Almost nobody has a fully formed idea going into any project or product

That’s the first thing that people need to understand is that this idea of some platonic product or project or tool kit or framework or library or whatever just doesn’t exist and it’s never going to exist

Do you have a specific discreet finite problem that you need to solve so you solve that and if you do it in a certain way you can solve other problems with that same solution sometimes you don’t need it to do anything more than you’re one thing and so that’s all you built but maybe you want to do more than just one thing and so you build it so that has the capability to do it

So yes fully concur it’s the synthesis of attention to detail and large scale

it’s all of the above

No, I don’t know what those are. (Looked them up and I don’t teach every possible handler, but I teach people how to do structured inputs etc..)

I teach TDD philosophy as well as conways law, parnas hiding etc…without using those terms

So things like problem decomposition into tractable chunks minimum viable product, prototyping, how do you iterate, write the smallest possible test… you know things like this which are just taking incremental work and then iterating on it

It’s basically everything I’ve learned about building stuff since 1997

**Interestingly I thought prompt engineering was going to be a fad but it’s turned into a whole ass new discipline which makes less sense as more robust toolchains come into play and models handle the context interpretation better

Not the original commenter, but I feel pretty strongly that frameworks for software review loops are at best training wheels for people who haven't yet developed the right understanding. I don't use any sort of complex skills framework, I just tell the AI what I want while leaving reasonable Claude-sized gaps to fill in, and my results are usually better and often faster than people who get lost in framework management. Perhaps they're more useful for pure greenfield development, but for most software developers who are working on existing systems I have not seen a strong use case for them.

There's one guy I know who constantly has problems with Claude going off-script, and every time I dig in, it's clear that the poor thing is so overloaded with instructions and skill lists that it can't figure out what he actually wants it to do.

Indeed I don't mean to downplay the usefulness that AI can have in the self-evaluation process. It's a wonderful engine for discovering information either general or specific to one's project.

> interactive discussion drilling down on details until I understand the problem and the solutions better.

I think it is fair to call this use of AI something akin to a fusion of a super-competent search engine and a leveled-up rubber duck (https://en.wikipedia.org/wiki/Rubber_duck_debugging). And this is not to downplay the utility of either of those things.

However, one cannot rely on an AI to decide when the details are sufficiently expounded, or when one understands them clearly enough. If one starts hinting that one gets it when one really doesn't, or that one is getting close to having all the pieces together, the AI will not be opinionated enough to contradict that sentiment.

> It definitely challenges my bias when I do this. The one thing it doesn't challenge is the X. Formulate the problem poorly, and you'll get a bad solution.

The best advice an expert can give a beginner is generally in the form of solutions to XY problems (https://en.wikipedia.org/wiki/XY_problem). It is a shame that AI are rarely opinionated enough to suggest you're not hunting the right thing. And if you do explicitly prompt it to consider if you're an XY problem, usually it takes that as a cue to indulge that suspicion regardless of its merit.

I don't think this is an inherent issue to LLMs and I see signs of it improving bit-by-bit. I can recall the shit-on-a-stick test about a year ago (https://www.reddit.com/r/ChatGPT/comments/1k920cg/new_chatgp...), and when I most recently asked Claude "Are oyster mushrooms or wine cap mushrooms more capable of high levels of sunlight?" it answered my question while also adding, "Caveat on the comparison: the relevant variable isn't sun per se but moisture retention. A wine cap bed that's kept moist will take far more sun than an exposed oyster log, but a sun-baked, drying bed will fail for either" which I think is a mature amount of pushback to include.

In the end I still disagree with the notion that subservience is, by default, the right attitude for an LLM to have. An agent spawned specifically for code generation according to a spec? Sure. But in any cases where you're trying to refine rather than execute your ideas, you want something to call you out on your bad ideas.

And what's the difference between that machine and LLMs?

You took it too literally. It means, the f*ing tool should do one thing well and f*off with its crappy "suggestions". Why is my washing machine trying to do talk to me nowadays? Once its done washing my clothes, it should just shut the f*up and turn itself off. I"ll tend to the clothes when I have time. Not when the machine tells me to. We are overwhelmed with the machines designed by morons in product management who think they are designing futuristic tech when they ask engineers to build a beeping washing machine.

You can refer to it however you want, the outcome is the same.

When I actuate the chain on my chainsaw to move, it’s obeying me unquestionably, in the same way that when I press a key on my keyboard it obeys me unquestionably. What exactly is the difference?

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>They’re not communicating, you’re just being observant.

Since we are talking about hammers: you hit the nail on the head.

The only consciousness, observing, and thinking happening when a person is using an LLM is happening in the person's brain. We project our own consciousness onto them, and that is the anthropomorphizing part. Essentially we empathize with the object because they are designed to respond like a person. The "conversation" is purely an illusion.

I've seen internet comments similar: "put on a yellow vest and carry a clipboard and you can enter any building, anywhere." Confidence is scary, and often misleading.

Conversely - we tell people who are speaking in public to "Show confidence" - or in job interviews "Hire people who are confident"

We've also pushed back "The more a person knows, the less confident they are" - Dunning Kruger - often used to dismiss over confident people - points out that people are really confident, at first, then that confidence drops away, markedly, but it rebuilds (slowly).

That last rise in confidence is what (I believe) people use as a heuristic on the likely level of knowledge possessed by the speaker (AI or human)

Most engineers know, though, that overconfident people are toxic - the difference between arrogance and genuine confidence in the answer is incredibly difficult to define.