GoKawiilWe've all heard people say that local Qwen 27B or 35-A3B is "near-Opus level", but I have receipts from a software business and open source projects, and am here to be transparent with you.
This post is long-form for a reason. It's not a cursory glance, an unsubstantiated claim on X about cancelling Claude Max, or a hobbyist report from a model running at single-digit tokens per second with a 32K context window. It isn't written by a famous CEO tweeting about coding from an airplane. It's my journey as a founder in a small software business, where local models have produced real, caveated value. I have skin in the game, but no incentive to push either cloud or local models, and a strong desire for local models to become capable and reliable.
I'll cover how the card paid for itself in the first two or three months, how it keeps serving our specific business use case, why I still can't trust it unsupervised, and Qwen's worst trait: the infinite loops and hallucination risk. These show up most when you quantize it down to fit a consumer GPU.
Figuring out the power connectors for the RTX 6000 Pro
On my use case for AI
My journey as a maintainer and founder started with OpenFaaS - built completely by hand, as was all software in 2016 up until recently. That meant laying down the core of the project on my own, then inviting others to participate through community - not because I couldn't do it on my own, but because my goal was to build a successful open source project. Around 2017 I tried to fund my time by joining VMware, and in 2019 after changes in the market, I needed a way to fund the work myself, so moved towards open-core and built a bootstrapped company. Today our small team maintains OpenFaaS, SlicerVM - AI sandboxes and "the missing API for Linux", Actuated.com - self-hosted CI runners for GitHub/GitLab, and Inlets.com - self-hosted HTTP/TCP tunnels.
These products use very low level Linux primitives like containers, Kubernetes, Firecracker microVMs, and networked protocols. If you squint, they're all opinionated infrastructure products focused on: efficiency, user-experience, control and autonomy. They're written in Go, and some have React-based UI components, landing pages, docs, agent skills, and CLIs. Along with the code, we also provide the best-in-class support, because we are lean and willing to do things that don't scale to help customers.
I've been using AI tools for as long as they've been available - from tab completion in VS Code in the early days, through to getting ChatGPT to generate chunks of code, or find bugs, to living in tmux 12 hours per day. I found myself in tmux so much of the time that I wrote a free tool Superterm.dev to keep track of my sessions, notes, and to get visual feedback from coding agents. Over that time, I've seen the capabilities go from "reduce boilerplate" to "design, architect, and test end to end". It's Claude or Codex that do the majority of my work, and whilst I insist on doing my own writing, I rarely write code by hand - as much as it pains me to say that.
A turning point for frontier intelligence
I'd say it was roughly between November 2025 and January 2026 that we saw a turning point. Many developers on X started to espouse Claude Opus as having changed and how it was now capable of doing all of their work. Manual coding turned bad as quickly as milk sours left out the fridge. The costs of the top-end coding plans settled at roughly 200 USD / mo for individuals. A real number, but tolerable for the value they generated. Even today, if you avoid too much unattended work, you can make it last through the 5 hour limit, and weekly limit if you're careful.
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