Jun 29, 2026

The AI Agents Helping Home Services Book More Jobs

Avoca (YC W23) has achieved eight-figure revenue and a $1 billion valuation by building an AI workforce for home services, turning missed calls into revenue. Founders Apurva Shrivastava and Tyson Chen explain how AI expands software's market share beyond 1% by automating labor and operational costs, leading to a 15x larger opportunity. They emphasize that their AI agents augment human workers, reducing attrition in challenging CSR roles and creating new positions for training AI, driven by a deep customer obsession learned at YC.

Jun 28, 2026

Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc

Vaidas Razgaitis, Senior Research Engineer at Higharc, shares three tactical tips to accelerate the transition of novel AI/ML research into production-ready features. He emphasizes addressing the critical handoff challenge between ML researchers and software engineers through structured documentation (Research Prototype Taxonomy Document), a well-organized monorepo utilizing decoupled microservices, and a systematic approach to code decomposition and PR review. These strategies aim to improve legibility, maintainability, and delivery speed for ML-driven products.

Jun 18, 2026

The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks

Sandipan Bhaumik presents a five-pillar framework for successfully moving AI systems from demos to production, inspired by a retail bank's failed chatbot PoC. The framework covers defining numerical success (Evaluation), tracing every AI decision (Observability), building robust data pipelines (Data Foundation), managing multiple AI interactions (Multi-agent Orchestration), and ensuring accountability and security (Governance). He illustrates these concepts with a banking chatbot case study, emphasizing continuous evaluation, data quality, and a proactive incident playbook.

May 27, 2026

Power agents with full context of your experiments and traces with W&B MCP server

The W&B Model Context Protocol (MCP) is a hosted endpoint that enables AI agents to intelligently interact with all Weights & Biases data, including runs, traces, evaluations, and reports. It features discovery tools for smart queries, automated analysis for comparing experiments and identifying regressions, and seamless integration with IDEs, coding agents, and chat interfaces like Mistral AI for streamlined ML workflows and on-the-go reporting.

May 25, 2026

Scaling Meta's Multi-Agent Systems to a Billion Videos

Meta's approach to solving modality misalignment and content theft in short-form video using a multi-agent system of smaller, specialized models instead of a single large LLM. The talk covers the architecture (Perceiver, Retriever, Reasoner), evaluation stack, and key cost-saving optimizations.

May 22, 2026

Lobster Trap: OpenClaw in Containers from Local to K8s and Back — Sally Ann O'Malley, Red Hat

This talk presents a container-first methodology for developing, distributing, and managing AI agents. Using a stack of Podman for local development and Kubernetes for scalable deployment, this approach transforms personalized agent setups from messy collections of files into reproducible, secure, and portable container images that can serve as a team-wide baseline. The session covers practical techniques for secrets management, state persistence, and automated setup, highlighted by a real-world example from an Nvidia team using this pattern for model evaluations.