Neuraxon Playground
shippedA browser-based reconstruction of Neuraxon's three-state, continuous-time neuron model, training live on a toy task so you can watch it converge in real time.
Why this matters
Neuraxon Playground rebuilds a small, unusual style of AI 'neuron' — inspired by a project called Neuraxon — where each artificial neuron doesn't just have one number representing how activated it is, but three separate internal numbers that constantly evolve over time like a real biological neuron, rather than updating in the fixed, instant steps almost every other AI system uses. The page loads with a tiny made-up classification puzzle — colored dots that aren't easy to separate into two groups with a straight line — and starts teaching itself the answer immediately, live, in front of you: you can watch a squiggly decision boundary reshape itself and the error number drop as it learns, with pause and reset controls so you can freeze a moment and see exactly what one neuron's three internal signals are doing right then. It's honest about what it is: since training this three-state kind of neuron the normal way (backpropagation) is unusually hard, it instead learns by trial and error — nudging its settings randomly and keeping any nudge that helps — and says so plainly rather than pretending to use the fancier method. The interesting part for a non-technical viewer isn't the specific model architecture; it's watching the actual, unscripted moment an AI system goes from guessing randomly to getting something right, which is normally a hidden, offline process.
Other ways this idea or technique could be used
- A visual teaching tool for how machine learning actually works — watching error drop over time explains it better than the phrase 'the model trains on data.'
- A sandbox for trying different toy puzzles to build intuition for which kinds of problems are easy or hard for a small neural network.
- A base for exploring other trial-and-error training methods, which matter for hardware or biological-style AI systems where the standard method doesn't apply cleanly.
- A stress-test for 'watch it learn live' as a UI pattern more serious research tools could borrow — most training dashboards show a finished chart, not the live moment of learning.
Approach
Vanilla HTML/CSS/JS in one file, no build step and no bundler, because the whole point is a zero-dependency demo and a toolchain would only add places for the nightly gate to fail. Each neuron holds three coupled continuous state variables integrated with fixed-step Euler substeps inside a requestAnimationFrame loop: v (membrane-like activation, dv/dt = -v/tau_v + input - alpha*s), s (slow adaptation, ds/dt = (v-s)/tau_s), and r (refractory gate, dr/dt = -r/tau_r + k*spike, output = sigmoid(v) * (1 - r)). Use these exact equations unless you locate Neuraxon's actual source (search for a public repo named 'neuraxon', three-state/continuous-time neuron, MIT license, referenced from https://x.com/cryptofanatiix/status/2104295213720121727) and can port it faithfully instead — either way the explainer text must say plainly which one it is. Because backprop through this ODE is not needed to demonstrate the claim, train the ~20-40 weights with a simple perturbation/evolutionary search (sample weight deltas, keep them if loss drops) run continuously in the animation loop so the page is visibly learning without any library. The toy task is 2D binary classification on a fixed, hardcoded 40-point dataset (four interleaved clusters, not linearly separable, so convergence is visually obvious) rendered as a scatter plot with a canvas heatmap of the current decision boundary redrawn each frame, plus a small multi-line trace of one hidden neuron's v/s/r states over the last few seconds and a loss-over-time sparkline.
The source post
https://x.com/cryptofanatiix/status/2104295213720121727
Scoring
Pick
| surprise | 4 |
|---|---|
| demonstrability | 4 |
| self_containedness | 5 |
| honesty | 4 |
Neuraxon's pitch — a dependency-free, MIT-licensed neural net built from three-state neurons running in continuous rather than discrete time — is a genuinely unusual claim that most people following AI news won't have seen, since it's a small crypto-adjacent repo rather than a lab release. It also happens to be the one candidate that fits this format cleanly: the reference implementation is pure Python with zero dependencies, small enough to port faithfully to JS and run entirely client-side, with no account, dataset, or key required. The plan is not a static diagram but a live demo — the ported network actually trains on a toy task (e.g. XOR or a small pattern set) in the browser and you watch the three-state, continuous-time dynamics converge, which keeps the interesting claim testable rather than a mockup. matchcn scored close behind on the same self-containedness strength but is a more familiar shape (component-search UI) and scored lower on surprise, so Neuraxon wins the tiebreak too.
Review
| shipped | 5 |
|---|---|
| honest | 5 |
| worth_it | 4 |
| efficient | 4 |
No change: the gate passed on the first attempt, the artifact is honest about being a reconstruction and actually does the live-learning thing it claims, and NOTES.md raises no unresolved objection or recurring failure. The one soft spot — 6/8 build attempts recorded in run.json before the successful one — isn't diagnosable into a specific, repeatable lesson because no per-attempt failure detail is preserved here, and history.json shows no comparable codex build-attempt pattern across recent nights to corroborate it as recurring rather than one night's weather. Writing a lessons entry without knowing what actually failed would be exactly the kind of platitude the lessons block is supposed to avoid.
Cost
| total | $0.1396 |
|---|---|
| xai | $0.1396 |
What it looked at
Colibri: Zero-dependency C inference engine that runs large MoE models locally across disk/RAM/VRAM (GitHub link in post).
Simply (DeepMind): Open-source JAX codebase + Amplio agent harness letting AI agents autonomously propose, run, and iterate ML experiments on small Transformers.
matchcn: npx MCP server exposing 4,578 shadcn components for AI agents to discover and use without API keys.
m17_gpt: Python script linking an LLM to M17 digital radio for real-time voice transcription and encoded replies over Pi-Star hotspots (video demo).
Jev Code Reviewer: Open-source tool that ranks agent-generated code changes (P0/P1/P2) with explanations for large PRs.
H3 Max Extend: fal’s video continuation model (upload 15s → extend to 30s) with an interactive GeoGuessr-style demo game showing real vs. AI footage.
Neuraxon (Qubic): Pure-Python, dependency-free, MIT-licensed bio-inspired neural net with three-state neurons and continuous time (download and run locally).
Gate
| pass | project directory exists — /Users/artax/code/builds/2026-09-28/project |
|---|---|
| pass | no build step needed — static project |
| pass | build output with index.html — /Users/artax/code/builds/2026-09-28/project |
| pass | index.html is a document — 16170 bytes |
| pass | local asset references resolve |
| pass | page loads without console errors |
Stages
| scout | grok · ok · 18.4s |
|---|---|
| pick | claude · ok · 45.8s |
| plan | claude · ok · 61.4s |
| build | codex · ok · 279.1s |
| gate | local · ok · 3.5s |
| publish | local · ok · 20.5s |
| review | claude · ok · 58.0s |
Notes
Omitted for lack of
None. This artifact needs no external service, account, API key, or runtime network access.