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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 la

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.

YetAnotherNick 1y 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.
- szundi 1y ago
  [dead]
kuruczgy 1y 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.
- kubb 1y ago
  There are 2 kinds of people. Those who are outperformed on their most common tasks by LLMs and those who aren’t.
  - avs733 1y 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.
    - brookst 1y 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.
      - avs733 1y ago
        I'm sure if we work hard enough we can add a meta-meta-cognition level. Cognition is just 2^n series of binary states right?
      - kubb 1y ago
        Sure, but if a person can find an easier way to do their job, they’ll usually do it. Usually the bias is towards less energy expenditure.
        brookst 1y ago
        For many people, yes. For people who have their identity invested in being the smartest person in the room, life is considerably harder.
      - magicalhippo 1y ago
        > I give you a 2x2x2 matrix. That'd be a tensor, no?
phillipcarter 1y 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.
- ademup 1y 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.
  - phillipcarter 1y 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?
  - majormajor 1y 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.

YetAnotherNick 1y 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.

szundi 1y ago
[dead]

kuruczgy 1y 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.

kubb 1y ago
There are 2 kinds of people. Those who are outperformed on their most common tasks by LLMs and those who aren’t.
- avs733 1y 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.
  - brookst 1y 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.
    - avs733 1y ago
      I'm sure if we work hard enough we can add a meta-meta-cognition level. Cognition is just 2^n series of binary states right?
    - kubb 1y ago
      Sure, but if a person can find an easier way to do their job, they’ll usually do it. Usually the bias is towards less energy expenditure.
      - brookst 1y ago
        For many people, yes. For people who have their identity invested in being the smartest person in the room, life is considerably harder.
    - magicalhippo 1y ago
      > I give you a 2x2x2 matrix. That'd be a tensor, no?

phillipcarter 1y 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.

ademup 1y 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.
- phillipcarter 1y 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?
- majormajor 1y 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.