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.
I actually think that good prompting MOSTLY comes from good writing skills in general. Being able to more clearly state things to an agent, knowing what pieces of context are entirely unnecessary and which are important, having a larger vocabulary helps too.
Of course, there are other areas that can improve model output (Direction rather than open-ended assistance requests, using keywords + plugins that help, the "your output should include: " style prompting).
A few of us run almost the same exact setup at my shop (Base Claude Code w/ SuperPowers + a context repository) and the models are somewhat unhelpful to some, and give meaningful output to others. The only correlation I notice is that their prompts are no-good. Not from a meta "prompt" engineering standpoint, but from a general English 101 standpoint.
"dudde no i wanted the function to return 3 things. not like that. do it again"
VS something like
"Modify the "renderThreeVars()" function signature to accept another variable called "z" and add it to the return statement at line 64."
I ask it all the time about whether X is feasible, how we can get started on Y, and to investigate issue Z.
It is working great for me in a >100k LOC project.
Perhaps this works less well with weaker models. I suspect the people who say Qwen 3.6 27B is working well, are using prompts like "modify the renderThreeVars() function in rendering.py".
As part of a previous job I needed to audit internal AI usage from a largely non-technical employee population.
The prompts were, predictably, really bad. Broken English, sentence fragments, vague requests, lack of context. Yet somehow, the users always got the answer they were looking for. It might have taken a few extra turns with questions from the model, but the end result was the same.
It's humbling, but a flowery, carefully crafted prompt is at best slightly more efficient than a "CAN A DOG BE EATIN SUN FLOWER SEED?" peasant prompt.
You call that a peasant prompt, but it's actually almost perfect. Couple notes, but it's 95% of the way there. "Can a dog eat sunflower seed" is probably the perfect version, just 1 extraneous word in this version.
Unless the user wanted to know if a cat could eat sunflower seed or something.
I think some people who are better at "prompting" even without domain knowledge could be better at getting LLM agents to produce good results than people with good domain knowledge but without the skills to prompt well. Just a hypothesis though, would be fun to try it out for real sometime :)
I feel the same way about "prompt engineering" as I feel regarding the term "parkour" - you know, running and jumping on stuff.
Are people really putting on their resumes that they are capable of reading and writing and appropriately defining and limiting context? That's all prompt engineering is - it's being able to communicate effectively and elucidate your objectives.
Congratulations to all you English majors out there, you're about to make $350K/year.
RL has basically killed prompt engineering. You still need to provide the right context and process, but how you communicate with them beyond that is no longer so important.
> 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?
Yes, it is. Source: had models inplement complex things from scratch and bullshit regardless of whether it was a one-line prompt or a detailed "SOTA witchcraft magic spells that are guaranteed to work"
It's not really a skill. The models are at this point smarter than you are, so the idea that you can prompt them "better" is laughable really when discussing frontier models.
It's like imagining you could "prompt" Richard Feynman to be smarter at Physics.
That is, for 99.9% of engineers, if you want the model to do a code review of your project, the best solution is to just ask Fable, "Hey Fable, do a code review of this project." Throwing in extra text like "think like a senior engineer", "ensure you focus on DRY principles, KISS, self documenting code, etc", doesn't make a difference.
These sorts of tricks used to work with dumber models, but now, like I said before, it's like thinking you can prompt Linus Torvalds into writing better C than he already can do.
I find this very much untrue in my practice and my testing, where fable prompted to do a review found surface level issues, while prompting along the lines of "assume it's wrong, prove it's correct" found much more in depth and real issues.
> it's like thinking you can prompt Linus Torvalds into writing better C++ than he already can do.
Linus Torvalds, the inventor (and beloved dictator) of Linux, has always been quite harsh about C++ and why he rejects it for Linux kernel development. He’s not just been very vocal about it, but also brought up some arguments against the use of C++ that are worth reviewing in detail.
The models are beyond expert level in many areas at this point.
Do you really believe that adding extra junk to your prompt is going to make the model write code better than it does already?
Again, imagine going to Terrence Tao and "prompting" him to get better at Maths, do you think you can do it? What prompt would you give to him to make him produce better maths. Unless you're already a world-leading Mathematician I think you would find it hard.
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
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.
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.
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.
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.
Did you tell Claude "make an agent instructor skill, whatever you think is good, go for it," or did you use the knowledge you had gained about how AI works and how to write good instructions for it?
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”.
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.
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.
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?
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.
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.
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.
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'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.
Surprisingly, a lot of these recipe websites (even the worst offenders for fluff and ads) have a clean JSON API for getting the recipe directly. I self-host a Mealie instance (https://docs.mealie.io/) which has an "import from URL" feature that uses this. It has rarely if ever failed me.
The other day I did that with a TikTok video! It was infuriatingly vague, so I extracted the mp4 using yt-dlp and fed the video file into Gemini Flash and told it to fill in the missing information and give me back a proper recipe.
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.
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.
I actually think that good prompting MOSTLY comes from good writing skills in general. Being able to more clearly state things to an agent, knowing what pieces of context are entirely unnecessary and which are important, having a larger vocabulary helps too.
Of course, there are other areas that can improve model output (Direction rather than open-ended assistance requests, using keywords + plugins that help, the "your output should include: " style prompting).
A few of us run almost the same exact setup at my shop (Base Claude Code w/ SuperPowers + a context repository) and the models are somewhat unhelpful to some, and give meaningful output to others. The only correlation I notice is that their prompts are no-good. Not from a meta "prompt" engineering standpoint, but from a general English 101 standpoint.
"dudde no i wanted the function to return 3 things. not like that. do it again"
VS something like
"Modify the "renderThreeVars()" function signature to accept another variable called "z" and add it to the return statement at line 64."
Obvious exaggeration, but you get the point.
Why not open-ended assistance requests?
I ask it all the time about whether X is feasible, how we can get started on Y, and to investigate issue Z.
It is working great for me in a >100k LOC project.
Perhaps this works less well with weaker models. I suspect the people who say Qwen 3.6 27B is working well, are using prompts like "modify the renderThreeVars() function in rendering.py".
As part of a previous job I needed to audit internal AI usage from a largely non-technical employee population.
The prompts were, predictably, really bad. Broken English, sentence fragments, vague requests, lack of context. Yet somehow, the users always got the answer they were looking for. It might have taken a few extra turns with questions from the model, but the end result was the same.
It's humbling, but a flowery, carefully crafted prompt is at best slightly more efficient than a "CAN A DOG BE EATIN SUN FLOWER SEED?" peasant prompt.
You call that a peasant prompt, but it's actually almost perfect. Couple notes, but it's 95% of the way there. "Can a dog eat sunflower seed" is probably the perfect version, just 1 extraneous word in this version.
Unless the user wanted to know if a cat could eat sunflower seed or something.
I am tempted to try get me a lawyer dog
Nah, you might be confusing prompt engineering with having domain knowledge. :)
I think some people who are better at "prompting" even without domain knowledge could be better at getting LLM agents to produce good results than people with good domain knowledge but without the skills to prompt well. Just a hypothesis though, would be fun to try it out for real sometime :)
I feel the same way about "prompt engineering" as I feel regarding the term "parkour" - you know, running and jumping on stuff.
Are people really putting on their resumes that they are capable of reading and writing and appropriately defining and limiting context? That's all prompt engineering is - it's being able to communicate effectively and elucidate your objectives.
Congratulations to all you English majors out there, you're about to make $350K/year.
Parkour is for the more energetic peripatetic.
RL has basically killed prompt engineering. You still need to provide the right context and process, but how you communicate with them beyond that is no longer so important.
What's clear is that there is a lot of hype around LLM and people who were previously valued for their IC are now in the business of shilling.
> 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?
Yes, it is. Source: had models inplement complex things from scratch and bullshit regardless of whether it was a one-line prompt or a detailed "SOTA witchcraft magic spells that are guaranteed to work"
It's not really a skill. The models are at this point smarter than you are, so the idea that you can prompt them "better" is laughable really when discussing frontier models.
It's like imagining you could "prompt" Richard Feynman to be smarter at Physics.
That is, for 99.9% of engineers, if you want the model to do a code review of your project, the best solution is to just ask Fable, "Hey Fable, do a code review of this project." Throwing in extra text like "think like a senior engineer", "ensure you focus on DRY principles, KISS, self documenting code, etc", doesn't make a difference.
These sorts of tricks used to work with dumber models, but now, like I said before, it's like thinking you can prompt Linus Torvalds into writing better C than he already can do.
I find this very much untrue in my practice and my testing, where fable prompted to do a review found surface level issues, while prompting along the lines of "assume it's wrong, prove it's correct" found much more in depth and real issues.
FWiW
> it's like thinking you can prompt Linus Torvalds into writing better C++ than he already can do.
~ https://medium.com/@jankammerath/linus-torvalds-critique-of-...I mean, the point stands.
The models are beyond expert level in many areas at this point.
Do you really believe that adding extra junk to your prompt is going to make the model write code better than it does already?
Again, imagine going to Terrence Tao and "prompting" him to get better at Maths, do you think you can do it? What prompt would you give to him to make him produce better maths. Unless you're already a world-leading Mathematician I think you would find it hard.
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
Sure but lets not pretend this is engineering.
Definitely. But the special knowledge how to talk to a certain model is usually not worth it.
Being able to write clear, always is.
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.
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.
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.
>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.
Did you tell Claude "make an agent instructor skill, whatever you think is good, go for it," or did you use the knowledge you had gained about how AI works and how to write good instructions for it?
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.
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.
Not only that, but they change on a whim with new ideas on how to do things every few weeks.
> 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.
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.
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.
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?
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.
I've see people write "broooo pleaseeee!" into the box. Something about the universe providing better fools.
I do this often, I manage to get the results I’m looking for
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.
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.
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.
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.
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.
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.
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.
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.
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.
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?
Will they stop killing if the open models will also start killing?
That sounds like a great idea. Let's stop killing people altogether. It's almost entirely unnecessary.
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.
Surprisingly, a lot of these recipe websites (even the worst offenders for fluff and ads) have a clean JSON API for getting the recipe directly. I self-host a Mealie instance (https://docs.mealie.io/) which has an "import from URL" feature that uses this. It has rarely if ever failed me.
You can also hit "print" and get a nicely formatted, single page list of ingredients and recipe.
The other day I did that with a TikTok video! It was infuriatingly vague, so I extracted the mp4 using yt-dlp and fed the video file into Gemini Flash and told it to fill in the missing information and give me back a proper recipe.
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.
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.
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
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).
Recipes like this get baked into the next iteration of a model.
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.
Immensely disappointed; I expected an actual cookbook.