Jul 19, 2026

From Blind Spots to Merged PRs: Continuous Agentic Performance Optimization - May Walter, Hud

May Walter, CTO of Hud, details a real-world case study on integrating AI agents into the SDLC for continuous performance optimization. The talk covers Hud's runtime intelligence layer, which uses production context to identify and fix high-ROI performance issues like N+1 queries and missing database indexes. It highlights the technical approach, challenges, and the development of a human-friendly reporting system that delivers measurable P90 latency improvements, enabling proactive optimization in mature codebases.

Jul 19, 2026

Why Netflix is betting on systems thinkers—not specialists—in the AI era | Elizabeth Stone (CPTO)

Elizabeth Stone, Netflix CPTO, discusses how AI is transforming product development and company culture. She highlights the increasing importance of "systems thinking," managing the influx of AI-generated output with clear guardrails, fostering "AI fluency" across all roles, and maintaining Netflix's unique "excellence as an operating system" through high talent density and a comfort with risk-taking. The conversation also explores AI's impact on content creation, the future of entertainment, and talent management.

Jul 16, 2026

Has AI Finally Cracked Time Series Forecasting?

Ameet Talwalkar, CMU professor and Datadog's Chief Scientist, traces the journey of time series foundation models from early skepticism to their current impact. He details Datadog's Toto V1 and V2, highlighting breakthroughs in zero-shot performance, scaling, and the crucial role of data mix. The discussion extends to the vision of 'world models' for observability, integrating diverse data for self-healing software systems, and concludes with insights on open-weights models and AI's transformative effect on computer science and academic research.

Jul 15, 2026

Omnigent: Composition, Control, and Collaboration for AI Agents

Denny Lee discusses the industry's shift to meta-harnesses like Omnigent, which enables hot-swappable AI models and agents, illustrated by his personal project of using debating agents to plan a matcha farm in Taiwan. He highlights how "tokenomics" is replaying the CapEx-to-OpEx cost shift, emphasizing the need for developer visibility, central governance, and auto-model selection to manage AI spend. The conversation also touches on the importance of databases for agent memory and accountability in AI-assisted workflows.

Jul 10, 2026

Reddit cracks down on AI slop & the future of AI compute

This episode explores Reddit's aggressive AI spam combat strategy, revealing AI's dual role in fighting malicious AI. It then dissects Anthropic's Economic Index, highlighting how Claude integrates into daily life despite significant user selection bias. The discussion also covers Orin's $33M raise for a GPU compute marketplace, debating the fungibility of compute and the technical hurdles. Finally, Anthropic's chip ambitions are analyzed as an economic strategy to optimize models, reduce NVIDIA dependency, and manage rising token costs, with comparisons to existing hardware ecosystems and NVIDIA's market position.

Jul 06, 2026

Session on Inclusive AI: Data, Models, Evaluation

The Microsoft Research India Academic Research Summit 2026 session on "Inclusive AI" explored critical challenges in developing AI that serves diverse linguistic and cultural contexts. Speakers Niloy Ganguly, Danish Pruthi, Sunayana Sitaram, Anoop Kunchukuttan, and Ashutosh Modi addressed data gaps, model biases, and evaluation shortcomings, emphasizing the need for equitable and culturally relevant AI. Key themes included the use of synthetic data for low-resource languages, the impact of tokenization on model performance, geographical disparities in generative AI, and the application of AI for social good in legal and accessibility domains. The discussions underscored the importance of community involvement, open data, and designing AI for multilinguality from the outset, rather than as an afterthought.