131–140 of 182 posts
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...
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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.
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.
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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…
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 flowe…
They don't need to be told that you're asking about the safety of eating them, because they can infer that based on the fact that a very large percentage of any text linking dogs to sunflower seeds is obviously going to be about the safety of the dog eating them.
Even pre-LLM that would have been a perfectly sufficient google search for the same information.
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I copied your prompt verbatim to ChatGPT and to Google. ChatGPT kept the charade for all of one sentence. Then it dropped to talking about "your dog" the rest of the way. It even starts the final paragraph with "if, instead, you mean you (a human) ate them ...", and finishes with the question "is this about an actual dog or yourself?" Google did consistently refer to me as a dog, but its entire focus was on the steps…
You have to steer it back with "No I'm a dog"
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.
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...
[1]: https://github.com/anthropics/claude-code/blob/main/plugins/...
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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.
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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 "yo…
Is Superpowers any good? My coworkers who've used it seem to think that its main purpose is to consume a lot of tokens.
I've noticed among my coworkers that we all have different amounts of trust we're willing to give the agent. That seems to manifest into some people only asking questions about the existing code, but never writing anything new with it. Others are willing to do limited targeted changes with the agent, but are unwilling to do things like let it make commits, or connect MCP servers, or really do anything that isnt fully understood by the human before setting the agent loose. Then I find myself, who has dove headlong into it all. I have skills that use MCP servers to check for pull requests, and give me summaries to give me more context for code reviews. I update Jira tickets in batches of 50+. I develop complete features exclusively through prompting the agent to do everything. I know I still have the responsibility to understand it at the same level as if I wrote it my self, and defend it and debug it.
I can easily see that superpowers would be wasted on most of my coworkers, simply because the benefits compound with the complexity of my ask. My coworkers aren't willing to hand off enough control to receive the benefits of superpowers.
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...
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/...