I just discovered Pi Coding Agent and found that it's lean System Prompt + a tuned CLAUDE.md brought back a lot of the intelligence that Opus seemed to lose over the last month.
Sucks to be pushed back to Claude Code with opaque system behavior and inconsistency. I bet many would rather pay more for stability than less for gambling on the model intelligence.
We use Pi at work (where we pay per token) and I’d love to use it personally too. From what I’ve read, nobody has been banned for using Pi yet… I wonder if Anthropic minds this much as long as it’s still human usage, or if they’re mostly focused on stamping out the autonomous harnesses. Unfortunately Pi is also what OpenClaw uses so it could easily get swept up in the enforcement attention.
Or maybe I’ll just get a Codex subscription instead. OpenAI has semi-officially blessed usage of third party harnesses, right?
It appears that OpenAI has blessed third party harnesses. I know they officially support OpenCode and they have this on their developer portal:
"Developers should code in the tools they prefer, whether that's Codex, OpenCode, Cline, pi, OpenClaw, or something else, and this program supports that work."
Obviously, the context is that OpenAI is telling open source developers who are using free subscriptions/tokens from the Codex for Open Source program that they can use any harness they want. But it would be strange for that to not extend to paying subscribers.
They have, but they also just announced this week that for business and enterprise plans, they’re switching from quotas for codex to token use based pricing, and I would expect that to eventually propagate to all their plans for all the same reasons.
I’d be surprised if that propagated to personal subscription plans, simply because it would put them at a huge competitive disadvantage against Anthropic, which they’ve already signaled they care about by saying they allow third-party harnesses. But I wouldn’t be surprised if they required third-party harnesses to use per-token billing, since that’d put them on par with Anthropic.
This is one of the things that GitHub Spec Kit solves for me. The specify.plan step launches code exploration agents and builds itself the latest data model, migrations, etc etc. Really reduces the need to document stuff when the agent self discovers codebase needs.
Give Claude sqlite/supabase MCP, GitHub CLI, Linear CLI, Chrome or launch.json and it can really autonomously solve this.
Who else struggles with both sides of this? My engineer side values curiosity, brain power, and artistanship. My capitalist side says it's always the product not the process. My formula is something like this: product = money, process = happiness, money != happiness, no money = unhappiness.
I think the optimal solution is min/maxing this thing. Find the AI process that minimizes unhappiness, and maximizes money.
> My capitalist side says it's always the product not the process.
Your capitalist side needs to read some Deming. "Your system is perfectly tuned to produce the results that you are getting." Obviously, then, if you want better results, you need to improve your system.
Also "the product" is ambiguous. Is it the overall product, like how the product sits in the market, how the user interacts with it to achieve their goals, the manufacturability of the product, etc.? That is Steve Jobs sort of focus on the product, and it is really more of a system (how does the product relate to its user, environment, etc). However, AI doesn't produce that product, nor does any individual engineer. If "the product" means "the result of a task", you don't want to optimize that. That's how you get Microsoft and enterprise products. Nothing works well together, and using it is like cutting a steak with a spoon, but it has a truckload of features.
I definitely struggle with both sides, or maybe multiple sides. On the one hand most of my daily output at my job is coming from AI these days. On the other hand I find the explosion of AI-generated "writing" (and other forms of art) to be aesthetically abhorrent. And I've just recently started a ... weird sort of metaphysics / spirituality / but also AI related writing project, so the difference between creation with and without AI is in really sharp focus for me right now.
I wrote an article about this, but honestly I don't think I really captured the totality of my feelings. I really haven't decided where I land. I'm definitely using the tools for economic purposes, and I even have some "pure-fun" side project stuff where I'm getting value from it.
I did some napkin math the other day, and my kids at half my size prob hit the ground with 1/2 the stress that I do. Certainly could take more risks falling with a 50% reduction in harm. The extra rotational energy from 70" vs 40" will do it.
I'm starting to think for software it's produce 2,000 loaves per month. I'm realizing now software was supply-constrained and organizations had to be very strategic about what apps/UIs to build. Now everything and anything can be an app and so we can build more targeted frontends for all kinds of business units that would've been overlooked before.
Humans, like all animals, have instinctual and biological drives to survive besides, but it's interesting to think how much of our drive to survive is culturally transmitted too.
Yes my hot take is that the real risk isn't skill atrophy... it's failing to develop the new skill of using AI. It's all abstraction layers anyway and people always lament the next abstraction up.
0/1s → assembly → C → high-level languages → frameworks → AI → product
The engineer keeps moving up the abstraction chain with less and less understanding of the layers below. The better solution would be creating better verification, testing, and determinism at the AI layer. Surely we'll see the equivalent of high-level languages and frameworks for AI soon.
I find “maintainable code” the hardest bias to let go of. 15+ years of coding and design patterns are hard to let go.
But the aha moment for me was what’s maintainable by AI vs by me by hand are on different realms. So maintainable has to evolve from good human design patterns to good AI patterns.
Specs are worth it IMO. Not because if I can spec, I could’ve coded anyway. But because I gain all the insight and capabilities of AI, while minimizing the gotchas and edge failures.
> But the aha moment for me was what’s maintainable by AI vs by me by hand are on different realms. So maintainable has to evolve from good human design patterns to good AI patterns.
How do you square that with the idea that all the code still has to be reviewed by humans? Yourself, and your coworkers
I picture like semi conductors; the 5nm process is so absurdly complex that operators can't just peek into the system easily. I imagine I'm just so used to hand crafting code that I can't imagine not being able to peek in.
So maybe it's that we won't be reviewing by hand anymore? I.e. it's LLMs all the way down. Trying to embrace that style of development lately as unnatural as it feels. We're obv not 100% there yet but Claude Opus is a significant step in that direction and they keep getting better and better.
Then who is responsible when (not if) that code does horrible things? We have humans to blame right now. I just don’t see it happening personally because liability and responsibility are too important
And you don’t blame humans anyways lol. Everywhere I’ve worked has had “blameless” postmortems. You don’t remove human review unless you have reasonable alternatives like high test coverage and other automated reviews.
> But the aha moment for me was what’s maintainable by AI vs by me by hand are on different realms
I don't find that LLMs are any more likely than humans to remember to update all of the places it wrote redundant functions. Generally far less likely, actually. So forgive me for treating this claim with a massive grain of salt.
Sucks to be pushed back to Claude Code with opaque system behavior and inconsistency. I bet many would rather pay more for stability than less for gambling on the model intelligence.
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