101–107 of 107 posts
I am a long time fan of Steve Yegge but he's too much part of the groupthink at this point. You can't win with Claude Code. I understand that his API key isn't on the PID controller, so he gets a less bad deal, but he's still breaking even with some gee whizz factor. Agents are like people on a long enough timeline: they will eventually do the lazy thing. But this happens in minutes not years. If you don't have them…
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> I have the same experience and still use it. It's just that I learned to use it for simplistic work. OP said "I don't see the productivity boost from AI" and that they don't "believe the hype" without any qualification, but then went on to say that they use it every day. This makes no sense to me. Isn't this like saying "I don't get anything out of reading books" immediately followed by "I read books for 4 hours ev…
to be fair, every other comment is usually screaming about how if you aren't able to utilize LLMs effectively, you will be without a job soon. most people want to keep their job, or be employable, so if LLMs are a required tool to know, they're trying to become fluent in it by using it.
In other words, people claiming these high productivity increases may be the ones at actual risk. Why employ 3 people when 1 can write the prompts?
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I find that with more complex projects (full-stack application with some 50 controllers, services, and about 90 distinct full-feature pages) it often starts writing code that simply breaks functionality. For example, had to update some more complex code to correctly calculate a financial penalty amount. The amount is defined by law and recently received an overhaul so we had to change our implementation. Every model…
I noticed that you said "models" & not "agents". Agents can receive feedback from automated QA systems, such as linters, unit, & integration tests, which can dramatically improve their work. There's still the risk that the agent will try to modify the QA systems themselves, but that's why there will always be a human in the loop.
To provide a bit more context: - We use VS Code (plus derivatives like Cursor) hooked up to general modals and allowing general context access to the entire repository. - We have a MCP server that has access to company internal framework and tools (especially the documentation) so it should know how they are used.
So far, we've found 2 use-cases that make AI work for us: 1. Code Review. This took quite a bit of refinement for the instructions but we've got it to a point where it provides decent comments on the things we want it to comment on. It still fails on the more complex application logic, but will consistently point out minor things. It's used now as a Pre-PR review so engineers can use it and fix things before publishing a PR. Less noise for the rest of the developers. 2. CRUD croft like tests for a controller. We still create the controller endpoint, but providing it with the controller, DTOs, and an example of how another controller has its tests done, it will produce decent code. Even then, we still often have to fix a couple of things and debug to see where it went wrong like fixing a broken test by removing the actual strictlyEquals() call.
Just keeping up with newest AI changes is hard. We all have personal curiosity but at the end of the day, we need to deliver our product and only have so much time to experiment with AI stuff. Nevermind all the other developments in our regulatory heavy environment and tech stack we need to keep on top off.
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10x is doing a year's work in ~5 weeks No shot I'm seeing that some tickets are "finished" (i.e. ready for PR) more quickly, but they end up needing so many changes and to be re-reviewed so many times it takes longer than it ever did because someone is saying yes to the LLM instead of designing software. When it's clear your review comments are just going back into the maw of the LLM, I've given up trying to guide an…
Nothing is more infuriating at work than when you say something to someone in a message or PR comment, and they just paste the LLM response back to you.
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to be fair, every other comment is usually screaming about how if you aren't able to utilize LLMs effectively, you will be without a job soon. most people want to keep their job, or be employable, so if LLMs are a required tool to know, they're trying to become fluent in it by using it.
Perhaps the out of job prediction is actually reversed. True, LLMs will become an efficiency increasing tool. But in terms of job security, doesn't that mean that if your whole job can be driven by an LLM then demand for that job decreases? In other words, people claiming these high productivity increases may be the ones at actual risk. Why employ 3 people when 1 can write the prompts?
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I am with you on this, and you can't win, because as soon as you voice this opinion you get overwhelmed with "you dont have the sauce/prompt" opinions which hold an inherent fallacy because they assume you are solving the same problems as them. I work in GPU programming, so there is no way in hell that JavaScript tools and database wrapper tasks can be on equal terms with generating for example Blackwell tcgen05 warp…
Anthropic has a challenge of optimizing GPU code. The current leader is Opus 4.5 https://github.com/anthropics/original_performance_takehome
Every time I say I don't see the productivity boost from AI, people always say I'm using the wrong tool, or the wrong model. I use Claude with Sonnet, Zed with either Claude Sonnet 4 or Opus 4.6, Gemini, and ChatGPT 5.2. I use these tools daily and I just don't see it. The vampire in the room, for me, seems to be feeling like I'm the only person in the room that doesn't believe the hype. Or should I say, being in roo…
Exact same experience. Here's what I find Claude Code (Opus) useful for: 1. Copy-pasting existing working code with small variations. If the intended variation is bigger then it fails to bring productivity gains, because it's almost universally wrong. 2. Exploring unknown code bases. Previously I had to curse my way through code reading sessions, now I can find information easily. 3. Google Search++, e.g. for decidin…