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For me it’s meant a huge increase in productivity, at least 3X. Since so many claim the opposite, I’m curious to what you do more specifically? I guess differe

For me it’s meant a huge increase in productivity, at least 3X. Since so many claim the opposite, I’m curious to what you do more specifically? I guess different roles/technologies benefit more from agents than others. I build full stack web applications in node/.net/react, more importantly (I think) is that I work on a small startup and manage 3 applications myself.

datadrivenangel 1y ago
How do you structure your applications for maintainability?
dingnuts 1y ago
You have small applications following extremely common patterns and using common libraries. Models are good at regurgitating patterns they've seen many times, with fuzzy find/replace translations applied. Try to build something like Kubernetes from the ground up and let us know how it goes. Or try writing a custom firmware for a device you just designed. Something like that.
elevatortrim 1y ago
I think there are two broad cases where ai coding is beneficial: 1. You are a good coder but working on a new (to you) or building a new project, or working with a technology you are not familiar with. This is where AI is hugely beneficial. It does not only accelerate you, it lets you do things you could not otherwise. 2. You have spent a lot of time on engineering your context and learning what AI is good at, and using it very strategically where you know it will save time and not bother otherwise. If you are a really good coder, really familiar with the project, and mostly changing its bits and pieces rather than building new functionality, AI won’t accelerate you much. Especially if you did not invest the time to make it work well.
nicce 1y ago
> I build full stack web applications in node/.net/react, more importantly (I think) is that I work on a small startup and manage 3 applications myself. I think this is your answer. For example, React and JavaScript are extremely popular and aged. Are you using TypeScript and want to get most of the types or are you accepting everything that LLM gives as JavaScript? How interested you are about the code whether it is using "soon to be deprecated" functions or the most optimized loop/implementation? How about the project structure? In other cases, the more precision you need, less effective LLM is.
rs186 1y ago
3X if not 10X if you are starting a new project with Next.js, React, Tailwind CSS for a fullstack website development, that solves an everyday problem. Yeah I just witnessed that yesterday when creating a toy project. For my company's codebase, where we use internal tools and proprietary technology, solving a problem that does not exist outside the specific domain, on a codebase of over 1000 files? No way. Even locating the correct file to edit is non trivial for a new (human) developer.
- GenerocUsername 1y ago
  Your first week of AI usage should be crawling your codebase and generating context.md docs that can then be fed back into future prompts so that AI understands your project space, packages, apis, and code philosophy. I guarantee your internal tools are not revolutionary, they are just unrepresented in the ML model out of the box
  - nicce 1y ago
    Even then, are you even allowed to use AI in such codebase. Is some part of the code "bought", e.g. commercial compiler generated with specific license? Is pinky promise from LLM provider enough?
    - GenerocUsername 1y ago
      Are the resources to understand the code on a computer? Whether it's code, swagger, or a collection of sticky notes, your job is now to supply context to the AI. I am 100% convinced people who are not getting value from AI would have trouble explaining how to tie shoes to a toddler
  - orra 1y ago
    That sounds incredibly boring. Is it effective? If so I'm sure we'll see models to generate those context.md files.
    - cpursley 1y ago
evantbyrne 1y ago
The problem with these discussions is that almost nobody outside of the agency/contracting world seems to track their time. Self-reported data is already sketchy enough without layering on the issue of relying on distant memory of fine details.
andrepd 1y ago
Self-reports are notoriously overexcited, real results are, let's say, not so stellar. https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
- logicprog 1y ago
  Here's an in depth analysis and critique of that study by someone whose job is literally to study programmers psychologically and has experience in sociology studies: https://www.fightforthehuman.com/are-developers-slowed-down- https://www.fightforthehuman.com/are-developers-slowed-down-... Basically, the study has a fuckton of methodological problems that seriously undercut the quality of its findings, and even assuming its findings are correct, if you look closer at the data, it doesn't show what it claims to show regarding developer estimations, and the story of whether it speeds up or slows down developers is actually much more nuanced and precisely mirrors what the developers themselves say in the qualitative quote questionaire, and relatively closely mirrors what the more nuanced people will say here — that it helps with things you're less familiar with, that have scope creep, etc a lot more, but is less or even negatively useful for the opposite scenarios — even in the worst case setting. Not to mention this is studying a highly specific and rare subset of developers, and they even admit it's a subset that isn't applicable to the whole.
acedTrex 1y ago
I have yet to get it to generate code past 10ish lines that I am willing to accept. I read stuff like this and wonder how low yall's standards are, or if you are working on projects that just do not matter in any real world sense.
- spicyusername 1y ago
  4/5 times I can easily get 100s of lines output, that only needs a quick once over. 1/5 times, I spend an extra hour tangled in code it outputs that I eventually just rewrite from scratch. Definitely a massive net positive, but that 20% is extremely frustrating.
  - acedTrex 1y ago
    That is fascinating to me, i've never seen it generate that much code that is actually something i would consider correct. It's always wrong in some way.
  - LinXitoW 1y ago
    In my experience, if I have to issue more than 2 corrections, I'm better off restarting and beefing up the prompt or just doing it myself
- dillydogg 1y ago
  Whenever I read comments from the people singing their praises of the technology, it's hard not to think of the study that found AI tools made developers slower in early 2025. >When developers are allowed to use AI tools, they take 19% longer to complete issues—a significant slowdown that goes against developer beliefs and expert forecasts. This gap between perception and reality is striking: developers expected AI to speed them up by 24%, and even after experiencing the slowdown, they still believed AI had sped them up by 20%. https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
  - mstkllah 1y ago
    Ah, the very extensive study with 16 developers. Bulletproof results.
wiremine 1y ago
> Having spent a couple of weeks on Claude Code recently, I arrived to the conclusion that the net value for me from agentic AI is actually negative. > For me it’s meant a huge increase in productivity, at least 3X. How do we reconcile these two comments? I think that's a core question of the industry right now. My take, as a CTO, is this: we're giving people new tools, and very little training on the techniques that make those tools effective. It's sort of like we're dropping trucks and airplanes on a generation that only knows walking and bicycles. If you've never driven a truck before, you're going to crash a few times. Then it's easy to say "See, I told you, this new fangled truck is rubbish." Those who practice with the truck are going to get the hang of it, and figure out two things: 1. How to drive the truck effectively, and 2. When NOT to use the truck... when talking or the bike is actually the better way to go. We need to shift the conversation to techniques, and away from the tools. Until we do that, we're going to be forever comparing apples to oranges and talking around each other.
- oceanplexian 1y ago
  It's pretty simple, AI is now political for a lot of people. Some folks have a vested interest in downplaying it or over hyping it rather than impartially approaching it as a tool.
  - Gigachad 1y ago
    It’s also just not consistent. A manager who can’t code using it to generate a react todo list thinks it’s 100x efficiency while a senior software dev working on established apps finds it a net productivity negative. AI coding tools seem to excel at demos and flop on the field so the expectation disconnect between managers and actual workers is massive.
- jdgoesmarching 1y ago
  Agreed, and it drives me bonkers when people talk about AI coding as if it represents some a single technique, process, or tool. Makes me wonder if people spoke this way about “using computers” or “using the internet” in the olden days. We don’t even fully agree on the best practices for writing code without AI.
thanhhaimai 1y ago
I work across the stack (frontend, backend, ML) - For FrontEnd or easy code, it's a speed up. I think it's more like 2x instead of 3x. - For my backend (hard trading algo), it has like 90% failure rate so far. There is just so much for it to reason through (balance sheet, lots, wash, etc). All agents I have tried, even on Max mode, couldn't reason through all the cases correctly. They end up thrashing back and forth. Gemini most of the time will go into the "depressed" mode on the code base. One thing I notice is that the Max mode on Cursor is not worth it for my particular use case. The problem is either easy (frontend), which means any agent can solve it, or it's hard, and Max mode can't solve it. I tend to pick the fast model over strong model.
squeaky-clean 1y ago
I just want to point out that they only said agentic models were a negative, not AI in general. I don't know if this is what they meant, but I personally prefer to use a web or IDE AI tool and don't really like the agentic stuff compared to those. For me agentic AI would be a net positive against no-AI, but it's a net negative compared to other AI interfaces
deleted 1y ago
[deleted]
dmitrygr 1y ago
> For me it’s meant a huge increase in productivity, at least 3X. Quote possibly you are doing very common things that are often done and thus are in the training set a lot, the parent post is doing something more novel that forces the model to extrapolate, which they suck at.
- cambaceres 1y ago
  Sure, I won’t argue against that. The more complex (and fun) parts of the applications I tend to write myself. The productivity gains are still real though.
bcrosby95 1y ago
My current guess is it's how the programmer solves problems in their head. This isn't something we talk about much. People seem to find LLMs do well with well-spec'd features. But for me, creating a good spec doesn't take any less time than creating the code. The problem for me is the translation layer that turns the model in my head into something more concrete. As such, creating a spec for the LLM doesn't save me any time over writing the code myself. So if it's a one shot with a vague spec and that works that's cool. But if it's well spec'd to the point the LLM won't fuck it up then I may as well write it myself.
byryan 1y ago
That makes sense, especially if your building web applications that are primarily "just" CRUD operations. If a lot of the API calls follow the same pattern and the application is just a series of API calls + React UI then that seems like something an LLM would excel at. LLM's are also more proficient in TypeScript/JS/Python compared to other languages, so that helps as well.
lillecarl 1y ago
I'm currently unemployed in the DevOps field (resigned and got a long vacation). I've been using various models to write various Kubernetes plug-ins abd simple automation scripts. It's been a godsend implementing things which would require too much research otherwise, my ADHD context window is smaller than Claude's. Models are VERY good at Kubernetes since they have very anal (good) documentation requirements before merging. I would say my productivity gain is unmeasurable since I can produce things I'd ADHD out of unless I've got a whip up my rear.
qingcharles 1y ago
On the right projects, definitely an enormous upgrade for me. Have to be judicious with it and know when it is right and when it's wrong. I think people have to figure out what those times are. For now. In the future I think a lot of the problems people are having with it will diminish.
epolanski 1y ago
> Since so many claim the opposite The overwhelming majority of those claiming the opposite are a mixture of: - users with wrong expectations, such as AI's ability to do the job on its own with minimal effort from the user. They have marketers to blame. - users that have AI skill issues: they simply don't understand/know how to use the tools appropriately. I could provide countless examples from the importance of quality prompting, good guidelines, context management, and many others. They have only their laziness or lack of interest to blame. - users that are very defensive about their job/skills. Many feel threatened by AI taking their jobs or diminishing it, so their default stance is negative. They have their ego to blame.
darkmarmot 1y ago
I work in distributed systems programming and have been horrified by the crap the AIs produce. I've found them to be quite helpful at summarizing papers and doing research, providing jumping off points. But none of the code I write can be scraped from a blog post.

datadrivenangel 1y ago

How do you structure your applications for maintainability?

dingnuts 1y ago

You have small applications following extremely common patterns and using common libraries. Models are good at regurgitating patterns they've seen many times, with fuzzy find/replace translations applied. Try to build something like Kubernetes from the ground up and let us know how it goes. Or try writing a custom firmware for a device you just designed. Something like that.

elevatortrim 1y ago

I think there are two broad cases where ai coding is beneficial: 1. You are a good coder but working on a new (to you) or building a new project, or working with a technology you are not familiar with. This is where AI is hugely beneficial. It does not only accelerate you, it lets you do things you could not otherwise. 2. You have spent a lot of time on engineering your context and learning what AI is good at, and using it very strategically where you know it will save time and not bother otherwise. If you are a really good coder, really familiar with the project, and mostly changing its bits and pieces rather than building new functionality, AI won’t accelerate you much. Especially if you did not invest the time to make it work well.

nicce 1y ago

> I build full stack web applications in node/.net/react, more importantly (I think) is that I work on a small startup and manage 3 applications myself. I think this is your answer. For example, React and JavaScript are extremely popular and aged. Are you using TypeScript and want to get most of the types or are you accepting everything that LLM gives as JavaScript? How interested you are about the code whether it is using "soon to be deprecated" functions or the most optimized loop/implementation? How about the project structure? In other cases, the more precision you need, less effective LLM is.

rs186 1y ago

3X if not 10X if you are starting a new project with Next.js, React, Tailwind CSS for a fullstack website development, that solves an everyday problem. Yeah I just witnessed that yesterday when creating a toy project. For my company's codebase, where we use internal tools and proprietary technology, solving a problem that does not exist outside the specific domain, on a codebase of over 1000 files? No way. Even locating the correct file to edit is non trivial for a new (human) developer.

GenerocUsername 1y ago
Your first week of AI usage should be crawling your codebase and generating context.md docs that can then be fed back into future prompts so that AI understands your project space, packages, apis, and code philosophy. I guarantee your internal tools are not revolutionary, they are just unrepresented in the ML model out of the box
- nicce 1y ago
  Even then, are you even allowed to use AI in such codebase. Is some part of the code "bought", e.g. commercial compiler generated with specific license? Is pinky promise from LLM provider enough?
  - GenerocUsername 1y ago
    Are the resources to understand the code on a computer? Whether it's code, swagger, or a collection of sticky notes, your job is now to supply context to the AI. I am 100% convinced people who are not getting value from AI would have trouble explaining how to tie shoes to a toddler
- orra 1y ago
  That sounds incredibly boring. Is it effective? If so I'm sure we'll see models to generate those context.md files.
  - cpursley 1y ago

evantbyrne 1y ago

The problem with these discussions is that almost nobody outside of the agency/contracting world seems to track their time. Self-reported data is already sketchy enough without layering on the issue of relying on distant memory of fine details.

andrepd 1y ago

Self-reports are notoriously overexcited, real results are, let's say, not so stellar. https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...

logicprog 1y ago
Here's an in depth analysis and critique of that study by someone whose job is literally to study programmers psychologically and has experience in sociology studies: https://www.fightforthehuman.com/are-developers-slowed-down- https://www.fightforthehuman.com/are-developers-slowed-down-... Basically, the study has a fuckton of methodological problems that seriously undercut the quality of its findings, and even assuming its findings are correct, if you look closer at the data, it doesn't show what it claims to show regarding developer estimations, and the story of whether it speeds up or slows down developers is actually much more nuanced and precisely mirrors what the developers themselves say in the qualitative quote questionaire, and relatively closely mirrors what the more nuanced people will say here — that it helps with things you're less familiar with, that have scope creep, etc a lot more, but is less or even negatively useful for the opposite scenarios — even in the worst case setting. Not to mention this is studying a highly specific and rare subset of developers, and they even admit it's a subset that isn't applicable to the whole.

acedTrex 1y ago

I have yet to get it to generate code past 10ish lines that I am willing to accept. I read stuff like this and wonder how low yall's standards are, or if you are working on projects that just do not matter in any real world sense.

spicyusername 1y ago
4/5 times I can easily get 100s of lines output, that only needs a quick once over. 1/5 times, I spend an extra hour tangled in code it outputs that I eventually just rewrite from scratch. Definitely a massive net positive, but that 20% is extremely frustrating.
- acedTrex 1y ago
  That is fascinating to me, i've never seen it generate that much code that is actually something i would consider correct. It's always wrong in some way.
- LinXitoW 1y ago
  In my experience, if I have to issue more than 2 corrections, I'm better off restarting and beefing up the prompt or just doing it myself
dillydogg 1y ago
Whenever I read comments from the people singing their praises of the technology, it's hard not to think of the study that found AI tools made developers slower in early 2025. >When developers are allowed to use AI tools, they take 19% longer to complete issues—a significant slowdown that goes against developer beliefs and expert forecasts. This gap between perception and reality is striking: developers expected AI to speed them up by 24%, and even after experiencing the slowdown, they still believed AI had sped them up by 20%. https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
- mstkllah 1y ago
  Ah, the very extensive study with 16 developers. Bulletproof results.

wiremine 1y ago

> Having spent a couple of weeks on Claude Code recently, I arrived to the conclusion that the net value for me from agentic AI is actually negative. > For me it’s meant a huge increase in productivity, at least 3X. How do we reconcile these two comments? I think that's a core question of the industry right now. My take, as a CTO, is this: we're giving people new tools, and very little training on the techniques that make those tools effective. It's sort of like we're dropping trucks and airplanes on a generation that only knows walking and bicycles. If you've never driven a truck before, you're going to crash a few times. Then it's easy to say "See, I told you, this new fangled truck is rubbish." Those who practice with the truck are going to get the hang of it, and figure out two things: 1. How to drive the truck effectively, and 2. When NOT to use the truck... when talking or the bike is actually the better way to go. We need to shift the conversation to techniques, and away from the tools. Until we do that, we're going to be forever comparing apples to oranges and talking around each other.

oceanplexian 1y ago
It's pretty simple, AI is now political for a lot of people. Some folks have a vested interest in downplaying it or over hyping it rather than impartially approaching it as a tool.
- Gigachad 1y ago
  It’s also just not consistent. A manager who can’t code using it to generate a react todo list thinks it’s 100x efficiency while a senior software dev working on established apps finds it a net productivity negative. AI coding tools seem to excel at demos and flop on the field so the expectation disconnect between managers and actual workers is massive.
jdgoesmarching 1y ago
Agreed, and it drives me bonkers when people talk about AI coding as if it represents some a single technique, process, or tool. Makes me wonder if people spoke this way about “using computers” or “using the internet” in the olden days. We don’t even fully agree on the best practices for writing code without AI.

thanhhaimai 1y ago

I work across the stack (frontend, backend, ML) - For FrontEnd or easy code, it's a speed up. I think it's more like 2x instead of 3x. - For my backend (hard trading algo), it has like 90% failure rate so far. There is just so much for it to reason through (balance sheet, lots, wash, etc). All agents I have tried, even on Max mode, couldn't reason through all the cases correctly. They end up thrashing back and forth. Gemini most of the time will go into the "depressed" mode on the code base. One thing I notice is that the Max mode on Cursor is not worth it for my particular use case. The problem is either easy (frontend), which means any agent can solve it, or it's hard, and Max mode can't solve it. I tend to pick the fast model over strong model.

squeaky-clean 1y ago

I just want to point out that they only said agentic models were a negative, not AI in general. I don't know if this is what they meant, but I personally prefer to use a web or IDE AI tool and don't really like the agentic stuff compared to those. For me agentic AI would be a net positive against no-AI, but it's a net negative compared to other AI interfaces

deleted 1y ago

[deleted]

dmitrygr 1y ago

> For me it’s meant a huge increase in productivity, at least 3X. Quote possibly you are doing very common things that are often done and thus are in the training set a lot, the parent post is doing something more novel that forces the model to extrapolate, which they suck at.

cambaceres 1y ago
Sure, I won’t argue against that. The more complex (and fun) parts of the applications I tend to write myself. The productivity gains are still real though.

bcrosby95 1y ago

My current guess is it's how the programmer solves problems in their head. This isn't something we talk about much. People seem to find LLMs do well with well-spec'd features. But for me, creating a good spec doesn't take any less time than creating the code. The problem for me is the translation layer that turns the model in my head into something more concrete. As such, creating a spec for the LLM doesn't save me any time over writing the code myself. So if it's a one shot with a vague spec and that works that's cool. But if it's well spec'd to the point the LLM won't fuck it up then I may as well write it myself.

byryan 1y ago

That makes sense, especially if your building web applications that are primarily "just" CRUD operations. If a lot of the API calls follow the same pattern and the application is just a series of API calls + React UI then that seems like something an LLM would excel at. LLM's are also more proficient in TypeScript/JS/Python compared to other languages, so that helps as well.

lillecarl 1y ago

I'm currently unemployed in the DevOps field (resigned and got a long vacation). I've been using various models to write various Kubernetes plug-ins abd simple automation scripts. It's been a godsend implementing things which would require too much research otherwise, my ADHD context window is smaller than Claude's. Models are VERY good at Kubernetes since they have very anal (good) documentation requirements before merging. I would say my productivity gain is unmeasurable since I can produce things I'd ADHD out of unless I've got a whip up my rear.

qingcharles 1y ago

On the right projects, definitely an enormous upgrade for me. Have to be judicious with it and know when it is right and when it's wrong. I think people have to figure out what those times are. For now. In the future I think a lot of the problems people are having with it will diminish.

epolanski 1y ago

> Since so many claim the opposite The overwhelming majority of those claiming the opposite are a mixture of: - users with wrong expectations, such as AI's ability to do the job on its own with minimal effort from the user. They have marketers to blame. - users that have AI skill issues: they simply don't understand/know how to use the tools appropriately. I could provide countless examples from the importance of quality prompting, good guidelines, context management, and many others. They have only their laziness or lack of interest to blame. - users that are very defensive about their job/skills. Many feel threatened by AI taking their jobs or diminishing it, so their default stance is negative. They have their ego to blame.

darkmarmot 1y ago

I work in distributed systems programming and have been horrified by the crap the AIs produce. I've found them to be quite helpful at summarizing papers and doing research, providing jumping off points. But none of the code I write can be scraped from a blog post.