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.
Remember in 2023 when people thought "prompt engineering" would be the new software engineering and invested tons of time into learning CoT, ReAct, thread-of-thoughts, etc?
Those were mostly obviated by reasoning models and harness updates by 2024.
It seems pointless to invest energy into the latest/greatest AI technique or framework when they're going to either be absorbed or replaced on a 3 month cycle.
Isn't it clear that some people are better at working with/prompting LLMs than other people? Or is the idea that what you write to them and how you use them doesn't matter, it's all up to the model/harness? To me this seems clear, so then clearly this is a skill, which typically is called "prompt engineering". Specifically CoT or the other things you mention wasn't referred to as "prompt engineering" as far as I know, that skill is more about how you communicate with the LLMs and how you use them, rather than what specific processes/workflows/technologies you use.
Understanding how to write good prompts (prompt engineering) is still very much a relevant skill if you want to effectively use LLMs. Harnesses aren't magic.
> Remember in 2023 when people thought "prompt engineering" would be the new software engineering and invested tons of time into learning CoT, ReAct, thread-of-thoughts, etc?
Prompt engineering was a GPT-3 era term, which couldn't understand instructions. Then ChatGPT came out in late 2022, which made actual prompt engineering superfluous.
I still believe in writing good prompts or good instructions. Bad prompts can sometimes blow up the bill. A poorly written spec can waste a lot of tokens
Not only that, but all of this tooling around models has such a short shelf life as the models themselves grow in capabilities, they absorb the tooling. We've already seen it over and over again.
These tools work pretty well out-of-the-box. I'm sure I could squeeze out better token usage or streamline some tool calls, but it's not something I really want to focus on. Just like I don't want to endlessly configure my IDE, I don't really have patience with spending time on anything besides actually building something.
There was an article by Vercel on how ineffective tool calling is compared to just a single md file with clearly defined instructions. They showed a clever way on how to use compressed indexes. My experience with tool calling was similar to what Vercel described, and I spent hours trying to perfect it. Since reading Vercel’s findings, Claude.md and Agents.md, maybe a Project.md it’s all I use.
Yes, but I have had moderate luck with creating an "agent-instructor" skill that has strict instructions around keeping language strong, unambiguous, concise, and always presenting me with exact diffs to review before writing anything.
Another thing in it is a strict line count. Any increase in line count requires my approval. That last one is important because it plays well with two biases: models don't tend to create long lines so they won't try to cheat that way, and they're strongly inclined to keep churning out lines so I take that away from them.
First you have to know that "it" exists and is possible. A cookbook like this introduces readers to concepts and features they didn't know were there in the first place.
That's a fair response. I could see how some folks would benefit. I suppose what bothers me more is that they don't actually seem pedagogical in nature, they seem promotional.
Exactly. I think we all have to come to this conclusion ourselves, because the message we’ve been getting from the LLM companies is “sweet user, you DO add value to the LLM, you gave it that custom subagent, remember?”, and then they go and roll that idea into the next version. Once they do that a few times you go “eh why would I bother, this will be a default soon. I’ll just prompt it like normal”.
Right now I feel like I'm getting so much value out of working daily with Fable, but I'd be embarrassed to share my sessions because they're pretty much how I would talk to a colleague. Spelling mistakes and half-baked thoughts included. Gone are the formal sounding prompts and specs I was proudly writing 6 months ago. But it's getting the results.
Once you understand enough to spawn independent agents for discrete tasks, it's hard to justify investing brain space into some harness that will likely be obsolete next week, if not tomorrow. And by the time the harness game converges, I suspect most of the high-end models will behave like them! Intrinsically.
This is how I've gone about it with my Shopify site.
I appreciate there may be more efficient ways to do things if you're an actual developer or engineer but I'm not and the tools I've been able to build so far have been both fun to make and add value to our workflow as a small 2 person ecommerce business.
I do at least have a background in web as a designer for many years and having worked with developers I can at least spec and understand how a product might work which is one thing Claude/AI isn't hugely helpful with and often it's the testing and QA phase as always where the problems and shortcomings expose themselves.
+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.
If the bar is knowing how to type a question into a box, I'm confident almost everyone is better off starting with that then reading a "cookbook" that starts with installing python packages.
I suspect certain workflows like langchain and others like it will retain usefulness into the future. Having deterministic steps before and after the llm is the way to go for anything that might be potentially harmful. Which I guess is what the harness does, but why be limited to a generic harness when we can use it to make specific ones for our needs.
Ok, thanks, because I looked at one example and was like, “I am supposed to take someone’s OpenAPI doc and translate it back to English for the model?” And if they’re implying I should have an AI do that, why don’t they just build that step into the models rather than having someone prompt an AI to write these half-ass docs?
This is also where I'm at. "Just ask the model stuff" continues to be the best, most durable advice. Karpathy himself had something recently about this on Twitter.
You don't learn about progress if you don't take part in it.
Before /goal was ralph-wiggum it was def an interesting learning experience, it was interesting to see how Claude became a lot better doing this itself but it still took 6 Month and more.
You can wait and sit it out and suddenly you get fired if you miss the point when to start spending more time and energy on topics like this.
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?
You should read their frontend design skill [1], which lots of guides recommend blindly, in classic LLM cargo-cult fashion. It doesn't say what people think it does.
it’s crazy to see right there in writing the prompt go out of its way to avoid the bootstrap effect with homogeneous templated results and then proceeds to produce fully homogeneous obviously templated AI results.
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.
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?
There may be good options coupled with agents like Claudes Front End Design skill, I haven't used it.
I have been using Magic Patterns which is very good at generating initial prototypes, especially when driven by an agent familiar with it's facilities and constraints.
I assume the service is just a wrapper plus scaffolding but I like it. You have to push it though to get anything particularly creative but it's good at standard front end designs.
Agents aren't great at complex async state updates, mostly because it's hard to represent what's happening in their context. They're fine at creating complex components in isolation and implementing decoupled stuff like animations.
I've been cooking with the help of LLMs a few times a month for over a year now. It's worked out well 9/10 times.
It turns out the "average" version of a recipe that's baked into the weights is usually a solid recipe, and they're really good at offering substitutions for things like "I don't have ingredient X" or "make it vegetarian".
It's also fun promoting "make it tastier" once or twice after each recipe just to see what happens.
One of the most satisfying and useful applications for AI is handing it the URL for a recipe (replete with 8 paragraphs of useless introductory text, 2 paragraphs dedicated to how to refrigerate the meal, 9 inline advertisements and a mid-scroll ad) and having it return the actual recipe in Markdown in an Obsidian folder.
I have actually cooked with LLMs, specifically Gemini, few times this year.
Works surprisingly well, can even tell it "I have so and so ingredients in my pantry, I want a keto-friendly meal, what can I make?" followed with some narrowing down dialogue-style.
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.
> Because it's all very clearly spelled out within the code itself.
You're probably burning tokens. A markdown file with a summary, structure of your project, rules, and even goals prevents Claude from hunting for and reading files over and over. Each new session (or after compact) would be starting from scratch. I take it a step further and create a HANDOFF.md file between compacting that gives the next session full context of what was done and how to continue.
Also:
"Treat CLAUDE.md as the place you write down what you’d otherwise re-explain. Add to it when:
Claude makes the same mistake a second time
A code review catches something Claude should have known about this codebase
You type the same correction or clarification into chat that you typed last session"
I had to create some workarounds to avoid this bug where they include a "this might not be relevant" preamble to the CLAUDE.MD file: https://github.com/anthropics/claude-code/issues/18560 - I ended up switching to Codex and it just straight up follows the AGENTS.MD instructions, it's nice to see.
This is to say, Anthropic seems to be going down the path you are describing to make the CLAUDE.MD unnecessary. But I think that's the Claude Code harness.
I shouldn't have been so hyperbolic. I do have some general statements about tone and not to go down follow-up question rabbit holes and when to push back or not to install dotnet while on WSL and just go through Windows. Anything that won't change based on day-to-day dev work.
I think that's something that has changed in the last 6 months. It used to be standard practice to let Claude scan everything once and describe your repo in the CLAUDE.md - the logic being you don't want to do that every time for every prompt. But now I think the tooling has gotten so good at just grepping around your repos, and maintaining memory from previous sessions, that the best practice is only use CLAUDE.md for things that aren't obvious from the code.
> the logic being you don't want to do that every time for every prompt
That's definitely a downside of a minimal MD and I could def be wasting tokens. I've tried to scope the tasks such that it won't feel the need to scan unnecessary files, but it's not bulletproof.
I've found CLAUDE.md (etc.) make more sense when the agent is doing more generalized tasks rather than just coding.
If you're just having an agent produce code, then I'd agree. I'd argue that any additional context it might need ought to be included in README.md anyways, just like a human.
But I have projects where agents do more than just code, and retaining context about how I want the agents to operate, keeping track of gotchas, avoiding unnecessary steps in the future, etc., just ends up going into CLAUDE.md. It's not for me; it's for the agents. I also never edit this file directly. I may review it every once in a while to make sure there isn't anything weird in there, but I leave it to the agent to add what it thinks is necessary (or I'll tell it that something will be relevant in the future and that it should add it, etc.).
Although even this pattern isn't necessarily future-proof. Seems like skills are kind of taking that role. Still, I end up with a CLAUDE.md in these kinds of scenarios to catch all the little pieces of context that don't fit nicely anywhere else.
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.
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.
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.
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).
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.
Remember in 2023 when people thought "prompt engineering" would be the new software engineering and invested tons of time into learning CoT, ReAct, thread-of-thoughts, etc?
Those were mostly obviated by reasoning models and harness updates by 2024.
It seems pointless to invest energy into the latest/greatest AI technique or framework when they're going to either be absorbed or replaced on a 3 month cycle.
Isn't it clear that some people are better at working with/prompting LLMs than other people? Or is the idea that what you write to them and how you use them doesn't matter, it's all up to the model/harness? To me this seems clear, so then clearly this is a skill, which typically is called "prompt engineering". Specifically CoT or the other things you mention wasn't referred to as "prompt engineering" as far as I know, that skill is more about how you communicate with the LLMs and how you use them, rather than what specific processes/workflows/technologies you use.
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Understanding how to write good prompts (prompt engineering) is still very much a relevant skill if you want to effectively use LLMs. Harnesses aren't magic.
> Remember in 2023 when people thought "prompt engineering" would be the new software engineering and invested tons of time into learning CoT, ReAct, thread-of-thoughts, etc?
Prompt engineering was a GPT-3 era term, which couldn't understand instructions. Then ChatGPT came out in late 2022, which made actual prompt engineering superfluous.
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I think one day it will relatively plateau and then these approaches will be meaningful improvements in performance. But yes, pointless for now.
I still believe in writing good prompts or good instructions. Bad prompts can sometimes blow up the bill. A poorly written spec can waste a lot of tokens
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Any current way lasts 3-6 months. What's currently the way, will evolve too.
Not only that, but all of this tooling around models has such a short shelf life as the models themselves grow in capabilities, they absorb the tooling. We've already seen it over and over again.
Amen on both your and OP's comments.
These tools work pretty well out-of-the-box. I'm sure I could squeeze out better token usage or streamline some tool calls, but it's not something I really want to focus on. Just like I don't want to endlessly configure my IDE, I don't really have patience with spending time on anything besides actually building something.
There was an article by Vercel on how ineffective tool calling is compared to just a single md file with clearly defined instructions. They showed a clever way on how to use compressed indexes. My experience with tool calling was similar to what Vercel described, and I spent hours trying to perfect it. Since reading Vercel’s findings, Claude.md and Agents.md, maybe a Project.md it’s all I use.
Do you have a link?
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>I'm either going to ask the AI how to do it
LLMs seem terrible at using LLMs in harnesses. Have you seen how they rot their context with the stuff they put in .md files if you let them?
You'd have to have the LLMs search, and thus these resources could be for them more than you
Yes, but I have had moderate luck with creating an "agent-instructor" skill that has strict instructions around keeping language strong, unambiguous, concise, and always presenting me with exact diffs to review before writing anything.
Another thing in it is a strict line count. Any increase in line count requires my approval. That last one is important because it plays well with two biases: models don't tend to create long lines so they won't try to cheat that way, and they're strongly inclined to keep churning out lines so I take that away from them.
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> I'm either going to ask the AI how to do it
First you have to know that "it" exists and is possible. A cookbook like this introduces readers to concepts and features they didn't know were there in the first place.
That's a fair response. I could see how some folks would benefit. I suppose what bothers me more is that they don't actually seem pedagogical in nature, they seem promotional.
Thanks for saying the quiet part out loud. Everytime I do a demo it seems like a waste of time compared to just actually building.
Unless I'm doing something super complicated even taking time to set things up like subagents, etc. seems like a waste compared to just building.
The only things that really seem beneficial (for Claude Code) seems to be learning to set up loops, memory and finding relevant MCP servers.
Exactly. I think we all have to come to this conclusion ourselves, because the message we’ve been getting from the LLM companies is “sweet user, you DO add value to the LLM, you gave it that custom subagent, remember?”, and then they go and roll that idea into the next version. Once they do that a few times you go “eh why would I bother, this will be a default soon. I’ll just prompt it like normal”.
Just use the vanilla settings.
Right now I feel like I'm getting so much value out of working daily with Fable, but I'd be embarrassed to share my sessions because they're pretty much how I would talk to a colleague. Spelling mistakes and half-baked thoughts included. Gone are the formal sounding prompts and specs I was proudly writing 6 months ago. But it's getting the results.
Once you understand enough to spawn independent agents for discrete tasks, it's hard to justify investing brain space into some harness that will likely be obsolete next week, if not tomorrow. And by the time the harness game converges, I suspect most of the high-end models will behave like them! Intrinsically.
This is how I've gone about it with my Shopify site.
I appreciate there may be more efficient ways to do things if you're an actual developer or engineer but I'm not and the tools I've been able to build so far have been both fun to make and add value to our workflow as a small 2 person ecommerce business.
I do at least have a background in web as a designer for many years and having worked with developers I can at least spec and understand how a product might work which is one thing Claude/AI isn't hugely helpful with and often it's the testing and QA phase as always where the problems and shortcomings expose themselves.
+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.
It was more important with earlier models, as they were less RL'd to golden paths, so the context could steer them more.
Now process engineering is more important than prompt engineering.
if you know how to ask Ai about how to use Ai, you already know how to use Ai
the point of guides is to provide assurance for people unfamiliar to the process in the first place
If the bar is knowing how to type a question into a box, I'm confident almost everyone is better off starting with that then reading a "cookbook" that starts with installing python packages.
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I suspect certain workflows like langchain and others like it will retain usefulness into the future. Having deterministic steps before and after the llm is the way to go for anything that might be potentially harmful. Which I guess is what the harness does, but why be limited to a generic harness when we can use it to make specific ones for our needs.
Yea i do the same. I can't be bothered to watch videos and courses on how to use claude better when I can simply ask it myself.
If these things are so smart why do I have to coax it to be useful with all these magic spells scrawled on markdown parchment.
Ok, thanks, because I looked at one example and was like, “I am supposed to take someone’s OpenAPI doc and translate it back to English for the model?” And if they’re implying I should have an AI do that, why don’t they just build that step into the models rather than having someone prompt an AI to write these half-ass docs?
Maybe this “how to use ai” isn’t written for you, but for the agents who then have many ideas on what to do.
Not only that, but they change on a whim with new ideas on how to do things every few weeks.
This is also where I'm at. "Just ask the model stuff" continues to be the best, most durable advice. Karpathy himself had something recently about this on Twitter.
I appreciate the cookbooks bc sometimes I learn about ways to use ai that I did no know before.
I agree, there isn’t much of a skill to using AI. And the model keeps changing so frequently anyway making any/ old skills redundant.
I think there's "how to use AI" and "how to make frequent AI use economical in systems". Different things?
I think the main thing of interest in the linked site is the dates. You can quickly get a view of what was possible and when.
I found them handy to bring along coworkers who aren’t quite up to speed.
You don't learn about progress if you don't take part in it.
Before /goal was ralph-wiggum it was def an interesting learning experience, it was interesting to see how Claude became a lot better doing this itself but it still took 6 Month and more.
You can wait and sit it out and suddenly you get fired if you miss the point when to start spending more time and energy on topics like this.
[flagged]
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?
https://platform.claude.com/cookbook/coding-prompting-for-fr...
Thank you for pointing this out. All of the examples shown are regressions imo.
Fascinating! Makes me wonder if they were chosen to maximize the contrast between the before and after shots, just to "prove that it "works"".
Personally I prefer the before shots all the way down.
You weren't kidding. That's embarrassing.
"Use a black background and this hideously wide font!"
You should read their frontend design skill [1], which lots of guides recommend blindly, in classic LLM cargo-cult fashion. It doesn't say what people think it does.
[1]: https://github.com/anthropics/claude-code/blob/main/plugins/...
it’s crazy to see right there in writing the prompt go out of its way to avoid the bootstrap effect with homogeneous templated results and then proceeds to produce fully homogeneous obviously templated AI results.
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The "with aesthetics" sites look like early 2000s keygens. Just need some techno music now
To be fair. That was peak aesthetics.
If the only artifacts remaining on earth a million years past our demise were The Parthenon and 2002 keygens, our existence will have been worth it.
Ok, if there was chiptune music I think I _would_ like the after shots better.
Is the before/after backwards?
Of course not. It's vibe-slop all the way down.
https://platform.claude.com/cookbook/coding-prompting-for-fr...
Before: bland
After: bland with gradients
Yes, and a couple of them look better before, I would say only the Blog one is truly better.
The Blog after just looks like a Substack post.
Yeah, along with UPPERCASE labels, which is a staple of AI designed pages
It should not be difficult to change the CSS. LLMs have learned from us.
It is to a point where I prefer the non-"aesthetics" version.
Gradients fixed it. They fix everything.
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.
Thanks! I also like the OpenAI Cookbook: https://developers.openai.com/cookbook
Other 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.
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?
There may be good options coupled with agents like Claudes Front End Design skill, I haven't used it.
I have been using Magic Patterns which is very good at generating initial prototypes, especially when driven by an agent familiar with it's facilities and constraints.
I assume the service is just a wrapper plus scaffolding but I like it. You have to push it though to get anything particularly creative but it's good at standard front end designs.
https://www.magicpatterns.com/
That's not necessarily what you're after because I only use it for piloting stuff, not for adhoc feature addition and fixes.
Agents aren't great at complex async state updates, mostly because it's hard to represent what's happening in their context. They're fine at creating complex components in isolation and implementing decoupled stuff like animations.
I thought it would be a litteral cookbook on how to make plausible/feasible recipes with LLMs (we're not here yet sadly).
I've been cooking with the help of LLMs a few times a month for over a year now. It's worked out well 9/10 times.
It turns out the "average" version of a recipe that's baked into the weights is usually a solid recipe, and they're really good at offering substitutions for things like "I don't have ingredient X" or "make it vegetarian".
It's also fun promoting "make it tastier" once or twice after each recipe just to see what happens.
One of the most satisfying and useful applications for AI is handing it the URL for a recipe (replete with 8 paragraphs of useless introductory text, 2 paragraphs dedicated to how to refrigerate the meal, 9 inline advertisements and a mid-scroll ad) and having it return the actual recipe in Markdown in an Obsidian folder.
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Okay, but I think we all want to hear the story about the time it failed.
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I have actually cooked with LLMs, specifically Gemini, few times this year.
Works surprisingly well, can even tell it "I have so and so ingredients in my pantry, I want a keto-friendly meal, what can I make?" followed with some narrowing down dialogue-style.
I've been using ChatGPT to learn how to cook, specifically Italian cuisine, and with a few bumps here and there it has been working surprisingly well.
Same. I was hoping for a digital meal planner.
Ask it for one?
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.
> Because it's all very clearly spelled out within the code itself.
You're probably burning tokens. A markdown file with a summary, structure of your project, rules, and even goals prevents Claude from hunting for and reading files over and over. Each new session (or after compact) would be starting from scratch. I take it a step further and create a HANDOFF.md file between compacting that gives the next session full context of what was done and how to continue.
Also:
"Treat CLAUDE.md as the place you write down what you’d otherwise re-explain. Add to it when:
Claude makes the same mistake a second time
A code review catches something Claude should have known about this codebase
You type the same correction or clarification into chat that you typed last session"
https://code.claude.com/docs/en/memory#claude-md-files
I had to create some workarounds to avoid this bug where they include a "this might not be relevant" preamble to the CLAUDE.MD file: https://github.com/anthropics/claude-code/issues/18560 - I ended up switching to Codex and it just straight up follows the AGENTS.MD instructions, it's nice to see.
This is to say, Anthropic seems to be going down the path you are describing to make the CLAUDE.MD unnecessary. But I think that's the Claude Code harness.
I shouldn't have been so hyperbolic. I do have some general statements about tone and not to go down follow-up question rabbit holes and when to push back or not to install dotnet while on WSL and just go through Windows. Anything that won't change based on day-to-day dev work.
I think that's something that has changed in the last 6 months. It used to be standard practice to let Claude scan everything once and describe your repo in the CLAUDE.md - the logic being you don't want to do that every time for every prompt. But now I think the tooling has gotten so good at just grepping around your repos, and maintaining memory from previous sessions, that the best practice is only use CLAUDE.md for things that aren't obvious from the code.
> the logic being you don't want to do that every time for every prompt
That's definitely a downside of a minimal MD and I could def be wasting tokens. I've tried to scope the tasks such that it won't feel the need to scan unnecessary files, but it's not bulletproof.
I've found CLAUDE.md (etc.) make more sense when the agent is doing more generalized tasks rather than just coding.
If you're just having an agent produce code, then I'd agree. I'd argue that any additional context it might need ought to be included in README.md anyways, just like a human.
But I have projects where agents do more than just code, and retaining context about how I want the agents to operate, keeping track of gotchas, avoiding unnecessary steps in the future, etc., just ends up going into CLAUDE.md. It's not for me; it's for the agents. I also never edit this file directly. I may review it every once in a while to make sure there isn't anything weird in there, but I leave it to the agent to add what it thinks is necessary (or I'll tell it that something will be relevant in the future and that it should add it, etc.).
Although even this pattern isn't necessarily future-proof. Seems like skills are kind of taking that role. Still, I end up with a CLAUDE.md in these kinds of scenarios to catch all the little pieces of context that don't fit nicely anywhere else.
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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.
I don't think that is controversial.
My system CLAUDE.MD spells out my preferences in stack and architecture, and how I expect the agent to interact with me.
I have it generate a summary doc in large repos so it doesn't need to slurp in the world to review a PR or work on a corner of it.
if i don't write "use podman instead of docker" it will go on journey to install docker. so there is some use for it.
I thought this is a new product to generate recipes with Claude.
I was hoping too, because I use a Claude project for recipes and it’s great for suggestions and adapting recipes for what’s in the pantry.
You can use it for that.
I legitimately thought this was going to be a chef (the food kind) tool.
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.
So like OpenAI's Cookbook which has existed for years and no one reads?
Anthropic started their in August 2023 https://github.com/anthropics/claude-cookbooks/commit/2b3f4a...
OpenAI's started in June 2022 https://github.com/openai/openai-cookbook/commit/535f545be7e...
Have you ever thought that just because you don't read something doesn't mean that nobody reads it?
I would assume people read LLM generated summaries of it, almost nobody reads it directly.
Somehow all the with/without cookbook design examples look vibe coded on both sides 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?
They could have done at least one pass on the UI. Can't even get simple table spacing right
Recipes like this get baked into the next iteration of a model.
Well, that's silly. The batter goes in the pan; the recipe is supposed to stay out on the counter.
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.
The open models will also be used to kill people, unfortunately.
Aren't closed models used to kill people by the US and Israel?
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according to the warlords that area killing people with ai models while lying about everything that is happening, yes.
The best way to learn how to use LLM tools and harnesses is to use them.
They missed a great chance to call this "To Serve Man."
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).
Immensely disappointed; I expected an actual cookbook.
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