Daily Brief

Cold starts, proprietary SDKs and one-region deployments slow AI apps down. WebAssembly at the edge removes all three. Thorsten Hans, senior developer advocate at Akamai, shows how to build and ship MCP servers and AI agents with Spin, the open-source WebAssembly framework, and run them on Akamai Functions, a globally distributed serverless platform with no cold starts. He explains why WebAssembly on the server is fast, portable and sandboxed by default, then live-codes MCP servers with wasmcp: scaffolding tools in TypeScript and Rust, composing them into a full server, testing with MCP Inspe

Full episode: https://youtu.be/LwQQ7nBCGSs Me on twitter: https://x.com/dwarkesh_sp

Vector search can't answer "which PRs did I merge last week?" A relational context engine can, and it cuts token burn along the way. Peter Werry, founding engineer at Unblocked, explains what a context engine is and why coding agents need more than code search: the PRs, Slack threads and incident history that explain why the code is the way it is. He shows Claude sessions with and without that context, including one with about a 50% drop in cost and one that traced a regression to a model switch. Then he builds Unblocked's open-source document query engine step by step: automatic schema disco

You can write all the rules you want in a markdown file. Your agent can still call the API. Sam, CTO at Gravity, skips the slides and runs five live scenarios against a hotel-booking agent to show why governance belongs in the infrastructure, not in the agent's instructions. Running on the Gravity Gateway with a Databricks backend, Sonnet and Groq, he shows the gateway blocking a destructive "delete all bookings" tool call, stopping an attempt to pull every guest's personal data, rate-limiting a buggy agent that fires the same request eight times, serving repeated questions from a semantic ca

Most models learn text first and get vision bolted on later. MiniMax M3 learned both from the very first training step. Olive, who works on reinforcement learning research at MiniMax, goes inside MiniMax M3, an open-weight model with frontier-level coding and agentic ability, a 1 million token context window and native multimodality. She explains MSA, MiniMax's sparse attention design (about 9x faster prefill and 15x faster decoding than full attention): an index branch that picks which blocks matter and a sparse branch that attends only to those, and why existing designs like DeepSeek's DSA