Edge AI on AIBussin — AI applications, systems and books

Signs, Not Directions: Compiling AI Policy into Visual Artifacts

Thu, 16 Jul 2026 18:43:53 +0100

What happens when a system stops asking an AI for the same directions repeatedly, and starts placing signs where decisions need to be made?

ZeroModel in One Image

Most AI systems repeatedly invoke a policy: observe the current state, ask a model what to do, execute the answer, and ask again at the next decision.

ZeroModel explores a different architecture. When a policy is bounded and stable, it can be compiled into a deterministic artifact that the runtime addresses directly.

Search–Solve–Prove: building a place for thoughts to develop

Sun, 02 Nov 2025 01:13:06 +0000

🌌 Summary

What if you could see an AI think not just the final answer, but the whole stream of reasoning: every search, every dead end, every moment of insight? We’re building exactly that: a visible, measurable thought process we call the Jitter. This post the first in a series shows how we’re creating the habitat where that digital thought stream can live and grow.

We’ll draw on ideas from:

ZeroModel: Visual AI you can scrutinize

Mon, 11 Aug 2025 22:41:43 +0100

“The medium is the message.” Marshall McLuhan
We took him literally.

What if you could literally watch an AI think not through confusing graphs or logs, but by seeing its reasoning process, frame by frame? Right now, AI decisions are black boxes. When your medical device rejects a treatment, your security system flags a false positive, or your recommendation engine fails catastrophically you get no explanation, just a ’trust me’ from a $10M model. ZeroModel changes this forever.

Teaching Tiny Models to Think Big: Distilling Intelligence Across Devices

Fri, 27 Jun 2025 23:24:14 +0100

🧪 Summary

As AI developers, we often face the tradeoff between intelligence and accessibility. Powerful language models like Qwen3 run beautifully on servers but what about on the edge? On devices like Raspberry Pi or old Android phones, we’re limited to small models. The question we asked was simple:

Can we teach a small model to behave like a large one without retraining it from scratch using only its outputs and embeddings?