alt Hacker NewsYou have to hold AI hand to do even simple vanilla JS correctly. Or do framework code which is well documented all over the net. I love AI and use it for programming a lot, but the limitations are real.
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I must say, I do love how this comment has provoked such varying responses.
My own observations about using AI to write code is that it changes my position from that of an author to a reviewer. And I find code review to be a much more exhausting task than writing code in the first place, especially when you have to work out how and why the AI-generated code is structured the way it is.
Exactly that is also my experience also with Claude Code. It can create a lot of stuff impressively but with LOTS of more code than necessary. It’s not really effective in the end. I have more than 35 years of coding experience and always dig into the newest stuff. Quality wise it’s still not more than junior dev stuff even with latest models, sorry. And I know how to talk to these machines.
This is not my experience either. If you put the work in upfront to plan the feature, write the test cases, and then loop until they pass... you can build a lot of high quality software quickly. The difference between a junior engineer using it and a great architect using it is significant. I think of it as an amplifier.
Not in my experience. But then again, lots of programmers are limited in how they use AI to write code. Those limitations are definitely real.
that's just not even remotely my experience. and i am ~20k hours into my programming career. ai makes most things so much faster that it is hard to justify ever doing large classes of things yourself (as much as this hurts my aesthetic sensibilities, it simply is what it is).
AI assisted code can't even stick to the API documentation, especially if the data structures are not consistent and have evolved over time. You would see Claude literally pulling function after function from thin air, desperately trying to fulfill your complicated business logic and even when it's complete, it doesn't look neat at all. Yes, it will have test coverage, but one more feature request will probably break the back of the camel. And if you raise that PR to the rest of your team, good luck trying to summarise it all to your colleagues.
However if you just have an easy project, or a greenfield project, or don't care about who's going to maintain that stuff in 6 months, sure, go all in with AI.
Not what I've experienced
It’s crazy how some people feel the ai and others don’t. But one group is wrong. It’s a matter of time before everyone feels the AI.
Most of this thread is debating whether models are good or bad at writing code... however, I think a more important question is what we feed the AI with because that dramatically determines the quality of the output.
When your agent explores your codebase trying to understand what to build, it read schema files, existing routes, UI components etc... easily 50-100k tokens of implementation detail. It's basically reverse-engineering intent from code. With that level of ambiguous input, no wonder the results feel like junior work.
When you hand it a structured spec instead including data model, API contracts, architecture constraints etc., the agent gets 3-5x less context at much higher signal density. Instead of guessing from what was built it knows exactly what to build. Code quality improves significantly.
I've measured this across ~47 features in a production codebase with amedian ratio: 4x less context with specs vs. random agent code exploration. For UI-heavy features it's 8-25x. The agent reads 2-3 focused markdown files instead of grepping through hundreds of KB of components.
To pick up @wek's point about planning from above: devs who get great results from agentic development aren't better prompt engineers... they're better architects. They write the spec before the code, which is what good engineering always was... AI just made the payoff for that discipline 10x more visible.
The other day I (well, the AI) just wrote a Rust app to merge two (huge, GB of data) tables by discovering columns with data in common based on text distance (levenshtein and Dice) . It worked beautifully
An i have NEVER made one line of Rust.
I dont understand nay-sayers, to me the state of gen.AI is like the simpsons quote "worst day so far". Look were we are within 5 years of the first real GPT/LLM. The next 5 years are going to be crazy exciting.
The "programmer" position will become a "builder". When we've got LLMs that generate Opus quality text at 100x speed (think, ASIC based models) , things will get crazy.