In this episode, we sit down with Rohan Katyal and Raghav Sethi, co-founders of Milana, to discuss the shift from passive analytics to the world’s first AI Product Engineer. Rather than just providing another dashboard to monitor, Rohan and Raghav are building an agentic partner that you add to your product to bridge the gap between discovery and deployment. Drawing on their experience at Meta, Yelp, and Airtable, they explore how Milana enables autonomous improvement - turning deep user intelligence into shippable code and structural refinements that act as a tireless extension of your engineering team.

The conversation dives into why session replays — a mature but historically underused technology — are now a powerful data asset thanks to vision LLMs. Raghav explains how session replays are really just high-granularity logging of DOM changes, not screen recordings, and why feeding them through AI unlocks insights that traditional event-based analytics simply can’t capture. The team breaks down how they use just-in-time structuring to extract meaning from dense, unstructured session data without requiring upfront instrumentation.

Rohan shares hard-won lessons from building Yelp’s experimentation platform — including how teams that simply ran more experiments consistently outperformed those with better data resources. They discuss the tension between A/B testing rigor and iteration speed, why most experiments never ship, and how lowering the cost of generating and testing hypotheses changes everything about product development velocity.

We also get into the technical details of semantic clustering across millions of sessions, why video is actually a more compact representation than raw DOM for LLM reasoning, and how Milana analyzes sessions from multiple perspectives — user researcher, PM, founder — to surface real pain points. Plus, a bold prediction: analytics dashboards are dying, and the future belongs to agentic systems that don’t just deliver insights but actually own and drive your OKRs.

Topics covered:

• Why session replays are the ultimate untapped data asset for product teams
• How vision LLMs unlocked AI-powered analysis of user sessions
• Just-in-time data structuring: querying unstructured sessions without upfront instrumentation
• Lessons from building experimentation platforms at Yelp and Airtable
• Why running more experiments beats having better data
• Semantic clustering: separating signal from noise across millions of sessions
• Video vs. DOM vs. events — the best data representation for LLM reasoning
• Analyzing agent behavior through session replays
• The death of dashboards and the rise of agentic growth systems
• User research horror stories and the surprising things users do

Chapters

00:00 Introduction to Rohan and Raghav's Journey
04:47 The Importance of User Research
08:03 Making Solutioning a Science
11:09 Understanding Session Replays and Experimentation
14:50 Defining Sessions and Experimentation Platforms
18:54 The Need for Consistent Metrics
22:11 The Role of Events vs. Session Replays
29:46 Leveraging LLMs for Enhanced Insights
35:04 Determinism vs. Non-Determinism in Data Analysis
37:57 Understanding User vs. Agent Behavior
39:47 The Art of Structuring Data
45:25 Semantic Clustering and Its Importance
47:09 Building Infrastructure for Complex Data
51:24 The Future of User Simulation and Experimentation

Jamie, a seasoned startup founder and former Dropbox engineer, shares insights on building distributed systems, scaling storage solutions, and the impact of AI on infrastructure and application development. Discover practical lessons from scaling Dropbox, the evolution of data storage, and how Convex is shaping the future of app development.

In this episode, we sit down with the full founding team of Wild Moose — CEO Yasmin Dunsky, CTO Roei, and VP R&D Tom Tytunovich — to explore how they’re transforming production debugging from an art into a science using AI.

The trio shares their unconventional founding story — from meeting across three different cities to living together for three months in a California Airbnb to stress-test both their idea and their relationship. They discuss how they identified production debugging as a massive unsolved problem before ChatGPT even launched, recognizing that while code generation is fundamentally a text problem, debugging is a search problem that demands a completely different approach.

We dive deep into Wild Moose’s “microagents” architecture — fast, highly optimized AI agents that replicate the muscle memory of senior engineers to automatically investigate production incidents in under a minute. The team explains why accuracy trumps everything in their space (wrong answers are worse than no answers when you’re debugging at 3 AM), how they navigate the speed-cost-quality triangle, and why they built a test-driven approach to validate agents against past incidents.

We also get into the multi-agent vs. single-agent debate, handling multimodal observability data (logs, metrics, traces, dashboards, code), and how the rapidly evolving LLM landscape creates both opportunities and challenges for production AI systems. Plus, the team shares their favorite outage war stories — including a “WatchCat” hack and a three-month hunt for a single rogue bit.

Topics covered:

• The Wild Moose origin story and the California Airbnb experiment
• Why production debugging is a search problem, not a text generation problem
• Microagents: fast, specialized AI agents for incident investigation
• Building institutional knowledge into AI — capturing engineering muscle memory
• The speed-cost-quality triangle in real-time AI systems
• Multi-agent vs. single-agent architectures: when to use what
• Handling multimodal observability data with LLMs
• The future of AI SRE and self-healing production environments
• Favorite outage war stories from the trenches

Chapters

00:00 Introduction to the Wild Moose Team
04:12 The Spark Behind Wild Moose
08:41 Understanding the Debugging Landscape
12:45 The Role of AI in Debugging
17:31 Building Investigative Agents
21:55 Optimizing Workflows and Feedback Loops
29:12 Navigating Complexity in Software Systems
33:42 Adapting to Rapid Changes in AI Technology
40:02 Microagents: The Future of AI Architecture
44:46 Outage Stories: Lessons from the Trenches
50:49 Vision for the Future of AI in Production