Jul 14, 2026

Don't Ship Skills Without Evals — Philipp Schmid, Google DeepMind

Philipp Schmid from Google DeepMind emphasizes the critical, often-overlooked need for rigorous evaluation of AI agent skills. He argues that shipping skills without testing is akin to deploying code without unit tests, leading to unreliable agent behavior. The talk covers what defines an agent skill, strategies for writing effective and correctly triggering skills, and a practical guide to building lightweight evaluation harnesses to catch failures proactively.

Jul 10, 2026

Should AI Engineers Still Read Code in 2026? The Z/L Continuum — Alex Volkov, ThursdAI

Alex Volkov introduces the "Z/L Continuum," a framework for navigating the tension between rapid AI-generated code production and the critical need for human review. He argues that the key lies in understanding that the continuum applies to tasks, not individuals, and presents a pragmatic routing table for verifying changes based on their criticality, highlighting the shift towards engineering systems that build and verify code, rather than meticulously inspecting every line. The talk also touches on emerging capabilities like Fable and "loops" and the importance of flexibility and human judgment in the evolving AI engineering landscape.

Jun 03, 2026

Benchmarking semantic code retrieval on Claude Code — Kuba Rogut, Turbopuffer

A detailed benchmark analysis comparing raw Claude Code's performance with windowed grep and Turbopuffer's semantic search for code retrieval in LLM agents. The study reveals significant improvements in file precision (65% to 87%) and reduced wasted reads (1 in 3 to 1 in 8) with semantic search, while highlighting the importance of the agent's understanding of when to use retrieval tools.

Apr 08, 2026

Bending a Public MCP Server Without Breaking It — Nimrod Hauser, Baz

Learn practical strategies to adapt third-party MCP server tools for production AI applications. This talk covers five key practices: curating tools, enhancing descriptions, implementing deterministic guardrails, composing new tools from existing ones, and leveraging tools as simple functions, all demonstrated through a real-world "Spec Reviewer" example.

Apr 01, 2026

Large-scale agentic quant research with Weights & Biases

Explore how Weights & Biases (W&B) enhances reliability, reproducibility, and explainability in large-scale, agent-driven quantitative research. This video demonstrates two core applications: debugging multi-agent alpha research pipelines with W&B Weave to identify root causes and iterate on forecasts, and automating strategy optimization using W&B Models to tune agent weights and gain insights from performance convergence and parallel coordinate plots.

Mar 09, 2026

OpenClaw's Memory Sucks and the fix is simple — Dhravya Shah, Supermemory

Dhravya Shah, founder of Super Memory, details the evolution of his company from a simple RAG-based consumer app to a sophisticated, open-source context infrastructure for AI, and introduces a novel hooks-based memory solution for OpenClaw.