I’m happy just to make half baked slop for myself. Highly personalized software that is poorly implemented and yet yields the result I desire. E.g. software that generates these models that I can print https://wiki.roshangeorge.dev/w/Blog/2026-06-30/Modeling_a_Watch_Strap_Print_with_Claude https://wiki.roshangeorge.dev/w/Blog/2026-06-30/Modeling_a_W... https://wiki.roshangeorge.dev/w/Blog/2025-12-01/Grounding_Your_Agent https://wiki.roshangeorge.dev/w/Blog/2025-12-01/Grounding_Yo... Or blog post authoring software https://wiki.roshangeorge.dev/w/Blog/2026-04-25/The_rise_of_personal_software https://wiki.roshangeorge.dev/w/Blog/2026-04-25/The_rise_of_... There were so many things that no one will ever study and won’t give humanity any benefit but I use everyday to make my life better. That’s enough. The value far exceeds $200/mo. I’m getting it for cheap and now that I have my GPUs and my models they can’t even take it from me in the future if they wanted, haha! LLMs allow for human flourishing on a massive scale. One of the best inventions to occur in my life. Up there with the Internet/Web. Truly a marvelous time.
moron4hire 2mo ago
Those sort of ideas are unlikely to yield major benefit, though. This is basically replicating the plight of the solo open source dev, writ large. Individual programmers have long built the thing they've cared about on their own time (essentially "for free" because, despite kindergarten economics theory, a programmer cannot usually monetize a marginal hour). And it usually goes that the project never gets adopted anywhere. It might acrue more features and total man-hour effort than most of what FAANG does in open source to drown out the solo devs. But the market will decide that "no organizational buy-in" is a signal the project doesn't matter. Other developers will decide, "if he could do it, so could I" and also not adopt. Same exact thing is happening and will continue to happen with all these generated "but we wouldn't have done it otherwise" projects. It's just very, very unlikely to go anywhere. That which took very little effort to create will receive very little effort to promote.
- bloaf 2mo ago
  I don't think this is the right framing, because I think it confuses the means and the ends. Its like making a jig in woodworking. The measure of the jig's success is not whether it gets re-used or widespread adoption, its whether it made it easier to achieve some actual objective. Because the jig is a means to some other end. Lots of these "we wouldn't have done it otherwise" applications are means, not ends.
  - breuleux 2mo ago
    That is true, but that suggests that the value of software is reverting to the value of a jig. Software that gets deployed to millions of people gets a large valuation because it provides small convenience times a million. In the era of bespoke software, you’re basically 100x-ing the production of a millionth of the value of commercial software. That’s why it isn’t all that impactful in the end and I think it’s worth highlighting.
    - moron4hire 2mo ago
      It's the underpants gnomes again. - Vibe code a bunch of small projects that we couldn't justify ROI before - ??? - Profit
overfeed 2mo ago
> The real power of these tools is that there are so many ideas people would like to try, but never have the time or motivation to pursue. This can be a negative multiplier: code I thought I wanted that gets immediately abandoned is a net-negative if no one else wants it (lets face it, this is the safest default posture for software of unknown providence). In isolation, instant-abandonware takes up hdd space, burns dependabot's CPU-cycles, and wastes human attention when appearing in search results. In aggregate, it floods the zone with a deluge of forks with imperceptible differences between them, based on nit-picks, legitimate stand-out products will have a much harder time going forward.
- mardef 2mo ago
  Not every engineering effort is a product in search of market fit or a community. Some things are already very useful as just a one shot. I just made a quick app to help me pack for a trip, it updated forecasts every day, let me know when rain entered the forecast at one of my stops and gave me a checklist that helped me quell my travel anxiety. The greatest thing that LLMs have done is allow many to achieve things that they couldn't have before. I'm incredibly disinterested in "I can do the same thing I was already doing x% faster"
  - overfeed 2mo ago
    > Not every engineering effort is a product in search of market fit or a community I think you misunderstood my point: by "legitimate stand-out products", I meant exactly that, with no connotation of commercialization. Maybe you can agree that having a high signal-to-noise ratio for (open source) projects is a desirable goal? > Some things are already very useful as just a one shot. I agree. I too have made or forked about a dozen apps and tools in the past few months. It would be dishonest not to consider the flipside, that this software is overfitted to the needs of a single person. Further, this hyper-bespoke software typically feature-complete within moments of the final prompt, and I have,on occasion, completely forgot about the tool/app I spent a weekend created, it clearly wasn't worth the effort I put in. > The greatest thing that LLMs have done is allow many to achieve things that they couldn't have before. Let's not pretend there isn't a cost to this.
rayiner 2mo ago
LLMs open up huge possibilities to do work that you otherwise wouldn't do. I vibe coded a PDF viewer yesterday in a couple of hours using Claude and Grok. I've been itching for a native Mac app that meets some very basic requirements: one window with file tree on the left and tabbed view on the right, with the ability to call out to pdfgrep and jump to results. Like Zotero, but with better multi-file search and a viewer that doesn't choke on big files. Claude built it for me in an hour, and Grok iterated a couple of times to make it better. I didn't look at a single line of code. It's now my default PDF viewer, and I used it today to do a bunch of my real work. It's a braindead simple program that mostly hooks together pre-existing functionality, it just so happened that none of the widely available apps had the specific mix of features I wanted. I could probably have done it myself in a week if I took time off my non-coding day job to figure out Swift and AppKit. But I wasn't going to do that. I’m psyched. I hate web apps and now I can just write my own for all the little things I use every day.
- waynesonfire 2mo ago
  Cool story bro. I used a llm to help me name a variable.
switchbak 2mo ago
In this last week, I've performed experiments in an almost fully automated way - parallel to my main work, and easy to orchestrate. This has led to 3 parallel pieces of adjacent work that each speed up our build by quite a drastic margin. When combined, this is a massive improvement. None of this would have happened in the old days, as the research itself takes a long time to babysit and a lot of options to check. So I very much agree - the activation energy can be a lot lower on some kinds of tasks, and some of those get big returns for small inputs. It's not all like that, but part of the game is identifying when you can spot those high return efforts.
- rsoto2 2mo ago
  or the AI could lead you to a completely unusable experiment and lead you to believe it was successful wasting weeks of work. Not saying it will happen to you, but there is a huge "survivorship" bias already in software for what tools are useful or not. Maybe it's the person and not the tools.
- rsoto2 2mo ago
  Figured out a better way of explaining it: dev A knows exactly what the program should do and how to verify the AI output dev B thinks they know what they are doing but are actually misguided by bad psycophantic AI output they have incorrectly verified. both work on product C
sdevonoes 2mo ago
Im not so sure if this is a pro or a con. Take two extremes at product building: brute force vs deep research first. I guess llms allow you to experiment quickly with many approaches and see what sticks. Without llms, you need to spend more time researching and thinking what’s the most economical approach. Im biased towards the non-llm approach
pron 2mo ago
Yes, but only in the programming sense of productivity, not in the economic sense. When prices stop being subsidised and the novelty wears off, this will boil down to the question of how much money is that worth? We're already seeing an inflation in such projects, which are becoming hard to even show off. It could be similar to 3D printers. Fun and valuable, with a market size of $25B, but not quite world-changing (I'm not saying AI won't be world-changing, only that this tinkering aspect may not be).
nicman23 2mo ago
the real power is making the bot search a large code base for the relevant code you want to change
jaapz 2mo ago
Not even just building stuff with LLMS. I'm doing analysis on stuff that we previously simply couldn't do in my company, it would take way too much time or effort, and we didn't have the manpower.
gymbeaux 2mo ago
I often use LLMs to build locally running web apps and console apps that I otherwise never would have built, because the value-add wouldn’t be worth, say, 40 hours of my free time.

keeganpoppen 2mo ago

that and 2x is "i'm 5'10" and i round up to 6'" low.

infecto 2mo ago

Agree so much. So many small bugs, nits, tweaks I just send off to an LLM agent to figure it out.

SkepticalWhale 2mo ago

Yes but I’ve seen some devs waste a lot of time using AI to build something that was a bad idea to begin with. Without AI they might have first spent more time validating the idea was worth it.

petesergeant 2mo ago
> Without AI they might have first spent more time validating the idea was worth it. Seems optimistic
marssaxman 2mo ago
For me it is exactly the opposite. AI makes it so easy to create test fixtures and run experiments that I now spend much more time validating ideas than I could ever afford to do before. When I write code for production, it's not "this ought to work", it's "here are the figures showing how well this works, on this dataset", where the dataset is also much larger than anything I would have used before, because I used AI to generate the tools which collected and organized it.
- skydhash 2mo ago
  > it's not "this ought to work", it's "here are the figures showing how well this works, on this dataset" The thing with constraints is that you focus of the thing with high value first. So you focus on the most promising ideas first or choose experiments that can get rid of most ideas. Instead of trying to validate each ideas and generate what is most likely noise to the decision process. Like if I ever hire an assistant, I want like one to three options that are closely aligned to my needs, not a bible size report on 42 choices.
rayiner 2mo ago
Isn’t it better to be able to quickly test ideas and see what works and what doesn’t?

tails4e 2mo ago

Yes, I work in hardware design ans the cost to code up the RTL for an idea and compare it to the existing code in terms of PPA, its very expensive in terms of engineering time. This week I had 5 different architecites coded, pushed through a functional test bench, bit accurate models created and then run through ppa analysis all done via llms. So its a task tbis project could not afford to do without llms, but can save us 10% in ppa

nathell 2mo ago

I think the ideas worth pursuing still require substantial time and motivation, even with LLMs. Otherwise you just end up with a half-baked sloppy artifact that nobody will ever use or study, for a net negative benefit to humanity.

supersour 2mo ago
Yes this is true to some extent. I've been using LLMs to run some computer vision tests, and I've certainly noticed myself running into the trap of "just one more AI experiment", or "just one more change" while neglecting to actually properly integrate the learnings into my mental model. However, without an agent running its own experiments on a cloud GPU, would I realistically have invested my limited work hours and tried evaluating 10 different models, each with 10 different tuned parameters, to solve my specific use case? Or would I have tried 1-2 models and spent my time trying to optimize those models? I think there is some merit to the spray and pray approach when one is in the exploration phase of the solution space. Also, on more than one occasion now, I have had fable halve the inference latency of a model simply because the original implementation from an academic included unnecessary GPU-to-CPU-to-GPU transfers or similarly inefficient operations. Those optimizations came at essentially 0 time cost to me and I can verify that the outputs are byte-identical. Pretty sweet!
arjie 2mo ago
Well, I’m happy just to make half baked slop for myself. Highly personalized software that is poorly implemented and yet yields the result I desire. E.g. software that generates these models that I can print https://wiki.roshangeorge.dev/w/Blog/2026-06-30/Modeling_a_Watch_Strap_Print_with_Claude https://wiki.roshangeorge.dev/w/Blog/2026-06-30/Modeling_a_W... https://wiki.roshangeorge.dev/w/Blog/2025-12-01/Grounding_Your_Agent https://wiki.roshangeorge.dev/w/Blog/2025-12-01/Grounding_Yo... Or blog post authoring software https://wiki.roshangeorge.dev/w/Blog/2026-04-25/The_rise_of_personal_software https://wiki.roshangeorge.dev/w/Blog/2026-04-25/The_rise_of_... There were so many things that no one will ever study and won’t give humanity any benefit but I use everyday to make my life better. That’s enough. The value far exceeds $200/mo. I’m getting it for cheap and now that I have my GPUs and my models they can’t even take it from me in the future if they wanted, haha! LLMs allow for human flourishing on a massive scale. One of the best inventions to occur in my life. Up there with the Internet/Web. Truly a marvelous time.

moron4hire 2mo ago

Those sort of ideas are unlikely to yield major benefit, though. This is basically replicating the plight of the solo open source dev, writ large. Individual programmers have long built the thing they've cared about on their own time (essentially "for free" because, despite kindergarten economics theory, a programmer cannot usually monetize a marginal hour). And it usually goes that the project never gets adopted anywhere. It might acrue more features and total man-hour effort than most of what FAANG does in open source to drown out the solo devs. But the market will decide that "no organizational buy-in" is a signal the project doesn't matter. Other developers will decide, "if he could do it, so could I" and also not adopt. Same exact thing is happening and will continue to happen with all these generated "but we wouldn't have done it otherwise" projects. It's just very, very unlikely to go anywhere. That which took very little effort to create will receive very little effort to promote.

bloaf 2mo ago
I don't think this is the right framing, because I think it confuses the means and the ends. Its like making a jig in woodworking. The measure of the jig's success is not whether it gets re-used or widespread adoption, its whether it made it easier to achieve some actual objective. Because the jig is a means to some other end. Lots of these "we wouldn't have done it otherwise" applications are means, not ends.
- breuleux 2mo ago
  That is true, but that suggests that the value of software is reverting to the value of a jig. Software that gets deployed to millions of people gets a large valuation because it provides small convenience times a million. In the era of bespoke software, you’re basically 100x-ing the production of a millionth of the value of commercial software. That’s why it isn’t all that impactful in the end and I think it’s worth highlighting.
  - moron4hire 2mo ago
    It's the underpants gnomes again. - Vibe code a bunch of small projects that we couldn't justify ROI before - ??? - Profit

overfeed 2mo ago

> The real power of these tools is that there are so many ideas people would like to try, but never have the time or motivation to pursue. This can be a negative multiplier: code I thought I wanted that gets immediately abandoned is a net-negative if no one else wants it (lets face it, this is the safest default posture for software of unknown providence). In isolation, instant-abandonware takes up hdd space, burns dependabot's CPU-cycles, and wastes human attention when appearing in search results. In aggregate, it floods the zone with a deluge of forks with imperceptible differences between them, based on nit-picks, legitimate stand-out products will have a much harder time going forward.

mardef 2mo ago
Not every engineering effort is a product in search of market fit or a community. Some things are already very useful as just a one shot. I just made a quick app to help me pack for a trip, it updated forecasts every day, let me know when rain entered the forecast at one of my stops and gave me a checklist that helped me quell my travel anxiety. The greatest thing that LLMs have done is allow many to achieve things that they couldn't have before. I'm incredibly disinterested in "I can do the same thing I was already doing x% faster"
- overfeed 2mo ago
  > Not every engineering effort is a product in search of market fit or a community I think you misunderstood my point: by "legitimate stand-out products", I meant exactly that, with no connotation of commercialization. Maybe you can agree that having a high signal-to-noise ratio for (open source) projects is a desirable goal? > Some things are already very useful as just a one shot. I agree. I too have made or forked about a dozen apps and tools in the past few months. It would be dishonest not to consider the flipside, that this software is overfitted to the needs of a single person. Further, this hyper-bespoke software typically feature-complete within moments of the final prompt, and I have,on occasion, completely forgot about the tool/app I spent a weekend created, it clearly wasn't worth the effort I put in. > The greatest thing that LLMs have done is allow many to achieve things that they couldn't have before. Let's not pretend there isn't a cost to this.

rayiner 2mo ago

LLMs open up huge possibilities to do work that you otherwise wouldn't do. I vibe coded a PDF viewer yesterday in a couple of hours using Claude and Grok. I've been itching for a native Mac app that meets some very basic requirements: one window with file tree on the left and tabbed view on the right, with the ability to call out to pdfgrep and jump to results. Like Zotero, but with better multi-file search and a viewer that doesn't choke on big files. Claude built it for me in an hour, and Grok iterated a couple of times to make it better. I didn't look at a single line of code. It's now my default PDF viewer, and I used it today to do a bunch of my real work. It's a braindead simple program that mostly hooks together pre-existing functionality, it just so happened that none of the widely available apps had the specific mix of features I wanted. I could probably have done it myself in a week if I took time off my non-coding day job to figure out Swift and AppKit. But I wasn't going to do that. I’m psyched. I hate web apps and now I can just write my own for all the little things I use every day.

waynesonfire 2mo ago
Cool story bro. I used a llm to help me name a variable.

switchbak 2mo ago

In this last week, I've performed experiments in an almost fully automated way - parallel to my main work, and easy to orchestrate. This has led to 3 parallel pieces of adjacent work that each speed up our build by quite a drastic margin. When combined, this is a massive improvement. None of this would have happened in the old days, as the research itself takes a long time to babysit and a lot of options to check. So I very much agree - the activation energy can be a lot lower on some kinds of tasks, and some of those get big returns for small inputs. It's not all like that, but part of the game is identifying when you can spot those high return efforts.

rsoto2 2mo ago
or the AI could lead you to a completely unusable experiment and lead you to believe it was successful wasting weeks of work. Not saying it will happen to you, but there is a huge "survivorship" bias already in software for what tools are useful or not. Maybe it's the person and not the tools.
rsoto2 2mo ago
Figured out a better way of explaining it: dev A knows exactly what the program should do and how to verify the AI output dev B thinks they know what they are doing but are actually misguided by bad psycophantic AI output they have incorrectly verified. both work on product C

sdevonoes 2mo ago

Im not so sure if this is a pro or a con. Take two extremes at product building: brute force vs deep research first. I guess llms allow you to experiment quickly with many approaches and see what sticks. Without llms, you need to spend more time researching and thinking what’s the most economical approach. Im biased towards the non-llm approach

pron 2mo ago

Yes, but only in the programming sense of productivity, not in the economic sense. When prices stop being subsidised and the novelty wears off, this will boil down to the question of how much money is that worth? We're already seeing an inflation in such projects, which are becoming hard to even show off. It could be similar to 3D printers. Fun and valuable, with a market size of $25B, but not quite world-changing (I'm not saying AI won't be world-changing, only that this tinkering aspect may not be).

nicman23 2mo ago

the real power is making the bot search a large code base for the relevant code you want to change

jaapz 2mo ago

Not even just building stuff with LLMS. I'm doing analysis on stuff that we previously simply couldn't do in my company, it would take way too much time or effort, and we didn't have the manpower.

gymbeaux 2mo ago

I often use LLMs to build locally running web apps and console apps that I otherwise never would have built, because the value-add wouldn’t be worth, say, 40 hours of my free time.