Jul 31, 2026

Learning on the Job: The Future of Post-Training — Raymond Feng, Applied Compute

Raymond Feng presents Applied Compute's approach to training custom AI models that learn "on the job" using reinforcement learning. He details the evolution from controlled Q&A to synthetic environments, highlighting the core GRPO-style loop. A major focus is tackling the challenges of environment fidelity and "reward hacking" in simulated settings. The discussion then moves to the complexities of training directly within real-world enterprise harnesses, addressing issues like non-replayability and off-policy data. Feng concludes by outlining frontier research in self-distillation, automated data pipelines, and qualitative feedback, envisioning a future where models continuously learn and self-evaluate from every interaction, making "experience the dominant medium of improvement."

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.

Jul 20, 2026

Sandboxing, Agent Harnesses, and Agent Teamwork

Shahram Anver, CEO of Cleric, details how AI agents for SRE are evolving beyond fast triage to continuous learning and operational memory. He discusses Cleric's architectural shifts, from complex early designs to simpler, sandboxed query agents, and the unique challenges SRE agents face in diverse production environments. A core focus is on human-agent interaction, redefining roles as managers overseeing agents, and how agents learn from unstructured data like Slack to build robust, actionable knowledge for autonomous, self-healing infrastructure. The discussion also touches on the future of software, differentiating between durable systems and rapidly developed "skills" or "vibe-coded" solutions.

Jul 20, 2026

The First Dedicated YC GPU Cluster - With Together AI

YC and Together AI have partnered to launch the first dedicated YC GPU cluster, addressing the critical compute bottleneck faced by AI-native startups. This initiative provides flexible, cost-effective access to GPU resources, enabling companies from early-stage research to major players to train, fine-tune, and run inference on AI models, and mitigating the financial strain of long-term compute commitments.

Jul 18, 2026

Agents Need Feature Flags - Sachin Gupta

AI teams are deploying advanced agent systems without the fundamental safety mechanisms (feature flags, canaries, kill switches) that web teams adopted over a decade ago. This oversight leads to critical incidents like data deletion and financial loss. This talk introduces six agent-specific feature flag types—for prompts, tools, models, memory, autonomy, and sub-agents—and outlines a practical playbook for secure AI deployment, emphasizing the critical role of a pre-wired kill switch to manage the high blast radius of AI agents.

Jul 17, 2026

On AI and Knowledge — Pablo Castro, Distinguished Engineer & CVP for AI Knowledge, Microsoft

Pablo Castro explores the three pillars of AI knowledge—intrinsic, extrinsic, and learned—explaining how models leverage parametric memory, agents integrate with enterprise data through sophisticated RAG systems like Foundry IQ, and how continuous learning loops, facilitated by tools like the Agent Optimizer, enable self-improving agents, driving the next wave of AI applications.