zlacker

>>tablet+(OP)
I tried the agent thing on:

- Large C codebase (new feature and bugfix)

- Small rust codebase (new feature)

- Brand new greenfield frontend for an in-spec and documented openAPI API

- Small fixes to an existing frontend

It failed _dramatically_ in all cases. Maybe I'm using this thing wrong but it is devin-level fail. Gets diffs wrong. Passes phantom arguments to tools. Screws up basic features. Pulls in hundreds of line changes on unrelated files to refactor. Refactors again and again, over itself, partially, so that the uncompleted boneyard of an old refactor sits in the codebase like a skeleton (those tokens are also sent up to the model).

It genuinely makes an insane, horrible, spaghetti MESS of the codebase. Any codebase. I expected it to be good at svelte and solidJS since those are popular javascript frameworks with lots of training data. Nope, it's bad. This was a few days ago, Claude 4. Seriously, seriously people what am I missing here with this agents thing. They are such gluttonous eaters of tokens that I'm beginning to think these agent posts are paid advertising.

>>mlsu+ur
You’re not providing a key piece of information to provide you with an answer: what were the prompts you used? You can share your sessions via URL.

A prompt like “Write a $x program that does $y” is generally going to produce some pretty poor code. You generally want to include a lot of details and desires in your prompt. And include something like “Ask clarifying questions until you can provide a good solution”.

A lot of the people who complain about poor code generation use poor prompting.

>>runjak+uw
Prompt engineering isn't really that important anymore imo. If you're using a reasoning model, you can see if it understood your request by reading the reasoning trace.

>>lexand+Qw
That's a very dangerous thought. Prompt engineering evolved is just clear and direct communication. That's a hard thing to get right when talking to people. Heck, personally I can have a hard time with clear and coherent internal dialog. When I am working with models and encounter unexpected results, it often boils down to the model giving me what I asked for instead of what I want. I've never met anyone who always knows exactly what they want and is able to articulate it with perfect clarity. Some of the models are surprisingly good at figuring out intent, but complexity inevitably requires additional context. Whether you are working with a model or a person, or even your future self, you must spend time developing and articulating clear specifications, that is prompt engineering. Furthermore, models don't "think" like people--there's technique in how you struture specifications for optimal results.