Amrit Kumar writes about machine learning training, moving from per-example scheduling experiments that failed to beat uniform sampling toward Aethos, a platform that decides early in a run whether continuing is worth the compute. The posts are candid about experimental bias and negative results, backed by specific benchmarks and numbers.
Website: https://amrkumar.com/
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.
Long-form technical articles on machine learning and AI research, covering new model releases, architectures and the history behind them. Issues include explanations of concepts such as looped transformers and text watermarking, plus commentary on recent papers.
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.
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