I found jaredpalmer/kev while scrolling trending repos: a small family of decision models built on Qwen3.5, designed to be trained and run on your own hardware instead of called through an API. I haven't tested it, and I'm not going to pretend I have. This isn't a review, it's a case for why the idea deserves attention before anyone opens the code.

Here's the actual point. Most of the model conversation right now is about scale, bigger context windows, bigger parameter counts, bigger bills. Kev bets on the opposite: models small enough that you own them outright. Not "call an API and hope the vendor doesn't change the pricing tier next quarter", something you fine-tune on your own data and keep running when the network is down.

"Decision model" is the phrase worth sitting with. If this means classification-style output, pick an action from a bounded set, route a request, approve or deny, score a lead, that's a different design target than a chat model entirely. You don't need a massive general-purpose model to decide which of five support queues a ticket belongs in. You need something fast, cheap per inference, and predictable enough that you can write tests against its output distribution. Starting from Qwen3.5 instead of pretraining from scratch is the sane move if that's the actual goal.

Who benefits if the idea holds up: anyone running high-volume, low-latency classification or routing behind an API, where a full LLM call is overkill and the cost compounds fast. Teams that need to fine-tune on proprietary decision data and can't ship that data to a third-party endpoint. And agent orchestration generally, a layer of small, cheap "decide what happens next" models sitting in front of one expensive general-purpose model is a pattern that's going to show up more, because it's the only way the economics work once you're running agents at any real volume.

The real test is calibration, not size. "Tiny" plus "trainable on your own data" pushes the burden onto you to prove it isn't just confidently wrong. That's not a flaw in the idea, it's the question anyone should answer before putting a model like this near a production decision path.

Worth tracking if small, ownable models are on your radar: https://github.com/jaredpalmer/kev