MNIST
Beyond Market Intelligence keeps MNIST in one place: 6 stories so far. The section currently leads with “Peek inside a neural network as it learns, layer by layer”, “Accelerate Local LLM Learning: A New Prototype for Faster Fact Correction”, and “Discover a smarter path to your next great white wine recipe.”. Open the training view and you'll watch a small neural network learn to read digits, layer by layer. Watching a toddler learn taught Kavanutz something about AI. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every MNIST story on Beyond Market Intelligence, newest first.
Peek inside a neural network as it learns, layer by layer
Open the training view and you'll watch a small neural network learn to read digits, layer by layer. Built with plain NumPy and manual backprop, this tool exposes the messy, instructive details most courses skim over: gradient norms, inactive neurons, and weight shifts from initialization. It reaches about 98.5% on MNIST, but the real value is the lab, where you can ablate neurons or tweak temperature and see accuracy move instantly. For teachers and self-learners, it turns abstract theory into something you can touch.
Accelerate Local LLM Learning: A New Prototype for Faster Fact Correction
Watching a toddler learn taught Kavanutz something about AI. Jayce, his new prototype, skips the heavy lifting of RAG pipelines and fine-tuning entirely. Instead, it shifts raw context vectors across a fixed pool of 4,048 prototype slots, correcting mistakes in real time. The benchmarks are telling: faster updates, better sample efficiency on sequential tests, and a strict memory ceiling. It's a lean, framework-free proof of concept built on NumPy and Java.
Discover a smarter path to your next great white wine recipe.
A white wine recipe scored between 7.30 and 7.58 is a strong result, and the approach here is genuinely clever. This project navigates a latent space to find where the best wines cluster, then takes 100 careful steps toward the highest possible score. The loss question is a fair one; plateaus matter more than absolute numbers with MSELoss. This is thoughtful experimentation, not just tinkering. For anyone exploring similar generative modeling, our piece on the Forrester function offers a related angle on optimization.
Discover How Predictive Coding Networks Achieve Near-Perfect Accuracy at Speed
Backpropagation has dominated deep learning for decades, but Deepity makes a compelling case that it's not the only path forward. This C++ library's Predictive Coding Networks hit 97.73% on MNIST in under a minute, edging close to PyTorch's 98.27% while offering biological plausibility and continual learning potential. The trick lies in algorithmic caching and recent feedback alignment research, closing the performance gap on CPU.
When a scientific calculator becomes a platform for AI discovery
A 67% accuracy on handwritten digits sounds unimpressive, until you remember the entire model fits in your head. This builder trained a binary classifier on the Casio FX-82CE X, hand-punching weights from just six images. The architecture is brutally simple: a 3x3 grid feeding one neuron. That it works at all is a quiet win for minimalism. The real lesson? Even a clunky, manual process reveals how much signal lives in raw data.
Why weight-space perception fails when networks train independently
The symmetry story in weight-space learning finally has the hard numbers it needed. This study, built on roughly 1.8 million fitted SIRENs, shows that randomizing only the exact symmetry group destroys 79.1 of the 80.4 accuracy points separating shared-init from random-init networks. That is sufficiency, cleanly demonstrated. The deeper insight, though, is computational. If a complete invariant matches function access informationally, then weight-space's real edge must be efficiency, not insight. That reframing deserves attention.