Comment by theshrike79 | Hacker News Reader

Comment by theshrike79 | Hacker News Reader

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Yyep.

IME Claude is the most "creative" of the bunch, you can get surprising ideas out of it that were kinda tickling the back of your head but didn't really connect.

BUT it's also "relentlessly proactive" like simonw put it. It _will_ get the job done, it's the smartest idiot in town. Why use a library to parse $format when you can just write a custom 1000 line parser? Or if it can't access something, it'll pursue the goal of accessing it in the most creative ways - instead of stopping, asking the user "yo, can you give me access to X" and then continuing.

My solution is to use Claude as a pair programmer. I _very_ rarely just do /goal fix this shit, I watch what it does and interrupt if it gets to the "smart idiot" phase. Also I communicate with it like I would a coworker, never had it berate me or get combative. There's a Finnish proverb for that too[0]

As for Codex, Deepseek, GLM, those I use when the goal is 100% clear like "convert this Brewfile to a list of packages for Arch and Debian, use these two Docker containers to test that pacman and apt work correctly". Boom, done.

But I won't give any creative open-ended tasks to any other model than Claude.

[0] https://en.wiktionary.org/wiki/niin_mets%C3%A4_vastaa_kuin_s...

The parsing thing, or the willingness to instantly drop into janky unsanitized string manipulations, or to constantly push back against work on infra projects because some random package on GitHub has 200 stars so it’s totally the safer approach, is driving me insane.

On one hand I’m glad Anthropic is only just now starting to get into infrastructure because it means there’s opportunity there, but it’d be great for their models to be more knowledgeable or able to seek out that knowledge on their own, or for the UX of Claude code to be more amenable to launching 5 in parallel and picking the best one, so I don’t have to spend time arguing with a robot. I think there’s a much better balance to strike between just charging ahead towards the goal at all costs vs being lazy and pushing everything back up to the user. Basically they write too much code that’s too contingent/brittle outside its exact current context and don’t do a good job distilling out the essence of the problem “cleanly”. Almost all of them are like this right now, it’s partially a problem with long-range planning but I think a real bias from over optimization for certain RLVR outcomes vs others.

I feel like this is really due to the harness.

Gemini CLI at work has the same issue: it'll prefer hacking your workstation over just asking you how to proceed.

I think the harnesses are setup to have a bias to action otherwise the LLM would just stop all the time when doing trivial task but it also mean they'll keep going when the "obvious" path is to just prompt the user.

While I agree that the harness is part of it, I think it's also a lack of epistemic understanding or awareness for what it means to actually solve a problem vs just get something kinda working; maybe if Claude Code or other harnesses made web search more likely or had a better way to make technical documentation and specs available to models, it would be better solvable there.

I often tell it to stop asking me and just keep going until it accomplishes X task; unfortunately it tends to assume I want something that only just barely works, in the sense that it means it's time to stop once its there, which is I don't think a harness by itself could easily address (ultimately the model itself needs to determine the stopping points unless I literally specify by hand hidden evaluation criteria).

That's why think it's at least partially a training issue where the model gets rewarded for "solving" the problem within a certain amount of context/time without access to grounded knowledge (eg looking up the actual spec for a format) nor adversarially/rigorously evaluated against a reviewer capable of finding all the edge cases/shortcuts preventing something from being a properly generalized solution. I don't want it to ask me for guidance when it's working on a well-specified problem, I want it to either find the right parser and use it, or to completely implement one against the spec, rather than write some half-assed string inserter that eg only works on the specific select statements my examples use right now. My understanding is that the Mythos/Fable models were better trained for this but from my brief foray into using Fable for work I wasn't that impressed. For me they need to get better at agentic search and self-eval still

There are still billion dollar opportunities in the harness/LLM space.

Having a reliable shared memory for hundreds of agentic AI users is something that's 95% snake oil at the moment. There are a few successes on an individual level (I really like Hermes[0]) but nothing scales to a company level easily.

It should be possible to (pre)configure all agentic harnesses used in a company to use a single source for information so that it'd automatically pick up internal libraries, conventions, licensing decisions etc and remember them across sessions.

I've had limited success with this on a personal level, but it's still not ingrained in the model because it would really need a custom harness. Hooks, skills, prompts get you like 80% of the way. I still need to do a "please check that the project matches the conventions defined in ..." regularly to catch any drift - especially on more vague stuff that can't be locked down with unit testing.

[0] https://hermes-agent.nousresearch.com

FWIW I find that GPT can be very creative when discussing a high-level design. Once it starts writing code snippets it will offer to take things in a bunch of different directions.

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