Posted by kmelve 9/2/2025
Typescript on the other hand, seems to do much better on first pass. Still not always beautiful code, but much more application ready.
My hypothesis is that this is due to the billions LOC of Jupyter Notebook it was probably trained on :/
It will fix those if you catch them, but I haven't been able to figure out a prompt that prevents this in the first place.
I notice what worked and what didn't, what was good and what was garbage -- and also how my own opinion of what should be done changed. I have Claude Code help me update the initial prompt, help me update what should have been in the initial context, maybe add some of the bits that looked good to the initial context as well, and then write it all to a file.
Then I revert everything else and start with a totally blank context, except that file. In this session I care about the code, I review it, I am vigilant to not let any slop through. I've been trying for the second session to be the one that's gonna work -- but I'm open to another round or two of this iteration.
OK I made up the statistic, but the core idea is true, and it's something that is rarely considered in this debate. At least with code you wrote, you can probably recognize it later when you need to maintain it or just figure out what it does.
I think I can also end up with a better result, and having learned more myself. It's just better in a whole host of directions all at once.
I don't end up intimately familiar with the solution however. Which I think is still a major cost.
> This isn't failure; it's the process!
> The biggest challenge? AI can't retain learning between sessions
ai slop
for the record, I've been bullish on the tooling from the beginning
My dev-tooling AI journey has been chatGPT -> vscode + copilot -> early cursor adopter -> early claude + cursor adopter -> cursor agent with claude -> and now claude code
I've also spent a lot of time trying out self-hosted LLMs such as couple version of Qwen coder 2.5/3 32B, as well as deepseek 30B - and talking to them through the vscode continue.dev extension
My personal feelings are that the AI coding/tooling industry has seen a major plateau in usefulness as soon as agents became apart of the tooling. The reality is coding is a highly precise task, and LLMs down to the very core of the model architecture are not precise in the way coding needs them to be. and it's not that I don't think we won't one day see coding agents, but I think it will take a deep and complete bottom up kind of change and an possibly an entirely new model architecture to get us to what people imagine a coding agent is
I've accepted to just use claude w/ cursor and to be done with experimenting. the agent tooling just slows my engineering team down
I think the worst part about this dev tooling space is the comment sections on these kinds of articles is completely useless. it's either AI hype bots just saying non-sense, or the most mid an obvious takes that you here everywhere else. I've genuinely have become frustrated with all this vague advice and how the AI dev community talks about this domain space. there is no science, data, or reason as to why these things fail or how to improve it
I think anyone who tries to take this domain space seriously knows that there's limit to all this tooling, we're probably not going to see anything group breaking for a while, and there doesn't exist a person, outside the AI researchers at the the big AI companies, that could tell ya how to actually improve the performance of a coding agent
I think that famous vibe-code reddit post said it best
"what's the point of using these tools if I still need a software engineer to actually build it when I'm done prototyping"
I havn't put a huge effort into learning to write prompts but in short, it seems easier to write the code myself than determine prompts. If you don't know every detail ahead of time and ask a slightly off question, the entire result will be garbage.