Superpowers: How I'm Using Coding Agents in October 2025

The most challenging part when working with coding agents is that they seem to do well initially on a small code base with low complexity. Once the codebase gets bigger with lots of non-trivial connections and patterns, they almost always experience tunnel vision when asked to do anything non-trivial, leading to increased tech debt.

C'mon, such self-congratulatory "Look at My Potency: How I'm using Nicknack.exe" fluffies always were and always will be a staple of the IT industry.

maybe explicit support from providers would make it feasible?

The packaged collection is very cool and so is the idea of automatically adding new abilities, but I’m not fully convinced that this concept of skills is that much better than having custom commands+sub-agents. I’ll have to play around with it these next few days and compare.

Admittedly that stance is easiest to take if you were old enough, experienced enough already by the time this era hit.

I've found that a single CLAUDE.md does really well at guiding it how I want it to behave. For me that's making it take small steps and stop to ask me questions frequently, so it's more like we're pairing than I'm sending it off solo to work on a task. I'm sure that's not to everyone's taste but it works for me (and I say this as someone who was an agent-sceptic until quite recently).

Fwiw my ~/.claude/CLAUDE.md is 2.2K / 49 lines.

just remember that it works the same for everyone: you input text, magic happens, text comes out.

if you can properly explain a software engineering problem in plain language, you're an expert in using LLMs. everything on top of that people experimenting or trying to build the next big thing.

I’ve found you have to use Claude Code to do something small. And as you do it iterate on the CLAUDE.md input prompt to refine what it does by default. As it doesn't do it your way, change it to see if you can fix how it works. The agent is then equivalent to calling chatgpt / sonnet 1000 times a hour. So these refinements (skills in the post are a meta approach) are all about how to tune the workflow to be more accurate for your project and fit your mental model. So as you tune the md file you’ll start to feel what is possible and understand agent capabilities much better.

So short story you have to try it, but long story its the iteration of the meta prompt approach that teaches you whats possible.

Just give it a few months. If some technics really work, it’ll get streamlined.

No matter what you are told, there is no silver bullet. Precisely defining the problem is always the hard part. And the best way to precisely define a problem and its solution is code.

I’ll let other people fight swarms of bots building… well who knows what. Maybe someday it will deliver useful stuff, but I’m highly skeptical.

Jesse on Bluesky: https://bsky.app/profile/s.ly/post/3m2srmkergc2p

> The core of it is VERY token light. It pulls in one doc of fewer than 2k tokens. As it needs bits of the process, it runs a shell script to search for them. The long end to end chat for the planning and implementation process for that todo list app was 100k tokens.

> It uses subagents to manage token-heavy stuff, including all the actual implementation.

That paper is about using persuasion prompts to overcome trained in "safety" refusals, not to improve prompt conformance.

It is the thing I find most irritating about working with llms and agents. They seem forever a generation behind in capabilities that are self referential.

The whole world is changing around us and nothing is secure. I would not gamble that the market for our engineering careers is safe with so much disruption happening.

Tools like Lovable are going to put lots of pressure on technical web designers.

Business processes may conform to the new shape and channels for information delivery, causing more consolidation and less duplication.

Or perhaps the barrier to entry for new engineers, in a worldwide marketplace, lowers dramatically. We have accessible new tools to teach, new tools to translate, new tools to coordinate...

And that's just the bear case where nothing improves from what we have today.

I was struggling to find the exact reason this type of article bugs me so much, and I think "voodoo" is precisely the correct phrase to sum up my feelings.

I don't mean that as a judgement on the utility of LLMs or that reading about what different users have tried out to increase that utility isn't valuable. But if someone asked me how to most effectively get started with coding agents, my instinct is to answer (a) carefully and (b) probably every approach works somewhat.

I would say that systems like this are about getting the agent to correctly choose the precisely correct context snippet for the exact subtask it's doing at a given point within a larger workflow. Obviously you could also do that manually, but that doesn't scale to running many agents in parallel, or running automomously for longer durations.

I’ve found the most helpful things for me is just voice to Whisper to LLMs, managing token usage effectively and restarting chats when necessary, and giving it quantified ways to check when its work is done (say, AI-Unit-Tests with apis or playwright tests.) Also, every file I own is markdown haha.

And obviously having different AI chats for specialized tasks (the way the math works on these models makes this have much better results!)

All of this has allowed me to still be in the PM role like he said, but without burning down a needless forest on having it reevaluate things in its training set lol. But why would we go back to vendor lock in with Claude? Not to mention how much more powerful 5o-codex-high is, it’s not even close

The good thing about what he said is getting AI to work with AI, I have found this to be incredibly useful in promoting, and segmenting out roles