A developer built a tiny language model in pure Node.js with no dependencies, implementing tokenization, embeddings, causal Transformer blocks, and backpropagation from scratch. The model trains on si…
A guide explains that a neuron is a weighted sum followed by a non-linear activation, without which a deep network would be a linear regression. The training workflow consists of forward pass, loss ca…
A technical blog post explains that AdamW with warmup and cosine decay is the optimizer almost everyone uses today to train large language models, tracing the evolution from vanilla SGD through moment…
A new study on arXiv (2607.13246v1) finds that Muon, an optimizer that reshapes gradient updates through approximate orthogonalization and has outperformed Adam and AdamW in large language model train…
A new neural network optimization method using a symmetric-exponential weight reparameterization achieved up to 30% faster training on transformers, reaching equivalent validation loss in 1.32 to 1.49…
A researcher developed a physics-informed AI method that integrates a PDE residual into the training loop of a language model by using a differentiable numerical head, enabling the model to learn phys…
Researchers introduce Depth-wise Gradient Augmentation, a new optimization paradigm that transforms layer-wise updates to exploit structured relationships across deep neural network layers. Their inst…
A developer completed building an LSTM cell using PyTorch and Lightning AI, implementing the forward pass and optimizer. The LSTM unit processes stock price inputs over four days, updating long-term a…
A detailed breakdown of the PyTorch training loop reveals common placement errors that silently break training, such as moving the model to a device after optimizer creation or calling gradient clippi…
A developer implemented an LSTM from scratch using PyTorch and Lightning AI, detailing the initialization of weights and biases for the forget, input, cell candidate, and output gates. The tutorial us…
A new study on arXiv presents an experimental analysis of neural network-based image classification on the CIFAR-10 dataset, comparing fully connected and convolutional architectures. The convolutiona…
Adam, a YC W25 startup, launched CADAM, an open-source text-to-CAD web application that runs entirely in the browser. It uses LLMs to generate parametric OpenSCAD code, compiled via WebAssembly and re…
Adam, a Y Combinator W25 startup, launched CADAM, an open-source AI-powered CAD tool that generates parametric 3D models from natural language or images in a browser. The tool exports to STL, SCAD, an…
Developer Adam launched MangaBuilder, a new AI-powered tool for manga creators that maintains character and location consistency across panels. The tool integrates reference images directly into the p…
ElevenLabs hosted a New York City pop-up where every experience was run by a voice agent, including an AI shopkeeper that negotiated prices with visitors. Business Insider reported that the shopkeeper…
Researchers from Sakana AI and the University of Tokyo developed DiffusionBlocks, a block-wise training framework that converts residual networks into independently trainable denoising modules. The me…
A developer with 17 years of distributed systems and SRE experience built torchdiag, a diagnostic toolkit for PyTorch that provides five commands to measure gradient flow, detect dead neurons, and ver…
A developer spent two days wrestling with the Swamp automation tool for AI agents, first getting stuck on a naming bug that has since been fixed, then reading the manual to understand its architecture…
Recent AI/ML research as of May 16, 2026, highlighting three key areas: faster inference through techniques like distillation and low-rank adapters, extended context windows via hierarchical memory, a…
Everyday devices like refrigerators can pose national security risks due to their vulnerability to cyberattacks. CrowdStrike’s Global Threat Report reveals that 281 known adversaries are using AI, clo…