Editorial Library
Library
How technology, markets, and modern software actually work. Visual breakdowns and practical explanations from Travis Raymond covering AI, developer tools, infrastructure, prediction markets, and emerging technology.
AfterQuery: Why Expert Work Is Becoming AI Training Data
AfterQuery turns expert workflows into training and evaluation data for AI models. Its reported rise from a $300 million valuation to $3.2 billion shows how valuable human judgment has become in the race to improve reasoning models.
How Claude Code Actually Works
Claude Code can read a codebase, edit files, run commands, work through an agentic loop, connect to outside tools with MCP, and continue across local, cloud, and mobile workflows. Here is how the pieces fit together.
What Kalshi's AGI Market Actually Measures
Kalshi's AGI prediction market does not simply ask whether AGI exists. It resolves on a qualifying company announcement under specific contract rules. Here is how to read the market correctly.
Why a16z Just Raised $2.85B Across AI Infrastructure and Growth
Andreessen Horowitz announced $2.85 billion across two funds in four days: $1.1 billion for the Machine Age Fund and $1.75 billion added to Growth V. The split shows how capital is moving toward both AI infrastructure and later-stage company scale.
How Prediction Markets Actually Work
A practical guide to how prediction markets like Kalshi and Polymarket work, how prices translate into probabilities, how contracts settle, and where traders can misread the market.
How AI Is Really Being Paid For
AI infrastructure is being funded through hyperscaler cash flow, corporate debt, leases, private credit, joint ventures, and long-term customer commitments. Here is how the financing behind the AI buildout actually works.