51–60 of 478 posts
The only approach I've tried that seems to work reasonably well, and consistently, was the following: Make a commit. Give Claude a task that's not particularly open ended, the closer to pure "monkey work" boilerplate nonsense the task is, the better (which is also the sort of code I don't want do deal with myself). Preferably it should be something that only touches a file or two in the codebase unless it is a trivia…
* I came up with a list of 9 performance improvement ideas for an expensive pipeline. Most of these were really boring and tedious to implement (basically a lot of special cases) and I wasn't sure which would work, so I had Claude try them all. It made prototypes that had bad code quality but tested the core ideas. One approach cut the time down by 50%, I rewrote it with better code and it's saved about $6,000/month for my company.
* My wife and I had a really complicated spreadsheet for tracking how much we owed our babysitter – it was just complex enough to not really fit into a spreadsheet easily. I vibecoded a command line tool that's made it a lot easier.
* When AWS RDS costs spiked one month, I set Claude Code to investigate and it found the reason was a misconfigured backup setting
* I'll use Claude to throw together a bunch of visualizations for some data to help me investigate
* I'll often give Claude the type signature for a function, and ask it to write the function. It generally gets this about 85% right
Works pretty great for me, especially Spec-driven development using OpenSpec - Cleaner code - Easily 5x speed minimum - Better docs, designs - Focus more on the product than than the mechanics - More time for family
I have the same experience despite using claude every day. As an funny anecdote: Someone I know wrote the code and the unit tests for a new feature with an agent. The code was subtly wrong, fine, it happens, but worse the 30 or so tests they added added 10 minutes to the test run time and they all essentially amounted to `expect(true).to.be(true)` because the LLM had worked around the code not working in the tests
There was an article on HN last week (?) which described this exact behaviour in the newer models. Older, less "capable", models would fail to accomplish a task. Newer models would cheat, and provide a worthless but apparently functional solution. Hopefully someone with a larger context window than myself can recall the article in question.
Purely anecdotally, I've found agents have gotten much better at asking clarifying questions, stating that two requirements are incompatible and asking which one to change, and so on.
Just start smaller. I'm not sure why people try to jump immediately to creating an entire app when they haven't even gotten any net-positive results at all yet. Just start using it for small time saving activities and then you will naturally figure out how to gradually expand the scope of what you can use it for.
I used Claude Opus 4.5 inside Cursor to write RISC-V Vector/SIMD code. Specifically Depthwise Convolution and normal Convolution layers for a CNN. I started out by letting it write a naive C version without intrinsic, and validated it against the PyTorch version. Then I asked it (and two other models, Gemini 3.0 and GPT 5.1) to come up with some ideas on how to make it faster using SIMD vector instructions and write…
IIRC, Depthwise is memory bound so the bar might be lower. Perhaps you can try some thing with higher compute intensity like a matrix multiply. I have observed, it trips up with the columnar accesses for SIMD.
I have the same experience despite using claude every day. As an funny anecdote: Someone I know wrote the code and the unit tests for a new feature with an agent. The code was subtly wrong, fine, it happens, but worse the 30 or so tests they added added 10 minutes to the test run time and they all essentially amounted to `expect(true).to.be(true)` because the LLM had worked around the code not working in the tests
But when I use Claude code, I also supervise it somewhat closely. I don't let it go wild, and if it starts to make changes to existing tests it better have a damn good reason or it gets the hose again.
The failure mode here is letting the AI manage both the implementation and the testing. May as well ask high schoolers to grade their own exams. Everyone got an A+, how surprising!
Yes, agentic coding works and has massive value. No, you can't just deploy code unreviewed. Still takes much less time for me to review the plan and output than write the code myself.
So typing was a bottleneck for you? I’ve only found this true when I’m a novice in an area. Once I’m experienced, typing is an inconsequential amount of time. Understanding the theory of mind that composes the system is easily the largest time sink in my day to day.
Now in terms of using AI, the key is to view yourself as a technical lead, not a people manager. You don't stop coding completely or treat underlying frameworks as a black box, you just do less of it. But at some point fixing a bug yourself is faster than writing a page of text explaining exactly how you want it fixed. Although when you don't know the programming language, giving pseudocode or sample code in another language can be super handy.