This resonates a lot with me. I often use AI for the plan and let it propose multiple possible implementations, I often have to point out the glaring easier / logical solution.

When implementing its often a lot of misses with a few golden hits. The other day it used flex for a table layout while our app uses tables everywhere sigh.

Another typical one is that it tends to prefere frontend aggregation and looping of data instead of letting the database and backend deal with it.

Using mix of claude, cursor composer and codex.

LLMs diverge, not converge. They slightly increase entropy if not controlled. While you can have DRY skills and use AI to organize AI (in loops(tm) like Boris does) but eventually if you don’t understand the code, you are taking yourself out of the loop. And not just the job security that’s on the line, it’s the increasing cost for AI to babysit AI. If you or your “loops” (or paperclip, Hermes, gastown, or next in class agents of agents that runs your entire company) let it gradually sneak in slop-debt, the cost to fix it later will become prohibitive. (You can always just rewrite it, but as the race for “feature complete” and “zero backlog” continues, rewriting an ever growing set of new daily table stakes will become an economical moat)

TLDR: Keeping your codebase human readable and reason-about-able is not just helping humans to stay relevant. It will save costs for LLMs to maintain it.

> Before coding agents, when given a task, I would explore the codebase, think of different solutions, experiment, and only then implement. That could take days of consolidating all that context. When I finally submitted that PR, confidence was higher, and explaining each of my changes to my coworkers was easier.

Now we are getting to the point where we are speed-running the deskilling of engineers into comprehension debt and they themselves rapidly losing confidence in reviewing code they did not write.

I think this blog post [0] is the best example of what could go entirely wrong and even worse when you do not know the technology.

If you cannot explain a change even when "the CI is green" or "all tests passing", I will immediately reject it.

Maybe great for vibe coding prototypes, but it all changes when that code is deployed onto mission critical systems. Just ask Amazon with Kiro. [1]

[0] https://sketch.dev/blog/our-first-outage-from-llm-written-co...

[1] https://www.reuters.com/business/retail-consumer/amazons-clo...

"Even if it works?"

How do you verify that it 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 ))";

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