Weco let an AI coding agent rewrite the harness around a fixed language model for eight days. Zhengyao Jiang explains what the reported gains show, where the evidence stops short of recursive self-improvement, how reward hacking complicates evaluation, and why human-designed abstractions still anchor the search.
Robert Lange from Sakana AI discusses Shinka Evolve, a framework combining LLMs with evolutionary algorithms for open-ended program search. The conversation explores how Shinka Evolve addresses the limitations of systems like AlphaEvolve by co-evolving problems and solutions, its sample-efficient architecture using UCB bandits and quality-diversity search, and its applications in circle packing, competitive programming, and evolving MoE loss functions. The discussion also delves into the philosophical debate on whether these systems produce true novelty or are parasitic on their starting conditions, and the future role of the "AI Scientist" as a human co-pilot.
Today's AI, despite its impressive capabilities, may be an "impostor" with a messy, unstructured internal understanding—a "spaghetti" representation. This summary explores an alternative, open-ended approach to building AI that fosters a deep, modular, and truly intelligent foundation, moving beyond brute-force optimization to embrace serendipitous discovery and "evolvability."