Aug 22, 2026

Preferences Over Benchmarks: Model Routing — Archana Kamath & Tyler Gillam, DigitalOcean

This talk argues against the common practice of picking LLMs based solely on leaderboards, emphasizing that there's no single best model, only the right one for a given request. It introduces Digital Ocean's Inference Router, a customizable, open-source solution that intelligently selects models based on user-defined preferences (cost, latency, task, quality) rather than benchmarks, demonstrating significant cost savings and performance improvements in live demos.

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 09, 2026

Multiplayer agentic engineering — Arjun Singh, Superconductor

Arjun Singh, co-founder of Superconductor, discusses the six crucial lessons learned from integrating AI agents into their software development workflow. He emphasizes the importance of building "multiplayer agentic engineering" systems where human teams and AI agents collaborate seamlessly, focusing on model agnosticism, pervasive agent interfaces, transparent work visibility, automated signal-to-code conversion (like their "meeting bot"), secure isolated cloud environments, and internal code-base benchmarking to optimize cost, quality, and speed.

Aug 06, 2026

The State of Model Routing — NVIDIA, Cognition, OpenRouter

A deep dive into model routing strategies for AI/ML production, featuring experts from Cognition, OpenRouter, and NVIDIA. Key topics include optimizing costs with multi-model systems, delegating tasks between frontier and smaller models, managing context efficiently (sidekicks, compaction), and adapting to dynamic task complexities. The panel discusses the fragility of naive routing, the cost implications of in-distribution vs. out-of-distribution tasks, and the evolution of auto-routers driven by real-world usage patterns like OpenClaw's heartbeats. Insights also cover NVIDIA's Flex Run for dynamic model sizing, hallucination probes for detecting model limitations, and the future of hybrid local/cloud routing and model collaboration.

Jul 24, 2026

Build Hour: Valuemaxxing with GPT-5.6

This Build Hour focuses on "value maxing" with GPT-5.6, shifting from simply tracking token usage to measuring the actual outcomes and efficiency gained from AI. It covers how to select the right GPT-5.6 model (Sol, Terra, Luna) based on intelligence, latency, and cost, and provides practical strategies for optimizing cost-performance. Key topics include leveraging programmatic tool calling, prompt caching, persistent reasoning, and context compaction for API users, along with CodeX-specific tips. A customer spotlight on Ploy demonstrates real-world application, showcasing their migration to GPT-5.6 Sol, which resulted in 2.2x faster builds at 27% lower cost through advanced caching and tool optimization techniques.

Jul 21, 2026

2026 State of AI Engineering — Barr Yaron, Amplify Partners

Barr Yaron's 2026 AI engineering survey reveals key trends: audio and image generation are rapidly gaining traction, while cost is now a primary engineering constraint. Agents are evolving to take actions within systems, but control mechanisms remain primitive. Evaluation (eval) is still the top infrastructure challenge. AI positively impacts job satisfaction and experimentation but also raises concerns about technical skill erosion and non-developers shipping code, fundamentally changing engineering culture. Predictions include a likely AGI declaration within five years and a shift away from Transformers as state-of-the-art.