Hey everyone,

I’d like to share my latest « research » in minimalist AI: the NeuroStochastic Heuristic Learner (NSHL)—a single-layer perceptron that technically learns through stochastic weight perturbation (or as I like to call it, « educated guessing »).

🔗 GitHub: https://github.com/nextixt/Simple-perceptron

Key « Features »

✅ Zero backpropagation (just vibes and random updates)
✅ Theoretically converges (if you believe hard enough)
✅ Licensed under « Do What You Want » (because accountability is overrated)

Why This Exists

To prove that sometimes, randomness works (until it doesn’t). To serve as a cautionary tale for proper optimization. To see if anyone actually forks this seriously.

Discussion Questions:

Is randomness the future of AI, or just my coping mechanism? Should we add more layers (or is that too mainstream)?