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Comment by dingdong2026 | Hacker News Reader

dingdong2026

Only someone who doesn't do any work of any meaningful difficulty could think these models have anything to do with AGI.

Today I spent half a day trying to solve a moderately interesting software engineering problem. I was switching between GPT-5.6 Sol and Fable 5.1 to check each other's work in Cursor.

And the result was gradually driving me insane. As the models struggled to find a solution that would actually work, they dug themselves deeper into a hole. The work grew in complexity beyond my ability to understand what's happening and recover.

At some point, when I felt like throwing the keyboard out the window, I just gave up. Tomorrow I'm starting from scratch, having burned god knows how many tokens and hours of my life.

But sure, they can create a decent website or CRUD app, so they must be really smart.

That's AGI for you.

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But that happens with humans as well. You are having the same experience with an AI that many managers have with their direct reports.

The smarter AI gets, the easier it becomes to move the AGI goalposts. Seems at this point there are people who will refuse to call anything less than omniintelligence AGI.

(And then the excuse will be, but it’s not omniscient! And even if it were, is it omnipotent?)

My success rate for solving software engineering challenges encountered in my day jobs has been near 100% for my entire career. I can only think of a few tasks I kicked back and said they were impossible. For example, after trying to get a signal processing system working reliably I decided to sit down and calculate the actual limits of the channel we were sending the data over and found that from a basic estimation it would not be possible to do. In start ups you don't really get to get stuck in a spiral and not fix things.

I find agents often get into these cases during research tasks.

Yep, people are typing comments with a computer that is powered by several layers of software that will be stored on another computer powered by several layers of software to be read on a computer also powered by layer of software. And then they hope to make the argument that humans cannot produce software.

yeah for me it's "I wanna add this new thing to an existing system" and the AI responds "we should just add some arbitrary state here to facilitate this feature". The real issue is the existing system needs to change entirely to facilitate, I know this, Good developers know this, The AI however knows the shitty solution would solve the immediate problem because it's been trained on shitty solutions. the problem could simply be the AI doesn't have all nebulous loose context I have about the goals of the project and future plans, but I would have to write a novel to give it that context.

> The real issue is the existing system needs to change entirely to facilitate, I know this, Good developers know this

1. I’ll often include boilerplate in a prompt to tell it to make the broader fix. [1]

2. However, a top HN AGENTS.md post 11 days ago included the standard guidance “As much as possible try to minimize the number of changed lines when implementing a feature.” I.e. some devs want LLMs to avoid broader changes and so some of that likely makes it into the training, even if others like us want the opposite.

[1] As far as whether my boilerplate is effective, I don’t know.

The default case when implementing a change should obviously be to make changes with as minimal a blast radius as is reasonable. This is basic software engineering and current LLMs fail it. LLMs also don't have a good idea of what is "reasonable" and a human judgement call is needed.

The case where a (sub)system needs a complete rewrite to admit a feature without incurring too much technical debt should be the exception. When exactly to make that exception is something that clearly currently requires a human judgement call, as models aren't yet nearly smart enough to make such calls.

I still routinely have this experience too. But Sol and Fable feel closer and I have this experience less with them than with their predecessors.

What was the problem?

Humans dig ourselves into holes as well. Sometimes more intelligent humans are better at realising they are digging a hole and clamber out, but sometimes they just dig deeper.

And: Is your work more difficult than finding proofs of or counterexamples to decades-old open problems in mathematics?

Care to share the problem?

Not OP but here's a problem just today:

I was using an AI to help me set up a container to be used as the Nix build environment for another AI. This build environment would not have Internet access. I was having it base its approach off a previous container used for a Stack build environment.

In its initial analysis of my proposed strategy, it insists as its premier point /against/ the strategy, "you will have to rebuild the container every time your flake.nix changes."

Two head-slapping errors of judgement in saying something like that:

(1) The Stack solution is identical. Change stack.yaml, the container must rebuild. (2) It is not physically possible to do better than this while insisting on an internet-free environment.

So on this point, it was just parroting advice irrelevant to the context at hand. LLMs always have such a bizarre mix of technical knowledge and lack of good judgment.

Not the person who asked, but yes, that sounds like a small lack of judgement, but hardly a huge problem. You just correct it and move on.

I work on some reasonably sophisticated stuff (not inventing a new form of compression sophisticated, but still) and I just don't seem to encounter so many of the issues people talk about with these models.

It's really hard to say why, everyone uses them differently. I generally start complex tasks with a "here is what I am trying to achieve as a high level, here is a file that lists the technical constraints, here are my initial thoughts, here is where I am uncertain, what am I missing, lets have a deep back and forth discussion about it with the aim of ...."

Not always the same prompt, definitely not when the task is simpler, but this usually gets me to a good place before it I get it to write any code.

And our repo has very strong opinions and guidelines for how testing is done - we're lucky enough that we write mostly single threaded low latency code, so testing it end to end is very easy.

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