Clear, practical breakdowns of the AI papers and ideas that matter: agents, reasoning, safety, multi-agent systems. Written for practitioners, not academics.
Every frontier AI model runs on transformers. This guide explains self-attention, scaling laws, Mixture of Experts, FlashAttention, and the modern innovations that determine cost and capability.
AI agents create attack surfaces that chatbots don't. This playbook covers prompt injection, tool misuse, data exfiltration, multi-agent attacks, defense-in-depth, and the compliance timeline.
Only 5.2% of engineering teams have AI agents live in production. This guide covers the infrastructure, reliability, and cost management patterns that separate working deployments from abandoned prototypes.
Benchmarks are contaminated, gamed, and misleading. Here's how to build evaluation systems that predict real-world model performance.
Every AI builder hits the crossroads: better prompts, retrieval, or fine-tuning? This guide provides a concrete decision tree based on data freshness, accuracy needs, cost, and latency.
Raw API pricing is 30-50% of total agent cost. This guide breaks down where the money actually goes, from orchestration overhead to the Jevons paradox, and how to cut spend without cutting capability.
Chain-of-thought is the most studied prompting technique in AI, and the most misapplied. A decision framework for when it helps, when it hurts, and what it costs.
What AI alignment actually means as an engineering problem. The three core challenges, the techniques that exist today, and why agents make everything harder.
A practical guide to reading AI research papers. Learn the three-pass method, spot red flags in benchmarks and methodology, and build a sustainable reading practice.
Roughly 70% of Earth science datasets hosted in large repositories like PANGAEA go uncited after publication. The data exists. The agents can access it....
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