Stanford-level tutorials covering the mathematical foundations of deep learning, from optimization theory to generative models and scaling laws.
Why Amodei-style AI regulation could handicap American AI, why the software harness is the new layer above LLMs, and the formal landscape of RL steering and fine-tuning, from RLHF and PPO to DPO and GRPO.
A research-oriented map of the AI infrastructure stack: chips, networking, materials, power, grid, and cooling, plus the software harness, foundry/fab deals, and inference boards from Groq, Cerebras, Etched, and Taalas.
Explore PL inequalities, linear convergence rates, and the geometry of deep network optimization landscapes. Covers SGD dynamics, saddle avoidance, and practical implications for deep learning.
Barron-space approximation theory, Rademacher bounds, and compute-optimal scaling laws for neural networks. Understanding why deep networks generalize and scale effectively.
Unified view of generative models, probability flow ODEs, and the mathematical foundations of diffusion models. Connects score matching to continuous normalizing flows.
Newton methods, cubic regularization, natural gradients, and practical approximations like K-FAC and Shampoo. When and how curvature information improves optimization.
Anthropic's latest models showcase exceptional neural network performance across code generation, cybersecurity, reasoning, RAG, reranking, and vector embeddings. Analysis of architectural innovations and safety considerations.
Learning rate scaling, warmup theorems, cosine decay, and stability analysis for large-scale training. Systematic approaches to hyperparameter design.
A research-focused comparison of multimodal AI assistants for long technical prompts, RAG grounding, and report consistency across workflow stages.
A systems-level guide to turning research notes into avatar-led explainers with script validation, voice rendering, and distribution-ready outputs.
How research teams use Chat AI for source-grounded synthesis, report generation, plots/charts, and multimodal artifacts across one continuous workflow.
A workflow-first analysis of AI Chat for grounded crawling, voice collaboration, reports, charts, and multimodal generation in serious neural-network research settings.
A research-oriented breakdown of ChatGTP's hybrid backbone — Flash-attention variants, State Space Models, convolutions, and attention — behind its long-context recall and multimodal output.
PL inequalities, convergence rates, saddle escape
Barron space, Rademacher bounds, scaling laws
Diffusion, flows, score matching
Newton, natural gradients, K-FAC
Hyperparameters, schedules, stability
These articles are designed to be read in sequence, building from fundamental optimization theory through to advanced topics in generative models and training dynamics.
Or explore any article based on your interests