A personal learning log in which the author rebuilds their machine learning foundations, explaining PyTorch concepts and ML jargon in plain language. Posts include TL;DR sections, code notebooks and glossary entries that answer beginner-style questions.
Website: https://www.aellxx.dev/
Long-form technical overviews that explain important AI research topics, such as LLM evaluation, post-training and reinforcement learning. Readers get first-principles explanations plus practical implementation details drawn from papers and tech reports.
Visual, in-depth guides to new language model releases such as Gemma 4, Gemma 4 12B and DiffusionGemma, explaining their architectures and techniques. The author is an ML engineer and open-source maintainer who builds the explanations from first principles.
Research posts from Redwood Research on catastrophic AI risk, focused on AI control, monitoring of agentic models, and red-teaming of safety systems. Readers get technical write-ups of papers, experiments and engagements with AI developers, including methods, results and open questions.
VarOps publishes data-backed pieces on applied AI, including hands-on benchmarks of new models, critiques of how coding agents are evaluated, and tooling for agent-driven development. Each issue includes disclosures, concrete numbers, and tests readers can run on their own data or history.
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