I’ve been experimenting with local AI for a while, and one limitation kept coming back: Running very large models locally wasn’t always practical on the hardware available to me. So instead of only asking: “How can I run a bigger model?” I started asking a different question: “How much more capable can a smaller model become if the system around it is better?” That question became SIA. The goal is not to pretend that a small model can magically become a frontier model. The goal is to move more reliability into the system around the model. SIA is currently focused on areas such as: • structured planning before execution • tool routing and schema validation • verification before declaring a task complete • bounded repair and retry logic • context and state management • detecting repeated failure patterns • cost and resource telemetry • measuring the difference between a raw model and the same model running through the system I’m building and testing it incrementally with smaller local models, including real failure cases instead of only successful demos. The wider work happening around MCP, open and local models, AI agents, and developer tooling has also been useful context while I’ve been exploring this direction. I’ve now brought SIA and my other AI work together under TYKAIRO-AI: https://tykairoai.wordpress.com/ I’d genuinely like to hear from people building agentic workflows with smaller local models. Where does reliability usually break first for you — planning, tool use, context management, verification, or recovery after a bad action? And what have you found actually helps? I’m especially interested in hearing from people building with smaller local models rather than relying only on frontier APIs.   submitted by   /u/TYKAIRO-AI [link]   [comments]