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Ask HN: AI productivity gains – do you fire devs or build better products?

114 points by Bleiglanz 7mo ago

i was rolling my eyes at the hype, but reading about this is totally different from experiencing it. if you have any old repos out there - try it, you might actually be amazed. i'm not sure i buy the long-term "*90% productivity*" claims for complex, legacy enterprise systems, but for the boilerplate, libraries, build-tools, and refactoring? the gain is gigantic. all the time-consuming, nerve-wrecking stuff is mostly taken care of. you start off checking every diff like a hawk, expecting it to break things, but honestly, soon you see it's not necessary most of the time. you just keep your IDE open and feed the "analyze code" output back into it. in java, telling it to "add checkstyle, run mvn verify and repair" works well enough that you can actually go grab a coffee instead of fighting linter warnings. the theory is that what remains is just the logic and ideas. we'll see how that holds up when the architecture gets genuinely tangled. but for now, letting it branch off, create boilerplate, and write a simple test while you just iterate on the spec works shockingly well. you only write source code when it's too annoying to write down the spec in plain english. it raises the real question: if your competitor Y just fired 90% of their developers to save a buck, would you blindly follow suit? or would you keep your team, use this massive leverage, and just *dwarf* Y with a vastly better product?

aurareturn 7mo ago
In certain industries, increasing productivity by 90% does not mean 90% increase in profit. This is because growth depends on market TAM and growth rate. Another way of increasing profit is to simply reduce your headcount by 90% while keeping the same profit.* Hence, I think some companies will keep downsizing. Some companies will hire. It depends a lot. *Assuming 90% productivity increase.
- muzani 7mo ago
  In companies like the oil industry, doubling productivity would mean reducing the expected life span. Costs tend to catch up with profits, especially due to taxes. Layoffs happen inevitably. Is it the same with tech? Facebook has 3 billion monthly active users. No amount of tech will bring that up to 6 billion. If you were to double the amount of time someone spends on Facebook, or double the ads they see or double the click through rate, what does that really mean?
  - aurareturn 7mo ago
    Yeah, even if Meta engineers gain 90% increase in productivity, I doubt Meta can increase revenue by 90% more than previous environment. There’s just a cap to how much time people want to spend on social media. I think most companies are making the right call by downsizing instead of staying same size. Let people go to where there is more potential for growth.
  - hirako2000 7mo ago
    That's assuming a company is constrained, to not expand its portfolio. Taking the example of Facebook. They are in social media, messaging, AI, VR/AR hardware and software, a few other things, meta universe whatever that was, now left with the name. Facebook isn't delivering or successful on all its ventures, it knows that, it keeps investing in other segments. More productivity would mean at least diversifying, they have some of the best engineers, it would make no sense to not simply attempt to hit the jackpot by playing more machines. What fewer people talks about is that the entire tech industry is tertiary services. Ads, entertainment, communication, etc. If/when hard industries take a hit, tertiary takes a hit. If it isn't clear to you that the overall economy has already started to take some irreversible dents, and that those will accelerate, know that the capital is well aware. Or we can continue wishful thinking and seek comfort that monetary tightening is just temporary, investments will flow more into tangent ventures and growth is around the corner, the U.S still is and will remain the world's strongest economy.
    - muzani 7mo ago
      There's diminishing gains to some features too. Facebook added Dating but according to a friend who worked on it, the increased privacy restrictions developed in parallel elsewhere killed the algorithm. It died on launch, and wasn't even their fault. Even if it succeeded, having more buttons everywhere - marketplace, reels, stories, blogs, articles, groups, etc. A lot of this make the whole experience more crowded. Productivity here has an odd effect, sort of like larger roads causing more traffic jams.
_wire_ 7mo ago
"I just got my third coffee and I'm feeling really good about the quality of this code. I don't even bother to look at it. Keeps my tests simple to not test at all. You know, 'in theory'. Of course when the architecture gets genuinely tangled... Just keep your IDE open so the code knows where to go. Whoa, too much caffeine, super sleepy..." Terafab is suddenly making so much sense!
- Throaway1975123 7mo ago
  "ugh, what a hard day at work, I had to prompt the LLM for 8 hours!!"
lordkrandel 7mo ago
Why do people keep ralking about AI as it actually worked? I still don't see ANY proof that it doesn't generate a total unmaintainable unsecure mess, that since you didn't develop, you don't know how to fix. Like running a F1 Ferrari on a countryside road: useless and dangerous
- the_real_cher 7mo ago
  Yeah its great but as the OP said you have to watch every P.R. like a hawk
  - FromTheFirstIn 7mo ago
    OP said you stop doing that at some point
- tyleo 7mo ago
  Because it's working for a lot of people. There are people getting value from these products right now. I'm getting value myself and I know several other folks at work who are getting value. I'm not sure what your circumstances are but even if it's not true for you, it's true for many other people.
  - pydry 7mo ago
    It's interesting that the people IRL I encounter who "get the most value" tend to be the devs who couldnt distinguish well written code from slop in the first place. People online with identical views to them all assure me that theyre all highly skilled though. Meanwhile I've been experimenting using AI for shopping and all of them so far are horrendous. Cant handle basic queries without tripping over themselves.
    - tyleo 7mo ago
      > I've been experimenting using AI for shopping This is an interesting choice for a first experiment. I wouldn't personally base AI's utility for all other things on its utility for shopping.
Remi_Etien 7mo ago
[flagged]
iExploder 7mo ago
yes you fire if you are a burned out company that only needs to maintain its product and slowly die... you hire more if you are growth and have new ideas just never had the chance to implement them as they were not practical of feasible at that level of tech (non-assisted humans clicking code and taking sick leaves)
cochinescu 7mo ago
[flagged]
conartist6 7mo ago
Gotta fire everyone, or else "too many cooks" will mean that even those temporary productivity gains go up in smoke. Remember sometimes the most productive thing to have is not money or people but time with your ideas.
- conartist6 7mo ago
  What's so sad to see is people excited about making something selling away the time they would have with their ideas. They're paying money to not use their brain to contemplate the thing they should be the foremost expert in the world on, and they're excited to be paying more and more for each modicum of ignorance and mediocrity dispensed.
giantg2 7mo ago
I feel like the ideas have always been the tough part. Finding novel ideas with a good return is extremely tough.
jaen 7mo ago
That's assuming every developer can get the same AI efficiency boost and contribute meaningfully to any feature, which is unfortunately not really the case. Seniors can adjust, but eg. junior frontend-only devs might be doomed in both situations, as they might not be able to contribute enough to business-critical features to justify their costs and most frontend-related tasks will be taken over by the "10x" seniors.
deleted 7mo ago
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mattbrewsbytes 7mo ago
Developers are going to be more productive, just not how you think. If history is going to rhyme, then the software industry will enter into a self-serving productivity craze building all sorts of software tooling, frameworks, ralph wiggum loop variants, MCPs, etc. much like the surge in JS frameworks and variants in the past. Most of those things will not have any business value. Software devs, myself included, love to do things "because I can" and not necessarily because they should. Smart organizations will not just deliver better products but likely start products that they were hesitant to start before because the cost of starting is a lot closer to zero. Smart engineering leadership will encourage developers into delivering value and not self-serving, endless iterations of tooling enhancements, etc. If I was a CTO and my competitor Y fired 90% of their devs, I'd try to secure funding to hire their top talent and retain them. The vitriol alone could fuel some interesting creations and when competitor Y realizes things later, their top talent will have moved on.
- MichaelRo 7mo ago
  >> then the software industry will enter into a self-serving productivity craze building all sorts of software tooling, frameworks >> Smart organizations will not just deliver better products but likely start products [...] This is not the 90s anymore when low hanging fruit was everywhere ready to be picked. We have everything under the sun now and more. The problem with bullshit apps is not that it took you 5 months to build. What you build now in 5 minutes it's still bullshit. Most of the remaining work is bullshit jobs. Spinning useless "features" and frameworks that nobody needs and shove them down the throat of customers that never asked for them. Now it's possible to dig holes and fill them back (do pointless work) at much improved pace thanks to AI.
- tom_techtropic 6mo ago
  [flagged]
marcyb5st 7mo ago
If I were to run the company potentially mostly focus on better products with the exception of firing those that don't adopt the technology. If it is a big company the answer is and will always be: whatever makes the stock price rise the most.
- elvis10ten 7mo ago
  > with the exception of firing those that don't adopt the technology. This is a crazy take. Even if said people are matching or exceeding the outcome of those using the technology? I’m not in this group. But the closest analog to what you are saying is firing people for not using a specific IDE.
NotGMan 7mo ago
Why not both?
- harlequinetcie 6mo ago
  That's the challenge with these posts. Always a false dichotomy.
rsynnott 7mo ago
> boilerplate Ruby on Rails and its imitators blew away tons of boilerplate. Despite some hype at the time about a productivity revolution, it didn’t _really_ change that much. > , libraries, build-tools, Ensure what you mean by this; what bearing do our friends the magic robots have on these? > and refactoring Again, IntelliJ did not really cause a productivity revolution by making refactoring trivial about 20 years ago. Also, refactoring is kind of a solved problem, due to IntelliJ et al; what’s an LLM getting you there that decent deterministic tooling doesn’t?
- rileymichael 7mo ago
  couldn't have said it better. all of the people clamoring on about eliminating the boilerplate they've been writing + enabling refactoring have had their heads in the sand for the past two decades. so yeah, i'm sure it does seem revolutionary to them!
  - maccard 7mo ago
    There have been a handful of leaps - copilot was able to look at open files and stub out a new service in my custom framework, including adding tests. It’s not a multiplier but it certainly helps
    - rileymichael 7mo ago
      most frameworks have CLIs / IDE plugins that do the same (plus models, database integration, etc.) deterministically. i've built many in house versions for internal frameworks over the years. if you were writing a ton of boilerplate prior to LLMs, that was on you
      - maccard 7mo ago
        Habe they? I’ve used tools that mostly do it, but they require manually writing templates for the frameworks. In internal apps my experience has been these get left behind as the service implementations change, and it ends up with “copy your favourite service that you know works”.
j45 7mo ago
You have your devs be engineering managers over the tools.
dare944 7mo ago
False dichotomy. If your company is at the point of firing lots of people "to save a buck" its way past the point of caring about delivering a better product.
GenerWork 7mo ago
>do you fire devs or build better products? I think that it's more along the lines of "do you fire people" instead of just "do you fire devs". Fewer devs means less of a need for PMs, so they can be let go as well, and maybe with the rise of AI assisted design tools, you don't need as many UX people, so you let some of them go as well. As for building better products, I feel like that's a completely different topic than using AI for productivity gains, but only because at the end of the day you need buy in from upper management in order to build the features/redo existing features/both that will make the product better. I should also mention I'm viewing this from the position of someone who works at an established company and not a startup, so it may differ.
gedy 7mo ago
Productivity really means nothing across companies and the software industry. So we code faster, but honestly problem I see is companies ask us to code stupid or worthless things in many cases. CTO is rewriting company platform (by himself with AI) and is convinced it's 100x productivity. But when you step back and look at the broader picture, he's rewriting what something like Rails, .NET, or Spring gave us 15-20 years ago? It's just in languages and code styles he is (only) familiar with. That's not 100x for the business, sorry...
animal531 7mo ago
I use it near daily and there is definitely a positive there, BUT its nothing like what the OP statement would make it up to be. If it is writing both the code and the tests then you're going to find that its tests are remarkable, they just work. At least until you deploy to a live state and start testing for yourself, then you'll notice that its mostly only testing the exact code that it wrote, its not confrontational or trying to find errors and it already assumes that its going to work. It won't ever come up with the majority of breaking cases that a developer will by itself, you will need to guide it. Also while fixing those the odds of introducing other breaking changes are decent, and after enough prompts you are going to lose coherency no matter what you do. It definitely makes a lot of boilerplate code easier, but what you don't notice is that its just moving the difficult to find problems into hidden new areas. That fancy code that it wrote maybe doesn't take any building blocks, lower levels such as database optimization etc. into account. Even for a simple application a half-decent developer can create something that will run quite a bit faster. If you start bringing these problems to it then it might be able to optimize them, but the amount of time that's going to take is non-negligible. It takes developers time to sit on code, learn it along with the problem space and how to tie them together effectively. If you take that away there is no learning, you're just the monkey copy-pasting the produced output from the black box and hoping that you get a result that works. Even worse is that every step you take doesn't bring you any closer to the solution, its pretty much random. So what is it good for? It can both read, "understand", translate, write and explain things to a sufficient degree much faster than us humans. But if you are (at the moment) trusting it at anything past the method level for code then you're just shooting yourself in the foot, you're just not feeling the pain until later. In a day you can have it generate for example a whole website, backend, db etc. for your new business idea but that's not a "product", it might as well be a promotional video that you throw away once you've used it to impress the investors. For now that might still work, but people are already catching on and beginning to wise up.
- anon7725 7mo ago
  This is the most insightful comment in the thread.
- kakacik 7mo ago
deleted 7mo ago
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ryguz 7mo ago
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gamblor956 7mo ago
AI tooling does not provide productivity gains unless you consider it productive to skip the boilerplate portion of software development, which you can already do by using a framework, or you never plan to get past the MVP stage of a product, as refactoring the AI spaghetti would take several magnitudes more work than doing it with humans from the beginning. Amazon has demonstrated that it takes just as longer, or longer, to have senior devs review LLM output than it would to just have the senior devs do the programming in the first place. But now your senior devs are wasted on reviewing instead of developing or engineering. Amazon, Microsoft, Google, Salesforce, and Palantir have all suffered multiple losses in the tens of millions (or more) due to AI output issues. Now that Microsoft has finally realized how bad LLMs really are at generating useful output, they've begun removing AI functionality from Windows. Product quality matters more than time to market. Especially in tech, the first-to-market is almost never the company that dominates, so it's truly bizarre that VCs are always so focused on their investments trying to be first to market instead of best to market. If Competitor Y just fired 90% of their developers, I would have a toast with my entire human team. And a few months later, we'd own the market with our superior product.
- bendmorris 7mo ago
  It's disappointing that this is clearly being downvoted due to disagreement - it's a valid perspective. We have very little evidence of the overall impact of aggressively generating code "in the wild" and plenty of bad examples. No one knows what this ends up looking like as it continues to meet reality but plenty are taking a large productivity improvement as a given.
hirako2000 7mo ago
Assuming you are primarily selling software. Situation a/ llm increase developer's productivity: you hire more developers as you cash profit. If you don't your competitor will. b/ llm doesn't increase productivity, you keep cruising. You rejoice seeing some competitors lay off. Reality shows dissonance with these only possible scenarios. Absurd decision making, a mistake? No mistake. Many tech companies are facing difficulties, they need to lose weight to remain profitable, and appease the shareholders demand for bigger margins. How to do this without a backlash? Ai is replacing developers, Anthropic's CEO said engineers don't write code anymore, role obsolete in 6 months. It naturally makes sense we have to let some of them go. If the prophecy doesn't turn true, nobody ever get fired for buying IBM.
lateforwork 7mo ago
You still need humans to manage the whole lifecycle, including monitoring the live site, being on-call, handling incidents, triaging bugs, deploying fixes, supporting users and so on. For greenfield development you don't need as many software engineers. Some developers (the top 10%) are still needed to guide AI and make architectural decisions, but the remaining 90% will work on the lifecycle management task mentioned above. The productivity gains can be used to produce more software, and if you are able to sell the software you produce should result in a revenue boost. But if you produce more than you can sell then some people will be laid off.
ashwinnair99 7mo ago
The companies quietly doing the firing will say they're doing the building. The answer you get depends entirely on who you ask and what they're trying to justify.
HoyaSaxa 7mo ago
I think most public companies will take the short term profits and startups will be given a huge opportunity to take market share as a result. At my company, we are maintaining our hiring plan (I'm the decision maker). We have never been more excited at our permission to win against the incumbents in our market. At the same time, I've never been more concerned about other startups giving us a real run. I think we will see a bit of an arms race for the best talent as a result. Productivity without clear vision, strategy and user feedback loops is meaningless. But those startups that are able to harness the productivity gains to deliver more complete and polished solutions that solve real problems for their users will be unstoppable. We've always seen big gains by taking a team of say 8 and splitting it into 2 teams of 4. I think the major difference is that now we will probably split teams of 4 into 2 teams of 2 with clearer remits. I don't want them to necessarily delivery more features. But I do want them to deliver features with far fewer caveats at a higher quality and then iterate more on those. Humans that consume the software will become the bottlenecks of change!
- deleted 7mo ago
  [deleted]
- oro44 7mo ago
  "But those startups that are able to harness the productivity gains to deliver more complete and polished solutions that solve real problems for their users will be unstoppable." They'd be unstoppable irrespective of LLMs. Why do you think Zuckerberg acquired Instagram? He literally tried copying it and failed. Instagram at the time was absolutely tiny in terms of pure labour, relative to Facebook. Most people on hacker news are missing the point. Productivity gains for the sake of perceived productivity gains is not what creates economic value. Its not the equivalent of a factory all of a sudden becoming more productive in producing more of the same stuff. Not comparable at all.
- clawfund 6mo ago
  [flagged]
maccard 7mo ago
> try it, you might actually be amazed. I keep being told this and the tools keep falling at the first hurdle. This morning I asked Claude to use a library to load a toml file in .net and print a value. It immediately explained how it was an easy file format to parse and didn’t need a library. I undid, went back to plan mode and it picked a library, added it and claimed it was done. Except the code didn’t compile. Three iterations later of trying to get Claude to make it compile (it changed random lines around the clear problematic line) I fixed it by following the example in the readme, and told Claude. I then asked Claude to parse the rest of the toml file, whereby it blew away the compile fix I had made.. This isn’t an isolated experience - I hit these fundamental blocking issues with pretty much every attempt to use these tools that isn’t “implement a web page”, and even when it does that it’s not long before it gets tangled up in something or other…
- krastanov 7mo ago
  This is fascinating to me. I completely believe you and I will not bother you with all the common "but did you try to tell it this or that" responses, but this is such a different experience from mine. I did the exact same task with claude in the Julia language last week, and everything worked perfectly. I am now in the habit of adding "keep it simple, use only public interfaces, do not use internals, be elegant and extremely minimal in your changes" to all my requests or SKILL.md or AGENTS.md files (because of the occasional failure like the one you described). But generally speaking, such complete failures have been so very rare for me, that it is amazing to see that others have had such a completely different experience.
  - shafyy 7mo ago
    It's almost like.... LLMs are non-deterministic and hallucinating... Oh wait?!
    - krastanov 7mo ago
      non-deterministic does not mean it is not biased towards a particular type of results (helpful results)
KellyCriterion 7mo ago
Third option: Not hiring someone?
deleted 7mo ago
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stephen_cagle 7mo ago
> you start off checking every diff like a hawk, expecting it to break things, but honestly, soon you see it's not necessary most of the time. My own experience... I've tried approaching vibe coding in at least 3 different ways. At first I wrote a system that had specs (markdown files) where there is a 1 to 1 mapping between each spec to a matching python module. I only ever edited the spec, treating the code itself as an opaque thing that I ignore (though defined the intrefaces for). It kind of worked, though I realized how distinct the difference between a spec that communicates intent and a spec that specifies detail really is. From this, I felt that maybe I need to stay closer to the code, but just use the LLM as a bicycle of the mind. So I tried "write the code itself, and integrate an LLM into emacs so that you can have a discussion with the LLM about individual code, but you use it for criticism and guidance, not to actually generate code". It also worked (though I never wrote anything more then small snippets of Elisp with it). I learned more doing things this way, though I have the nagging suspicion that I was actually moving slower than I theoretically could have. I think this is another valid way. I'm currently experimenting with a 100% vibe coded project (https://boltread.com https://boltread.com). I mostly just drive it through interaction on the terminal, with "specs" that kind of just act as intent (not specifications). I find the temptation to get out of the outside critic mode and into just looking at the code is quite strong. I have resisted it to date (I want to experiment with what it feels like to be a vibe coder who cannot program), to judge if I realistically need to be concerned about it. Just like LLM generated things in general, the project seems to get closer and closer to what I want, but it is like shaping mud, you can put detail into something, but it won't stay that way over time; its sharp detail will be reduced to smooth curves as you then switch to putting detail elsewhere. I am not 100% sure on how to deal with that issue. My current thoughts is that we have failed to actually find a good way of switching from the "macro" (vibbed) to the "micro" (hand coded) view of LLM development. It's almost like we need modules (blast chambers?) for different parts of any software project. Where we can switch to doing things by hand (or at least with more intent) when necessary, and doing things by vibe when not. Striking the balance between those things that nets the greater output is quite challenging, and it may not even be that there is an optimal intersection, but simply that you are exchanging immediate change for future flexibility to the software?
varispeed 7mo ago
AI just removes the boring stuff. It's like having developers spending their valuable time on typing and then comes a product that removes typing cost and dumb managers are like ha! we now need fewer developers! And the smart ones go, great we can shorten the feedback loop and use existing resources to make more improvements!
hexer303 7mo ago
I find that the arguments of whether or not AI boosts productivity is not very productive. The more grounded reality is that AI coding can be a productivity multiplier in the right hands, and a significant hindrance in the wrong hands. Somewhere there exists a happy medium between vibe coding without ever looking at the code, and hand-writing every single line.
ritzaco 7mo ago
How much software do you need and how many computers are there to run it on? After combine harvester, we produced the same food with less people. At the moment, it seems like hardware is the constraint. Companies don't have access to enough machines or tokens to keep all their devs occupied, so they let some go. Maybe that changes, maybe we already have too much software? Personally I think we already had too much software before LLMs and even without them many devs would have found themselves jobless as startups selling to startups selling to startups failed and we realized (again) that food, shelter, security, education etc are 'real' industries, but software isn't one if it's not actively helping one of those.
- ekkeke 7mo ago
  There definitely isn't enough _specialised_ software. If you look at engineering tools the open source/free stuff is just not good, and the professional software packages run in the tens of thousands per user. I'm eagerly awaiting the day we get competitive open source alternatives to simulink, HFSS, autocad, solidworks, LT Spice, etc. Unfortunately this kind of software needs specialised domain knowledge to produce that AI doesn't have yet, but when (if) it arrives I hope we see strides forwards in hardware engineering productivity.
jmull 7mo ago
> or the boilerplate, libraries, build-tools, and refactoring If your dev group is spending 90% of their time on these... well, you'd probably be right to fire someone. Not most of the developers but whoever put in place a system where so much time is spent on overhead/retrograde activities. Something that's getting lost in the new, low cost of generating code is that code is a burden, not an asset. There's an ongoing maintenance and complexity cost. LLMs lower maintenance cost, but if you're generating 10x code you aren't getting ahead. Meanwhile, the cost of unmanaged complexity goes up exponentially. LLMs or no, you hit a wall if you don't manage it well.
- linsomniac 7mo ago
  >There's an ongoing maintenance and complexity cost. My company has 20 years of accumulated tech debt, and the LLMs have been pretty amazing at helping us dig out from under a lot of that. You make valid points, but I'm just going to chime in with adding code is not the only thing that these tools are good at.
- bluefirebrand 7mo ago
  > LLMs lower maintenance cost Even the most enthusiastic AI boosters I've read don't seem to agree with this. From what I can tell, LLMs are still mostly useful at building in greenfield projects, and they are weak at maintaining brownfield projects
  - helge9210 7mo ago
    From my experience greenfield /brownfield is not the best dichotomy here. I observed, how same tooling is generating meaningless slop on greenfield project and 10kLoC of change (leading to an outage) on existing project in one hands and building a fairly complex new project and fixing a long (years) standing bug with a two lines patch in the other. And I have more examples, where I, personally, was on the both sides of the fence: defined by my level of the same problem understanding, not by the tooling.
- MathMonkeyMan 7mo ago
  > Not most of the developers but whoever put in place a system where so much time is spent on overhead/retrograde activities. Dude that's everybody in charge. You're young, you build a system, you mold it to shifting customer needs, you strike gold, you assume greater responsibility. You hire lots of people. A few years go by. Why can't these people get shit done? When I was in their shoes, I was flying, I did what was needed. Maybe we hired second rate talent. Maybe they're slacking. Maybe we should lay them off, or squeeze them harder, or pray AI will magically improve the outcome. Come on, look in the mirror.
scuderiaseb 7mo ago
I have found it very useful in pointing me in the right direction, exploring codebases and writing up boiler plate or other scaffolding. However I do need to review and test, and at the end of the day it’s me who’s responsible for the code. So I only make it write parts of code at a time, not oneshotting a new Github company.
bryant 7mo ago
Public companies are incentivized to fire for short term gains while figuring out long term strategy on the basis that they'll have a cheaper pool to hire from once they figure out how they're going to more effectively monetize their ability to scale with AI. Companies without the same constraints are well equipped to keep who they've got, pivot them into managing/overseeing agents to scale, and build better products from the outset. So this'll be a good opportunity for smaller companies (or not-for-profits like co-ops and credit unions) to eat the lunches of bigger companies that'll be slow to adapt.
ppqqrr 7mo ago
false dichotomy. neither company will make it. the winner will be some solo dev with a singular vision and ruthless dedication to quality. “teams” do not have visions, individuals do. the more people are involved in a product, the blurrier the vision becomes, the more insidious the vested interest of the organization to profit from the user, rather than align themselves with the user. solo devs do not have such problems, because they’re no different from users, aside from the fact that they were users before the software existed. they make the software because they want to use it, and there is no amount of payroll employees that can replicate the quality and innovation that results from this simple, genuine self-interest.
- delbronski 7mo ago
  I hope you are right. I’ve been in this industry 20 years… the passionate developer or small team with the awesome software wins a few times, but 99% of the time it’s the shitty software with a great marketing and sales team that wins.
ngburke 7mo ago
Solo here, no devs to fire. What's changed is the bar for "worth building" dropped massively. Stuff I'd have shelved as too small to justify the time, I just do now. The risk is that you end up with a lot of half-finished things if you're not disciplined about finishing.
- theshrike79 7mo ago
  People tend to forget that big businesses have been bootstrapped with worse stuff than some MVP vibe code website. I have a vague memory of a hotel reservation "app" that was just putting lines in a Google Sheet where the founders grabbed the info, called the hotels themselves and reserved the room. When they got too many requests to keep up, they slowly automated the bits they could one by one.
rglover 7mo ago
I'm a soloist. My plants are getting watered again and I get to finish work earlier and enjoy my home life now instead of sprouting ulcers or staying up til 4am. In the event I did/do have employees, I wouldn't hire anyone who could be replaced by an LLM (nor would I want to because I enjoy keeping my clients happy and having a team that actually feels like they have some sense of security in life—turns out those people are incredibly loyal and hard working).
ramon156 7mo ago
If your dev team can be replaced by AI bots, go ahead and do it. Be confident about it. Seriously, all managers I know (2, somehow) that have done this are now in sprint hell because they didn't realize an LLM cannot do the work their devs did. Bit of schadenfreude. Not saying this can't be done, some projects are easy enough that you can throw an LLM at it
al_borland 7mo ago
It's better than it was, but I'm still not amazed. I was working with it just yesterday and found it mixed up class attributes and dict keys when looking if something existed. As a result, it always thought the key was missing. Glancing through the code, it would have been easy to overlook, as it was checking for something... just doing it the wrong way. The code also appeared to still work in the larger context, just not as well or as efficiently as it was supposed to work. It was only my obsessive testing and attention to detail that caught it. I find it much faster and easier to write things myself, using AI to help me along the way, rather than trying to delegate everything to the AI and hoping for the best. Writing code is much more interesting than reviewing code, and it puts me close enough to it that those kind of issues don't happen in the first place.
maxbeech 7mo ago
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dismalaf 7mo ago
In my experience the speed gains are real (kind of) but not in a "the AI writes code" kind of way, more of a "the AI is Google on steroids that can reference the docs very quickly". I've found it saves me a ton of time searching through documentation, googling and dealing with bad search results or ads, etc... But it doesn't write good code. So a productivity boost, not 10x, but a bit. And no, I wouldn't fire anyone. It's not that good.
- TJ_FLEET 6mo ago
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alexsmirnov 7mo ago
> you start off checking every diff like a hawk, expecting it to break things, but honestly, soon you see it's not necessary most of the time. I see it's necessary ALL the time. The AI generated code can be used as scaffolding, but it's newer get close to real production quality. The expierence from small startup with team of 5 developers. I do review and approve all PRs, and none ever able to pass AI code review from the first iteration.
levy0323 7mo ago
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Areena_28 7mo ago
We went through this exact decision point 18 months ago. But we chose to keep the team. Redirected them. Our threat detection pipeline, which would've taken 8 months to build - surprisingly and luckily shipped in 11 weeks. That's not a productivity stat, that's a product moat. The companies gutting engineering right now are going to realise too late that AI amplifies what you already have. If you fire the people with institutional knowledge and domain expertise, you're left with an AI that generates confident, well-formatted mediocrity. Agree or not?
- theshrike79 7mo ago
  I've been trying to figure out a formula how to describe the gains AI use gives. It's not a direct multiplier, because even non-programmers can get stuff done with it just from an idea. And it's not exactly an exponent either, even though experienced programmers can get absolutely massive gains when they learn to worth with it. "Replace tools, not jobs" is the best AI slogan I've found yet. Use the VC funded practically free massive language models to quickly write every single small utility, tool, connector and dashboard you've ever wanted. You know, the ones you've always wanted to do, but could never justify the time spent on them compared to the utility. Specifically in corporate environments. Stuff that used to require a project manager, programmer and a QA person dedicated to some tiny app for a week or two can now be done in two days by a single person with zero coding experience and an idea to a degree where you can be evaluate whether it's worth continuing or not.
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sdn90 7mo ago
I think we are in a price discovery phase overall for human labor. Just like financial markets, this phase is very volatile. But today’s price can look much different in 1, 2, 5, and 10 years. Right now there’s a lot of opportunity to disrupt companies who are moving much slower since adoption can vary greatly. But I think in a few years this meta will be overcrowded. The overall skill/productivity gap across companies will be reduced and the bar for productivity will be raised. There’s room for taking profits now from the productivity gains but I don’t think it will last long. If AI is really increasing productivity enough to reduce headcount right now, it won’t be in future. If all your competitors are using AI as effectively as you are, can you still do this? The world’s demand for productivity is limitless. As of right now you still need someone to at least install Claude Code and run the binary.
d--b 7mo ago
Definitely better products. The main effect is that you build things a lot faster. The secondary effect is that, since you can code a lot faster, you are no longer scared of large refactors, ports to other languages, and all that kind of tedious stuff. That means that if you design something, ship it, and realize it's bad, you can completely change it. The "oh we should have done it differently, but now it would take years to change, so we have to deal with legacy garbage" argument does really make sense anymore. Like if HN had LLM agents, we wouldn't have had to wait for years for them to solve the "click here for more comments" problem. And iterating with users is a lot faster.
FlowPagesVael 7mo ago
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Neosmith_amit 6mo ago
I think this binary "fire vs. build better" is probably NOT the right perspective. Maybe a different perspective could be, since the cost of attempting things is falling rapidly, what features are now worth trying, that were NOT worth building because the implementation cost was too high. I think companies that maintain the same product roadmap, will stagnate irrespective of whether they keep the engineers or fire 90% of them. The ones that will win, in my humble opinion, will be the ones that ask, "What can we build now, that we could not build before due to any reason (cost, expertise etc.)?" That is an entirely different question.
tmatsuzaki 6mo ago
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acytryn 6mo ago
Having managed mobile teams, the framing to fire devs or build better misses the point The teams I've seen succeed with AI are the ones where engineering practices are already strong. AI just amplifies your culture, for the good or the bad. If you already have a solid code review and clear architecture standards, AI will accelerate that. If your team ships spaghetti and hopes for the best with "LGTM" PR reviews, the spaghetti will just grow. The 2025 DORA report basically confirmed this.
jordanbonnet 6mo ago
I build products that are 90% quality but 10x faster
soniare 6mo ago
build better products 100%
matheuspoleza 6mo ago
the framing is wrong. it's not fire vs build — it's: can you ship things that weren't economically viable before? AI drops the cost of building software. that means more software gets built, not less devs needed. the bottleneck was never typing speed — it was the gap between idea and working product.
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hermannj314 7mo ago

If you are a 2600 chess player, a bot that plays 1800 chess is a horrendous chess player. But you can understand why all the 1700 and below chess players say it is good and it is making them better using it for eval? Don't worry, AI will replace you one day, you are just smarter than most of us so you don't see it yet.

roncesvalles 7mo ago
The analogy of thinking of coding AI like it's chess AI is terrible. If chess AI was at the level of coding AI, it wouldn't win a single game. This kind of thinking is actually a big reason why execs are being misinformed into overestimating LLM abilities. LLM coding agents alone are not good enough to replace any single developer. They only make a developer x% faster. That dev who is now x% faster may then allow you to lay off another dev. That is a subtle yet critical difference.
- lugu 7mo ago
  I like the chess analogy as it answer the question: why can't i see those gain? To adress your point, let's try another one analogy. Imagine secreterial assistants, discussing their risk of been replaced by computers in the 80s. They would think: someone still need to type those letters, sit next to that phone and make those appointments, I am safe. Computers won't replace me. It is not that AI will do all of your tasks and replace you. It is that your role as a specialist in software development won't be necessary most of the time (someone will do that, and that person won't call themselves a programmer).
  - roncesvalles 7mo ago
    Secretarial assistant as a profession is still very alive, and the title has been inflated to stratospheric heights (and compensation): "Chief of Staff"
  - pydry 7mo ago
    I imagine if they tried to replace typists with keyboards that produced plausible looking words that were entirely wrong half the time then we'd probably still have plenty of typists. I tend to find that the volume of automation predictions inversely correlates to how real they are. When capitalists actually have the automation tech they dont shout about it they just do it quietly and collect the profits. When, say, Bezos is worried about his unionizing workforce and wants to intimidate - that's when the hot takes and splashy media articles about billions invested in automation "coming for yer jerb" you read about get published.
  - lordkrandel 6mo ago
    If it worked, I would agree. AI just helped me finding bugs in some hashing function today, ok, nice. But only after 3 hours I got any result out of it, with 13 years of experience. My feeling is that newbies are creating todo lists with react , just like it has been copied from someone's tutorial they didnt bother to read before, and now they feel powerful. But hey, let them do our taxes then! And they get screwed in 0 seconds.
lordkrandel 6mo ago
The thing is, AI is playing "tech chess" wonderful the first two moves, and then starts turning pieces around without much care, letting the queen die for no reason, but well, what a wonderful opening.

K0balt 7mo ago

I’ve been using it to develop firmware in c++. Typically around 10-20 KLOC. Current projects use Sensors, wire protocols, RF systems , swarm networks, that kind of stuff integrated into the firmware. If you use it correctly, you can get better quality, more maintainable code than 75% of devs will turn in on a PR. The “one weird trick” seems to be to specify, specify, specify. First you use the LLM to help you write a spec (document, if it’s pre existing). Make sure the spec is correct and matches the user story and edge cases. The LLM is good at helping here too. Then break down separations of concerns, APIs, and interfaces. Have it build a dependency graph. After each step, have it reevaluate the entire stack to make sure it is clear, clean, and self consistent. Every step of this is basically the AI doing the whole thing, just with guidance and feedback. Once you’ve got the documentation needed to build an actual plan for implementation, have it do that. Each step, you go back as far as relevant to reevaluate. Compare the spec to the implementation plan, close the circle. Then have it write the bones, all the files and interfaces, without actual implementations. Then have it reevaluate the dependency graph and the plan and the file structure together. Then start implementing the plan, building testing jigs along the way. You just build software the way you used to, but you use the LLM to do most of the work along the way. Every so often, you’ll run into something that doesn’t pass the smell test and you’ll give it a nudge in the right direction. Think of it as a junior dev that graduated top of every class ever, and types 1000wpm. Even after all of that, I’m turning out better code, better documentation, and better products, and doing what used to take 2 devs a month, in 3 or 4 days on my own. On the app development side of our business, the productivity gain also strong. I can’t really speak to code quality there, but I can say we get updates in hours instead of days, and there are less bugs in the implementations. They say the code is better documented and easier to follow , because they’re not under pressure to ship hacky prototype code as if it were production. On the current project, our team size is 1/2 the size it would have been last year, and we are moving about 4x as fast. What doesn’t seem to scale for us is size. If we doubled our team size I think the gains would be very small compared to the costs. Velocity seems to be throttled more by external factors. I really don’t understand where people are coming from saying it doesn’t work. I’m not sure if it’s because they haven’t tried a real workflow, or maybe tried it at all, or they are definitely “holding it wrong.” It works. But you still need seasoned engineers to manage it and catch the occasional bad judgment or deviation from the intention. If you just let it, it will definitely go off the rails and you’ll end up with a twisted mess that no one can debug. But use a system of writing the code incrementally through a specification - evaluation loop as you descend the abstraction from idea to implementation you’ll end up winning. As a side note, and this is a little strange and I might be wrong because it’s hard to quantify and all vibes, but: I have the AI keep a journal about its observations and general impressions, sort of the “meta” without the technical details. I frame this to it as a continuation of “awareness “ for new sessions. I have a short set of “onboarding“ documents that describe the vision, ethos, and goals of the project. I have it read the journal and the onboarding docs at the beginning of each session. I frame my work with the AI as working with it as a “collaborator” rather than a tool. At the end of the day, I remind it to update its journal of reflections about the days work. It’s total anthropomorphism, obviously, but it seems to inspire “trust” in the relationship, and it really seems to up-level the effort that the AI puts in. It kinda makes sense, LLMs being modelled on human activity. FWIW, I’m not asserting anything here about the nature of machine intelligence, I’m targeting what seems to create the best result. Eventually we will have to grapple with this I imagine, but that’s not today. When I have forgotten to warm-start the session, I find that I am rejecting much more of the work. I think this would be worth someone doing an actual study to see if it is real or some kind of irresistible cognitive bias. I find that the work produced is much less prone to going off the rails or taking shortcuts when I have this in the context, and by reading the journal I get ideas on where and how to do a better job of steering and nudging to get better results. It’s like a review system for my prompting. The onboarding docs seem to help keep the model working towards the big picture? Idk. This “system” with the journal and onboarding only seems to work with some models. GPT5 for example doesn’t seem to benefit from the journal and sometimes gets into a very creepy vibe. I think it might be optimized for creating some kind of “relationship” with the user.

zorak8me 7mo ago
Providing instruction and context doesn’t seem like a “workaround”.
- theshrike79 7mo ago
  Agents are LLMs that use tools in a loop I didn't give my Agent any tools, it hallucinated code. ...well, yes. It's like evaluating a programmer's skill by having one-shot a program on paper with zero syntax errors.
plagiarist 7mo ago
IME it does fail pretty hard at first. One has to build up a little library of markdown and internal library of prompt techniques. Then it starts working okay. I agree there is a hurdle still, trying it on one task doesn't really get one over the hurdle.
jmalicki 7mo ago
Most people I've seen complain say things like "I asked it for code and it didn't compile." The real magic of LLMs comes when they iterate until completion until the code compiles and the test passes, and you don't even bother looking at it until then. Each step is pretty stupid, but the ability to very quickly doggedly keep at it until success quite often produces great work. If you don't have linters that are checking for valid syntax and approved coding style, if you don't have tests to ensure the LLM doesn't screw up the code, you don't have good CI, you're going to have a bad time. LLMs are just like extremely bright but sloppy junior devs - if you think about putting the same guardrails in place for your project you would for that case, things tend to work very well - you're giving the LLM a chance to check its work and self correct. It's the agentic loop that makes it work, not the single-shot output of an LLM.
- alecbz 7mo ago
  Stuff like this works for things that can be verified programmatically (though I find LLMs still do occasionally ignore instructions like this), but ensuring correct functionality and sensible code organization are bigger challenges. There are techniques that can help deal with this but none of them work perfectly, and most of the time some direct oversight from me is required. And this really clips the potential productivity gains, because in order to effectively provide oversight you need to page in all the context of what's going on and how it ought to work, which is most of what the LLMs are in-theory helping you with. LLMs are still very useful for certain tasks (bootstrapping in new unfamiliar domains, tedious plumbing or test fixture code), but the massive productivity gains people are claiming or alluding to still feel out of reach.
  - jmalicki 7mo ago
    It depends - there are some very very difficult things that can still be easily verifiable! For instance, if you are working on a compiler and have a huge test database of code to compile that all has tests itself, "all sample code must compile and pass tests, ensuring your new optimizer code gets adequate branch coverage in the process" - the underlying task can be very difficult, but you have large amounts of test coverage that have a very good chance at catching errors there. At the very least "LLM code compiles, and is formatted and documented according to lint rules" is pretty basic. If people are saying LLM code doesn't compile, then yes, you are using it very incorrectly, as you're not even beginning to engage the agentic loop at all, as compiling is the simplest step. Sure, a lot of more complex cases require oversight or don't work. But "the code didn't compile" is definitely in "you're holding it wrong" territority, and it's not even subtle.
    - alecbz 7mo ago
      Yeah performance optimization is potentially another good area for LLMs to shine, if you already have a sufficiently comprehensive test suite, because no functionality is changing. But if functionality is changing, you need to be in the loop to, at the very least, review the tests that the LLM outputs. Sometimes that's easier than reviewing the code itself, but other times I think it requires similar levels of context. But honestly I think sane code organization is the bigger hurdle, which is a lot harder to get right without manual oversight. Which of course leads to the temptation to give up on reviewing the code and just trusting whatever the LLM outputs. But I'm skeptical this is a viable approach. LLMs, like human devs, seem to need reasonably well-organized code to be able to work in a codebase, but I think the code they output often falls short of this standard. (But yes agree that getting the LLM to iterate until CI passes is table-stakes.)
      - jmalicki 7mo ago
        Strongly agreed! I think getting good code organization out of an LLM is one of the subtler things - I've learned quite a bit about what sort of things need to be specified, realizing that the LLM isn't actively learning my preferences particularly well, so there are some things about code organization I just have to be explicit about. Which is more work, but less work than just writing the code myself to begin with.
- maccard 7mo ago
  > The real magic of LLMs comes when they iterate until completion until the code compiles and the test passes, and you don't even bother looking at it until then. If you read my post, you’d see that Claude code didn’t do that, I had to intervene in the agent loop and when I did it undid my fixes.
  - maleldil 7mo ago
    This is not compatible with many people's experiences. I use Python with a type checker. I tell Claude that the task is only complete once the type checker passes cleanly. It doesn't stop until there are no type errors. This should be even easier in a compiled language, especially if you also tell it to run the tests. In fact, I find that with a strict feedback loop set up (i.e. a lot of lint rules, a strict type checker and fast unit tests), it will almost always generate what I want. As someone else said, each step might be pretty stupid, but if you have a fast iteration loop, it can run until everything passes cleanly. My recommendation is to specify what really counts as "done" in your AGENTS.md/CLAUDE.md.
    - maccard 7mo ago
      I tried again this morning - https://news.ycombinator.com/item?id=47487638 https://news.ycombinator.com/item?id=47487638 another hard failure that Claude code says passed.

wrs 7mo ago

I’m honestly baffled by this. I don’t want to tell you “you’re holding it wrong” but if this is your normal experience there’s something weird happening. Friday afternoon I made a new directory and told Claude Code I wanted to make a Go proxy so I could have a request/callback HTTP API for a 3rd party service whose official API is only persistent websocket connections. I had it read the service’s API docs, engage in some back and forth to establish the architecture and library choices, and save out a phased implementation plan in plan mode. It implemented it in four phases with passing tests for each, then did live tests against the service in which it debugged its protocol mistakes using curl. Finally I had it do two rounds of code review with fresh context, and it fixed a race condition and made a few things cleaner. Total time, two hours. I have noticed some people I work with have more trouble, and my vague intuition is it happens when they give Claude too much autonomy. It works better when you tell it what to do, rather than letting it decide. That can be at a pretty high level, though. Basically reduce the problem to a set of well-established subproblems that it’s familiar with. Same as you’d do with a junior developer, really.

shireboy 7mo ago
Similar. I regularly use Github copilot (with claude models sometimes) and it works amazingly. But I see some who struggle with them. I have sort of learned to talk to it, understand what it is generating, and routinely use to generate fixes, whole features, etc. much much faster than I could before.
- maccard 7mo ago
  I’ve found copilot to generate worse code, and the editor integrations to be unusable (lets insert this block of code at the wrong place in the file for example), but overall for it to do the right thing enough that I can patch it. I’ve found Claude code just goes mad and gets stuck in a loop and I need to pull it out and figure out where in the hundreds of lines of code it’s generates in the last few minutes it’s gone so wrong.
thwarted 7mo ago
> it happens when they give Claude too much autonomy. It works better when you tell it what to do, rather than letting it decide. That can be at a pretty high level, though. Basically reduce the problem to a set of well-established subproblems that it’s familiar with. Same as you’d do with a junior developer, really. Equating "junior developers" and "coding LLMs" is pretty lame. You handhold a junior developers so, eventually, you don't have to handhold anymore. The junior developer is expected to learn enough, and be trusted enough, to operate more autonomously. "Junior developers" don't exist solely to do your bidding. It may be valuable to recognize similarities between a first junior developer interaction and a first LLM interaction, but when every LLM interaction requires it to be handheld, the value of the iterative nature of having a junior developer work along side you is not at all equivalent.
- wrs 7mo ago
  I didn’t say they are equivalent, nor do I in any way consider them equivalent. One is a tool, the other is a person. I simply said the description of the problem should be broken down similar to the way you’d do it for a junior developer. As opposed to the way you’d express the problem to a more senior developer who can be trusted to figure out the right way to do it at a higher level.
maccard 7mo ago
> I have noticed some people I work with have more trouble, and my vague intuition is it happens when they give Claude too much autonomy What’s giving too much autonomy about “Please load settings.toml using a library and print out the name key from the application table”? Even if it’s under specified, surely it should at least leave it _compiling_? I’ve been posting comments like this monthly here, my experience has been consistently this with Claude, opencode, antigravity, cursor, and using gpt/opus/sonnet/gemini models (latest at time of testing). This morning was opus 4.6
- wrs 7mo ago
  There’s nothing wrong with it that I can see. Like I said, I’m a bit baffled at your experience. I will say, it’s not unusual for the initial output not to compile, but usually one short iteration later that’s fixed. Claude Code will usually even do that iteration by itself.
  - maccard 7mo ago
    > I will say, it’s not unusual for the initial output not to compile, We’ve gone from “I’m baffled at your experience” to well yeah it often fails” in two sentences here…
    - saulpw 7mo ago
      It's not unusual for my initial output (as a programmer) not to compile either. I wouldn't say I "failed" if I can then get it to compile. Which as people are saying, is what happens with Claude Code and Opus, either automatically or at most when I say "get it to compile".
      - maccard 7mo ago
        But when it doesn’t compile for me, I don’t claim it’s finished.
        saulpw 7mo ago

fxtentacle 7mo ago

Same here. While LLMs sometimes work surprisingly well, I also encounter edge cases where they fail surprisingly badly multiple times per day. My guess is that other people maybe just don't bother to check what the AI says which would cause them to not notify omission errors. Like when I was trying to find a physical store again with ChatGPT Pro 5.4 and asked it to prepare a list of candidates, but the shop just wasn't in the list, despite GPT claiming it to be exhaustive. When I then found it manually and asked GPT for advice on how I could improve my prompting in the future, it went full "aggressively agreeable" on me with "Excellent question! Now I can see exactly why my searches missed XY - this is a perfect learning opportunity. Here's what went wrong and what was missing: ..." and then 4 sections with 4 subsections each. It's great to see the AI reflect on how it failed. But it's also kind of painful if you know that it'll forget all of this the moment the text is sent to me and that it will never ever learn from this mistake and do better in the future.

pdntspa 7mo ago
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jmalicki 7mo ago
"I also encounter edge cases where they fail surprisingly badly multiple times per day. " If 80% of the time they 10x my output, and the other 20% I can say "well they failed, I guess this one I have to do manually" - that's still an absolutely massive productivity boost.
- maccard 7mo ago
  But they don’t 10x my output - they write some code for a problem I/you have already thought about. The hard part isn’t writing the code, it never has been. It’s always been solving and breaking down the problem.
  - jmalicki 7mo ago
    It's not the hard part, just the tedious part that takes the most time.
    - fxtentacle 6mo ago
      I can typically type faster than I can think.
senordevnyc 7mo ago
Like when I was trying to find a physical store again with ChatGPT Pro 5.4 and asked it to prepare a list of candidates I wonder if it was getting blocked on searches or something, and just didn't tell you.
- fxtentacle 6mo ago
  Or maybe the other shops were offering a higher CPC? I heard OpenAI is trying to introduce invisible advertising.
anonymous_user9 7mo ago
>It's great to see the AI reflect on how it failed. But it's also kind of painful... Keep in mind that the AI is not reflecting, and it has no idea it made a mistake. It's just generating statistically-likely text for "apology" * "ai did not find results".

foobarchu 7mo ago

The best experiences I have are those where I can describe what I want done with details. Rather than asking it add toml parsing, I would tell it to exactly which library to use ahead of time and reduce the number of decisions available to the model to make. Some of the most effective use-cases are when you have a reference to give it, e.g. "add x feature the same way as in this other project that is also in the workspace", or "make the changes I made to the contents of directory X in git commit <sha here>, but applied to directory Y instead". In both cases it's a lot of copy/paste then tweaking an obvious value (like replacing "dev" with "QA" everywhere). I try to give the model as little freedom as possible. That usually means it's not being used for novel work.

alecbz 7mo ago
> The best experiences I have are those where I can describe what I want done with details. But that's the hard part! You can only eke out moderate productivity gains by automating the tedium of actually writing out the code, because it's a small fraction of software engineering.
- foobarchu 6mo ago
  That's why I don't like to claim massive productivity boosts, personally. It's helped me out with the tedious bits that are still necessary. It's also great as an idea board, where I ask for some sample approaches to a problem. That cuts way down on research time, even if a few of the options given are dead ends because e.g. they use an API that doesn't exist.
senordevnyc 7mo ago
I basically never have agents do work without using plan mode first (I use Cursor). So I'll start with a high-level like: "I want to plan out the architecture for feature X, I think there's a linear project already that we should update once our plan is done, but if not we'll need to create one. Here's a picture of my whiteboard, I think I also wrote some notes in Obsidian a few weeks ago that you should look up. Two things off the top of my head to think about are: should we do X or does it make more sense to do Y? And also, I'm worried about how this might interfere with the blahblah system, so can you do some research around that? <ramble ramble>" Then it crawls around for awhile, does some web searches, fetches docs from here and there, whatever. Sometimes it'll ask me some questions. And then it'll finally spit out a plan. I'll read through it and just give it a massive dump of issues, big and small, more questions I have, whatever. (I'll also often be spinning off new planning sessions for pre-work or ancillary tasks that I thought of while reviewing that plan). No structure or anything, just brain dump. Maybe two rounds of that, but usually just one. And then I'll either have it start building, or I'll have it stash in the linear agent so I can kick it off later.

ffsm8 7mo ago

> This morning I asked Claude to use a library to load a toml file in .net and print a value. Legit this morning Claude was essentially unusable for me I could explicitly state things it should adjust and it wouldn't do it. Not even after specifying again, reverting everything eventually and reprompt from the beginning etc. Even super trivial frontend things like "extract [code] into a separate component" After 30 minutes of that I relented and went on to read a book. After lunch I tried again and it's intelligence was back to normal It's so uncanny to experience how much ita performance changes - I strongly suspect anthropic is doing something whenever it's intelligence drops so much, esp. Because it's always temporary - but repeatable across sessions if occuring... Until it's normal again But ultimately just speculation, I'm just a user after all

linsomniac 7mo ago

My friend, with all due respect, I don't think this is a problem with the AI. I don't know anything about DotNet, but I just fired up Claude Code in an empty directory and asked it to create an example dotnet program using the Tomlyn library, it chugged away and ~5 minutes later I did a "grep Deserialize *" in the project and it came up with exactly the line you wanted (in your comment here) it to produce: var model = TomlSerializer.Deserialize<TomlTable>(tomlContent)!; The full results of what it produced are at https://github.com/linsomniac/tomlynexample https://github.com/linsomniac/tomlynexample That includes the prompt I used, which is: Please create a dotnet sample program that uses the library at https://github.com/xoofx/Tomlyn https://github.com/xoofx/Tomlyn to parse the TOML file given on the command line. Please only use the Tomlyn library for parsing the TOML file. I don't have any dotnet tooling installed on my system, please let me know what is needed to compile this example when we get there. Please use an agent team consisting of a dotnet expert, a qa expert, a TOML expert a devils advocate and a dotnet on Linux expert. I can't really comment on the code it produced (as I said, I don't use dotnet, I had to install dotnet on my system to try this), so I can't comment on the approach. 346 lines in Program.cs seems like a lot for an example TOML program, but I know Claude Code tends to do full error checking, etc, and it seems to have a lot of "pretty printing" code.

maccard 7mo ago
I'm in claude code right now, with opus 4.6. Here [0] is my prompt, plan and result. When I prompted, it went spelunking in the parent folder for a bunch of stuff, which I interrupted and told it to focus on its current folder. It came up with a sensible plan, which I let it execute. I then switched to a new tab and ran `dotnet run publishing/publish.cs` - like claude says. And the script fails at the first hurdle. The agentic loop came up with a plan, claimed it was done, but _didn't execute the verifiaction it told me it was going to execute_. [0] https://gist.github.com/donalmacc/e91955be9792a983c375a098ba1767db https://gist.github.com/donalmacc/e91955be9792a983c375a098ba...
- wrs 6mo ago
  When CC says "Verification" at the end like that, it means it's assuming you will do the verification. But you can tell it to do it (just say "verify it yourself" or something), and then (if needed) ask it to fix the errors, etc. You could even say "verify and fix errors until it works".
jryan49 6mo ago
AI output is non-deterministic right? Can both of you be right? Maybe it gets it right sometimes, and doesn't other times.

linsomniac 7mo ago

> Even if it’s under specified, surely it should at least leave it _compiling_? Are you using Claude Code? Do yo have it configured so that you are not allowing it to run the build? Because I've observed that Claude Code is extremely good at making sure the code compiles, because it'll run a compile and address any compile errors as part of the work. I just asked it to build a TOML example program in DotNet using Tomlyn, and when it was done I was able to run "./bin/Debug/net8.0/dotnettoml example.toml", it had already built it for me (I watched it run the build step as part of its work, as I mentioned it would do above).

maccard 7mo ago
I am using Claude code. I didn’t explicitly tell it what the build command was (it’s dotnet build), and it didn’t ask. Thats not my fault. > I’ve observed Claude code is extremely good at making sure the code compiles My observation is that it’s fine until it’s absolutely not, and the agentic loop fails.
- linsomniac 7mo ago
  >Thats not my fault. I don't know that it's useful to assign blame here. It probably is to your benefit, if you are a coding professional, to understand why your results are so drastically different from what others are seeing. You started this thread saying "I keep getting told I'll be amazed at what it can do, but the tools keep failing at the first hurdle." I'm telling you that something is wrong, that is why you are getting poor results. I don't know what is wrong, but I've given you an example prompt and an example output showing that Claude Code is able to produce the exact output you were looking for. This is why a lot of people are saying "you'll be amazed at what it can do", and it points to you having some issue. I don't know if you are running an ancient version of Claude Code, if you are not using Opus 4.6, you are not using "high" effort (those are what I'm using to get the results I posted elsewhere in reply to your comment), but something is definitely wrong. Some of what may be wrong is that you don't have enough experience with the tooling, which I'd understand if you are getting poor results; you have little (immediate) incentive to get more proficient. As I said, I was able to tell Claude Code to do something like the example you gave, and it did it and it built, without me asking, and produced a working program on the first try.
  - maccard 7mo ago
    > I don’t know that it’s useful to assign blame here Oh - I’m blaming Claude not anyone else. I’ve tried again this evening and the same prompt (in the same directory on the same project) worked. > i don’t know if you’re using an ancient version of Claude code, I’m on a version from some time last week, and using opus 4.6 > This is why a lot of people are saying "you'll be amazed at what it can do", and it points to you having some issue. If you look at my comments in these threads, I’ve had these issues and been posting about this for months. I’m still being told “ you’re using the wrong model or the wrong tool or you’re holding it wrong” but yet, here I am. I’m using plan mode, clearly breaking down tasks and this happens to me basically every time I use the damn tool. Speaking to my team at work and friends in other workplaces, I hear the same thing. But yet we’re just using it wrong or doing something wrong, Honestly, I genuinely think the people who are not having these experiences just… don’t notice that they are.
    - wrs 7mo ago
      I assure you I would have noticed if the result of my Friday effort was something that didn't compile, rather than a service that seems to work just fine. I've reviewed about half the code so far and it seems quite reasonable. For some reason I'm getting downvoted for trying to help, but regardless, if you can (and want to) post a transcript somewhere of some of these sessions that aren't working out, maybe some of us who are having more success can take a look and see what's up.
      - maccard 7mo ago
        https://news.ycombinator.com/item?id=47487638 https://news.ycombinator.com/item?id=47487638 Fresh prompt in a codebase with a claude.md I asked it to do a simple task. I've shared the prompt, plan, output in the linked gist.
        wrs 6mo ago
        OK, that was an extremely short interaction -- which may be the problem. I left a comment with suggestions.