241–250 of 312 posts
In the end it will still produce slop you need to review line by line.
The question is: Do you want to write code you know and verified that works, or review code written by AI that is of junior dev quality that is not verified.
I wish all model providers would converge on a standard set of files, so I could switch easily from Claude to Codex to Cursor to Opencode depending on the situation
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its not though if you're working in a massive codebase or on a distributed system that has many interconnected parts. skills that teach the agent how to pipe data, build requests, trace them through a system and datasources, then update code based on those results are a step function improvement in development. ai has fundamentally changed how productive i am working on a 10m line codebase, and i'd guess less than 5%…
Even the most complex distributed systems can be understood with the context windows we have. Short of 1M+ loc, and even then you could use documentation to get a more succinct view of the whole thing.
And also we know why: effective context depends on inout and task complexity. Our best guess right now is that we are often between 100k to 200k effective context length for frontier, 1m NIHS type models
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Let me give you a counterexample. I'm working on a product for the national market, and i need to do all financial tasks, invoicing, submit to national fiscal databse etc. through a local accounting firm. So i integrate their API in the backend; this is a 100% custom API developed by this small european firm, with a few dozen restful enpoints supporting various accounting operations, and I need to use it programmatic…
That's what you should be doing. Start from plain Claude, then add on to it for your specific use cases where needed. Skills are fantastic if used this way. The problem is people adding hundreds or thousands of skills that they download and will never use, but just bloat the entire system and drown out a useful system.
Though, you get such a huge bang from customizing your config that I can easily see how you could go down that slippery slope.
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Lots of money being made by luring people into this trap. The reality is that if you actually know what you want, and can communicate it well (where the productivity app can be helpful), then you can do a lot with AI. My experience is that most people don't actually know what they want. Or they don't understand what goes into what they want. Asking for a plan is a shortcut to gaining that understanding.
This is why the grill me skill went viral - https://github.com/mattpocock/skills/blob/main/grill-me/SKIL...
Building your AI agent "toolkit" is becoming the equivalent of the perfect "productivity" setup where you spend your time reading blog posts, watching YouTube videos telling you how to be productive and creating habits and rituals...only to be overtaken by a person with a simple paper list of tasks that they work through. Plain Claude, ask it to write a plan, review plan, then tell it to execute still works the best…
So when Anthropic releases a new model that "breaks compatibility" with some Markdown files, do we call it "refactoring" to find (guess) the required changes to have the desired outcome again? Don't we create brittle specifications to fit a version of a model?
Coming soon, unit, behavioural and regression tests for your prompts and skills :P