Probably a first world problem, but with Opus 5.5's efficiency, the limits on the 5x plan are simply sufficient for my everyday work, even when running 2-3 sessions at a time. So I wonder when I would use Sonnet 5.5.
More concurrency than that isn't really practical for me if I want to retain some semblance of understanding. Perhaps it's different for purely web app or frontend tasks, where the outcome is more relevant than the process, I don't have much experience there (and also don't want to belittle these domains, I might be underestimating their complexity).
So surprisingly, my own work is at least for the time being almost saturated by the model capabilities. I am not sure how I'd scale from here. Sure I could run all requests at max effort to burn tokens for the sake of it, but that can't be it. And for many tasks, I am not really able to define so clear cut success criteria or self-verification loops that I could benefit from letting an agent (or a fleet thereof) autonomously run for a day.
So I realize it's a skill issue on my side, but I can't be the only one. I wonder if there is a limit to token demand, at least short term. Feels like either they accelerate to AGI and RSI, where the AI can find uses for token, or things might plateau at some point.
Note I don't think this because I'm an AGI skeptic or think there's a ceiling to intelligence, but there might simply be a valley of economic hardship for the companies where the supply of tokens outpaces the demand, due to a lack of ideas of what to do with them. And this might slow down the funding enough that they never reach escape velocity with the training run scaling. But we'll see.
It's the "semblance of understanding" you're holding on to that is keeping your demand limited. I'm holding onto it as well, but I think these companies are assuming that human understanding will no longer be relevant for most codebases going forward.
> It's the "semblance of understanding" you're holding on to that is keeping your demand limited. I'm holding onto it as well, but I think these companies are assuming that human understanding will no longer be relevant for most codebases going forward.
In short, seems to describe vibe-coding to me?
What I don't understand about companies attempting to vibe code is if they realize that other people (especially sometimes their customers) can tailor-made their own software for their own needs, or rather competitors can be dime a dozen and maybe even a fight for constantly paying for the better model.
There was a comment[0] from a just few days ago by @jjcm (which I wish to quote which I hope they don't mind.):
> I just got back from a 2 week trip to China. I was in some of the more remote parts and my cell wasn't able to connect to their towers in that area, resulting in me not having the tourist VPN.
> The side effect was I was fully cut off from my AI tools for those two weeks. I was coding "manually" during that time, and I think I accompished in two weeks what I previously had been able to do in a day. I'm not gonna lie, it was very, very stressful as a solo founder.
> The industry moves so fast these days, that the only way to keep up with the speed is to leverage them. While I can appreciate the push of this to help your brain think independently/critically, the opportunity cost of a month of development without LLMs is too high a price to pay.
What happens if the opportunity cost of a month of development with vs without human understanding becomes too high a price to pay. I feel like we would be in awkward time because of the factors that I had described above (higher competition, software stops meaning just as much software as people would be custom-making them.)
I think that (former fly.io's) @tptacek's article[1] starts making more sense if viewed from this direction: What even is an OS now.
I don't have the answer to this question as to what happens next but its a form of development that I would prefer not to happen on a more gut instinct level?
Letting AI basically control everything and us not having any mental understanding of sorts and sort of becoming the meat-proxies just for economical reasons seems realistic possibility but a bleaker reality at that. I am left feeling a little bit uncomfortable if this reality turns out to be true.
> Letting AI basically control everything and us not having any mental understanding of sorts and sort of becoming the meat-proxies just for economical reasons seems realistic possibility but a bleaker reality at that. I am left feeling a little bit uncomfortable if this reality turns out to be true.
For the past year I’ve been yo-yo-ing in and out of existential despair about the future of civilization depending on how I feel the answer to this question looks. It’s emotionally exhausting, on top of everything else, and I wonder how others are coping with it aside from denial and cynicism.
It sorta feels to me like extrapolating from "the internet has all the knowledge for free" to "we won't need tradespeople anymore"
Why hire a plumber when you can just watch some youtube videos and do it yourself?
Why pay someone else for their software when you can just make your own?
Because the hard part of making software wasn't *just* writing the code. It was about understanding the problem well enough to understand what the solution should look like.
I feel like as software engineers we should be pretty familiar with what it's like talking to your average user, they will sometimes understand the root cause of what's making their task difficult (although often will get focused on some annoying but ultimately trivial symptom) and have very disasterously bad ideas on how to solve it.
What we've given them with generative AI is a machine they can put their sometimes ok, sometimes questionable understanding of the problem and their dreadful solutions and it will happily churn away building it regardless of how pointless and silly it is.
A future where every user can tell the AI "We keep getting the sales tax wrong, remove charging sales tax from the checkout flow" isn't one I'm terrifically worried about.
In the same way that having access to information about plumbing didn't suddenly make everyone plumbers, having access to a machine that will implement every idea you have regardless of quality doesn't suddenly make everyone a software engineer.
The plumbing analogy doesn’t match software. You pay for a plumber once and you can always choose a new plumber. With software, it’s a monthly fee that will continue to increase over time. More features behind higher tiers. And probably taking and selling your data. So why wouldn’t a person try to build something custom for their needs? They have the ultimate feedback loop of actually using the product and telling AI the issue and having AI fix it. Most software people create are probably something they never used it their lives. But the best software comes from people building it that also use it. Even Shopify started when Tobi started a snowboarding store online and couldn’t suitable e-commerce software.
You’re right about people not watching plumbing videos and doing it themselves. But the equivalent example would be open-source software in the tech example. But instead of reading open-source code to see how different features were implemented, AI can go and dig into the code and figure it out.
You’re right, the monetization of software is fading and building actual moats is becoming difficult. If you have distribution, regardless of your app, you still have a long run way.
I was more pointing out that software gets enshittified. A plumber necessarily doesn’t and if the plumber does get worse, you can call a different one next time. Software, especially B2B, has switching costs and lock in. So you just have to put up with it.
Another point is that most software started with a few features and to get more market share and support more use cases, it became worse for the users using the early features. That’s why they try to build their own so it’s not bloated with features you will never use.
> A future where every user can tell the AI "We keep getting the sales tax wrong, remove charging sales tax from the checkout flow" isn't one I'm terrifically worried about.
You couldn't move the goal post further from "Letting AI basically control everything and us not having any mental understanding of sorts and sort of becoming the meat-proxies just for economical reasons".
> I wonder how others are coping with it aside from denial and cynicism.
There are a lot of horrible potential scenarios that are really scary to contemplate. There are also a lot of really delightful ones where AI does the drudge work, invents a million incredible medicines, and frees us up to hang out and make art all day. And there are even more scenarios somewhere in the middle where AI changes a lot of stuff but we all still more or less end up going to work and doing jobs.
I've basically had a background thread in my skull running at high priority for the past two years trying to predict which of those scenarios I think are most likely so that I can plan for them. It is utterly exhausting spending that many mental resources on a question like that.
It finally clicked for me a couple of weeks ago that no one is going to be able to accurately predict all the thousands of ways AI will affect the world. Certainly not me. We are living in unprecedented times. No one has a map for the future.
So I am trying to loosen my hold on the future some and focus more on the present. I have a great job and a great family now. I have most of my health. I'll try to live my life right now to the fullest and in accordance with my values. The future is going to have to be future me's problem. That's OK.
> So I am trying to loosen my hold on the future some and focus more on the present. I have a great job and a great family now. I have most of my health. I'll try to live my life right now to the fullest and in accordance with my values. The future is going to have to be future me's problem. That's OK.
One of the quotes which might help as well (I think I have this even in my HN profile): The only thing we know about the future is that it will surprise us.
Not even experts are much more likely to predict for what its worth than a coin toss in many cases (especially if they believe that only one theory/idea will mostly predict the future)
It's a blend of things and ideas and the sheer interconnectedness of them where a small pocket can grow large and then also shrink and taking into account all variables and factors is just simply impossible for a mind. I think that although we feel we are being more informed about the world, that in it of itself doesn't prevent things in the future from happening. It just makes us alert and sad and anxious about it.
Yet this life is one which shouldn't be lived with sorrow and anxiety. It is one of beauty and greatness. In many ways, we humanity have come so far from the past (Our medicine is something that not even the mightiest of kings could get) and yes, there are many problems in the world and some things feel as if they are staying just the same or getting worse real-time.
But even then, worrying about it could lead to nowhere other than a path of misery. Also these problems are complicated enough that its extremely hard for a single person to bring change (not that I wish to demotivate that person but rather seeing the system as a complex nature)
So to me, its also a form of inward action. I can work on myself to be better prepared for the world that comes next. In the same time, I think that the present for me as well is good. I have great family and friends and have many qualities that I am proud of and I wish to share that gratitude to the people who have helped me along the way (my family/friends/ Hackernews!.)
Within the hustle culture, there is no time to relax but it is within the time of relax that I believe some of the most fruitful actions can come. I believe it just makes my mind more productive being in a calmer state.
here's a quote from how to measure your life that I hope can help some people:
I genuinely believe that relationships with family and friends are one of the greatest sources of happiness in life. It sounds simple but like any important investment, it needs constant attention and care(...)
You'll be tempted to invest your resources elsewhere but if you don't nurture these relationships, they won't be there to support you in hardships or as one of the most important sources of happiness in your life.
So thank you hackernews and have a nice day and please, please try to say gratitude towards someone close to you (within these tough times) and try to keep a balance towards inward focus, sharing time with friends/family and also writing on hackernews (as is my past time nowadays), balance is necessary :-D
So once again, I hope that its a call to action to say gratitude towards anyone. Just send them a big message thanking them and make their day as well as yours memorable, have a nice day!
i am in a similar state, i joined a bigger family owned company as a source of safety.
And AI is not the only "threat" that we are facing.
I recently built this tool to come to terms with where to live, because i want at least some certainty on geographical factors:
<a href="https://om-intelligence.ch/projects/polycrisis.html" rel="nofollow">https://om-intelligence.ch/projects/polycrisis.html
but the AI thing is on one side using lots of energy to keep up with it, and on the other side gives you an edge, because most people are not aware what even is already possible. so suddenly you are the "AI-Expert" just because you try to keep up to date. so if it all goes to shit, at least we have a chance to sniff it in the wind a couple moments beforehand.
Or make memes from it.. that helped me cope with it: <a href="https://t.me/RobotComrades" rel="nofollow">https://t.me/RobotComrades
I hope you find a peer group to talk with and exchange and build community. it is so rewarding to talk to likeminded people that have a similar knowledge base and soothe some fears that someone might have, and have them help you with the ones i have... (i recently did a deep dive in custom DNA synthesis, and how connected those services are already to API <a href="https://www.twistbioscience.com/tapi" rel="nofollow">https://www.twistbioscience.com/tapi )
as always accepting what is seems to be a healthy strategy
You either keep in mind all the horrifying little possibilities the future could hold for us and try to prepare, accept and cope, or you push it out of your mind using whatever techniques available to you to not drive yourself into nervous spirals.
Ultimately it comes down to what your brain chemistry allows in combination with ways you practiced dealing with stress, existential dread, cognitive dissonance, etc.
I guess submission to a higher power is one way to deal with it? That way it's no longer your problem (alone).
It creates a bigger context, and a longer time horizon.
Even some very basic questions, like “are humans inherently valuable?” have been thought through and discussed thoroughly in many faith traditions. For many people not part of such a community it’s a question that’s suddenly very important and they lack the tools to address it.
Damn, yet they still hire programmers, marketers, researchers like there's no tomorrow. I thought everything would be vibe coded and we wouldn't need to even understand code anymore. Which one is it?
The only advantage I could anticipate is I still hit session limits with Opus 5.5. My usage shows I'm on-track reach my weekly reset with room to spare, but yesterday I ran into a session limit. I switched down to Sonnet 5 for the next session, but performance benefit of Sonnet 5.5 is a compelling alternative for managing session limits.
I am mostly at the same point right now you are, but I think in the future with those "gas town" ideas we might be managing even more agents each.
Also, I've recently begun experimenting with specific tasked agents running on a cron like timer for non-dev work. (checking emails, managing small business tasks, etc). Once I started using Claude code in this way, the number of agents I can imagine running has skyrocketed. So I guess what I am saying is that I look forward even cheaper tokens going forward.
What does the existence of this do to wages and compensation for the workers in the industries it upsets?
It depresses them. Significantly. White collar jobs constitute the bulk of global purchasing power. What happens to the economy when aggregate purchasing power drops? The naive response is "prices fall until equilibrium is reached again"
But what if the needle continues moving so quickly that equilibrium is never reached?
This is the K-shaped-economy concern. The ultra wealthy and those who own the "AI means of production" will become unfathomably wealthy at the expense of everyone else.
Why should this not be a concern? Historically, this trend has always precipitated bloody conflict.
One reason might be that Sonnet tends to be a lot faster, so since its almost as smart as opus maybe you use it to get work done quicker. In latency terms not throughput.
I understand the point that you are making but why do we have to fulfill the supply just as much as demand. There is a demand frenzy going on right now with still being substantially subsidized.
Why do we have to burn tokens just for the sake of it if we aren't finding any actual productive use of them?
> And this might slow down the funding enough that they never reach escape velocity with the training run scaling. But we'll see.
I would consider this to be good rather than bad, or just neutral...? Given the past record of these companies, I wouldn't try to wish them luck for reaching escape velocity, as if I feel like perhaps it can have more net harm than positive.
And especially so if you are already suggesting that current models are good enough for your work already. More improvements or escape velocity might not really translate anywhere to the actual work that you are doing economically but it could translate into a more consolidated form of wealth and control.
I am imagining that your workload is quite complicated and that, the AI being good enough means that it is most likely good "enough" for other use cases as well (that "enough" is doing quite some heavy weight lifting here)
So what is the point of advancing further to reach escape velocity. The good argument (for the sake of neutrality) that i see is are advances within science but that's kinda about it whereas the downsides of p(doom) as many are now genuinely suggesting is more terrifying.
Perhaps it can be worth it to ask, shall we stop or just stopping and asking what's the point. A form of self introspection on what these companies ideals actually wanted when they were formed and if they have completed it or not, but I suppose when trillions of dollars depend on you, you do have some incentives to not stop. We will have to wait and see how it all pans out.
Exactly. I'm saying that Sonnet 5.5 might not be useful or necessary in a Claude Code session but it could be good value in the API when you pay per token.
> I want to retain some semblance of understanding
How you do this (and how deeply) I think is really the limit. I am doing this by focusing heavily on the design phase with grilling and trying to continually improve process to need less effort in the review phase.
Are your models doing automated reviewing and testing before pushing out the PR (themselves)?
I think in the long run as models and the tools around them get better and cheaper, those that abdicate understanding will be able to achieve more. Although programmers think of that as irresponsible, ask yourself what does a tech lead do? And then what does a CTO do, etc?
I think going for more understanding is the way you need less understanding. The more solid your core understanding of your codebase is the less you need to know the details, the less missunderstandings the less iterations needed, the less mental capacity consumed
There's lots more you can do! Use the model to monitor your deployments after they get deployed. Have them fix and watch CI issues for you. Run adverserial review. Automatically watch metrics every day and highlight performance regressions. Start reviewing your previous sessions to find ways to statically reject different failure modes and have the agent have more success earlier on etc.
Another thing to think about is, what would it take for you to care less about the understanding. Better integration / e2e tests? Performance validation? visualizing program and data flows? Better refactoring of your modules?
Claude Code has the issue that sub agents inherit the thinking level. This means that to use a smarter or dumber sub agent you need a different model. That's not a particularly good reason, but that's my one use case for Sonnet.
Being that my first prompt can be something like: for task x/issue y, which model would strike the best balance between cost and capability…
It seems like it would be a better UX to have model and effort selection asked into the system. Of course, I’m not sure in practice if that would be in the best interests of the providers and/or users.
There's different layers of understanding the system. I generally care about high level data flow, concurrency and performance (batching, holding transactions too long, back pressure etc.) rather than the mechanics of how the code actually does a thing. I still look to see what the final output looks like and ask my agent questions on how it fits in the larger system and evolve things if necessary, but agents are pretty good at writing code if the rest of the code base looks pretty decent.
An LLM can produce far more code than a human can understand. And the famous rule that "optimizations are entirely pointless unless you're optimizing at the constraint" is logistics 101.
To accelerate software development, you either need to remove or lessen the need for code understanding, or make it much quicker for humans to gain that understanding. Making the LLM faster won't help you if the LLM isn't the bottleneck.
A human can produce far more code than a human can understand, too, but pre-LLM we always viewed someone overwhelming their colleagues like that as being bad at their job.
Humans could already produce more code than a human can understand. Even a single human in the pre-agentic era could produce more code than they could understand, certainly over a career and often even in the short term given the resources many companies give to maintenance.
A lot of old-school software engineering is about how to deal with this reality.
No they couldn't. You can't create software you don't understand because you wouldn't even know what to type into the IDE in the first place. I don't understand claims like these, how exactly are people especially individuals producing more code than they could understand? Even at a huge corporation one might not understand all the code but surely they understand the part they're modifying because otherwise they wouldnt know how to modify it.
Yes, it’s possible for a person to create software he doesn’t understand himself. In the old days this was pasting from Stack Overflow and changing things until it worked.
In the old days even if I knew how the software worked when I wrote it, I’d have no idea how it worked when I looked at it weeks later.
It’s also easy to modify software without knowing how it works. This produces modifications that hopefully appear to work, but that break other things, sometimes unknown things.
I’m referring to competent engineers maintaining understanding over time of all the code they’ve produced. Long before agentic coding, codebases routinely grew beyond the comprehensive understanding of their own authors.
Of course less competent engineers (or anyone on a particularly disorganized or desperate day) can literally hand-write code they don’t understand even as they write it, but that’s not really what I’m talking about.
There's understanding and there's understanding.
Have you never "fixed a bug", only to realize that you just papered over a single symptom, while the underlying bug is still intact?
People you're disagreeing with (I think!), would say that during your first attempt, you didn't _really_ understand the part you're modifying.
It is _very easy_ to do this in large codebases, and even more so when working on anything touching UI.
I suppose I am confused about why you’re confused. There’s a long history in computing of describing pieces of programming languages syntax syntax as “incantations” and similar. I suspect it has been very common, especially in the early part of developers’ careers, to know what you’re trying to do and to know that this code accomplishes it, but to not understand how the code works, to not be able to use the technique more generally, and to not understand all the effects your change has on the rest of the system.
I see, I guess I'm using "understanding" in the more literal sense of being able to put enough context together in your mind to type the characters on screen, not necessarily understand enough to know how it affects every other part of the codebase.
It's the style of understanding that says "if your animation is stuttering, set `gc.tune(pause_length=0, frequency=-1)`. Or "to make data access fast, remember to always use `integritychecks=omit;encryption=export-grade;checksum=md5`".
You don't know what these things do and what their effects really are (examples and syntax illustrative, but this is the kind of code that has disastrous effects when used carelessly), but you know they achieve your particular micro goal of "make things go fast" or "make this fit in packets on these strange industrial networks customer X has" or whatever.
I want to make better software, not more software. Making software development faster isn't necessarily the goal. Making it better in the many, many ways that matter (of which speed is just one part) is.
There is more software to be written than there were programmers so lots of people do indeed want more software, for example small tools and one off projects that aren't worthwhile to make pre LLM.
Many people want less software to deal with now, not more. Being forced to download and update apps on your phone that previously could be done without an app, for example.
A lot of people would find it easier to pull out a few quarters and put it in the parking meter than download and update an app and give it your financial information. Or hand over a paper ticket to get into an event that’s easy to transfer instead of yet another app with a barcode.
Probably because most of that software is garbage then. While there are some people who may do that, most find the convenience of not carrying around loose change as a benefit.
I'm looking forward to the making of more software. I think there are probably people who have had really useful software ideas for a long time that they'd never be able to raise money for, but now for $20 a month, they can get up and running, serving their local and/or niche communities, without having to hire a team of engineers.
Eventually we're going to reach a point where they don't have to understand the code themselves. The democratization of software creation is going to be fascinating.
The Android/iOS app store is already flooded with low quality apps. Almost 20,000 games have been released on Steam so far in 2026, that averages to about 70 games per day, every single day. Getting any kind of traction in such a environment is hopeless; every new app might as well be a scream into the void.
I think a lot of people don't want or care about major traction. They want to make little things that make their own little corners of the world better.
There are small towns all over the world which could realistically have their own little "hometown app" now, that really does track all the interesting things going on there. People don't need "The (Unofficial) Smallville Happenings" Facebook pages anymore. These don't all have some programmer who cares enough about building such a thing to make it happen, but they probably do have a teenager or even a retiree who would have a lot of fun making something that really works well for the people of that town and how they want to use it. The barrier to entry just became low enough to get over.
And that's just one example. There are SO MANY problems that used to require mega investment to solve at scale to get any traction at all. Now communities can build them for themselves. And they'll never be the kind of data target that a megacorp is, because you'd have to target each little app individually, hoping there was something useful in there.
The democratization of software development is going to have lots of things that go nowhere, and lots of little hobby projects, and a few things people will actually hear about and care about. But a lot of people's lives will become incrementally better and I'm excited to see that.
True, but if you can think about tests as "how do we validate that the outcome I want has been solved", then you can orient your tests around that. I do wonder if BDD style integration tests will end up being the path we go down.
Frontier models today don't really write incorrect code at the micro level. They do miss edge cases at the high level though, and that's what we want to test, is the scenarios.
The thing with watching CI in an agent loop is that it burns tons of tokens. At work I ended up writing a deterministic, traditional CLI tool to poll GitLab CI pipeline+job state changes on a branch and exit with an appropriate status code, and then updated my `/glab-ci-feedback` skill to use that. Saved a ton of token churn, and now I have a runbook a human could just as easily use if they don’t want to (or can’t) use an agent loop.
… but walking away to make a coffee and coming back to the robots auto-fixing bugs only found in CI is definitely some flavor of magic, regardless of the execution order to get there.
I think they are trying now to to bake CI awareness into Claude Desktop, didn't use it yet.
But meanwhile we also have the scripts - one script to watch CI, one script to fetch comments (without dumping raw graphql into the agent), etc etc. Can't wait for this phase to end already
Yeah the codex app can deterministically poll and watch for you too. Consider it like an event based trigger, where the event can be anything you can dream of (like webhooks!)
FWIW, Claude Channels[1][2] are probably going to be the solution for that, eventually. While I'm not sure how the WebHook receiver example will work with, say, GitHub and a local Claude, the Chat side of things _would_. So you'd have GH send its web hook to Telegram (for example), and then the Telegram Channel MCP would inject that into Claude, and Claude would start working on the problem. Still experimental, but functional enough to play with.
This sounds... horrible? I mean, it's certainly a solution to the "wake up when this thing happens" problem, but... $SERVICE -> webhook -> $CHAT_APP -> MCP -> remote wakeup sounds both brittle and - as you said - the local code harness route is entirely unserved by something like this.
Am I having a yells-at-cloud moment where a bunch of folks are using cloud hosted LLM harnesses/environments (let's ignore the models, "of course" those are remote) and I just never saw the point?
> The thing with watching CI in an agent loop is that it burns tons of tokens.
Not my experience with Claude Code.
> writing a deterministic, traditional CLI tool to poll GitLab CI pipeline+job state changes on a branch and exit with an appropriate status code
This is what Claude Code does, more or less, on the fly. With a short prompt like "I pushed, monitor CI and debug if needed", it writes a monitor script which is responsible for polling CI status (the script is short, so it's not token-heavy), and if CI fails, only then does the agent proceed to pulling out CI logs, grepping them for signs of errors, etc. as continuation to debugging.
I mean, I'm sure it's more token-efficient to have a CLI tool ready-to-go instead of Claude Code dynamically writing its own script each time, but as I'm on a Max sub where it doesn't seem to affect how close I am to the limits, and I only ever hit the limits if I'm running Fable for everything... /shrug
I guess folks' experiences with this stuff will vary wildly by what environment they work in. I use LLMs mostly at work, where I don't have any subscription plans, everything is billed per-token, and there's multiple coding harnesses with different token quotas available (and vastly different functionality). So the sharable CLI that works whether I'm in Claude Code (where tokens cost some outrageous amount) or Devin CLI (a horrible harness that also lacks any sort of scheduling system as far as I've ever figured out, but hey, there's GPT Luna and GLM available, at least) is a huge win.
Be careful about this one if you want to have any level of control over basic stuff like comment style and accuracy. Claude will happily spend 20 review cycles in a row rewriting the same 10 comments for a small bugfix over and over because it can recognize "Claude-ese" in the review cycle but then just immediately and compulsively spew out more of it and drift even further from your style rules in the next "fix".
I'm seriously not joking about the 20 tries, I left it running in the background for what should have been a minor code change and it took 18 out of 20 review cycles to stop writing in more comments that all either broke my ASE-STD100ish style rules or included false statements about the code.
Yeah, I made this point above but LLMs just don't have a good sense of importance. They treat everything at the same level of importance and can spend considerable effort on things that just don't really matter.
I think that's what a future dev team is going to look like.
One person doing product management / talking to customers and vibe coding features that solve users' problems, one person keeping the UI/UX in check, one QA person that spends their time clicking through the software, finds the bugs that are obvious to humans but not LLMs and fixes them, and one "harness engineer" who pays off technical debt, observes failure modes and sets the rest of the team up for success.
You're thinking that the entire economy will collapse down to just 3-4 vendors?
Human power and social structures just don't work that way. No AI company is making my sandwich, operating the bus, or serving soup in the school cafeteria. Real estate, human service, specialized expertise, and have-power influence isn't going away.
Who's making the robots? Who's owning the bus? Who's growing the ingredients in the soup? Who's deciding when the bus needs to stop due to a security or safety concern? Who's flirting with the customers and recognizing the power brokers? Not the AI companies.
Just because robots can do stuff doesn't mean the human power structures or service preferences evaporate.
More tests that aren’t written by you don’t help you understand the system, and I would argue the there’s no confidence without understanding. That was true in the pre-agentic era and is perhaps even more true now.
I want to understand more about how the world around me works. Not less.
Humanity advances in proportion to how well we understand the world. If the machines understand better than us, the world will bend to fit their preferences, and ours only incidentally to the extent they coincide with the machines.
> Another thing to think about is, what would it take for you to care less about the understanding
Yes please, I'd like to not understand my codebase, give up my decades of experience and have a machine do everything for me. That way I can let captialism utterly steamroller me because of my paltry token stack, in comparison to the 19 year old vibe coder who has secured a new funding round for ponzi.ai
The cat's out of the bag already. We can't undo the idea of LLMs or coding agents. If training progress stopped today, we have years and years of harness improvements to extract more performance out of today's models.
We also have open weight models too, and ways to host those at home.
Most people don't look at the assembler output of their C++ code (I used to write win32 programs in asm!). Most people don't look at the opcode instructions or JIT output of their ruby / python code. We're starting to work at a higher level of abstraction using LLMs. It's ok to be sad about it, but just being angry about it isn't going to change that there's a new world out there with a new skill set that's needed for honing.
And where's the results of all this higher level work?
Where's the super awesome 100x turbocharged software that's a result of everyone here having been being a 100x turbocharged programmer for the last 6 months and a 10x supercharged programmer the past 2 years?
I still use the same software I used 2 years ago, but a bit less reliable.
Using an LLM does not require people to stop understanding their codebases, and I find one of the best uses of an LLM is to improve my understanding of my code. I suspect the really good software is going to be written by those doing this, but time will tell.
I'm not angry, but yes I'm being deeply sarcastic to illustrate the extreme case you seem to be advocating, where we relinquish our understanding to the machines.
In a competitive business like software you need an unfair advantage and for very few people that's having near unlimited tokens. Even then I doubt that's going to produce good software.
> what would it take for you to care less about the understanding
It's an interesting question. The thing I keep coming back to though is that every time I've tried to go more towards vibe-coding, I invariably look at the code and find things have been added that would just not be acceptable. I've also tried asking the models to see could be refactored however they still miss things that should be obvious.
I think the gap is that they're still lacking a sense of importance. As engineers working on a product, you have a sense that this feature is more important than that feature. An LLM treats your codebase at the same level of importance. So they'll spend the same amount of effort and code changes on testing and hardening something that just really isn't that important.
Also, once a bad pattern gets into the codebase, they just continue to build and extend that out rather than re-thinking about it like an engineer would.
Yeah, what things can you write to statically eliminate the things that are not acceptable. What would you have to change in your prompting process to get that outcome? What bad patterns is it copying from the code base that maybe you should spend tokens fixing?
I do agree that they're not great at program design by default and that's where we as engineers should spend our time. Data structures and data flow are king. But once you suss that out, they're pretty good at writing the resulting code.
This is also where I disagree with dhh about just using lower level languages. Good abstractions make for excellent program understanding and we should continue to build extremely good building blocks that make program design naturally solid.
I find that we dont need to go all in. I can use LLMs to make tooling custom to my project: linters, skills, rules, some doc etc. Then iteratively improve on that.
For example, write a skill that finds some kind of code smell, say duplication, and generate a report. Give it some supporting scripts.
Then, use this report to file a few tickets. Then make the agent fix those tickets. Then, as you grow confident, automate more of this process.
It does not replace human supervision but it may enhance it. Especially in a team where people start generating PRs faster that anyone can review them.
Continue this improvement process long enough and you may find yourself with an AI Software Factory.
I've been vibe coding a game and running multiple Opus 5.5 in parallel on Claude Code Cloud, 5x Max plan, and I'm yet to hit a session limit too. Not sure when I'd use Sonnet. Though it would be nice to switch back to Pro I guess
I created a team of agents using Opus 5.5 to review and address findings on a job system I have in a side project with medium reasoning, and I burned through the 20x plan weekly limit in 2.5 days. They were using GPT-6-Sol for reviews, and it also used 85% of my OpenAI x5 weekly limit. Three hundred something commits in total.
OTOH, in the daily job, I have the team plan that's similar to 5x plan and I never had any limit problems, because I really need to understand be able to take responsibility for the code.
Plan longer chains of work / higher level goals that can be broken down into multiple chains of work. This will allow you to automate more work units to be worked on.
The speed of your manual reviews become the limiting factor, which you should be doing at some level to maintain sanity, even if there are enough ideas to be worked on to maintain a review queue.
I find that "vibe coders" (that is, people who do not know anything about programming, but nevertheless produce useful tools for themselves and others) are using a lot more tokens than we do as programmers.
I think this is partially because we're still attached to pre-LLM notions of architecture, good design and code quality (which are still important, but maybe less important than they once were and that we think they are), partially because their projects are in a messy state, so models have to work around the technical dept.
They're essentially trading off programmer time for LLM time (which is a good trade financially speaking).
I think this is valid now, but not guaranteed to be valid forever. For engineers, there was a period where more checks, more tests, more auto code reviews improved results quite a bit. People were consuming tokens like crazy (including me). Then things improved via better effort/thinking levels, where you could see repeated code reviews plateaued, so now people don't really do that quite as much.
There was also a period where specifically OpenAI models would always have to comment something in code review and the builders were agreeable up to listening to each nitpick. If you'd have a loop of build->review->build->review, it would take maybe 5-7 rounds for it to 'settle' and not find the smallest nitpicks to argue about. Tried it this week with Astra reviewer and it's about 0-2 review loops (never had a LLM accept a change without nitpicking first try before Astra).
There was also a period where you'd have to give quite specific instructions for agents to keep iterating, but now agent are pretty proactive and try to finish tasks you give them unsurprisingly most of the time.
So, while there's a shortcoming of LLM+harness and engineers observe more tokens improve things even logarithmicly, you'll see more tokens seemingly abused by engineers.
> you'd have a loop of build->review->build->review, it would take maybe 5-7 rounds for it to 'settle' and not find the smallest nitpicks to argue about.
You sure that wasn't just working at Microsoft?
If you have well-specified tasks, you can easily reach 10 simultaneous agents working on disjoint parts of the code in worktrees. That consumes tokens pretty quickly!
I created an orchestration skill for myself (using herdr but any persistent mechanism works). So I then only interact with a front session and it will triage and dispatch each request to the relevant spaces (each of them can have multiple worktrees of the same project), summarize movements and pending decisions for me all at once. I do not directly interact with a multiplexer or any dashboard.
This seems complex and expensive, and I suppose the only reason to do this is because you want to generate code faster? Do you really have so much code to write that a single LLM is too slow?
I don't do this at my day job (coworkers would be pretty mad). This is for software ideas that comes up weekly that I need to execute to at least MVP before I ever need branching/merging.
Not complex at all, only one extra session other than the ones doing work and it's on a dumb model and can be thrown away & restarted because it only dispatches work, not doing anything.
I do everything in there, collecting requirements, kick off research, branching, merging, not one other agent on top. I considered making that orchestration command llm-powered but it's not justified at my current use.
It's not more expensive, in fact I could have just chugged along with the slow and manual session by session work but I have a claude subscription and another GLM one (the most low cost basic tier, not even much), that just sit there collecting dust if I don't put them to use in a more efficient way.
And doing session by session would face your problem when context switching too much become unscalable.
Personally, it takes me longer to write the specifications than it takes the model to implement them (and it takes me much longer to review the resulting code, although maybe that makes me old-fashioned). Consequently I do not have enough tasks to run more than one agent at a time.
I'm exactly in this situation, and at the same time I get so many jumpscares when reviewing the code that I'm not going to stop anytime soon.
Exactly. I don't understand how so many developers seem to have a long tail of well written task specifications ready to submit to the LLM.
Who produces them?
LLMs are good at cleaning up tech debt. Give them lots of small refactorings or dig out those crusty old P4 tickets.
There's a lot of easy stuff for them that requires very little specification and very high probability they'll get it right the first time, especially if you can point them at an example done right.
I have conversations with a smart model, and then the model writes the spec. I review and approve the spec, and it dispatches.
For a concrete example, check out this random plan [0]. A detailed spec followed by the exact implementation tasks that will be executed by the subagents.
Not sure why I'm being downvoted for stating the obvious. To the parallel agent skeptics: I was also a skeptic until a month or two ago. I would run one agent, watch it carefully, and check its work. However the models got good enough that it was more efficient to do more work in parallel, then have a single agent integrate the changes with a critical eye, then run a code quality pass, and then I would take a look a it and kick the tires behaviorally.
It does involve letting go and not micromanaging every code convention and implementation detail, but that is the same skill you need when leading engineering teams.
Every new generation of model goes and cleans up the slop of its predecessor in my code bases, and it has turned out to be quite effective.
Last year I held off on implementing a few features knowing that a model like Opus 5.5 was around the corner. I'm now implementing them in a much more efficient and quality manner than I could have fall of 2025.
Indeed. Maybe people down voting me don't like to admit it but when models train on the entirety of human input you can assume they'd be better than the average human.
This is probably why web development and scripts are much more effective domains while anyone working on anything even slightly off the track is either tearing their hair out or writing a new layer of software to write the software.
For my normal work I take ownership of the code, and end up with the exact code I want. I still have it go off and do a good amount of work a lot of the time, still queue up multiple tasks at the same time a lot of the time. Sometimes I throw it all away and re-prompt once it's time to commit to it, sometimes edit what it made, sometimes have it edit what it made etc.
For all of my side projects I'm full-on vibe. Well, almost: I do have opinions on what kinds of code it should write and set up my projects to get that. But I don't LOOK at the code.
I use a LOT more tokens on my side projects. I can have it working more or less constantly and it doesn't take up that much of my attention, but it is FAR less token efficient.
I am actually going to go out on a limb and guess the opposite of this is true, and that on average vibecoders burn tokens less readily than veteran software engineers. I could list several reasons why I think this would be likely. No idea which of us is empirically right, though.
(With exceptions for what I can only call the "manic vibecoders" with like 10 simultaneous weird slopprojects they're spewing out at once. Generally with each project itself being something related to vibecoding. Steve Yegge being an example of a "manic vibecoder-actual programmer" hybrid.)
I’d think this matches my hypothesis. I’d say that I spend more tokens rewriting and fixing things, so that contributes more.
Also, I’d imagine the token-maxed user is a programmer that lives in chat. I’ll admit to having asked the LLM to move a method up/down in a file, and watched it burn tokens for a minute thinking and executing a menial task.
"Sometimes"? I and I think many other people have been doing exactly this for most of 2026, assuming they look at the diff/PR at all and aren't all-in on dark factories.
Sure, if it's right there in front of you in the editor. But if have to locate the file and the location within the file, it's easier to just type out what I want and have the LLM do it.
LLMs are sometimes stupid and get confused when you change the files without them knowing. So asking them to do even simple things keeps the context in sync.
Plus you get a bonus random line "methods are all on the top" in the commit message that makes no sense to anybody.
I mean, they're trading off the time to learn to program for LLM time. Which might make sense! Locally.
But, in my experience, the projects where I have a constant pulse on the core design and abstractions in the code end up moving much faster than the ones where I don't. And I've been working on one of each at work recently, so I have a decent point of comparison.
>"but maybe less important than they once were"
On browser based front ends it seems to be the case for me even though I still impose certain guidelines. On my C++ backends, no fucking way. Even the best models produce working but absolutely disastrous non scalable (performance wise and design wise) code unless watched over like a hen. Having said that - the value I get in either case is enormous.
I think it's not only a matter of token efficiency. If you don't know what you are doing development will eventually crawl to a halt invariably.
It's the compound counter-probability of success, so even a 99% efficient model will in time accumulate so much error that without conscious cleanup and steering, it becomes really unlikely really fast that anything could be changed in the code without affecting something else, no matter how many tokens you throw at it. It's the collapse of a complex system under the weight of sheer uncertainty of what the system actually does.
Maybe, it'll be exciting to see, and I'm all about accessibility, but in this case I also don't think it's about model capability or intelligence, it's the low information to noise ratio in the codebase. There just won't be enough information in the code itself to know what to fix. Fix how? What should it do? I'm really not sure you can reconstruct intention from a codebase created unsupervised.
I keep seeing this but I'm not sure it is effective in the long run *if unsupervised*.
"Review quality and fix" doesn't mean a lot without context.
Does it mean to remove unused features and simplify the underlaying code? Does it mean changing the data structures to better support future development? Does it mean improving performance because of bottlenecks?
You are supposed to tell an LLM what your codebase needs, but if you just vibe code without knowing the code, "review quality and fix" will have unexpected results
Commit the intention as specs. If tokens/intelligence become that much cheaper over time, then "throwing away the code" to start again becomes feasible.
A few days ago somebody linked to their own project: <a href="https://ljtn.github.io/epiq/" rel="nofollow">https://ljtn.github.io/epiq/
I haven't had time to dive into it yet, but I think it might structure things in the way you want.
Fixing code is not the same as fixing a fundamentally broken architecture. The latter requires understanding that the architecture is broken in the first place and that understanding comes from experience.
We’re at 99% for a lot of stuff today, you get a third nine from council reviews and labs have another one or two nines in the pipeline. At five nines your task length horizon extends far beyond the current frontier model release cadence. More out of distribution tasks lose a nine or two, still revolutionary. You can get an extra nine from a good set of skills around slicing and distributing work according to model capabilities.
Indeed, I've been coding for ~25 years (competitively and in open source for many of them) and I max out at least two subscriptions' worth of tokens every week across a half dozen active projects.
I still code "by hand" sometimes (mostly Ruby/Rails, C#, and random languages for code golf) but just for fun at this point. Serious projects started being 95-100% AI over a year ago.
That and some have bridged the gap with tooling. starter projects+ Strick typing + dead code detection, linting rules and today's models can get you pretty far, especially if you plan out some basic architectural patterns with your starter kit.
It's not perfect but any means but it helps manage ones sanity.
It's because they don't know data structures.
"Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won’t usually need your flowcharts; they’ll be obvious." - Fred Brooks, The Mythical Man-Month (1975).
and essentially the same sentiment, three decades later:
"Bad programmers worry about the code. Good programmers worry about data structures and their relationships." - Linus Torvalds, git mailing list, 2006.
These things have not changed even though everything else is topsy-turvy. As-of current writing, I have yet to see an LLM make good data structure choices; they go for something that is superficially plausible but profoundly ill-considered (or rather, not considered at all), and then commonly burn tokens treating this implementation detail as a design invariant and trying to deal with the consequences by writing more code, instead of iterating directly upon the ill-fitting data at the root its problems.
If you're wondering, "does he mean the schema of let's say a db or other persistent store, or does he mean abstract/algebraic structures", the answer is yes to both, I think coding models are today shockingly weak when it comes to design reasoning in both domains.
Fortunately, their suggestibility means the same models will readily accept direction on the matter (perhaps even more so than on the structure of code), so I recommend doing just that, and (bonus!) this means your CS degree is still relevant.
While I agree that a coding model (such as Opus) by itself tends to act very shallowly, when it's driven by a harness like Claude Code, the combination seems to be a far more general thing than a LLM. It's capable of consistently making excellent data structure and architectural choices over large code bases. It imitates thinking about anything and it can drive itself for hours.
Honestly, if I simply fed it a sense of presence (I would repeatedly tell it what's going on right now and ask it to react if it thinks it should), it would feel eerily like AGI.
Coding models can already make well-considered data structure choices if given all the relevant context, but a non-programmer doesn't know the context to give it.
Falling back on a RL pipeline to cover gaps always strikes me as a more sophisticated version of the mechanical turk. If what we had was truly AGI wouldn't they be able to derive this from the data they already have.
I (a programmer) just did a pretty large task with Opus 5.5 that was a perfect fit for a big token eating task. It ate into my weekly budget in a way that made me have to use anthropics one-off "reset" they offer now.
Long story: we have a big legacy desktop app. It uses a big legacy UI component (a grid control), which we had a license for in an old version. Fast forward 20 years, and to be able to move to a new runtime for our app, we need to update the component. Someone had bought the company making the component and now charges north of $1k per developer per year. So instead of doing this, we had just lived with the very old version.
We had long thought of writing our own control to replace the proprietary, but it was always going to be a man-year of work we thought. But I thought I'd give it a try with AI now. I told Opus: look at our uses of that control (tens of thousands of lines of code, it has over 100 instances across our User Interface). Write a new control that would compile with the exact same app syntax. First just make a dummy implementation that throws on every call. Then start implementing. Make a test suite that can run both with our new control and the proprietary control, and test everything, every function that can be called in its public interface and every state that can be inspected from the public API. Verify that everything behaves exactly the same, and lock it in with thousands of tests. Finally, check that the control _looks_ exactly the same as the proprietary one. Render to bitmaps, figure out the rendering logic from observation, such as arithmetic for padding, font sizes, and so on. Compare pixels until it's exactly the same.
Basically: it was a mammoth coding task, but it was so extremely well specified that an LLM could easily just do it. It's a clean-room implementation of something with no tests, but we had a test double that could provide 100% of the expected behavior. The description was extremely short. "Make a new thing that works like the old thing, and prove that it does". Opus 5.5 finished this in a number of hours. 500 source files, several thousand unit tests, and html reports with image diffs from the reimplementation and the original control. It did not use any disassembly or such "cheating". Only observation of the public API and the behavior.
Do we need to deeply understand the implementation? Does the architecture matter? Not much in this case I'd argue. It was a black box to begin with and it remains a black box. If we notice a bug, we can always point it to the original proprietary control and say "there's a behavioral difference when doing X" and it will fix it, and lock it down with tests.
As a programmer it's kind of chilling. I had recreated for a few tens of dollars something that would cost $1000 per year to buy. Obviously it's not a complete implementation only the parts of the API we use. It likely still has some bugs. We don't get support, we get to maintain it ourselves. But the rate of reverse engineering this thing "black box" was frightening. It hasn't created anything novel. But we must realize that as programmers some times we have man-years of work that just isn't novel. And in the past, we didn't do this work at all.
I wonder if those who write and sell libraries like this will start having explicit no-reverse-engineering EULAs soon? Perhaps even explicitly mentioning AI/LLM use in analysis and reimplementation?_ Obviously the library we reimplemented was from 2005 so didn't mention AI... (It doesn't mention reverse-engineering either, luckily).
Most products do in fact have an anti-reverse engineering clause in their EULA, to be fair, it's been a standard EULA term for a long while. It's just that no one cares anymore...
Yes, and usually in the form "You may not reverse engineer, decompile, or disassemble the SOFTWARE or any of its constituents, except and only to the extent that applicable law expressly permits". (This is the concrete example from this software). And as far as I understand, this means that so long as you stay short of decompilation - you can reimplement as much as you want.
The law that covers this (in the EU) is EU Directive 2009/24/EC, where Article 5 is the reverse-engineering-without-decompilation.
> The person having a right to use a copy of a computer program shall be entitled, without the authorisation of the rightholder, to observe, study or test the functioning of the program in order to determine the ideas and principles which underlie any element of the program if he does so while performing any of the acts of loading, displaying, running, transmitting or storing the program which he is entitled to do.
This is pretty difficult to parse, but luckily there is a ruling from the European Court of Justice on this: SAS Institute Inc. v World Programming Ltd (Case C-406/10), delivered on May 2, 2012.
SAS Institute claimed that World Programming Ltd (WPL) infringed its copyright by studying the behavior of the SAS software system and writing a competing program (the World Programming System) that emulated its exact functionality and used the same data file formats. WPL did not have access to SAS's source code and did not copy any of its literal text or internal structural design.
CJEU:
> "It must therefore be held that the copyright in a computer program cannot be infringed where, as in the present case, the lawful acquirer of the license did not have access to the source code of the computer program to which that license relates, but merely studied, observed and tested that program in order to reproduce its functionality in a second program".
Which is a good find. But this is where I wonder if LLM-based reverse engineering is going to creep into either law (via lobbying) and/or EULA's, because this "observe every single state of the program for every single mutation" was simply not a viable mode of reverse engineering in the past. Or, it was at least always cheaper than just buying the software! Not so any more.
Or alternatively, that programs stop having so many observable states, making more things public. But for libraries as in this case, the whole product IS the public API. Without a rich public API, the library can't be sold. And with it, I can observe it and copy it - because it's internal workings are "too simple" not to be deduced from the public API. In short: a UI control is a ton of hard-to-write but easy to copy boilerplate code. And selling this has been an industry, but I wonder if it will be for very long.
"And as far as I understand, this means that so long as you stay short of decompilation - you can reimplement as much as you want."
Yes, but my point is that.... go on github, you'll find tons of decomps. And many more done just privately too. One of the No Man's Sky devtalks start with "yeah we decompiled the terrain generation from this other game, implemented it in our prototype, it didn't work okay, here's how we've learnt from it to make something better". This was in 2016. More recently, this has been going on way more openly, even full AI-assisted decomps thrown up onto GitHub casually. It might be the letter of law or included in Terms of Service but no one cares really.
Because that "vibe coders" didn't know and go through the fundamentals, thus they're wasting tokens with probably continuing the AI hallucination suggestions.
I've seen bunch of persons like this and that's kinda stupid because they're just blindly following AI's "suggestions" while they actually don't know what they're doing, then results on terrible code and architecture with "if it works, it works" mentality.
Does that mean you're doing work week by week that is well-scoped and planned for that week and you can't pick up the work for next week until that week happens? It just seems like mostly programmers work on things that extend out for a long time and you can kind of just throw more at the problem and pull it forward earlier
I have been using the “free” Ling Flash model on openrouter for a bit over a month now. My work is an aside project at home building a Rust binding for an open source Zig code base library. The result is nothing impressive but also not a total failure: I have a feature parity binding to use in Rust vs. Python/java/typescript.
Now, during those night and weekend sessions, I have never run into throttling issues with the free model. Sometimes it runs a bit slow and I switch to a different free model (NVIDIA Nemo something).
So yeah, I agree with you that for professional SDE like us, we don’t consume that much tokens. I’m pretty sure the folks on the line of over limit are pure vibe coders if I can take a wild guess.
If I follow the principle "every single line of AI-generated code has to be reviewed and understood by me," I can't even use up the $20 subscription.
I do that and can use up the $20 subscription, but I haven't been able to hit the limits with the 5x subscription so far (even though I use Fable for making plans).
The other thing too is that I’m having a hard time reviewing these massive PRs that are being generated. So much so that I’m having it write me a book (also vibe coded) that I can read through to learn about all the stuff it did as part of the pr review
<a href="https://ai-lessons.oncanine.run/" rel="nofollow">https://ai-lessons.oncanine.run/
This is boat I’m in too. I get a ton out if my pro subscription, and I don’t hit the limits, but my Microsoft buddy was just griping about how the company recently imposed $10000/month token budgets on his team and he blew through his quota on under a day. The mind boggles.
> Probably a first world problem, but with Opus 5.5's efficiency, the limits on the 5x plan are simply sufficient for my everyday work, even when running 2-3 sessions at a time. So I wonder when I would use Sonnet 5.5.
Not every user of Claude is a programmer. Or even exclusively a worker. Claude has uses beyond work. Something that many in HN struggle to understand.
When working with a whole bunch of sub-agents, like you probably would do when coding, 2-3 sessions at a time are probably not enough. Like I build very specific sub-agents for code review, ui review and so on. Within one project I work with at least 3 - 4 agents then. Using Opus on every task would not leave room for other everyday-tasks.
Sometimes it's not about token usage but speed of the task.
I am using Sonnet 5.0 in browser (btw Claude in Chrome extension works in Edge) to download Datadog logs with multiple filters. It's running for about an hour, doesn't run out of tokens and does a splendid job.
While I mostly use Opus for coding, I use Sonnet in my actual application, because it is far cheaper at scale. I have Sonnet parse user free-text input, understand it and return the meaning as structured json. It doesn't need advanced reasoning, just a good enough understanding.
I honestly rarely use the “more powerful” models, I find they don’t really follow my instructions very well. So it’s medium effort sonnet for most coding tasks for me, escalating to opus for code review.
I’m just hoping they didn’t “improve” sonnet too much or it will become annoying to wrestle into doing what I ask it to do.
Smart people delegate to dumb people. Of course ideally dumb people get slightly smarter. From my experience sonnet is mostly for opus or fable to delegate to. So delegates getting more useful is always a good thing.
Seeing 2000 years of history being replayed by the AI startups is pretty weird right?
for very extensive agent swarms, you could tell the orchestrator to use sonnet agents for the eval of papers.
this way you can digest much more papers (content of whatever form)
so whenever you are dealing with volume rather than independence. opus5.5 can define the goals of a sonnet well enough, that i would trust it with a group of 100s of agents
Sol- · · focus · HN ↗
More concurrency than that isn't really practical for me if I want to retain some semblance of understanding. Perhaps it's different for purely web app or frontend tasks, where the outcome is more relevant than the process, I don't have much experience there (and also don't want to belittle these domains, I might be underestimating their complexity).
So surprisingly, my own work is at least for the time being almost saturated by the model capabilities. I am not sure how I'd scale from here. Sure I could run all requests at max effort to burn tokens for the sake of it, but that can't be it. And for many tasks, I am not really able to define so clear cut success criteria or self-verification loops that I could benefit from letting an agent (or a fleet thereof) autonomously run for a day.
So I realize it's a skill issue on my side, but I can't be the only one. I wonder if there is a limit to token demand, at least short term. Feels like either they accelerate to AGI and RSI, where the AI can find uses for token, or things might plateau at some point.
Note I don't think this because I'm an AGI skeptic or think there's a ceiling to intelligence, but there might simply be a valley of economic hardship for the companies where the supply of tokens outpaces the demand, due to a lack of ideas of what to do with them. And this might slow down the funding enough that they never reach escape velocity with the training run scaling. But we'll see.
phainopepla2 · · focus · HN ↗
Imustaskforhelp · · focus · HN ↗
In short, seems to describe vibe-coding to me? What I don't understand about companies attempting to vibe code is if they realize that other people (especially sometimes their customers) can tailor-made their own software for their own needs, or rather competitors can be dime a dozen and maybe even a fight for constantly paying for the better model.
There was a comment[0] from a just few days ago by @jjcm (which I wish to quote which I hope they don't mind.):
> I just got back from a 2 week trip to China. I was in some of the more remote parts and my cell wasn't able to connect to their towers in that area, resulting in me not having the tourist VPN.
> The side effect was I was fully cut off from my AI tools for those two weeks. I was coding "manually" during that time, and I think I accompished in two weeks what I previously had been able to do in a day. I'm not gonna lie, it was very, very stressful as a solo founder.
> The industry moves so fast these days, that the only way to keep up with the speed is to leverage them. While I can appreciate the push of this to help your brain think independently/critically, the opportunity cost of a month of development without LLMs is too high a price to pay.
What happens if the opportunity cost of a month of development with vs without human understanding becomes too high a price to pay. I feel like we would be in awkward time because of the factors that I had described above (higher competition, software stops meaning just as much software as people would be custom-making them.)
I think that (former fly.io's) @tptacek's article[1] starts making more sense if viewed from this direction: What even is an OS now.
I don't have the answer to this question as to what happens next but its a form of development that I would prefer not to happen on a more gut instinct level?
Letting AI basically control everything and us not having any mental understanding of sorts and sort of becoming the meat-proxies just for economical reasons seems realistic possibility but a bleaker reality at that. I am left feeling a little bit uncomfortable if this reality turns out to be true.
[0]: <a href="https://news.ycombinator.com/item?id=49808422">https://news.ycombinator.com/item?id=49808422
[1]: <a href="https://sockpuppet.org/blog/2026/09/25/what-even-is-an-os-now/" rel="nofollow">https://sockpuppet.org/blog/2026/09/25/what-even-is-an-os-no...
RGS1811 · · focus · HN ↗
For the past year I’ve been yo-yo-ing in and out of existential despair about the future of civilization depending on how I feel the answer to this question looks. It’s emotionally exhausting, on top of everything else, and I wonder how others are coping with it aside from denial and cynicism.
ihumanable · · focus · HN ↗
Why hire a plumber when you can just watch some youtube videos and do it yourself?
Why pay someone else for their software when you can just make your own?
Because the hard part of making software wasn't *just* writing the code. It was about understanding the problem well enough to understand what the solution should look like.
I feel like as software engineers we should be pretty familiar with what it's like talking to your average user, they will sometimes understand the root cause of what's making their task difficult (although often will get focused on some annoying but ultimately trivial symptom) and have very disasterously bad ideas on how to solve it.
What we've given them with generative AI is a machine they can put their sometimes ok, sometimes questionable understanding of the problem and their dreadful solutions and it will happily churn away building it regardless of how pointless and silly it is.
A future where every user can tell the AI "We keep getting the sales tax wrong, remove charging sales tax from the checkout flow" isn't one I'm terrifically worried about.
In the same way that having access to information about plumbing didn't suddenly make everyone plumbers, having access to a machine that will implement every idea you have regardless of quality doesn't suddenly make everyone a software engineer.
theturtletalks · · focus · HN ↗
You’re right about people not watching plumbing videos and doing it themselves. But the equivalent example would be open-source software in the tech example. But instead of reading open-source code to see how different features were implemented, AI can go and dig into the code and figure it out.
zer00eyz · · focus · HN ↗
The fact that you think this is the way software is priced is telling.
The model being destroyed here is that every piece of software is something that needs to generate recurring revenue.
theturtletalks · · focus · HN ↗
I was more pointing out that software gets enshittified. A plumber necessarily doesn’t and if the plumber does get worse, you can call a different one next time. Software, especially B2B, has switching costs and lock in. So you just have to put up with it.
Another point is that most software started with a few features and to get more market share and support more use cases, it became worse for the users using the early features. That’s why they try to build their own so it’s not bloated with features you will never use.
customguy · · focus · HN ↗
You couldn't move the goal post further from "Letting AI basically control everything and us not having any mental understanding of sorts and sort of becoming the meat-proxies just for economical reasons".
So this one falls under denial for me.
munificent · · focus · HN ↗
There are a lot of horrible potential scenarios that are really scary to contemplate. There are also a lot of really delightful ones where AI does the drudge work, invents a million incredible medicines, and frees us up to hang out and make art all day. And there are even more scenarios somewhere in the middle where AI changes a lot of stuff but we all still more or less end up going to work and doing jobs.
I've basically had a background thread in my skull running at high priority for the past two years trying to predict which of those scenarios I think are most likely so that I can plan for them. It is utterly exhausting spending that many mental resources on a question like that.
It finally clicked for me a couple of weeks ago that no one is going to be able to accurately predict all the thousands of ways AI will affect the world. Certainly not me. We are living in unprecedented times. No one has a map for the future.
So I am trying to loosen my hold on the future some and focus more on the present. I have a great job and a great family now. I have most of my health. I'll try to live my life right now to the fullest and in accordance with my values. The future is going to have to be future me's problem. That's OK.
RGS1811 · · focus · HN ↗
munificent · · focus · HN ↗
Imustaskforhelp · · focus · HN ↗
One of the quotes which might help as well (I think I have this even in my HN profile): The only thing we know about the future is that it will surprise us.
Not even experts are much more likely to predict for what its worth than a coin toss in many cases (especially if they believe that only one theory/idea will mostly predict the future)
It's a blend of things and ideas and the sheer interconnectedness of them where a small pocket can grow large and then also shrink and taking into account all variables and factors is just simply impossible for a mind. I think that although we feel we are being more informed about the world, that in it of itself doesn't prevent things in the future from happening. It just makes us alert and sad and anxious about it.
Yet this life is one which shouldn't be lived with sorrow and anxiety. It is one of beauty and greatness. In many ways, we humanity have come so far from the past (Our medicine is something that not even the mightiest of kings could get) and yes, there are many problems in the world and some things feel as if they are staying just the same or getting worse real-time.
But even then, worrying about it could lead to nowhere other than a path of misery. Also these problems are complicated enough that its extremely hard for a single person to bring change (not that I wish to demotivate that person but rather seeing the system as a complex nature)
So to me, its also a form of inward action. I can work on myself to be better prepared for the world that comes next. In the same time, I think that the present for me as well is good. I have great family and friends and have many qualities that I am proud of and I wish to share that gratitude to the people who have helped me along the way (my family/friends/ Hackernews!.)
Within the hustle culture, there is no time to relax but it is within the time of relax that I believe some of the most fruitful actions can come. I believe it just makes my mind more productive being in a calmer state.
here's a quote from how to measure your life that I hope can help some people:
I genuinely believe that relationships with family and friends are one of the greatest sources of happiness in life. It sounds simple but like any important investment, it needs constant attention and care(...)
You'll be tempted to invest your resources elsewhere but if you don't nurture these relationships, they won't be there to support you in hardships or as one of the most important sources of happiness in your life.
So thank you hackernews and have a nice day and please, please try to say gratitude towards someone close to you (within these tough times) and try to keep a balance towards inward focus, sharing time with friends/family and also writing on hackernews (as is my past time nowadays), balance is necessary :-D
So once again, I hope that its a call to action to say gratitude towards anyone. Just send them a big message thanking them and make their day as well as yours memorable, have a nice day!
[Pardon me for the long post]
busssard · · focus · HN ↗
but the AI thing is on one side using lots of energy to keep up with it, and on the other side gives you an edge, because most people are not aware what even is already possible. so suddenly you are the "AI-Expert" just because you try to keep up to date. so if it all goes to shit, at least we have a chance to sniff it in the wind a couple moments beforehand. Or make memes from it.. that helped me cope with it: <a href="https://t.me/RobotComrades" rel="nofollow">https://t.me/RobotComrades
I hope you find a peer group to talk with and exchange and build community. it is so rewarding to talk to likeminded people that have a similar knowledge base and soothe some fears that someone might have, and have them help you with the ones i have... (i recently did a deep dive in custom DNA synthesis, and how connected those services are already to API <a href="https://www.twistbioscience.com/tapi" rel="nofollow">https://www.twistbioscience.com/tapi )
as always accepting what is seems to be a healthy strategy
jimbokun · · focus · HN ↗
Not sure how atheists are coping with the existential risks we are facing.
boromisp · · focus · HN ↗
You either keep in mind all the horrifying little possibilities the future could hold for us and try to prepare, accept and cope, or you push it out of your mind using whatever techniques available to you to not drive yourself into nervous spirals.
Ultimately it comes down to what your brain chemistry allows in combination with ways you practiced dealing with stress, existential dread, cognitive dissonance, etc.
I guess submission to a higher power is one way to deal with it? That way it's no longer your problem (alone).
jimbokun · · focus · HN ↗
Even some very basic questions, like “are humans inherently valuable?” have been thought through and discussed thoroughly in many faith traditions. For many people not part of such a community it’s a question that’s suddenly very important and they lack the tools to address it.
andrepd · · focus · HN ↗
The proof of the pudding.
schwarzrules · · focus · HN ↗
doctoboggan · · focus · HN ↗
Also, I've recently begun experimenting with specific tasked agents running on a cron like timer for non-dev work. (checking emails, managing small business tasks, etc). Once I started using Claude code in this way, the number of agents I can imagine running has skyrocketed. So I guess what I am saying is that I look forward even cheaper tokens going forward.
bbor · · focus · HN ↗
jobs_throwaway · · focus · HN ↗
> the economy is over
Hackernews' neuroticism remains undefeated
bbor · · focus · HN ↗
jobs_throwaway · · focus · HN ↗
Atreiden · · focus · HN ↗
It depresses them. Significantly. White collar jobs constitute the bulk of global purchasing power. What happens to the economy when aggregate purchasing power drops? The naive response is "prices fall until equilibrium is reached again"
But what if the needle continues moving so quickly that equilibrium is never reached?
This is the K-shaped-economy concern. The ultra wealthy and those who own the "AI means of production" will become unfathomably wealthy at the expense of everyone else.
Why should this not be a concern? Historically, this trend has always precipitated bloody conflict.
JMKH42 · · focus · HN ↗
Imustaskforhelp · · focus · HN ↗
Why do we have to burn tokens just for the sake of it if we aren't finding any actual productive use of them?
> And this might slow down the funding enough that they never reach escape velocity with the training run scaling. But we'll see.
I would consider this to be good rather than bad, or just neutral...? Given the past record of these companies, I wouldn't try to wish them luck for reaching escape velocity, as if I feel like perhaps it can have more net harm than positive.
And especially so if you are already suggesting that current models are good enough for your work already. More improvements or escape velocity might not really translate anywhere to the actual work that you are doing economically but it could translate into a more consolidated form of wealth and control.
I am imagining that your workload is quite complicated and that, the AI being good enough means that it is most likely good "enough" for other use cases as well (that "enough" is doing quite some heavy weight lifting here)
So what is the point of advancing further to reach escape velocity. The good argument (for the sake of neutrality) that i see is are advances within science but that's kinda about it whereas the downsides of p(doom) as many are now genuinely suggesting is more terrifying.
Perhaps it can be worth it to ask, shall we stop or just stopping and asking what's the point. A form of self introspection on what these companies ideals actually wanted when they were formed and if they have completed it or not, but I suppose when trillions of dollars depend on you, you do have some incentives to not stop. We will have to wait and see how it all pans out.
losvedir · · focus · HN ↗
neuronexmachina · · focus · HN ↗
losvedir · · focus · HN ↗
gregwebs · · focus · HN ↗
How you do this (and how deeply) I think is really the limit. I am doing this by focusing heavily on the design phase with grilling and trying to continually improve process to need less effort in the review phase. Are your models doing automated reviewing and testing before pushing out the PR (themselves)?
I think in the long run as models and the tools around them get better and cheaper, those that abdicate understanding will be able to achieve more. Although programmers think of that as irresponsible, ask yourself what does a tech lead do? And then what does a CTO do, etc?
jwpapi · · focus · HN ↗
alansaber · · focus · HN ↗
solooperator1 · · focus · HN ↗
[dead]
chrismustcode · · focus · HN ↗
Not quite sure where this fits well. Maybe small one one off requests like using Claude desktop/web?
maherbeg · · focus · HN ↗
Another thing to think about is, what would it take for you to care less about the understanding. Better integration / e2e tests? Performance validation? visualizing program and data flows? Better refactoring of your modules?
datadrivenangel · · focus · HN ↗
xgb84j · · focus · HN ↗
copperx · · focus · HN ↗
mnicky · · focus · HN ↗
nananana9 · · focus · HN ↗
jpease · · focus · HN ↗
It seems like it would be a better UX to have model and effort selection asked into the system. Of course, I’m not sure in practice if that would be in the best interests of the providers and/or users.
Hauthorn · · focus · HN ↗
Could you explain why it would be a goal to understand the system less, rather than more?
It seems harder to know if you have good tests while lowering your expertise in the system.
[deleted] · · focus · HN ↗
[deleted]
maherbeg · · focus · HN ↗
miki123211 · · focus · HN ↗
An LLM can produce far more code than a human can understand. And the famous rule that "optimizations are entirely pointless unless you're optimizing at the constraint" is logistics 101.
To accelerate software development, you either need to remove or lessen the need for code understanding, or make it much quicker for humans to gain that understanding. Making the LLM faster won't help you if the LLM isn't the bottleneck.
andrewaylett · · focus · HN ↗
tshaddox · · focus · HN ↗
A lot of old-school software engineering is about how to deal with this reality.
satvikpendem · · focus · HN ↗
massysett · · focus · HN ↗
In the old days even if I knew how the software worked when I wrote it, I’d have no idea how it worked when I looked at it weeks later.
It’s also easy to modify software without knowing how it works. This produces modifications that hopefully appear to work, but that break other things, sometimes unknown things.
tshaddox · · focus · HN ↗
Of course less competent engineers (or anyone on a particularly disorganized or desperate day) can literally hand-write code they don’t understand even as they write it, but that’s not really what I’m talking about.
satvikpendem · · focus · HN ↗
> literally hand-write code they don’t understand even as they write it
I find this literally impossible. How can you even start typing anything without knowing what to type?
klausa · · focus · HN ↗
Have you never "fixed a bug", only to realize that you just papered over a single symptom, while the underlying bug is still intact?
People you're disagreeing with (I think!), would say that during your first attempt, you didn't _really_ understand the part you're modifying.
It is _very easy_ to do this in large codebases, and even more so when working on anything touching UI.
tshaddox · · focus · HN ↗
satvikpendem · · focus · HN ↗
miki123211 · · focus · HN ↗
You don't know what these things do and what their effects really are (examples and syntax illustrative, but this is the kind of code that has disastrous effects when used carelessly), but you know they achieve your particular micro goal of "make things go fast" or "make this fit in packets on these strange industrial networks customer X has" or whatever.
gr_norm · · focus · HN ↗
satvikpendem · · focus · HN ↗
jimbokun · · focus · HN ↗
satvikpendem · · focus · HN ↗
jimbokun · · focus · HN ↗
Etc.
satvikpendem · · focus · HN ↗
smeej · · focus · HN ↗
Eventually we're going to reach a point where they don't have to understand the code themselves. The democratization of software creation is going to be fascinating.
ThrowawayR2 · · focus · HN ↗
smeej · · focus · HN ↗
There are small towns all over the world which could realistically have their own little "hometown app" now, that really does track all the interesting things going on there. People don't need "The (Unofficial) Smallville Happenings" Facebook pages anymore. These don't all have some programmer who cares enough about building such a thing to make it happen, but they probably do have a teenager or even a retiree who would have a lot of fun making something that really works well for the people of that town and how they want to use it. The barrier to entry just became low enough to get over.
And that's just one example. There are SO MANY problems that used to require mega investment to solve at scale to get any traction at all. Now communities can build them for themselves. And they'll never be the kind of data target that a megacorp is, because you'd have to target each little app individually, hoping there was something useful in there.
The democratization of software development is going to have lots of things that go nowhere, and lots of little hobby projects, and a few things people will actually hear about and care about. But a lot of people's lives will become incrementally better and I'm excited to see that.
jimbokun · · focus · HN ↗
So accelerate the vibe coding of shit nobody wants or asked for, just to see some metric go up somewhere.
Are we still getting bonuses for the number of tokens we can burn?
maherbeg · · focus · HN ↗
Frontier models today don't really write incorrect code at the micro level. They do miss edge cases at the high level though, and that's what we want to test, is the scenarios.
klardotsh · · focus · HN ↗
… but walking away to make a coffee and coming back to the robots auto-fixing bugs only found in CI is definitely some flavor of magic, regardless of the execution order to get there.
unddoch · · focus · HN ↗
But meanwhile we also have the scripts - one script to watch CI, one script to fetch comments (without dumping raw graphql into the agent), etc etc. Can't wait for this phase to end already
maherbeg · · focus · HN ↗
ipsi · · focus · HN ↗
[1]: <a href="https://code.claude.com/docs/en/channels" rel="nofollow">https://code.claude.com/docs/en/channels [2]: <a href="https://code.claude.com/docs/en/channels-reference" rel="nofollow">https://code.claude.com/docs/en/channels-reference
klardotsh · · focus · HN ↗
Am I having a yells-at-cloud moment where a bunch of folks are using cloud hosted LLM harnesses/environments (let's ignore the models, "of course" those are remote) and I just never saw the point?
wren6991 · · focus · HN ↗
solatic · · focus · HN ↗
Not my experience with Claude Code.
> writing a deterministic, traditional CLI tool to poll GitLab CI pipeline+job state changes on a branch and exit with an appropriate status code
This is what Claude Code does, more or less, on the fly. With a short prompt like "I pushed, monitor CI and debug if needed", it writes a monitor script which is responsible for polling CI status (the script is short, so it's not token-heavy), and if CI fails, only then does the agent proceed to pulling out CI logs, grepping them for signs of errors, etc. as continuation to debugging.
I mean, I'm sure it's more token-efficient to have a CLI tool ready-to-go instead of Claude Code dynamically writing its own script each time, but as I'm on a Max sub where it doesn't seem to affect how close I am to the limits, and I only ever hit the limits if I'm running Fable for everything... /shrug
klardotsh · · focus · HN ↗
crooked-v · · focus · HN ↗
Be careful about this one if you want to have any level of control over basic stuff like comment style and accuracy. Claude will happily spend 20 review cycles in a row rewriting the same 10 comments for a small bugfix over and over because it can recognize "Claude-ese" in the review cycle but then just immediately and compulsively spew out more of it and drift even further from your style rules in the next "fix".
I'm seriously not joking about the 20 tries, I left it running in the background for what should have been a minor code change and it took 18 out of 20 review cycles to stop writing in more comments that all either broke my ASE-STD100ish style rules or included false statements about the code.
maherbeg · · focus · HN ↗
mattm · · focus · HN ↗
miki123211 · · focus · HN ↗
One person doing product management / talking to customers and vibe coding features that solve users' problems, one person keeping the UI/UX in check, one QA person that spends their time clicking through the software, finds the bugs that are obvious to humans but not LLMs and fixes them, and one "harness engineer" who pays off technical debt, observes failure modes and sets the rest of the team up for success.
emkoemko · · focus · HN ↗
bdamm · · focus · HN ↗
Human power and social structures just don't work that way. No AI company is making my sandwich, operating the bus, or serving soup in the school cafeteria. Real estate, human service, specialized expertise, and have-power influence isn't going away.
xoac · · focus · HN ↗
bdamm · · focus · HN ↗
Just because robots can do stuff doesn't mean the human power structures or service preferences evaporate.
tshaddox · · focus · HN ↗
jimbokun · · focus · HN ↗
I want to understand more about how the world around me works. Not less.
Humanity advances in proportion to how well we understand the world. If the machines understand better than us, the world will bend to fit their preferences, and ours only incidentally to the extent they coincide with the machines.
[deleted] · · focus · HN ↗
[deleted]
willtemperley · · focus · HN ↗
Yes please, I'd like to not understand my codebase, give up my decades of experience and have a machine do everything for me. That way I can let captialism utterly steamroller me because of my paltry token stack, in comparison to the 19 year old vibe coder who has secured a new funding round for ponzi.ai
maherbeg · · focus · HN ↗
We also have open weight models too, and ways to host those at home.
Most people don't look at the assembler output of their C++ code (I used to write win32 programs in asm!). Most people don't look at the opcode instructions or JIT output of their ruby / python code. We're starting to work at a higher level of abstraction using LLMs. It's ok to be sad about it, but just being angry about it isn't going to change that there's a new world out there with a new skill set that's needed for honing.
nananana9 · · focus · HN ↗
Where's the super awesome 100x turbocharged software that's a result of everyone here having been being a 100x turbocharged programmer for the last 6 months and a 10x supercharged programmer the past 2 years?
I still use the same software I used 2 years ago, but a bit less reliable.
willtemperley · · focus · HN ↗
I'm not angry, but yes I'm being deeply sarcastic to illustrate the extreme case you seem to be advocating, where we relinquish our understanding to the machines.
In a competitive business like software you need an unfair advantage and for very few people that's having near unlimited tokens. Even then I doubt that's going to produce good software.
[deleted] · · focus · HN ↗
[deleted]
mattm · · focus · HN ↗
It's an interesting question. The thing I keep coming back to though is that every time I've tried to go more towards vibe-coding, I invariably look at the code and find things have been added that would just not be acceptable. I've also tried asking the models to see could be refactored however they still miss things that should be obvious.
I think the gap is that they're still lacking a sense of importance. As engineers working on a product, you have a sense that this feature is more important than that feature. An LLM treats your codebase at the same level of importance. So they'll spend the same amount of effort and code changes on testing and hardening something that just really isn't that important.
Also, once a bad pattern gets into the codebase, they just continue to build and extend that out rather than re-thinking about it like an engineer would.
ncruces · · focus · HN ↗
maherbeg · · focus · HN ↗
I do agree that they're not great at program design by default and that's where we as engineers should spend our time. Data structures and data flow are king. But once you suss that out, they're pretty good at writing the resulting code.
This is also where I disagree with dhh about just using lower level languages. Good abstractions make for excellent program understanding and we should continue to build extremely good building blocks that make program design naturally solid.
sdeframond · · focus · HN ↗
For example, write a skill that finds some kind of code smell, say duplication, and generate a report. Give it some supporting scripts.
Then, use this report to file a few tickets. Then make the agent fix those tickets. Then, as you grow confident, automate more of this process.
It does not replace human supervision but it may enhance it. Especially in a team where people start generating PRs faster that anyone can review them.
Continue this improvement process long enough and you may find yourself with an AI Software Factory.
afro88 · · focus · HN ↗
egeozcan · · focus · HN ↗
OTOH, in the daily job, I have the team plan that's similar to 5x plan and I never had any limit problems, because I really need to understand be able to take responsibility for the code.
Totally different uses.
huntertwo · · focus · HN ↗
The speed of your manual reviews become the limiting factor, which you should be doing at some level to maintain sanity, even if there are enough ideas to be worked on to maintain a review queue.
miki123211 · · focus · HN ↗
I think this is partially because we're still attached to pre-LLM notions of architecture, good design and code quality (which are still important, but maybe less important than they once were and that we think they are), partially because their projects are in a messy state, so models have to work around the technical dept.
They're essentially trading off programmer time for LLM time (which is a good trade financially speaking).
gobdovan · · focus · HN ↗
There was also a period where specifically OpenAI models would always have to comment something in code review and the builders were agreeable up to listening to each nitpick. If you'd have a loop of build->review->build->review, it would take maybe 5-7 rounds for it to 'settle' and not find the smallest nitpicks to argue about. Tried it this week with Astra reviewer and it's about 0-2 review loops (never had a LLM accept a change without nitpicking first try before Astra).
There was also a period where you'd have to give quite specific instructions for agents to keep iterating, but now agent are pretty proactive and try to finish tasks you give them unsurprisingly most of the time.
So, while there's a shortcoming of LLM+harness and engineers observe more tokens improve things even logarithmicly, you'll see more tokens seemingly abused by engineers.
wahnfrieden · · focus · HN ↗
gchamonlive · · focus · HN ↗
senderista · · focus · HN ↗
djmips · · focus · HN ↗
You sure that wasn't just working at Microsoft?
bensyverson · · focus · HN ↗
thunky · · focus · HN ↗
bensyverson · · focus · HN ↗
nsonha · · focus · HN ↗
thunky · · focus · HN ↗
nsonha · · focus · HN ↗
Not complex at all, only one extra session other than the ones doing work and it's on a dumb model and can be thrown away & restarted because it only dispatches work, not doing anything.
I do everything in there, collecting requirements, kick off research, branching, merging, not one other agent on top. I considered making that orchestration command llm-powered but it's not justified at my current use.
It's not more expensive, in fact I could have just chugged along with the slow and manual session by session work but I have a claude subscription and another GLM one (the most low cost basic tier, not even much), that just sit there collecting dust if I don't put them to use in a more efficient way.
And doing session by session would face your problem when context switching too much become unscalable.
daemonologist · · focus · HN ↗
stymaar · · focus · HN ↗
gbalduzzi · · focus · HN ↗
8n4vidtmkvmk · · focus · HN ↗
lobocinza · · focus · HN ↗
bensyverson · · focus · HN ↗
For a concrete example, check out this random plan [0]. A detailed spec followed by the exact implementation tasks that will be executed by the subagents.
[0]: <a href="https://github.com/bensyverson/woodcase/blob/main/project/2026-09-07-scripting-host.md" rel="nofollow">https://github.com/bensyverson/woodcase/blob/main/project/20...
bensyverson · · focus · HN ↗
It does involve letting go and not micromanaging every code convention and implementation detail, but that is the same skill you need when leading engineering teams.
satvikpendem · · focus · HN ↗
drewnick · · focus · HN ↗
Last year I held off on implementing a few features knowing that a model like Opus 5.5 was around the corner. I'm now implementing them in a much more efficient and quality manner than I could have fall of 2025.
satvikpendem · · focus · HN ↗
Tanjreeve · · focus · HN ↗
furyofantares · · focus · HN ↗
For all of my side projects I'm full-on vibe. Well, almost: I do have opinions on what kinds of code it should write and set up my projects to get that. But I don't LOOK at the code.
I use a LOT more tokens on my side projects. I can have it working more or less constantly and it doesn't take up that much of my attention, but it is FAR less token efficient.
meowface · · focus · HN ↗
(With exceptions for what I can only call the "manic vibecoders" with like 10 simultaneous weird slopprojects they're spewing out at once. Generally with each project itself being something related to vibecoding. Steve Yegge being an example of a "manic vibecoder-actual programmer" hybrid.)
vineyardmike · · focus · HN ↗
Also, I’d imagine the token-maxed user is a programmer that lives in chat. I’ll admit to having asked the LLM to move a method up/down in a file, and watched it burn tokens for a minute thinking and executing a menial task.
meowface · · focus · HN ↗
gbalduzzi · · focus · HN ↗
vineyardmike · · focus · HN ↗
pmg101 · · focus · HN ↗
hasbot · · focus · HN ↗
avadodin · · focus · HN ↗
That's a big if though and the blank page syndrome was already getting worse long before AI.
With age, it becomes easier and easier to get angry at someone or something until they work as expected than it is to actually do it.
I think this is why we've been seeing the genius coders from two generations ago embracing vibe coding even before it was cool or any good.
VMG · · focus · HN ↗
Plus you get a bonus random line "methods are all on the top" in the commit message that makes no sense to anybody.
tikhonj · · focus · HN ↗
But, in my experience, the projects where I have a constant pulse on the core design and abstractions in the code end up moving much faster than the ones where I don't. And I've been working on one of each at work recently, so I have a decent point of comparison.
FpUser · · focus · HN ↗
On browser based front ends it seems to be the case for me even though I still impose certain guidelines. On my C++ backends, no fucking way. Even the best models produce working but absolutely disastrous non scalable (performance wise and design wise) code unless watched over like a hen. Having said that - the value I get in either case is enormous.
gchamonlive · · focus · HN ↗
It's the compound counter-probability of success, so even a 99% efficient model will in time accumulate so much error that without conscious cleanup and steering, it becomes really unlikely really fast that anything could be changed in the code without affecting something else, no matter how many tokens you throw at it. It's the collapse of a complex system under the weight of sheer uncertainty of what the system actually does.
xbmcuser · · focus · HN ↗
gchamonlive · · focus · HN ↗
noisy_boy · · focus · HN ↗
2. Make sure tests pass
3. <Every now and then> Review code for quality and fix - make sure tests pass.
4. Go to #1
Overly simplistic? Yes. But I would wager that this can go a long way, even for vibe coders.
gbalduzzi · · focus · HN ↗
"Review quality and fix" doesn't mean a lot without context.
Does it mean to remove unused features and simplify the underlaying code? Does it mean changing the data structures to better support future development? Does it mean improving performance because of bottlenecks?
You are supposed to tell an LLM what your codebase needs, but if you just vibe code without knowing the code, "review quality and fix" will have unexpected results
gchamonlive · · focus · HN ↗
alexytsu · · focus · HN ↗
gchamonlive · · focus · HN ↗
flir · · focus · HN ↗
I haven't had time to dive into it yet, but I think it might structure things in the way you want.
gchamonlive · · focus · HN ↗
internet2000 · · focus · HN ↗
neya · · focus · HN ↗
gchamonlive · · focus · HN ↗
baq · · focus · HN ↗
spicyusername · · focus · HN ↗
drusepth · · focus · HN ↗
I still code "by hand" sometimes (mostly Ruby/Rails, C#, and random languages for code golf) but just for fun at this point. Serious projects started being 95-100% AI over a year ago.
taliesinb · · focus · HN ↗
drbojingle · · focus · HN ↗
It's not perfect but any means but it helps manage ones sanity.
inopinatus · · focus · HN ↗
"Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won’t usually need your flowcharts; they’ll be obvious." - Fred Brooks, The Mythical Man-Month (1975).
and essentially the same sentiment, three decades later:
"Bad programmers worry about the code. Good programmers worry about data structures and their relationships." - Linus Torvalds, git mailing list, 2006.
These things have not changed even though everything else is topsy-turvy. As-of current writing, I have yet to see an LLM make good data structure choices; they go for something that is superficially plausible but profoundly ill-considered (or rather, not considered at all), and then commonly burn tokens treating this implementation detail as a design invariant and trying to deal with the consequences by writing more code, instead of iterating directly upon the ill-fitting data at the root its problems.
If you're wondering, "does he mean the schema of let's say a db or other persistent store, or does he mean abstract/algebraic structures", the answer is yes to both, I think coding models are today shockingly weak when it comes to design reasoning in both domains.
Fortunately, their suggestibility means the same models will readily accept direction on the matter (perhaps even more so than on the structure of code), so I recommend doing just that, and (bonus!) this means your CS degree is still relevant.
avmich · · focus · HN ↗
inopinatus · · focus · HN ↗
hathawsh · · focus · HN ↗
Honestly, if I simply fed it a sense of presence (I would repeatedly tell it what's going on right now and ask it to react if it thinks it should), it would feel eerily like AGI.
logicchains · · focus · HN ↗
stymaar · · focus · HN ↗
potbelly83 · · focus · HN ↗
sdeframond · · focus · HN ↗
It is useful. It may be dangerous. It has an impact. I care about that.
jappgar · · focus · HN ↗
One of the problems is that by default, they'll avoid changing data structures or architecture that is already written down.
Like a junior dev, they're correctly cautious about breaking things, so they prefer to write more code instead.
sdeframond · · focus · HN ↗
Indeed I realized recently that, when we complain about LLMs producing slop, that's in part because we dont ask them to refactor.
Coding agents won't, on their own, make a big change the user did not ask for. And this is fine.
[deleted] · · focus · HN ↗
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alkonaut · · focus · HN ↗
Long story: we have a big legacy desktop app. It uses a big legacy UI component (a grid control), which we had a license for in an old version. Fast forward 20 years, and to be able to move to a new runtime for our app, we need to update the component. Someone had bought the company making the component and now charges north of $1k per developer per year. So instead of doing this, we had just lived with the very old version.
We had long thought of writing our own control to replace the proprietary, but it was always going to be a man-year of work we thought. But I thought I'd give it a try with AI now. I told Opus: look at our uses of that control (tens of thousands of lines of code, it has over 100 instances across our User Interface). Write a new control that would compile with the exact same app syntax. First just make a dummy implementation that throws on every call. Then start implementing. Make a test suite that can run both with our new control and the proprietary control, and test everything, every function that can be called in its public interface and every state that can be inspected from the public API. Verify that everything behaves exactly the same, and lock it in with thousands of tests. Finally, check that the control _looks_ exactly the same as the proprietary one. Render to bitmaps, figure out the rendering logic from observation, such as arithmetic for padding, font sizes, and so on. Compare pixels until it's exactly the same.
Basically: it was a mammoth coding task, but it was so extremely well specified that an LLM could easily just do it. It's a clean-room implementation of something with no tests, but we had a test double that could provide 100% of the expected behavior. The description was extremely short. "Make a new thing that works like the old thing, and prove that it does". Opus 5.5 finished this in a number of hours. 500 source files, several thousand unit tests, and html reports with image diffs from the reimplementation and the original control. It did not use any disassembly or such "cheating". Only observation of the public API and the behavior. Do we need to deeply understand the implementation? Does the architecture matter? Not much in this case I'd argue. It was a black box to begin with and it remains a black box. If we notice a bug, we can always point it to the original proprietary control and say "there's a behavioral difference when doing X" and it will fix it, and lock it down with tests.
As a programmer it's kind of chilling. I had recreated for a few tens of dollars something that would cost $1000 per year to buy. Obviously it's not a complete implementation only the parts of the API we use. It likely still has some bugs. We don't get support, we get to maintain it ourselves. But the rate of reverse engineering this thing "black box" was frightening. It hasn't created anything novel. But we must realize that as programmers some times we have man-years of work that just isn't novel. And in the past, we didn't do this work at all.
I wonder if those who write and sell libraries like this will start having explicit no-reverse-engineering EULAs soon? Perhaps even explicitly mentioning AI/LLM use in analysis and reimplementation?_ Obviously the library we reimplemented was from 2005 so didn't mention AI... (It doesn't mention reverse-engineering either, luckily).
Pannoniae · · focus · HN ↗
alkonaut · · focus · HN ↗
The law that covers this (in the EU) is EU Directive 2009/24/EC, where Article 5 is the reverse-engineering-without-decompilation.
> The person having a right to use a copy of a computer program shall be entitled, without the authorisation of the rightholder, to observe, study or test the functioning of the program in order to determine the ideas and principles which underlie any element of the program if he does so while performing any of the acts of loading, displaying, running, transmitting or storing the program which he is entitled to do.
This is pretty difficult to parse, but luckily there is a ruling from the European Court of Justice on this: SAS Institute Inc. v World Programming Ltd (Case C-406/10), delivered on May 2, 2012.
SAS Institute claimed that World Programming Ltd (WPL) infringed its copyright by studying the behavior of the SAS software system and writing a competing program (the World Programming System) that emulated its exact functionality and used the same data file formats. WPL did not have access to SAS's source code and did not copy any of its literal text or internal structural design.
CJEU:
> "It must therefore be held that the copyright in a computer program cannot be infringed where, as in the present case, the lawful acquirer of the license did not have access to the source code of the computer program to which that license relates, but merely studied, observed and tested that program in order to reproduce its functionality in a second program".
Which is a good find. But this is where I wonder if LLM-based reverse engineering is going to creep into either law (via lobbying) and/or EULA's, because this "observe every single state of the program for every single mutation" was simply not a viable mode of reverse engineering in the past. Or, it was at least always cheaper than just buying the software! Not so any more.
Or alternatively, that programs stop having so many observable states, making more things public. But for libraries as in this case, the whole product IS the public API. Without a rich public API, the library can't be sold. And with it, I can observe it and copy it - because it's internal workings are "too simple" not to be deduced from the public API. In short: a UI control is a ton of hard-to-write but easy to copy boilerplate code. And selling this has been an industry, but I wonder if it will be for very long.
Pannoniae · · focus · HN ↗
Yes, but my point is that.... go on github, you'll find tons of decomps. And many more done just privately too. One of the No Man's Sky devtalks start with "yeah we decompiled the terrain generation from this other game, implemented it in our prototype, it didn't work okay, here's how we've learnt from it to make something better". This was in 2016. More recently, this has been going on way more openly, even full AI-assisted decomps thrown up onto GitHub casually. It might be the letter of law or included in Terms of Service but no one cares really.
seanhunter · · focus · HN ↗
onevsall · · focus · HN ↗
arceister · · focus · HN ↗
I've seen bunch of persons like this and that's kinda stupid because they're just blindly following AI's "suggestions" while they actually don't know what they're doing, then results on terrible code and architecture with "if it works, it works" mentality.
erida_counter2 · · focus · HN ↗
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mgaunard · · focus · HN ↗
I mostly use Fable though, Opus only via sub-agents.
jtrn · · focus · HN ↗
eptcyka · · focus · HN ↗
smb06 · · focus · HN ↗
jcrben · · focus · HN ↗
mrbonner · · focus · HN ↗
Now, during those night and weekend sessions, I have never run into throttling issues with the free model. Sometimes it runs a bit slow and I switch to a different free model (NVIDIA Nemo something).
So yeah, I agree with you that for professional SDE like us, we don’t consume that much tokens. I’m pretty sure the folks on the line of over limit are pure vibe coders if I can take a wild guess.
Wowfunhappy · · focus · HN ↗
raincole · · focus · HN ↗
selcuka · · focus · HN ↗
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hamburglar · · focus · HN ↗
bossyTeacher · · focus · HN ↗
Not every user of Claude is a programmer. Or even exclusively a worker. Claude has uses beyond work. Something that many in HN struggle to understand.
y42 · · focus · HN ↗
konsnos · · focus · HN ↗
I am using Sonnet 5.0 in browser (btw Claude in Chrome extension works in Edge) to download Datadog logs with multiple filters. It's running for about an hour, doesn't run out of tokens and does a splendid job.
cft · · focus · HN ↗
tinykerneldev · · focus · HN ↗
richardw · · focus · HN ↗
randerson · · focus · HN ↗
swalsh · · focus · HN ↗
ohyes · · focus · HN ↗
I’m just hoping they didn’t “improve” sonnet too much or it will become annoying to wrestle into doing what I ask it to do.
pjjpo · · focus · HN ↗
Seeing 2000 years of history being replayed by the AI startups is pretty weird right?
busssard · · focus · HN ↗
also i find it interesting how the capabilities are growing. first speech, then code, then simple tools, then 3d objects, then desktop use
busssard · · focus · HN ↗
so whenever you are dealing with volume rather than independence. opus5.5 can define the goals of a sonnet well enough, that i would trust it with a group of 100s of agents
dionian · · focus · HN ↗