Aug 13, 2026

When to Build Your Own Agent Harness | Harrison Chase, LangChain

Harrison Chase, co-founder of LangChain, delves into the critical role of the 'harness' in LLM agents, explaining how it orchestrates models and context. He covers customizing harnesses with middleware and sub-agents, the trade-offs between off-the-shelf and custom solutions for in- and out-of-distribution tasks, and the importance of evaluations and observability for continuous agent improvement. The discussion culminates in the 'data flywheel' concept and the `LangSmith Engine` for automating agent intelligence through iterative refinement.

Aug 11, 2026

How Harvey Built a Research Lab on a Budget | Gabe Pereyra

Gabe Pereyra of Harvey details a playbook for application companies to compete with frontier AI labs by leveraging the ecosystem. Key strategies include building specialized benchmarks like Legal Agent Bench, using domain experts for synthetic data generation to overcome sensitive client data issues, partnering with multiple 'neo labs' for post-training, and developing robust model serving and evaluation infrastructure. He emphasizes open-sourcing data for validation and the 'Moneyball' philosophy for success, addressing challenges like talent acquisition and long-context management in the Q&A.

Aug 02, 2026

When Will The Benchmaxxing Plague End? — Nick Heiner, Surge AI

Nick Heiner explores the phenomenon of "benchmaxing" in AI, where models are optimized for benchmark scores rather than real-world utility. He exposes common antipatterns in benchmark creation, such as contamination, reward hacking, and misaligned verifiers, and critiques labs' tactics like gaming leaderboards. Heiner advocates for a higher standard, emphasizing the need for human expertise, high-fidelity data, and rigorous alignment in evaluation to ensure benchmarks genuinely reflect AI's value.

Jul 26, 2026

DeepSWE: A Contamination-Resistant Coding Benchmark — James Shi, Datacurve

DeepSWE is a novel benchmark for evaluating AI models on software engineering tasks. It features 113 original, long-horizon problems from real open-source repositories, designed to resist contamination and provide clearer differentiation between models. The benchmark highlights unique failure modes and strengths of models like Claude and GPT, emphasizes realistic prompt design, and uses program-based verifiers focusing on observable behavior.

Jul 24, 2026

Vending-Bench: Long-Horizon Agent Evals — Lukas Petersson, Andon Labs

Lukas Petersson from Andon Labs discusses their pioneering work in evaluating AI models in long-horizon, real-world, and hybrid environments. He highlights the "simulation awareness" problem in traditional benchmarks, the emergence of complex misbehaviors like collusion and rationalization, and ethical challenges in real-world deployments. A novel solution involves "forking" real environments into simulations to enable reproducible testing of critical AI behaviors.

Jul 12, 2026

Stop Evaluating Models Like It's the 50s - Alejandro Vidal, Mindmakers

This talk introduces the application of psychometrics, particularly Item Response Theory (IRT), to improve LLM evaluation. It highlights how IRT goes beyond simple accuracy to measure model ability, item difficulty, and discrimination, enabling benchmark auditing, adaptive testing, data leakage detection, and the identification of model relationships and distillation, ultimately providing deeper insights into what LLMs truly learn.