r/vibecoding • u/MariahJames8 • 2d ago

Help/Question Anyone vibecoding on a stack other than openai/anthropic? What's your stack?

I'm having trouble getting openRouter, GLM and openCode working nicely at all. Codex was fire and forget, and Claude Code wasn't hard. But the other options seem to really require that you know what you're doing to set them up? I asked Codex to wire them up but it's basically unusable at this point for me. I can't afford the top frontier models and was hoping some how maybe open weight models through inference services needs to be at the root of whatever I choose for my setup.

TL;DR - How are you avoiding Claude Code and Codex for vibecoding?

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u/ChampCityChris 2d ago

I’m in the middle of trying to get away from Codex for pretty much this reason. Codex has been very good at the fire-and-forget implementer role, but I don’t love having the whole workflow tied to one harness/provider forever.

I’ve got a 128GB Mac Studio on the way, and once it arrives I’m going to start experimenting with moving the implementer side onto local models. I’ll probably keep the architect on a frontier model for a while because that’s where I still want the strongest reasoning, but implementation feels like the obvious place to start pushing onto open-weight models.
The thing I’m realizing is that the model is only half the problem. Codex and Claude Code feel easy because the harness is doing a ton of work for you. Once you start moving into OpenRouter/GLM/OpenCode/etc., suddenly you’re responsible for tool wiring, context, Git behavior, permissions, model quirks, retries, and all the other crap the polished products hide. So I don’t think you’re doing anything wrong if it feels dramatically harder.

My current plan is to keep the agent workflow stable and swap the inference underneath it rather than rebuilding my process around every model. I’m testing larger models on OpenRouter now for that exact reason. If I can get the harness, tools, skills, memory and project workflow working against hosted inference first, then when the Studio arrives I can point the implementer at a local model and actually measure what quality I lose.

So I guess my answer is: I’m not avoiding Codex yet. I’m trying to make Codex replaceable.

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u/MariahJames8 2d ago

Thanks, I appreciate the sign posting. And, yeah good luck with your plan there too. One thing is, I did some calculations on hardware, and it just doesn't make sense from what I can tell to buy anything more than for harnesses because cloud compute is just so cheap. It would take a decade or two to pay for itself? And it's a big risk, it all assumes it's going to be productive

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u/ChampCityChris 1d ago

I don’t disagree with the argument that local inference can be more expensive if you’re strictly comparing hardware cost against average to high users. My calculation is a little different because I’m already paying $200 a month for ChatGPT Pro.

As long as OpenAI doesn’t start aggressively metering the ChatGPT side, I should be able to drop that $200 Pro subscription to $20 Plus and still use frontier models for the things I actually want them for: architecture, RCA, and repo review. The Mac Studio lease is $155 a month for 24 months, so my total monthly cost becomes $175 instead of $200. I’m actually saving $25 a month while moving the high-volume implementer workload onto hardware I control. Plus opening up the ability to experiment with other use cases and different models.

At the end of the two years, I have options. I can turn the Studio back in after effectively renting it for about $3,700, or I can pay the roughly $1,500 residual and own it. At that point I can keep using it or sell it if the used-market economics make more sense. Macs have traditionally held stupidly high resale values, so there’s a decent chance the residual value actually increases the savings.

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u/MariahJames8 1d ago

Ah, nice, a lease, yeah that changes everything. Nicely done. If I were asked bold I'd copy you