Okay, I've been really in a groove with @smallharness today, so decided to finally cut the feature I felt like I need for a true v1.0 release. And this is, model routing...but kinda model routing Morgan-style I guess, because I've been testing out different approaches lately, and found something pretty interesting. At a high level, I've been thinking that it doesn't make sense to have one model to orchestrate, one to write code, and one to review, and I've been playing around with different configurations. What I've determined, at least for me, lately, is that I actually want a different model to orchestrate simple tasks vs. complex tasks, and I also want different agents to do coding tasks, based on how much thinking depth/tool calling I need, etc. Also in some cases, I might want the same model but at different effort levels, like I learned with Fable where I could do a lot more with low than I expected, but there were some tasks I wanted medium for, and of course, crazy complex architecture stuff that I wanted high or even max for. Same for code review. For MVPs and stuff I'm playing with, I just want fast and cheap, simple code review. But for production code, then I want way more in-depth code review, a better, more expensive model that goes much deeper. I've come up with a series of roles, and this is all now built into Small Harness. Finally got my idea, into code, and into a harness that can help you write code, using this methodology. Here's the high-level on it. The Roles ----------- The config lives under modelSystem in agent.config.json: 👑 Selector: the decision model. This should usually be your strongest/highest-effort model. 🐙 Orchestrators: not just one orchestration model, but three, a different one for each level of task complexity: low, medium, high. 🧑‍💻 Coders: like the orchestrators, not just one model to execute/write code, but different models based on the complexity of the coding task. Some plans might use something like two low and one medium, and never need a high. ✅ Code reviewers: three types, play, production, and security. You don't need as detailed code review for stuff you're just playing around with, but you do for production, and your security review model might be different from both. And I made a chart, aptly titled, Morgan's Wacky Model Routing Idea. That you can look at if you want to do a little deeper dive into what I'm thinking here. Now live on Github, free and open source, link to the rep in first comment below.

Okay, officially too excited about Fusion from OpenRouter not to add a dedicated command for it directly to Small Harness. Don't wait for Anthropic to make Fable 5 available, get the same level of intelligence for half the cost. Now built-into Small Harness. Small harness is free and open source, so use it out of the box, or fork it and make it your own. Link to gh repo in first comment below.

Alibaba Qwen3.7 slowly fading into irrelevance at the frontier due to proprietary stance. In it's place we have Minimax M3 and... *checks notes* Rio 3.5 397b, made by the municipal IT company of Rio de Janeiro's city government. huggingface.co/prefeitura-ri…

friendly reminder that even in black swan events like fable being taken down @openrouter is here to help you gracefully fallback to the next best model (opus 4.8) the future is multi model unbelievably bullish on this product

Quick reminder that Small Harness, a fast, transparent, bare bones harness that I built, works wonderfully with local llms. Soon, you will be able to connect both frontier models for planning, with local models for execution, and with VulcanBench, optimize when you use each. And the less you rely on frontier models, the more free you become. The future is smaller than you think, try Small Harness. Free, open source, use as it is, or fork it and make it your own. github.com/GetSmallAI/SmallH…

You can club them with @getsmallai and you won't regret.

Small Harness just broke 100 stars on Github ⭐️ Thanks to everyone who has tried Small Harness and provided feedback 🙏 It is getting better every week.

just walked into one of our ai inference teammates’ workplace at @liquidai Cambridge! lots of ice packs, toasters, and coffee machines. 🖱️