Oct 09, 2026

Why 80% Reliability Isn't Good Enough — Felipe Blanes, Amazon AGI Lab

Felipe Blanes from the Amazon AGI Lab shares lessons from working directly with customers of Nova Act, Amazon's browser agent service, from research preview to general availability on AWS. He argues that static benchmarks leave teams hoping their evals match real usage, and proposes a customer-driven eval flywheel, the trust cliff around 90% reliability, the value of being open about limits, and principles for keeping evals fresh.

Jul 31, 2026

Data and Environment Curation for Post-Training LLMs — Mahesh Sathiamoorthy, Bespoke Labs

Mahesh Sathiamoorthy of Bespoke Labs argues that high-quality data and curated RL environments are the true bottlenecks for post-training LLMs, especially for building reliable, autonomous agents. He grounds this in experiences with OpenThoughts, a reasoning dataset, highlighting counterintuitive lessons like the importance of diverse reasoning traces and the fact that stronger teachers aren't always best. A key takeaway, reinforced by their Curator tooling, is that a disciplined curation stack is essential for transforming base models into capable, post-trained agents for real-world applications like credit card compliance.