Voyager

From 36% to 100%: How Self-Improving Agents Write Their Own Skills — Rafal Wilinski, Runlayer

From 36% to 100%: How Self-Improving Agents Write Their Own Skills — Rafal Wilinski, Runlayer

Rafal Wilinski of Runlayer argues that when one agent solves a hard problem, that knowledge should become a skill shared across the whole company. He covers why skills matter more as agents run longer, three problems with current skills, using MCP as a governed distribution layer, distilling skills from successful and failed agent traces inspired by the Voyager paper, and the organizational flywheel as a durable competitive advantage.

Robot-Use Agents: Why General-Purpose Models May Win in Robotics

Robot-Use Agents: Why General-Purpose Models May Win in Robotics

A discussion with the founders of Waddle Labs and RoboCurve on how general-purpose language models are beginning to control robots. The conversation covers the lineage from RT-2 and code-as-policies to in-context learning, harness design, skill consolidation, Platonic representations, and the path toward general-purpose robots.