Gotta be honest, almost every "how to use AI" resource seems pointless to me. I'm either going to ask the AI how to do it, or if it's about using the AI then we can just bake it into the harness or wait for Anthropic/OpenAI to do it for me because they're always trivial.
All of these resources on agentic workflows, managing agent memory, harness engineering, etc. appear to just be theatre to me.
+1, by the time everybody sworn about MCPs, skills became a more efficient alternative. People learned to aggressively context manage, then longer context windows and agentic made that a lot less important.
If the technique is any good, it will be baked in the next version.
My philosophy is to avoid plugins / MCPs unless strictly required, and to have thorough prompts. This way I’ve successfully avoided to be in the way of progress by forcing obsolete optimisations on my LLMs
Yeah 100% agree, I would even go as far as it's kind of harmful it seems to just pollute the context or stagnant as the models and tooling changes so rapidly or it is just better to do something bespoke for your own project.
After all this effort of not opening the models, then trying to forbid people to use open models and all the rubbish their CEO keeps talking about common people I am done with Anthropic.
I can't use it on good faith
Same goes for grok, chat gpt and others all involved in killing people and monopolising the market.
Does anybody here have a frontend development workflow that works well with coding agents? In my experience coding agents ship buggy, broken, incomplete, or awkward frontend features at a way higher frequency than they do for backend features. The reason seems obviously to
be the difference in verifiability between the two. Basic test suites dont seem to help much. I assume something like Garry Tan’s gstack is the right direction but I dont know if that particular tool is mature enough to adopt yet. I was surprised too see here on HN recently that gemini 3.5 flash may outperform Opus/gpt-5.5 on frontend tasks (maybe because of Gemini’s supposed edge w.r.t. multimodality? or maybe it understands Chrome more deeply?), can anyone here second that take?
I've been using Matt Pocock's skills at home, and they seem pretty great. He makes a great case for them vs other skills in his videos. I don't remember the exact arguments, but basically they're designed to be called by the user instead of called automatically, and that makes them take less context merely because they exist.
I've also found that they tend to coerce the programmer into thinking about the end result, rather than try to push them out of that role. His grill skills are about making sure the actual requirements are known, and his prototype skill can help explore things that need to be experienced to make a hard decision on.
I'm a bit disappointed. Based on the title, I was hoping this was going to be a list of popular recipes Claude has suggested for cooking food. It's one of the not-so-obvious use cases I have a Claude/ChatGPT. It's been really helpful for figuring out how to cook something new (for me). It is easy to ask "how do I cook X" when I have no idea of where to start. Claude has also been helpful when trying to figure out how to substitute different ingredients when I've forgotten something at the store. I've also gotten corrections in recipes, with Claude (correctly) identifying different issues that I didn't notice (like when Claude suggested adding more sugar to a sorbet too avoid having excess ice when freezing in a sorbet).
This is definitely controversial, but I think the best CLAUDE.MD is no CLAUDE.MD at all.
Every time I found myself wanting to put information into that file, I asked myself "Why isn't this already obvious from the code itself?"
I don't need to tell the agent my tech stack, what DB access library I'm using, the way in which I write my tests, etc. Why? Because it's all very clearly spelled out within the code itself. I've gone great lengths to make sure that my fairly complex domain can be understood by a person with little domain knowledge, which means it should certainly be understood by a tool that has a wealth of it.
It is more like make the CLAUDE.MD file as small as possible. What is the least amount of info it needs to be more efficient for the repo. I think the best thing is to point out where important things are so Claude is not just reading random files.
The before and after images in “prompting for frontend aesthetics” are hilarious. Did no one look at these to make sure the skill actually improved the design?
this has a non-zero benefit to those who are trying to do x which might be covered here.
have seen this with copilot, then with openai, and usually they tend to be bombarded with some examples at first. only to be not maintained down the line or become irrelevant.
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[ 3.1 ms ] story [ 58.9 ms ] threadOther AI labs also tend to publish examples and cookbooks on GitHub and Hugging Face, so it's always worth keeping an eye on those as well.
All of these resources on agentic workflows, managing agent memory, harness engineering, etc. appear to just be theatre to me.
I'm not enough of a designer to be able to point exactly at what makes it so, but Claude does seem to have a somewhat limited repertoire of styles.
Maybe if you could point more precisely at the required changes, you could discourage it?
I can't use it on good faith Same goes for grok, chat gpt and others all involved in killing people and monopolising the market.
Before: bland
After: bland with gradients
I've also found that they tend to coerce the programmer into thinking about the end result, rather than try to push them out of that role. His grill skills are about making sure the actual requirements are known, and his prototype skill can help explore things that need to be experienced to make a hard decision on.
I don't need to tell the agent my tech stack, what DB access library I'm using, the way in which I write my tests, etc. Why? Because it's all very clearly spelled out within the code itself. I've gone great lengths to make sure that my fairly complex domain can be understood by a person with little domain knowledge, which means it should certainly be understood by a tool that has a wealth of it.
https://platform.claude.com/cookbook/coding-prompting-for-fr...
have seen this with copilot, then with openai, and usually they tend to be bombarded with some examples at first. only to be not maintained down the line or become irrelevant.