Siraj Gupta of Warp outlines three practical ways software factories can improve over time: self-updating skills driven by outer-loop agents with pull-request review, versioned and traceable persistent memory that works across harnesses, and cost-aware model routing with custom rules and evals.
Molly Graham revisits her classic "Give Away Your Legos" career advice for an AI era, exploring grief and burnout in tech, the difference between delegating to AI and to humans, the overblown fear of job displacement, which Legos should never be given away, and what effective managers should do now. Key ideas include treating AI as a junior intern, the human sandwich model of vision and review, and reframing careers as evolving rather than disappearing.
Jeff Crume explains how AI agents, RAG pipelines, prompts, tools, and models move sensitive data through modern AI systems, and why traditional DLP falls short. He outlines a unified approach built on data lineage, workload and workforce visibility, proactive risk identification, and compliance reporting.
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.
Elie Bakouch, a research engineer at Prime Intellect, reports on setting Claude Code and Codex loose on the community Optimizer Speedrun, a race to train a GPT-2-level model in the fewest steps. Both agents beat the human record, but they behaved very differently, and none of them invented a new optimizer. The talk closes with a proposed AlphaEvolve-style discovery loop and an argument for doing this research openly.
Lakshya Agrawal, creator of GEPA and a PhD student at UC Berkeley's Sky Computing Lab, presents reflective optimization as an alternative to scalar-reward reinforcement learning. GEPA lets a model read full rollout traces, including reasoning, tool calls, and error messages, and rewrite prompts from that feedback, with a Pareto candidate pool guarding against local optima. The talk covers the GEPA vs. GRPO sample-efficiency comparison, domain-knowledge discovery, the Optimize Anything API for text-based artifacts like code, harnesses, and skills, and production results from Databricks, Snorkel, and others.