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:
What if the most valuable insights in AI evaluation aren’t in model agreements, but in systematic disagreements?
This post reveals that the “gap” between large and small reasoning models contains structured, measurable intelligence about how different architectures reason. We demonstrate how to transform model disagreements from a problem into a solution, using the space between models to make tiny networks behave more like their heavyweight counterparts.
We start by assembling a high-quality corpus (10k–50k conversation turns), score it with a local LLM to create targets, and train both HRM and Tiny models under identical conditions. Then we run fresh documents through both models, collecting not just final scores but rich auxiliary signals (uncertainty, consistency, OOD detection, etc.) and visualize what these signals reveal.
We asked a blunt question: Can we see reasoning?
The answer surprised us: Yes, and you can click on it.
This post shows the complete stack that turns AI reasoning from a black box into an editable canvas. Watch as:
This isn’t just code it’s a visual way of interacting with AI, where reasoning becomes something you can see, explore, and refine.
“The eye sees only what the mind is prepared to comprehend.” Henri Bergson
For decades, we’ve measured artificial intelligence with numbers loss curves, accuracy scores, reward signals.
We’ve plotted progress, tuned hyperparameters, celebrated benchmarks.
But we’ve never actually seen learning happen.
Not really.
Sure, we’ve visualized attention maps or gradient flows but those are snapshots, proxies, not processes.
What if we could watch understanding emerge not as a number going up, but as a pattern stabilizing across time?
What if reasoning itself left a visible trace?
Imagine an AI that gets smarter every time it works not by retraining on massive datasets, but by learning from its own reasoning and reflection, just like humans.
Most AI systems are frozen in time. Trained once, deployed forever, they never learn from mistakes or build on successes. Real intelligence human or artificial doesn’t work that way. It learns from experience.
This is the vision behind Stephanie: a self-improving AI that gets better every time it acts, not by fine-tuning, but by remembering, reusing, and revising its reasoning.
“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.