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>10x More Efficient Pretraining (15 minute read)
Without large amounts of compute, small labs can only compete through algorithmic efficiency. Magic's pretraining recipe is now more than 10 times more compute-efficient than that of leading open-weight base models. The startup believes that pretraining, agentic RL, and long-context are sufficient for building superhuman coding agents and automating AI research and development. This post discusses its pretraining and long-context work.
Aug 27 | Blog
The Agentic Survival Guide: How Security and Marketing Leaders Can Learn to Trust AI Agents
AI agent traffic grew 7,851% in 2025. Security and marketing teams are both flying blind, and the fix runs through visibility, identity, and governance, in that order.
SponsoredSep 09 | Tech
God Help Us, Let's Try To Learn About Mechanistic Interpretability Techniques (29 minute read)
Mechanistic interpretability is the science of reading an AI's mind.
Sep 09 | Design
AI-Powered Architecture Documentation (Website)
Archyl maps every system, service, and dependency into an interactive C4 model, then keeps it in sync on every push.
Sep 09 | DevOps
Kubernetes Promotes KYAML as a Safer, More Consistent Way to Work with Manifests (3 minute read)
KYAML is a stricter YAML dialect for Kubernetes that reduces syntactic and type ambiguity while remaining valid YAML and compatible with existing tooling. By standardizing configuration into a more explicit format, KYAML can improve consistency, validation, diffs, and machine-generated manifests, making it particularly relevant as Helm, GitOps systems, infrastructure tooling, and AI agents increasingly generate Kubernetes configuration.






























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