Verified Deep Learning with Lean 4
Formally verifies ResNet and ViT architectures using Lean 4 proofs.

Browser-based particle correction beats manual ImageJ macros for TEM workflows.
Materials scientists, microscopists, academic researchers
ImageJ · Fiji · Avizo
As far as I know, there's no widely available tool that does this end to end. The closest things are academic projects or ImageJ, which is more of a general purpose image processor without automatic particle detection. Nanostat handles the detection, measurement, and visualization in one place.
Posting here on the off chance that there are microscopists that may be open to trying it out and giving some feedback. There's a live demo on the homepage with real electron microscopy data, where you can interact with the full workflow (eraser, overlays, histograms, metrics, etc.) without signing up. If you want to try your own images, there's a free tier
Formally verifies ResNet and ViT architectures using Lean 4 proofs.
Decorators drive IPC code gen for Electron when tRPC feels too heavy.
CDP-based agent control beats screenshot approaches—finally efficient browser automation.
LibTorch bindings bring CUDA and MPS backends to Java with LLaMA-3 inference included.
Tiered curriculum from Day-zero to Operator level with mental model focus.
Clean Morse learning app, but Google's Learn Morse already did the image association thing.