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Comment by vablings

4 hours ago

That's pretty stupid. Most people who are incurring significant costs are just tokenmaxxing rather than being efficient with usage. You can get 99% of jobs and work done with Haiku/Luna in a collaberating working enviroment.

I feel like people who are later to the AI game just like to "oneshot" and sink a bunch of usage into generating garbage

There really is a skill to using it effectively. I've tried coaching some of the devs on my team. Some get it, some don't.

Our company has been tracking token usage and models used vs output (tickets, story points, PRs, deploys, etc...). A dev got chewed out, even after I warned him, because he spent over $2k in a single month almost exclusively on Opus while his actual productivity in terms of what he delivered was abysmal.

  • I get it but it goes against the grain for me. Isn't it ironic that we have to waste our precious and expensive human brain cycles to think about how to use AI cheaply so that it is not more expensive than us?

    In other words I want to spend 100% of my mental capacity in the problem domain for the things AI cannot do for me, like steering, grounding, verification and not for things AI could do.

    • > In other words I want to spend 100% of my mental capacity in the problem domain for the things AI cannot do for me, like steering, grounding, verification and not for things AI could do.

      It sounds like the parent is less talking about this, and more people burning tokens while not getting useful work done.

    • This is much like how devs got grilled for creating expensive test VMs on AWS. Someone has to pay for all this at the end of the day.

  • So you are going to blame this one that dev?

    Define productivity, and while at it, quality, maintainability , modularity and so forth.

  • rookie numbers. in one of the top companies, i know someone who tokenmaxed so hard they ended up spending $50000

  • It's funny, the thing that makes effective prompt also makes effective documentation/communication.

    It's bizzare to see people that made clown issues (not enough detail etc.) suddenly start writing detailed prompts just because it is AI that will do the task and not the human on the other side.

    • You mean that now rather than taking 30 seconds to write a ticket, because we all know what we're talking about, we must take 10 minutes to give all the background information to the AI?

  • there's a manifold to what "effective" means. The problem is once you get into the vibe flow, it's really difficult to eject yourself into the other realms of vscode or IDE or whatever it is you normal do because the vibing provides no anchor to what you're doing.

    Even if these models are smart enough to reorient themselves, they get entirely stuck in a desert and now you're asking someone to just pull up stakes and digg them out even thought they only watched them get there and the UI provides so much speed that no human can comprehend how they got there in the first place.

    It's like asking a pilot to take over in an emergency situation when they're not tasked with any of the every day requirements of the job. The orgs are relying on borrowed time of experienced professionals, and that's going to erode away and what replaces it is mostly people who understand how to navigate context but not use any of the _classic_ tools.

    It's a real conundrum and won't be easily surfaced but for a decade.

    • I’m trying really hard to keep my skills up but it doesn’t feel productive when I’m using it to write code. It feels like I’m slowing down the AI to the point that it’s not as effective as just letting it go. But I don’t get all the learning that comes from that time along the way.

      Have you found ways to stay sharp while using it? Or are you relying on other projects outside of work to keep your skills fresh?

That's what happens when token usage becomes a performance metric. As has been done at my company.

I feel like it's only within the past few months that opus got to the point where guiding the model is faster than doing things myself. I tried out sonnet recently and it was not a net positive to my work. I feel like anything that I'd trust haiku to handle isn't worth doing in the first place.

For context, I'm doing a range of tasks, everything from one-shotting adhoc scripts to having 4 hour 10M+ token conversations debugging things.

  • That is always amazing to me.

    There is no way I can beat even local models at generating complex Python scripts fast.

    hn is filled with uber geniuses.

in my company there are a few who keep sharing screenshots of reaching limits on 3 separate subscriptions, 2 of them their personal on top of the company subscription

  • Wonder when subscription-hopping attacks will become more often (jumping from a personal model to injecting instructions into the business account and exfiling data)

What on earth do you even do with these models?

Or does a "collaborating work environment" mean that everything is basically spoonfed to them? Or do you only ever use ghost suggestions?

I genuinely cannot even fathom. Just how do you even get into a state where tasks are so clear and cookie cutter? These things are abhorrent. Not only are they not useful, it's an outright form of psychological torture to try and use them. They almost fight you.

Luna doesn't even respond to steers properly! You try steering it and it immediately gets distracted and then just stops.

I can imagine coercing Sonnet into doing some of my tasks okay, but Haiku? Especially 4.5? Really?

  • I think you might be overestimating the sort of projects most of us have worked on throughout our careers -- we haven't been doing much groundbreaking work. LLMs can easily and successfully write most code.

    • I equate most LLM work to squeezing a glue bottle

      It's just glue code

      It's not complicated. Someone just has to be there to squeeze the bottle

    • It's possible it's my role distorting my perception, cause technically I don't write software, I work an SRE role. None of my items come pre-chewed or paced, it's all good luck and god bless.

      I'm desperately trying to classify and standardize my work items and delegate them to less capable models, because my usage is clearly unsustainable and this same sentiment as above keeps being pushed on me too. But all my tasks are genuinely fairly arbitrary, so there's no real way around the agent actually being able to reason about business and technical context proper. It's not even that they're hard, it's just that they're dynamic.

      I can get Luna to do things like walk our observability stack and perform a healthcheck, then defer to a stronger model if anything looks super off, but if I'm being entirely honest, this could basically be just a script. Which Opus 5.5 will immediately write for itself if it doesn't yet exist, run that, and then off it goes depending. But Luna will never actually do an investigation proper. Heck, it can't even read our dashboards most of the time, tripping up on Grafana minutia.

      It feels like that surgeon vs surgeon comparison, where you're made to decide based on their surgery success rate, and the better succeeding surgeon simply reward hacks the number by only operating on less dicey cases. Except there's no objective way to make this classification here, so jackasses like the above get to play with my insecurities with full obnoxious confidence, while I'm left desperately trying to slim my usage and failing to do so between two moments of crippling self doubt and blockers.

      3 replies →

Sorry, but that is nonsense. Compared to opus haiku doesn't cut it most of the time.

  • I think they mean the new Haiku, which is mildly above Luna now . If you have a plan written by a smarter model (so the hard parts are solved) they can be great at implementation.

> You can get 99% of jobs and work done with Haiku/Luna in a collaberating working enviroment.

Optimally? Opus will pay for itself if you save just 10% of your time