5 ms·

I don't understand the productivity that people get out of these AI tools. I've tried it and I just can't get anything remotely worthwhile unless it's something

I don't understand the productivity that people get out of these AI tools. I've tried it and I just can't get anything remotely worthwhile unless it's something very simple or something completely new being built from the ground up. Like sure, I can ask claude to give me the barebones of a web service that does some simple task. Or a webpage with some information on it. But any time I've tried to get AI services to help with bugfixing/feature development on a large, complex, potentially multi-language codebase, it's useless. And those tasks are the ones that actually take up the majority of my time. On the occasion that I'm spinning a new thing up quickly, I don't really need an AI to do it for me -- I mean, that's the easy part! Is there something I'm missing? Am I just not using it right? I keep seeing people talk about how addictive it is, how the productivity boost is insane, how all their code is now written by AI and then audited, and I just don't see how that's possible outside of really simple rote programming.

Starlevel004 1y ago
> Is there something I'm missing? Am I just not using it right? The talk about it makes more sense when you remember most developers are primarily writing CRUD webapps or adware, which is essentially a solved problem already.
- tptacek 1y ago
  I'm not doing either of those things with it.
  - rockemsockem 1y ago
    What are some examples of things you are doing with it?
    - tptacek 1y ago
      Large scale server telemetry and fiddly OAuth2/Macaroon token management.
      - deleted 1y ago
        [deleted]
      - guyfhuo 1y ago
        This seems a self servingly literal interpretation of the op’s original comment. Clearly something like “server telemetry” is the datacenter’s “CRUD app” analogue. It’s a solved problem that largely requires rtfm and rote execution of well worn patterns in code structure. Please stick to the comment guidelines: > Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
        sshine 1y ago
        Just to say: setting up oauth and telemetry are harder than building CRUD, because it comes later in the learning progression. But it’s still mostly just following a recipe; LLMs are good at finding recipes that are uncommon, but available in the dataset. They break more when you go off script and you don’t take tiny steps.
        tptacek 1y ago
        I'm laughing at the idea of server telemetry being "the data center equivalent of a CRUD app". I'll just pull out my Telemetry On Rails framework and run some generators...
        guyfhuo 1y ago
        I’d rather write server telemetry logic than any customer facing ui app, and it sounds like you would too.
        tptacek 1y ago
        I'd rather write a new Device Mapper target than do either of those things, and LLMs have been helpful with that too. Is a Device Mapper target the "CRUD app of the Linux kernel"?
    - int_19h 1y ago
      I work on a VSCode debugger extension that uses gdb as a backend, and I've had some success tackling bugs in that using Cursor + Gemini 2.5 Pro. The codebase is in TypeScript. Based from what I've seen, Python and TypeScript are where it fares best. Other languages are much more hit and miss.
slurpyb 1y ago
You are not alone! I strongly agree and I feel like I am losing my mind reading some of the comments people have about these services.
- jckahn 1y ago
  For me it's an additional tool, not the only tool. LSPs are still better for half of the things I do daily (renaming things, extracting things, finding symbols, etc.). I can't imagine using AI for everything and even meeting my current velocity.
- SchemaLoad 1y ago
  I swear the people posting this stuff are just building todo list tier apps from scratch. On even the most moderately large codebase it completely breaks down.
hx8 1y ago
Probably 80% of the time I spend coding, I'm inside a code file I haven't read in the last month. If I need to spend more than 30 seconds reading a section of code before I understand it, I'll ask AI to explain it to me. Usually, it does a good job of explaining code at a level of complexity that would take me 1-15 minutes to understand, but does a poor job of answering more complex questions or at understanding more complex code. It's a moderately useful tool for me. I suspect the people that get the most use out of are those that would take more than 1 hour to read code I would take 10 minutes to read. Which is to say the least experienced people get the most value.
lukan 1y ago
Yesterday I gave cursor a try and made my first (intentionally very lazy) vibe coding approach (a simple threejs project). It accepted the task and did things, failed, did things, failed, did things ... failed for good. I guess I could work on the magic incantations to tweak here and there a bit until it works and I guess that's the way it is done. But I wasn't hooked. I do get value out of LLM's for isolated broken down subtasks, where asking a LLM is quicker than googling. For me, AI will probably become really usefull, once I can scan and integrate my own complex codebase so it gives me solutions that work there and not hallucinate API points or jump between incompatible libary versions (my main issue).
- rockemsockem 1y ago
  I did almost the same thing and had pretty much the same experience. A lot of times it felt so close to being great, but it ultimately wasted more time than if I had just worked on the project and occasionally asked chat GPT to generate some sample code to figure out an API
- christophilus 1y ago
  I’ve had the same experience with Cursor. Claude Code, though, has been a game changer. It is really excellent.
colechristensen 1y ago
Some people do really repetitive or really boilerplate things, others do not. Also you have to learn to talk to it and how to ask it things.
- __loam 1y ago
  "The interface is natural language" "The programming language of the future will be English" --- "Well are you using it right? You have to know how to use it"
jiggawatts 1y ago
I’ve had good experiences using it, but with the caveat that only Gemini Pro 2.5 has been at all useful, and only for “spot” tasks. I typically use it to whip up a CLI tool or script to do something that would have been too fiddly otherwise. While sitting in a Teams meeting I got it to use the Roslyn compiler SDK in a CLI tool that stripped a very repetitive pattern from a code base. Some OCD person had repeated the same nonsense many thousands of times. The tool cleaned up the mess in seconds.
- _345 1y ago
  >Some OCD person had repeated the same nonsense many thousands of times. The tool cleaned up the mess in seconds. What were they doing?
  - jiggawatts 1y ago
    try { ... } catch (Exception e) { throw e; } That does nothing except add visual noise. It's like a magic incantation to make the errors go away (it doesn't actually), probably by someone used to Visual Basic's "ON ERROR RESUME NEXT" or some such.
tptacek 1y ago
The first and most important question to ask here is: are you using a coding agent? A lot of times, people who aren't getting much out of LLM-assisted coding are just asking Claude or GPT for code snippets, and pasting and building them themselves (or, equivalently, they're using LLM-augmented autocomplete in their editor). Almost everybody doing serious work with LLMs is using an agent, which means that the LLM is authoring files, linting them, compiling them, and iterating when it spots problems. There's more to using LLMs well than this, but this is the high-order bit.
- __mharrison__ 1y ago
  What agent do you recommend?
  - tptacek 1y ago
    I think they're all fine. Cursor is popular and charges a flat fee for model calls (interposed through their model call router, however that works). Aider is probably the most popular open source command line one. Claude Code is probably the most popular command line agent overall; Codex is the OpenAI equivalent (I like Codex fine). later Oh, I like Zed a lot too. People complain that Zed's agent (the back-and-forth with the model) is noticeably slower than the other agents, but to me, it doesn't matter: all the agents are slow enough that I can't sit there and wait for them to finish, and Zed has nice desktop notifications for when the agent finishes. Plus you get a pretty nice editor --- I still write exclusively in Emacs, but I think of Zed as being a particularly nice code UI for an LLM agent.
  - haiku2077 1y ago
    I've been having okayish results with Zed + Claude 3.7
  - kasey_junk 1y ago
    Speaking up for Devin.ai here. What I like about it is that after the initial prompt nearly all of my interaction with it is via pull request comments. I have this workflow where I trigger a bunch of prompts in the morning, lunch and at the end of the day. At those same times I give it feedback. The async nature really means I can have it work on things I can’t be bothered with myself.
bandoti 1y ago
For my workflow a lot of the benefit is in the smaller tasks. For example, get a medium size diff from two change-sets, paste it in, and ask to summarize the “what” and “why”. You have to learn how to give AI the right amount of context to get the right result back. For code changes I prefer to paste a single function in, or a small file, or error output from a compile failure. It’s pretty good at helping you narrow things down. So, for me, it’s a pile of small gains where the value is—because ultimately I know what I generally want to get done and it helps me get there.
etler 1y ago
I find it's incredibly helpful for prototyping. These tools quickly reach a limit of complexity and put out sub par code, but for a green field prototype that's ok. I've successfully been able to test out new libraries and do explorations quickly with AI coding tools and I can then take those working examples and fix them up manually to bring them up to my coding standards. I can also extend the lifespan of coding tools by doing cleanup cycles where I manually clean up the code since they work better with cleaner encapsulation, and you can use them to work on one scoped component at a time. I've found that they're great to test out ideas and learn more quickly, but my goal is to better understand the technologies I'm prototyping myself, I'm not trying to get it to output production quality code. I do think there's a future where LLMs can operate in a well architected production codebase with proper type safe compilation, linting, testing, encapsulation, code review, etc, but with a very tight leash because without oversight and quality control and correction it'll quickly degrade your codebase.
protocolture 1y ago
>large, complex, potentially multi-language I find that token memory size limits are the main barrier here. Once the LLM starts forgetting other parts of the application, all bets are off and it will hallucinate the dumbest shit, or even just remove features wholesale.
tauoverpi 1y ago
I've had the same issue every time I've tried it. The code I generally work on is embedded C/C++ with in-house libraries where the tools are less than useful as they try to generate non-existant interfaces and generally generate worse code than I'd write by hand. There's a need for correctness and being able to explain the code thus use of those tools is also detrimental to explainability unless I hand-hold it to the point where I'm writing all of the code myself. Generating function documentation hasn't been that useful either as the doc comments generated offer no insight and often the amount I'd have to write to get it to produce anything of value is more effort than just writing the doc comments myself. For my personal project in zig they either get lost completely or gives me terrible code (my code isn't _that_ bad!). There seems to be no middle ground here. I've even tried the tools as pair programmers but they often get lost or stuck in loops of repeating the same thing that's already been mentioned (likely falls out of the context window). When it comes to others using such tools I've had to ask them to stop using it to think as it becomes next to impossible to teach / mentor if they're passing that I say to the LLM or trying to have it perform the work. I'm confident in debugging people when it comes to math / programming but with an LLM between it's just not possible to guess where they went wrong or how to bring them back to the right path as the throught process is lost (or there wasn't one to begin with). This is not even "vibe coding", I've just never found it generally useful enough to use day-to-day for any task and my primary use of say phind has been to use it as an alternative to qwant when I cannot game the search query well enough to get the search results I'm looking for (i.e I ignore the LLM output and just look at the references).
- motorest 1y ago
  > I've had the same issue every time I've tried it. The code I generally work on is embedded C/C++ with in-house libraries where the tools are less than useful as they try to generate non-existant interfaces and generally generate worse code than I'd write by hand. That's because whatever training the model had, it didn't covered anything remotely similar to the codebase you worked on. We get this issue even with obscure FLOSS libraries. When we fail to provide context to LLMs, they generate examples by following supperficial queues like coding conventions. In extreme cases, such as code that employs source code generators or templates, LLMs even fill in function bodies that code generators are designed to generate for you. That's because, if LLMs are oblivious to the context, they resort to hallucinate their way into something seemingly coherent. Unless you provide them with context or instruct them not to make up stuff, they will resort to bullshit their way into an example. What's truly impressive about this is that often times the hallucinated code actually works. > Generating function documentation hasn't been that useful either as the doc comments generated offer no insight and often the amount I'd have to write to get it to produce anything of value is more effort than just writing the doc comments myself. Again,this suggest a failure on your side for not providing any context. If you give it enough context LLMs synthesize and present them almost instantly. If you're prompting a LLM to generate documentation, which boils down to synthesizing what an implementation does and what's their purpose,and the LLM comes up empty, that means you failed to give it anything to work on. The bulk of your comment screams failure to provide any context. If your code steers far away from what it expects, fails to follow any discernible structure, and doesn't even convey purpose and meaning in little things like naming conventions, you're not giving the LLM anything to work on.
danbolt 1y ago
I appreciate you voicing your feelings here. My previous employer requested we try AI tooling for productivity purposes, and I was finding myself in similar scenarios to what you mention. The parts that would have benefitted from a productivity gain weren’t seeing any improvement, while the areas that saw a speedup weren’t terribly mission-critical. The one thing I really appreciated though was the AI’s ability to do a “fuzzy” search in occasional moments of need. Or, for example, sometimes the colloquial term for a feature didn’t match naming conventions in source code. The AI could find associations in commit messages and review information to save me time rummaging through git-blame. Like I said though, that sort of problem wasn’t necessarily a bottleneck and could often be solved much more cheaply by asking around coworker on Slack.
theshrike79 1y ago
Just yesterday I wanted to adjust the Renovate setup in a repo. I could spend 5-10 minutes digging on through the docs for the correct config option, or I can just tap a hotkey, open up GitHub Copilot in Rider and tell it what I want to achieve. And within seconds it had a correct-looking setting ready to insert to my renovate.json file. I added it, tested it and it works. I kinda think people who diss AIs are prompting something like "build me Facebook" and then being disappointed when it doesn't :D
gdubs 1y ago
Incredibly useful for 'glue code' or internal apps that are for automating really annoying processes - but where normally the time it would take to develop those tools would add up and take away from the core work. For instance, dealing with files that don't quite work correctly between two 3D applications because of slightly different implementations. Ask for a python script to patch the files so that they work correctly – done almost instantly just by describing the problem. Also for prototyping. Before you spend a month crafting a beautiful codebase, just get something standing up so you can evaluate whether it's worth spending time on – like, does the idea have legs? 90% of programming problems get solved with a rubber ducky – and this is another valuable area. Even if the AI isn't correct, often times just talking it through with an LLM will get you to see what the solution is.
tom_m 1y ago
They aren't increasing productivity. In the short term. They are very handy tools that can help you learn a foreign code/base faster. They can help you when you run into those annoying blockers that usually take hours or days or a second set of eyes to figure out. They give you a sounding board and help you ask questions and think about the code more. Big IF here. IF you bother to read. The danger is some people just keep clicking and re-prompting until something works, but they have zero clue what it is and how it works. This is going to be the biggest problem with AI code editors. People just letting Jesus take the wheel and during this process, inefficient usage of the tools will lead to slower throughput and a higher bill. AI costs a good chunk of change per token and that's only going up. I do think it's addictive for sure. I also think the "productivity boost" is a feeling people get, but no one measures. I mean, it's hard to measure. Then again, if you do spend an hour on a problem you get stuck on vs 3 days then sure it helped productivity. In that particular scenario. Averaged out? Who knows. They are useful tools, they are just also very misunderstood and many people are too lazy to take the time to understand them. They read headlines and unsubstantiated claims and get overwhelmed by hype and FOMO. So here we are. Another tech bubble. A super bubble really. It's not that the tools won't be with us for a long time or that they aren't useful. It's that they are way way overvalued right now.

Starlevel004 1y ago

> Is there something I'm missing? Am I just not using it right? The talk about it makes more sense when you remember most developers are primarily writing CRUD webapps or adware, which is essentially a solved problem already.

tptacek 1y ago
I'm not doing either of those things with it.
- rockemsockem 1y ago
  What are some examples of things you are doing with it?
  - tptacek 1y ago
    Large scale server telemetry and fiddly OAuth2/Macaroon token management.
    - deleted 1y ago
      [deleted]
    - guyfhuo 1y ago
      This seems a self servingly literal interpretation of the op’s original comment. Clearly something like “server telemetry” is the datacenter’s “CRUD app” analogue. It’s a solved problem that largely requires rtfm and rote execution of well worn patterns in code structure. Please stick to the comment guidelines: > Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
      - sshine 1y ago
        Just to say: setting up oauth and telemetry are harder than building CRUD, because it comes later in the learning progression. But it’s still mostly just following a recipe; LLMs are good at finding recipes that are uncommon, but available in the dataset. They break more when you go off script and you don’t take tiny steps.
      - tptacek 1y ago
        I'm laughing at the idea of server telemetry being "the data center equivalent of a CRUD app". I'll just pull out my Telemetry On Rails framework and run some generators...
        guyfhuo 1y ago
        I’d rather write server telemetry logic than any customer facing ui app, and it sounds like you would too.
        tptacek 1y ago
        I'd rather write a new Device Mapper target than do either of those things, and LLMs have been helpful with that too. Is a Device Mapper target the "CRUD app of the Linux kernel"?
  - int_19h 1y ago
    I work on a VSCode debugger extension that uses gdb as a backend, and I've had some success tackling bugs in that using Cursor + Gemini 2.5 Pro. The codebase is in TypeScript. Based from what I've seen, Python and TypeScript are where it fares best. Other languages are much more hit and miss.

slurpyb 1y ago

You are not alone! I strongly agree and I feel like I am losing my mind reading some of the comments people have about these services.

jckahn 1y ago
For me it's an additional tool, not the only tool. LSPs are still better for half of the things I do daily (renaming things, extracting things, finding symbols, etc.). I can't imagine using AI for everything and even meeting my current velocity.
SchemaLoad 1y ago
I swear the people posting this stuff are just building todo list tier apps from scratch. On even the most moderately large codebase it completely breaks down.

hx8 1y ago

Probably 80% of the time I spend coding, I'm inside a code file I haven't read in the last month. If I need to spend more than 30 seconds reading a section of code before I understand it, I'll ask AI to explain it to me. Usually, it does a good job of explaining code at a level of complexity that would take me 1-15 minutes to understand, but does a poor job of answering more complex questions or at understanding more complex code. It's a moderately useful tool for me. I suspect the people that get the most use out of are those that would take more than 1 hour to read code I would take 10 minutes to read. Which is to say the least experienced people get the most value.

lukan 1y ago

Yesterday I gave cursor a try and made my first (intentionally very lazy) vibe coding approach (a simple threejs project). It accepted the task and did things, failed, did things, failed, did things ... failed for good. I guess I could work on the magic incantations to tweak here and there a bit until it works and I guess that's the way it is done. But I wasn't hooked. I do get value out of LLM's for isolated broken down subtasks, where asking a LLM is quicker than googling. For me, AI will probably become really usefull, once I can scan and integrate my own complex codebase so it gives me solutions that work there and not hallucinate API points or jump between incompatible libary versions (my main issue).

rockemsockem 1y ago
I did almost the same thing and had pretty much the same experience. A lot of times it felt so close to being great, but it ultimately wasted more time than if I had just worked on the project and occasionally asked chat GPT to generate some sample code to figure out an API
christophilus 1y ago
I’ve had the same experience with Cursor. Claude Code, though, has been a game changer. It is really excellent.

colechristensen 1y ago

Some people do really repetitive or really boilerplate things, others do not. Also you have to learn to talk to it and how to ask it things.

__loam 1y ago
"The interface is natural language" "The programming language of the future will be English" --- "Well are you using it right? You have to know how to use it"

jiggawatts 1y ago

I’ve had good experiences using it, but with the caveat that only Gemini Pro 2.5 has been at all useful, and only for “spot” tasks. I typically use it to whip up a CLI tool or script to do something that would have been too fiddly otherwise. While sitting in a Teams meeting I got it to use the Roslyn compiler SDK in a CLI tool that stripped a very repetitive pattern from a code base. Some OCD person had repeated the same nonsense many thousands of times. The tool cleaned up the mess in seconds.

_345 1y ago
>Some OCD person had repeated the same nonsense many thousands of times. The tool cleaned up the mess in seconds. What were they doing?
- jiggawatts 1y ago
  try { ... } catch (Exception e) { throw e; } That does nothing except add visual noise. It's like a magic incantation to make the errors go away (it doesn't actually), probably by someone used to Visual Basic's "ON ERROR RESUME NEXT" or some such.

tptacek 1y ago

The first and most important question to ask here is: are you using a coding agent? A lot of times, people who aren't getting much out of LLM-assisted coding are just asking Claude or GPT for code snippets, and pasting and building them themselves (or, equivalently, they're using LLM-augmented autocomplete in their editor). Almost everybody doing serious work with LLMs is using an agent, which means that the LLM is authoring files, linting them, compiling them, and iterating when it spots problems. There's more to using LLMs well than this, but this is the high-order bit.

__mharrison__ 1y ago
What agent do you recommend?
- tptacek 1y ago
  I think they're all fine. Cursor is popular and charges a flat fee for model calls (interposed through their model call router, however that works). Aider is probably the most popular open source command line one. Claude Code is probably the most popular command line agent overall; Codex is the OpenAI equivalent (I like Codex fine). later Oh, I like Zed a lot too. People complain that Zed's agent (the back-and-forth with the model) is noticeably slower than the other agents, but to me, it doesn't matter: all the agents are slow enough that I can't sit there and wait for them to finish, and Zed has nice desktop notifications for when the agent finishes. Plus you get a pretty nice editor --- I still write exclusively in Emacs, but I think of Zed as being a particularly nice code UI for an LLM agent.
- haiku2077 1y ago
  I've been having okayish results with Zed + Claude 3.7
- kasey_junk 1y ago
  Speaking up for Devin.ai here. What I like about it is that after the initial prompt nearly all of my interaction with it is via pull request comments. I have this workflow where I trigger a bunch of prompts in the morning, lunch and at the end of the day. At those same times I give it feedback. The async nature really means I can have it work on things I can’t be bothered with myself.

bandoti 1y ago

For my workflow a lot of the benefit is in the smaller tasks. For example, get a medium size diff from two change-sets, paste it in, and ask to summarize the “what” and “why”. You have to learn how to give AI the right amount of context to get the right result back. For code changes I prefer to paste a single function in, or a small file, or error output from a compile failure. It’s pretty good at helping you narrow things down. So, for me, it’s a pile of small gains where the value is—because ultimately I know what I generally want to get done and it helps me get there.

etler 1y ago

I find it's incredibly helpful for prototyping. These tools quickly reach a limit of complexity and put out sub par code, but for a green field prototype that's ok. I've successfully been able to test out new libraries and do explorations quickly with AI coding tools and I can then take those working examples and fix them up manually to bring them up to my coding standards. I can also extend the lifespan of coding tools by doing cleanup cycles where I manually clean up the code since they work better with cleaner encapsulation, and you can use them to work on one scoped component at a time. I've found that they're great to test out ideas and learn more quickly, but my goal is to better understand the technologies I'm prototyping myself, I'm not trying to get it to output production quality code. I do think there's a future where LLMs can operate in a well architected production codebase with proper type safe compilation, linting, testing, encapsulation, code review, etc, but with a very tight leash because without oversight and quality control and correction it'll quickly degrade your codebase.

protocolture 1y ago

>large, complex, potentially multi-language I find that token memory size limits are the main barrier here. Once the LLM starts forgetting other parts of the application, all bets are off and it will hallucinate the dumbest shit, or even just remove features wholesale.

tauoverpi 1y ago

I've had the same issue every time I've tried it. The code I generally work on is embedded C/C++ with in-house libraries where the tools are less than useful as they try to generate non-existant interfaces and generally generate worse code than I'd write by hand. There's a need for correctness and being able to explain the code thus use of those tools is also detrimental to explainability unless I hand-hold it to the point where I'm writing all of the code myself. Generating function documentation hasn't been that useful either as the doc comments generated offer no insight and often the amount I'd have to write to get it to produce anything of value is more effort than just writing the doc comments myself. For my personal project in zig they either get lost completely or gives me terrible code (my code isn't _that_ bad!). There seems to be no middle ground here. I've even tried the tools as pair programmers but they often get lost or stuck in loops of repeating the same thing that's already been mentioned (likely falls out of the context window). When it comes to others using such tools I've had to ask them to stop using it to think as it becomes next to impossible to teach / mentor if they're passing that I say to the LLM or trying to have it perform the work. I'm confident in debugging people when it comes to math / programming but with an LLM between it's just not possible to guess where they went wrong or how to bring them back to the right path as the throught process is lost (or there wasn't one to begin with). This is not even "vibe coding", I've just never found it generally useful enough to use day-to-day for any task and my primary use of say phind has been to use it as an alternative to qwant when I cannot game the search query well enough to get the search results I'm looking for (i.e I ignore the LLM output and just look at the references).

motorest 1y ago
> I've had the same issue every time I've tried it. The code I generally work on is embedded C/C++ with in-house libraries where the tools are less than useful as they try to generate non-existant interfaces and generally generate worse code than I'd write by hand. That's because whatever training the model had, it didn't covered anything remotely similar to the codebase you worked on. We get this issue even with obscure FLOSS libraries. When we fail to provide context to LLMs, they generate examples by following supperficial queues like coding conventions. In extreme cases, such as code that employs source code generators or templates, LLMs even fill in function bodies that code generators are designed to generate for you. That's because, if LLMs are oblivious to the context, they resort to hallucinate their way into something seemingly coherent. Unless you provide them with context or instruct them not to make up stuff, they will resort to bullshit their way into an example. What's truly impressive about this is that often times the hallucinated code actually works. > Generating function documentation hasn't been that useful either as the doc comments generated offer no insight and often the amount I'd have to write to get it to produce anything of value is more effort than just writing the doc comments myself. Again,this suggest a failure on your side for not providing any context. If you give it enough context LLMs synthesize and present them almost instantly. If you're prompting a LLM to generate documentation, which boils down to synthesizing what an implementation does and what's their purpose,and the LLM comes up empty, that means you failed to give it anything to work on. The bulk of your comment screams failure to provide any context. If your code steers far away from what it expects, fails to follow any discernible structure, and doesn't even convey purpose and meaning in little things like naming conventions, you're not giving the LLM anything to work on.

danbolt 1y ago

I appreciate you voicing your feelings here. My previous employer requested we try AI tooling for productivity purposes, and I was finding myself in similar scenarios to what you mention. The parts that would have benefitted from a productivity gain weren’t seeing any improvement, while the areas that saw a speedup weren’t terribly mission-critical. The one thing I really appreciated though was the AI’s ability to do a “fuzzy” search in occasional moments of need. Or, for example, sometimes the colloquial term for a feature didn’t match naming conventions in source code. The AI could find associations in commit messages and review information to save me time rummaging through git-blame. Like I said though, that sort of problem wasn’t necessarily a bottleneck and could often be solved much more cheaply by asking around coworker on Slack.

theshrike79 1y ago

Just yesterday I wanted to adjust the Renovate setup in a repo. I could spend 5-10 minutes digging on through the docs for the correct config option, or I can just tap a hotkey, open up GitHub Copilot in Rider and tell it what I want to achieve. And within seconds it had a correct-looking setting ready to insert to my renovate.json file. I added it, tested it and it works. I kinda think people who diss AIs are prompting something like "build me Facebook" and then being disappointed when it doesn't :D

gdubs 1y ago

Incredibly useful for 'glue code' or internal apps that are for automating really annoying processes - but where normally the time it would take to develop those tools would add up and take away from the core work. For instance, dealing with files that don't quite work correctly between two 3D applications because of slightly different implementations. Ask for a python script to patch the files so that they work correctly – done almost instantly just by describing the problem. Also for prototyping. Before you spend a month crafting a beautiful codebase, just get something standing up so you can evaluate whether it's worth spending time on – like, does the idea have legs? 90% of programming problems get solved with a rubber ducky – and this is another valuable area. Even if the AI isn't correct, often times just talking it through with an LLM will get you to see what the solution is.

tom_m 1y ago

They aren't increasing productivity. In the short term. They are very handy tools that can help you learn a foreign code/base faster. They can help you when you run into those annoying blockers that usually take hours or days or a second set of eyes to figure out. They give you a sounding board and help you ask questions and think about the code more. Big IF here. IF you bother to read. The danger is some people just keep clicking and re-prompting until something works, but they have zero clue what it is and how it works. This is going to be the biggest problem with AI code editors. People just letting Jesus take the wheel and during this process, inefficient usage of the tools will lead to slower throughput and a higher bill. AI costs a good chunk of change per token and that's only going up. I do think it's addictive for sure. I also think the "productivity boost" is a feeling people get, but no one measures. I mean, it's hard to measure. Then again, if you do spend an hour on a problem you get stuck on vs 3 days then sure it helped productivity. In that particular scenario. Averaged out? Who knows. They are useful tools, they are just also very misunderstood and many people are too lazy to take the time to understand them. They read headlines and unsubstantiated claims and get overwhelmed by hype and FOMO. So here we are. Another tech bubble. A super bubble really. It's not that the tools won't be with us for a long time or that they aren't useful. It's that they are way way overvalued right now.