All Claude models are huge suck ups. The "you're absolutely right" meme is real even if that exact phrase doesn't show up as much anymore.

I don't want to start a fight or anything but IME Codex has a bit more of a spine. If you point out something weird, it sometimes gives a good reason for it. Whereas Claude will always say "whoopsie you're right as always sir" even when it's me who missed something.

These "You're right to push back" scenarios are scary for me. I mostly code ML implementations, and some of the errors Claude Code (CC - have only used Opus 4.7) makes are very sneaky, and if you don't have sufficient experience in the area (I see this with people entering ML and writing their implementations with CC), you wouldn't know when to question CC and will let errors or future pitfalls silently slip into your code. A recent example was when there was data leakage in a model calibration step, which it refused to see as an error, till I wrote a detailed reason, and then it agreed that there was a "subtle leakage".

> There are a lot of workplaces where there isn’t a good mechanism to push back on this and the tech debt just keeps growing.

If the "big ball of spaghetti" theory holds, where software companies who can't manage the debt stumble over themselves as they continue to add to the big ball of spaghetti code, I guess we'll see a row of companies declaring "software bankruptcy" or something in some/many months, depending on how well these workspaces learn to care slightly more and get better at pushing back against slop.

> With enough of a token budget you can now wrap loops around an LLM and have it try things until the program appears to work. Ask it to do a code review and then submit the PR without having understood what it was doing. There are a lot of workplaces where there isn’t a good mechanism to push back on this and the tech debt just keeps growing.

I'm not making an argument in favor of people using LLMs for this, but people were doing this before we had LLMs it was just usually a bit slower. I can't even say it usually doesn't work out long term because I worked with a lot of guys who did this and took a ton of Adderall while working practically around the clock. Every incentive structure in the organizations rewarded it along with social credibility from more junior engineers. (The last cowboy I worked with who pulled this shit ended up becoming the most senior engineer in the company, a multi-millionaire and worshipped like a god by 90% of the mostly fresh grads we were hiring).

The problem is when invariably these people burn out eventually and leave, they leave a massive vacuum in their stead. Not from load they were carrying but creating.

I think the larger the organization I've been at, the more they reward the people making huge commits on nights and weekends. Worse, they could get away with TBRing their shit and merging it without review.

LLMs are often all of the bad habits and organizational problems that we already carryied just being speedrun. There are some places doing it right, but they already were.

Which means it doesn’t matter if the code is from AI or not.

If it’s not good it’s not good.

Depends on what it’s writing. There are times an LLM saves me a lot of time researching library functionality. Especially with testing frameworks. So many strange and arcane features out there beyond the basics, but not hard to understand what they do once you see the code. On that topic I should say I am careful when reviewing the actual test cases.

However if you’re highly familiar with a domain then LLMs are much less useful.

Yes this is the thing with these new tools. You have to know when to use them and when not to.

Good ol' software architecture tricks can also help you slot "vibe coded" components into a larger system safely.

> Adequate often means done and cheap

It really, REALLY depends what you're working on. If you're throwing together an internal tool or simple dashboard, it doesn't really matter what the code looks like. But if you're writing software that other programs will depend on, bad design choices ripple out and affect another generation of software. Imagine slop in the linux kernel, in google chrome, or in your compiler or runtime. Its not acceptable.

I know a lot of people spend their careers writing end user software and web UIs. AI is increasingly a good choice for this sort of code. But that's not all of us. And its not all of the software being written.

As long as safe and stable are assumed to be base-level requirements… maybe?

Disagree, adequate means adequate. Done and cheap is what you call it when a solution is adequate. If the solution isn't adequate, it doesn't matter if it's cheap, because it isn't done.

I was just watching a video about system engineering and the following stucks:

Stakeholder needs: What people wants to get done with the product

Management needs: How to manage the spending of resources (time, money,…) to create the product

Engineering needs: What is the product

You have to balance the three. Sometimes it’s simple and easy to get right. Sometimes it’s complex enough, you’re never truly sure until the product is out in the wild.

Software is malleable and we can do easily do iterations which is not possible with hardware. But today, we have a skew towards engineering, where the whole focus is to create a solution, whatever that is. No understanding of the problem, no proper allocation of resources, just do something. Even if it is plastering over the crack for the eleventh time.

For example, the following "works":

    json='{ "left":2, "right":2 }';    
    result="$(
        perl -e '($_)=<>; / "left":(\d+), "right":(\d+)/; print $1 + $2, "\n";' <<< "$json";
    )";
    printf '%s\n' "$result";

    printf '%s\n' "$(( 2 + 2 ))";

According to the author's intention, it is the code that he cannot understand or control. Even if the solution provided by the AI works, he will not adopt it. This is unless he can understand or control it. This should be an assumption.

However, if AI provides a solution, as the person using AI, one should conduct research before making a decision. This is not in conflict with or hindered by the use of the ideas provided by AI.