Aug 17, 2026

Context Engineering in 2026 — Louis-François Bouchard, Omar Solano & Samridhi Vaid, Towards AI

Experiments with an AI tutor reveal that not compacting context often outperforms summarization, leading to better recall, lower cost, and faster responses, primarily due to LLM API prompt caching. Compaction invalidates the cache, making it unexpectedly expensive. While cloud environments benefit from full history, local hardware limits necessitate compaction, highlighting the need to prioritize actual constraints over default compaction strategies.

Aug 17, 2026

Reading Group July 2026 - Loop Engineering

This session provides an in-depth exploration of Loop Engineering, a paradigm shift from manual AI prompting to designing autonomous systems that orchestrate AI agents. Speakers share practical experiences, from building production-grade platforms with automated code generation and adversarial AI reviews to experimental loop structures and foundational infrastructure layers. Key discussions address challenges like managing token costs, preventing agent chaos, and implementing robust verification mechanisms for industrializing software development.

Jul 24, 2026

Opencode CEO: Blocked, 20X Growth in 6 Months, Building the Coding Agent for the World

Jay V, founder and CEO of Opencode, shares insights into the explosive growth of his platform, an open-source alternative to proprietary coding agents. He details Opencode's journey to 13 million monthly active users and 7 trillion tokens processed daily, attributing its rapid rise partly to an unexpected controversy with Anthropic and the maturing open-source model ecosystem. The discussion delves into global user adoption, the economic shift in AI token consumption, and how Opencode's strategic product design, rooted in 16 years of entrepreneurial persistence, positions it as a critical marketplace for diverse AI models, serving both individual developers and Fortune 500 companies.

Jun 16, 2026

The State of Frontier Post-Training Recipes | Conversation with Finbarr Timbers

This discussion with Finbarr Timbers reviews the evolution of frontier post-training recipes, highlighting the shift from simpler SFT-DPO-RL to complex multi-teacher on-policy distillation (MOPD). It covers the organizational challenges of building models like Olmo, the rise of synthetic data and reasoning-focused RL in DeepSeek, and the complexities of integrating expert teachers, while also exploring open questions on environments, specialized APIs, and career strategies in the rapidly changing AI landscape.

May 07, 2026

Inside China's AI Labs with Interconnects & SAIL Media

Firsthand reflections from a visit to China’s most prominent AI labs, exploring the human side of the Chinese AI ecosystem, the technical constraints they face from chip supply, and how their research culture compares to the Bay Area.

Mar 11, 2026

Why AI Engineers Need to Understand GPU Hardware (with Chris Fregly)

Chris Fregly, author of 'AI Systems Performance Engineering', explains that true performance gains in AI come not from raw compute but from a deep, holistic understanding of the entire hardware and software stack. He emphasizes that memory bandwidth is the most critical GPU metric and introduces the concept of 'mechanical sympathy'—the co-design of hardware, software, and algorithms—as the key to unlocking efficiency and overcoming modern bottlenecks.