> Nowadays, it is better to write prompts
Very big doubt. AI can help for a few very specific tasks, but the hallucinations still happen, and making things up (especially APIs) is unacceptable.
Very big doubt. AI can help for a few very specific tasks, but the hallucinations still happen, and making things up (especially APIs) is unacceptable.
The real issues I'm struggling with are more subtle, like unnecessary code duplication, code that seems useful but is never called, doing the right work but in the wrong place, security issues, performance issues, not implementing the prompt correctly when it's not straight forward, implementing the prompt verbatim when a closer inspection of the libraries and technologies used reveals a much better way, etc. Mostly things you will catch in code review if you really pay attention. But whether that's faster than doing the task yourself greatly depends on the task at hand
The famous "It compiles on my machine." Is that where engineering is going? Spending $billions to get the same result as the laziest developer ever?
That obviously does not mean that it's good software. That's why the rest of my comment exists. But "AI is hallucinating libraries/APIs" is something that can be trivially solved with good software practices from the 00s, and that the AI can resolve by itself using those techniques. It's annoying for autocomplete AI, but for agents it's a non-issue
We used Claude Code the other day to add a new record type to an API and it was mostly right. CC decided (for some weird reason) to use a slightly different return shape on a list endpoint than the entire rest of the API. It changed two field names (count/items became total_count/data). This divergence was missed until the code was released because it 'worked' and had full tests and everything. But when the standard client lib code was used to access the API it failed on the list endpoint. Didn't take long to discover the issue. Luckily, it was a new feature so nothing broke, but it was a very clear reminder that you have to be very thorough when reviewing coding agent PRs.
FWIW, I use CC frequently and have mostly positive things to say about it as a tool.
Maybe it's because the libraries I use are made from small files which easily fit into the context window.
The new models are much better at reading the codebase first, and sticking to "use the APIs / libraries already included". Also, for new libraries there's context7 that brings in up-to-date docs. Again, newer models know how to use it (even gpt5-mini works fine with it).
I've had much more success with things under 20k LOC but that isn't the stuff that I really need any assistance with.
AI is no more capable of reliably one shotting solutions that you are.
Prompt / context engineering is still an underrated and underutilized activity (imo)
> with popular languages
Don't know, don't care. I write C++ code and that's all I need. JS and React can die a painful death for all I care as they have injected the worst practices across all the CS field. As for Python, I don't need help with that thanks to uv, but that's another story.
there is a learning curve, it reminds me of learning to use Google a long time ago
Agents use multiple models, can interact with the environment, and take many steps. You can get them to reflect on what they have done and what they need to do to continue, without intervention. One of the more important things they can do is understand their environment, the libraries and versions in use, fetch or read the docs, and then base their edits on those. Much of the hallucinating SDKs can be removed with this, and with running compile to validate, they get even better.
Models typically operate in a turn-by-turn basis with only the context and messages the user provides.
You're never going to get all, you don't have all today. Humans make mistakes too and have to run programs to discover their errors
Anecdotes are not facts, they are personal experiences, which we know are not equal and often come with biases
My Python work has to be told we are using uv, and sometimes that I am on a mac. This is not that different to what you would have to tell another programmer, not familiar with you tools.
And don't respond with a childish "skill issue lol" like it's Twitter. What specific skill do you think people are lacking?
I have extreme intolerance to boredom. I can't do the same job twice. Some people don't care.
This pain has caused me to become incredibly effective with LLMs because I'm always looking for an easier way to do anything.
If you keep hammering away at a problem - i.e. how to code with LLMs - you tend to become dramatically better at other people who don't do that.
LLMs only have so much context available, so larger projects are harder to get good results in.
Some tools (eg a fast compiler) are very useful to agents to get good feedback. If you don't have a compiler, you'll get hallucinations corrected more slowly.
Some people have schedules that facilitate long uninterrupted periods, so they see an agent work for twenty minutes on a task and think "well I could've done that in 10-30 minutes, so where's the gain?". And those people haven't understood that they could be running many agents in parallel (I don't blame people for not realizing this, no one I talk to is doing this at work).
People also don't realize they could have the agent working while they're asleep/eating lunch/in a meeting. This is why, in my experience, managers find agents more transformative than ICs do. We're in more meetings, with fewer uninterrupted periods.
People have an expectation that the agent will always one-shot the implementation, and don't appreciate it when the agent gets them 80% of the way there. Or that, it's basically free to try again if the agent went completely off the rails.
A lot of people don't understand that agents are a step beyond just an LLM, so their attempts last year have colored their expectations.
Some people are less willing to attempt to work with the agent to make it better at producing good output. They don't know how to do it. Your agent got logging wrong? Okay, tell it to read an example of good logging and to write a rule that will get it correct.