Teams

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

Jul 19, 2026 · 22:46

May Walter, CTO of Hud, presents a case study on using coding agents with runtime intelligence to automate continuous performance optimization in production. The system analyzes real production traces, queries, and latencies to surface high-ROI fixes like N+1 queries and missing indexes, scoring them by complexity and impact. It runs weekly via GitHub Actions and Claude Code, generating reports with verified fixes and evidence of impact, such as P90 latency improvements after deployment. Key challenges included 'plausible unverified' fixes, complex ClickHouse queries, and lazy fixes that only mask exceptions rather than root causes. Walter emphasizes the need for prod-to-code context mapping, scoring guardrails, and human review to achieve reliable automation. The talk concludes that context over cleverness works, and agents can automate the investigation phase that teams rarely do proactively, turning dormant performance debt into actionable merged PRs.

The UX of AI: Making AI-Powered Apps Your Users Don't Hate - Kathryn Grayson Nanz, Progress Software

Jul 18, 2026 · 35:59

Kathryn Grayson Nanz, Senior Design and Developer Advocate at Progress Software, argues that the success of AI-powered applications now depends on user experience rather than model performance. She identifies five pillars—trust, clarity, control, transparency, and meaningful benefit—and provides concrete patterns: citing sources to build trust, streaming text for clarity, allowing undo and version history for control, requesting granular permissions for transparency, and offering templates and next-step actions to ensure meaningful benefit. She emphasizes that developers must design these patterns themselves because AI can only remix existing interfaces, and that users need gradual introduction to AI features to avoid disengagement. The talk stresses that without addressing these UX challenges, users will abandon AI tools after a few failed attempts.