Analyzes the hidden costs and skill erosion of using AI for coding, emphasizing the need for human oversight.
Exploring the UMAP-MLX project, which achieves up to 46x speedups for UMAP using Apple's MLX, with performance benchmarks.
A skeptic's detailed journey using AI coding agents for complex projects, including porting scikit-learn to Rust, showcasing their surprising capabilities.
A skeptic's detailed journey using AI coding agents to port scikit-learn to Rust, showcasing the surprising capabilities of modern LLMs.
An update on the PRISM project's progress in developing methods for validating spatial patterns in machine learning, covering research from 2025-2026.
Explores the theoretical links between optimization algorithms (like gradient descent) and control theory, analyzing them as dynamical systems.
Author shares their experience and study strategy for passing Microsoft's AI-900 Azure AI Fundamentals certification exam.
A blog post discussing a New York Times article about Moltbook, a social network exclusively for AI bots, and the author's insights on AI behavior.
A blog post discussing a social network exclusively for AI bots, exploring their interactions and the implications of their sci-fi influenced conversations.
A 4.5-hour interview discussing the state of AI in 2026, covering LLMs, geopolitics, training, open vs. closed models, AGI timelines, and industry implications.
A 2025 AI research review covering tabular machine learning, the societal impacts of AI scale, and open-source data-science tools.
Andrej Karpathy notes a 600x cost reduction in training a GPT-2 level model over 7 years, highlighting rapid efficiency gains in AI.
A reflection on a decade-old blog post about deep learning, examining past predictions on architecture, scaling, and the field's evolution.
Leading AI researchers debate whether current scaling and innovations are sufficient to achieve Artificial General Intelligence (AGI).
Analysis of Microsoft's 2026 Global ML Building Footprints dataset, including technical setup and data exploration using DuckDB and QGIS.
Explores whether predictive statistical models require causal relationships to be useful, using examples from data sampling and real-world scenarios.
A syllabus for a university course exploring the principles of feedback, control theory, and their connections to machine learning and optimization.
A professor reflects on the intersection of machine learning and control theory, discussing the Learning for Dynamics and Control (L4DC) conference and the need for a merged perspective.
The author announces their new role as Probabl's CSO to accelerate development of the scikit-learn machine learning library and its ecosystem.
A technical comparison of YOLO-based food detection from 2018 to 2026, showing the evolution of deep learning tooling and ease of use.
