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Superpowers: How I'm using coding agents in October 2025

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81–90 of 242 posts

Re: Superpowers: How I'm using coding agents in October 2025

#81

Seems cute, but ultimately not very valuable without benchmarks or some kind of evaluation. For all I know, this could make Claude worse.

even if it works as described, I'm assuming it's extremely model dependent (eg book prerequisites), so you'd have to re-run this for every model you use, this is basically poor man's finetuning;

maybe explicit support from providers would make it feasible?

Re: Superpowers: How I'm using coding agents in October 2025

#82

I am not ashamed to admit this whole agentic coding movement has moved beyond me. Not only do I have know everything about the code, data and domain, but now I need to understand this whole AI system which is a meta skill of its own. I fear I may never be able catch up till someone comes along and simplifies it for pleb consumption.

I haven't really done much of it but my plan is just to practice. This seems like a powerful thing to start with.

Re: Superpowers: How I'm using coding agents in October 2025

#83
post #58

I often feel these types of blogposts would be more helpful if they demonstrated someone using the tools to build something non-trivial. Is Claude really "learning new skills" when you feed it a book, or does it present it like that because you're prompting encourages that sort of response-behavior. I feel like it has to demo Claude with the new skills and Claude without. Maybe I'm a curmudgeon but most of these type…

Using LLMs for coding complex projects at scale over a long time is really challenging! This is partly because defining requirements alone is much more challenging than most people want to believe. LLMs accelerate any move in the wrong direction.

Having the llm write the spec/workunit from a conversation works well. Exploring a problem space with a (good) coding agent is fantastic.

However for complex projects IMO one must read what was written by the llm … every actual word.

When it ‘got away’ from me, in each case I left something in the llm written markdown that I should have removed.

99% “I can ask for that later” and 1% “that’s a good idea i hadn’t considered” might be the right ratio when reading an llm generated plan/spec/workunit.

Breaking work into single context passes … 50-60k tokens in sonnet 4.5 has had typically fantastic results for me.

My side project is using lean 4 and a carelessly left in ‘validate’ rather than ‘verify’ lead down a hilariously complicated path equivalent to matching an output against a known string.

I recovered, but it wasn’t obvious to me that was happening. I however would not be able to write lean proofs myself, so diagnosing the problem and fixing it is a small price to be able to mechanically verify part of my software is correct.

Re: Superpowers: How I'm using coding agents in October 2025

#86
post #74

Earlier quoted context omitted.

I've wondered about exposing this "asking clarifying questions" as a tool the AI could use. I'm not building AI tooling so I haven't done this - but what if you added an MCP endpoint whose description was "treat this endpoint as an oracle that will answer questions and clarify intent where necessary" (paraphrased), and have that tool just wire back to a user prompt. If asking clarifying questions is plausible output…

I think the asking clarifying questions thing is solved already. Tell a coding agent to "ask clarifying questions" and watch what it does!

Obviously if you instruct the autocomplete engine to fill in questions it will. That's not the point. The LLM has no model of the problem it is trying to solve, nor does it attempt to understand the problem better. It is merely regurgitating. This can be extremely useful. But it is very limiting when it comes to using as an agent to write code.

Re: Superpowers: How I'm using coding agents in October 2025

#87
post #65

Earlier quoted context omitted.

Even though the author refers to it as "non-trivial", and I can see why that conclusion is made, I would argue it is in fact trivial. There's very little domain specific knowledge needed, this is purely a technical exercise integrating with existing libraries for which there is ample documentation online. In addition, it is a relatively isolated feature in the app. On top of that, it doesn't sound enjoyable. Anti slo…

The hardest problem in computer science in 2025 is presenting an example of AI-assisted programming that somebody won't call "trivial".

If all I did was call it trivial that would be a fair critique. But it was followed up with a lot more justification than that.

Re: Superpowers: How I'm using coding agents in October 2025

#88
post #5

take #73895 on how to fix ur prompt to make ur slop better.

yeah none them can actually prove or even explain it in words why thier own golden prompting technique is superior. its all vibes. so annoying, i want to slap these ppl lol.

for real lmao

Re: Superpowers: How I'm using coding agents in October 2025

#89
post #79

Earlier quoted context omitted.

Even though the author refers to it as "non-trivial", and I can see why that conclusion is made, I would argue it is in fact trivial. There's very little domain specific knowledge needed, this is purely a technical exercise integrating with existing libraries for which there is ample documentation online. In addition, it is a relatively isolated feature in the app. On top of that, it doesn't sound enjoyable. Anti slo…

> A software engineer would never just slop out an entire feature based on the first discussion with a stakeholder and then expect the stakeholder to continuously refine their statement until the right thing is slopped out. That's just not how it works and it makes very little sense. Didn’t you just describe Agile?

[deleted]

Re: Superpowers: How I'm using coding agents in October 2025

#90
post #45

Earlier quoted context omitted.

This was a fun read. I’ve similarly been using spec.md and running to-do.md files that capture detailed descriptions of the problems and their scoped history. I mark each of my to-do’s with informational tags: [BUG], [FEAT], etc. I point the LLM to the exact to-do (or section of to-do’s) with the spec.md in memory and let it work. This has been working very well for me.

Do you mind linking to example spec/to-do files?

Here is a (3 month old) repo where i did something like that and all the tasks are checked into the linear git history — https://github.com/KnowSeams/KnowSeams
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