Comment by mohsen1

Comment by mohsen1 21 hours ago

30 replies

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I’ve been exploring this too, since I rely on LLMs a lot to build software. I’ve noticed that our dev loop-writing, testing-is often mostly human-guided, but language models frequently outperform us in reasoning. If we plug in more automation; MCP tools controlling browsers, documentation readers, requirement analysers, we can make the cycle much more automated, with less human involvement.

This article suggests scaling up RL by exposing models to thousands of environments

I think we can already achieve something similar by chaining multiple agents:

1. A “requirement” agent that uses browser tools to craft detailed specs from docs.

2. A coding agent that sets up environments (Docker, build tools) via browser or CLI.

3. A testing agent that validates code against specs, again through tooling.

4. A feedback loop where the tester guides the coder based on results.

Put together, this system becomes a fully autonomous development pipeline-especially for small projects. In practice, I’ve left my machine running overnight, and these agents propose new features, implement them, run tests, and push to repo once they pass. It works surprisingly well.

The main barrier is cost—spinning up many powerful models is expensive. But on a modest scale, this method is remarkably effective.

phillipcarter 19 hours ago

> The main barrier is cost

I very much disagree. For the larger, more sophisticated stuff that runs our world, it is not cost that prohibits wide and deep automation. It's deeply sophisticated and constrained requirements, highly complex existing behaviors that may or may not be able to change, systems of people who don't always hold the information needed, usually wildly out of date internal docs that describe the system or even how to develop for it, and so on.

Agents are nowhere near capable of replacing this, and even if they were, they'd change it differently in ways that are often undesirable or illegal. I get that there's this fascination with "imagine if it were good enough to..." but it's not, and the systems AI must exist in are both vast and highly difficult to navigate.

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  • ademup 18 hours ago

    The status quo system you describe isn't objectively optimal. It sounds archaic to me. "We" would never intentionally design it this way if we had a fresh start. I believe it is this way due to a meriad of reasons, mostly stemming from the frailty and avarice of people.

    I'd argue the opposite of your stance: we've never had a chance at a fresh start without destruction, but agents (or their near-future offspring) can hold our entire systems "in nemory", and therefore might be our only chance at a redo without literally killing ourselves to get there.

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    • majormajor 16 hours ago

      It's not claimed to be an "objectively optimal" solution, it's claimed to represent how the world works.

      I don't know where you're going with discussion of destruction and killing, but even fairly simple consumer products have any number of edge cases that initial specifications rarely capture. I'm not sure what "objectively optimal" is supposed to mean here, either.

      If a spec described every edge case it would basically be executable already.

      The pain of developing software at scale is that you're creating the blueprint on the fly from high-level vague directions.

      Something trivial that nevertheless often results in meetings and debate in the development world:

      Spec requirement 1: "Give new users a 10% discount, but only if they haven't purchased in the last year."

      Spec requirement 2, a year later: "Now offer a second product the user can purchase."

      Does the 10% discount apply to the second product too? Do you get the 10% discount on the second product if you purchased the first product in the last year, or does a purchase on any product consume the discount eligibility? What if the prices are very different and customers would be pissed off if a $1 discount on the cheaper product (which didn't meet their needs in the end) prevented them from getting a 10$ discount 9 months later (which they think will)? What if the second product is a superset of the first product? What if there are different relevant laws in different jurisdictions where you're selling your product?

      Agents aren't going to figure out the intent of the company's principal's automatically here because the decision maker doesn't actually even realize it's a question until the implementers get into the weeds.

      A sufficiently advanced agent would present all the options to the person running the task, and then the humans could decide. But then you've slowed things back down the pace of the human decision makers.

      The complexities only increase as the product grows. And once you get into distributed or concurrent systems even most of our code today is ambiguous enough about intent that bugs are common.

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    • phillipcarter 16 hours ago

      Agents quite literally cannot do this today.

      Additionally, I disagree with your point:

      > The status quo system you describe isn't objectively optimal.

      On the basis that I would challenge you or anyone to judge what is objectively optimal. Google Search is a wildly complex system, an iceberg or rules on top of rules specifically because it is a digital infrastructure surrounding an organic system filled with a diverse group of people with ever-changing preferences and behaviors. What, exactly, would be optimal here?

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  • adidoit 17 hours ago

    "deeply sophisticated and constrained requirements"

    Yes this resonates completely. I think many are forgetting the purpose of formal language and code was because natural language has such high ambiguity that it doesn't capture complex behavior

    LLMs are great at interpolating between implicit and unsaid requirements but whether their interpolation matches your mental model is a dice throw

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  • orderone_ai 6 hours ago

    Overall, I agree - it would take far more sophisticated and deterministic or 'logical' AI better capable of tracking constraints, knowing what to check and double check, etc... Right now, AI is far too scattered to pull that off (or, for the stuff that isn't scattered, it's largely just incapable), but a lot of smart people are thinking about it.

    Imagine if...nevermind.

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  • dboreham 6 hours ago

    > they'd change it differently in ways that are often undesirable or illegal.

    So...like SAP then?

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kuruczgy 19 hours ago

> but language models frequently outperform us in reasoning

what

99% of the time their reasoning is laughable. Or even if their reasoning is on the right track, they often just ignore it in the final answer, and do the stupid thing anyway.

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  • Shorel 38 minutes ago

    Yes, if a LLM outperforms you, you have never reasoned in your life.

    I will assume you passed high-school based on your looks and not on your abilities.

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  • kubb 19 hours ago

    There are 2 kinds of people. Those who are outperformed on their most common tasks by LLMs and those who aren’t.

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    • avs733 17 hours ago

      there are also two kinds of people - those who are excited by that and those who are not.

      The result is a 2x2 matrix where several quadrants are deeply concerning to me.

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      • brookst 17 hours ago

        There are also two kinds of people - those who are objective enough to tell when it happens and those who will never even see when they’re outperformed because of their cognitive biases.

        I give you a 2x2x2 matrix.

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  • amluto 19 hours ago

    The best part when a “thinking” model carefully thinks and then says something that is obviously illogical, when the model clearly has both the knowledge and context to know it’s wrong. And then you ask it to double check and you give it a tiny hint about how it’s wrong, and it profusely apologizes, compliments you on your wisdom, and then says something else dumb.

    I fully believe that LLMs encode enormous amounts of knowledge (some of which is even correct, and much of which their operator does not personally possess), are capable of working quickly and ingesting large amounts of data and working quickly, and have essentially no judgment or particularly strong intelligence of the non-memorized sort. This can still be very valuable!

    Maybe this will change over the next few years, and maybe it won’t. I’m not at all convinced that scraping the bottom of the barrel for more billions and trillions of low-quality training tokens will help much.

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    • dimitri-vs 14 hours ago

      I feel like one coding benchmark should be just telling it to double check or fix something that's actually perfectly fine repeatedly and watch how bad it deep fries your code base.

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    • brookst 17 hours ago

      They key difference between that and humans, if course, is that most humans will double down on their error and insist that your correction is wrong, throwing a kitchen sink of appeals to authority, motte/bailey, and other rhetorical techniques at you.

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      • TheOtherHobbes 16 hours ago

        That's not any different in practice to the LLM "apologising" to placate you and then making a similar mistake again.

        It's not even a different strategy. It's just using rhetoric in a more limited way, and without human emotion.

        These are style over substance machines. Their cognitive abilities are extremely ragged and unreliable - sometimes brilliant, sometimes useless, sometimes wrong.

        But we give them the benefit of the doubt because they hide behind grammatically correct sentences that appear to make sense, and we're primed to assume that language = sentience = intelligence.

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  • copypaper 14 hours ago

    Yea I don't understand how people are "leaving it running overnight" to successfully implement features. There just seems to be a large disconnect between people who are all in on AI development and those who aren't. I have a suspicion that the former are using Python/JS and the features they are implementing are simple CRUD APIs while the latter are using more than simple systems/languages.

    I think the problem is that despite feeding it all the context and having all the right MCPs agents hooked up, is that there isn't a human-in-loop. So it will just reason against itself causing these laughable stupid decisions. For simple boilerplate tasks this isn't a problem. But as soon as the scope is outside of a CRUD/boilerplate problem, the whole thing crumbles.

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    • physix 9 hours ago

      I'd really like to know which use cases work and which don't. And when folks say they use agentic AI to churn through tokens to automate virtually the entire SDLC, are they just cherry picking the situations that turned out well, or do they really have prompting and workflow approaches that indeed increase their productivity 10-fold? Or, as you mention, is it possibly a niche area which works well?

      My personal experience the past five months has been very mixed. If I "let 'er rip" it's mostly junk I need to refactor or redo by micro-managing the AI. At the moment, at least for what I do, AI is like a fantastic calculator that speeds up your work, but where you still should be pushing the buttons.

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      • orderone_ai 6 hours ago

        Or - crazy idea here - they're just full of it.

        I haven't seen an LLM stay on task anywhere near that long, like...ever. The only thing that works better left running overnight that has anything to do with ML, in my experience, is training.

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YetAnotherNick 20 hours ago

RL is a training method and it improves the model itself. So basically one step(e.g. successful test run, finding search result) could create positive and negative examples for the other step(e.g. coding agent, search agent). And using this the base itself will improve to satisfy other demands and if it reaches close to 100% accuracy(which I believe it could as models mostly fail due to dumb mistakes in tests), you don't need the testing agent altogether.

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