https://speakerdeck.com/cweichen 2018-12-04 10:51:38 -0500 This is a talk given by Ching-Wei Chen at the LeadDev New York Meetup on Mar 25, 2026. The presentation uses a case study of a major Spotify outage to emphasize the need for proactive reliability and performance issue detection. It introduces Datadog APM Recommendations as a developer product that detects common anti-patterns like aggressive retries and N+1 queries. The rise of capable coding agents is "shrinking" the Software Delivery Loop, requiring a shift to building specialized tools for AI Agents. This shift necessitates building specialized tools that are designed to be consumed directly by AI Agents, rather than just humans. Effective agent tools must be optimized to "save cognition" by compressing deep insights from vast data (like trace spans) and minimizing friction through discoverability and sensible defaults. Specialized tools for tasks like identifying latency bottlenecks have shown significant performance improvements in internal agent investigations. The talk concludes that agent tools should be efficient, broadly useful, and discoverable to achieve the simplicity of tools like grep This is a talk given by Ching-Wei Chen at the LeadDev New York Meetup on Mar 25, 2026. The presentation uses a case study of a major Spotify outage to emphasize the need for proactive reliability and performance issue detection. It introduces Datadog APM Recommendations as a developer product that detects common anti-patterns like aggressive retries and N+1 queries. The rise of capable coding agents is "shrinking" the Software Delivery Loop, requiring a shift to building specialized tools for AI Agents. This shift necessitates building specialized tools that are designed to be consumed directly by AI Agents, rather than just humans. Effective agent tools must be optimized to "save cognition" by compressing deep insights from vast data (like trace spans) and minimizing friction through discoverability and sensible defaults. Specialized tools for tasks like identifying latency bottlenecks have shown significant performance improvements in internal agent investigations. The talk concludes that agent tools should be efficient, broadly useful, and discoverable to achieve the simplicity of tools like grep Thu, 26 Mar 2026 00:00:00 -0400 https://speakerdeck.com/cweichen/building-developer-products-in-the-agentic-age https://speakerdeck.com/cweichen/building-developer-products-in-the-agentic-age Talk given at WeAreDevelopers AI Congress Vienna, Dec 4th 2018. Spotify is the world's leading music streaming service, with over 191 million users in 78 markets. But no two users are exactly alike, so in order to serve each listener the content they will uniquely enjoy, Spotify must personalize every aspect of the user experience. From exclusive playlists like Discover Weekly and Your Daily Mix, to personalized Concert recommendations, Spotify utilizes Machine Learning technologies to learn from each user's unique listening habits and tastes, and surface the most relevant content for them. An important aspect of building Machine Learning-based systems is experimenting quickly and effectively, as well as fostering a culture and community around Machine Learning research, both inside and outside of the company. The first part of this talk will give an overview of Home screen personalization at Spotify: how the team uses Machine Learning to show users the most relevant content on the default screen of the Spotify app, and more importantly, how they experiment rapidly using infrastructure and tools for offline and online evaluation of Machine Learning models. The second part of this talk will introduce the Recommended Tracks feature for playlist creation on Spotify, and showcase the results of the RecSys Challenge 2018, which was an open data science challenge launched to promote the study of automatic playlist continuation in the greater research community. Talk given at WeAreDevelopers AI Congress Vienna, Dec 4th 2018. Spotify is the world's leading music streaming service, with over 191 million users in 78 markets. But no two users are exactly alike, so in order to serve each listener the content they will uniquely enjoy, Spotify must personalize every aspect of the user experience. From exclusive playlists like Discover Weekly and Your Daily Mix, to personalized Concert recommendations, Spotify utilizes Machine Learning technologies to learn from each user's unique listening habits and tastes, and surface the most relevant content for them. An important aspect of building Machine Learning-based systems is experimenting quickly and effectively, as well as fostering a culture and community around Machine Learning research, both inside and outside of the company. The first part of this talk will give an overview of Home screen personalization at Spotify: how the team uses Machine Learning to show users the most relevant content on the default screen of the Spotify app, and more importantly, how they experiment rapidly using infrastructure and tools for offline and online evaluation of Machine Learning models. The second part of this talk will introduce the Recommended Tracks feature for playlist creation on Spotify, and showcase the results of the RecSys Challenge 2018, which was an open data science challenge launched to promote the study of automatic playlist continuation in the greater research community. Tue, 04 Dec 2018 00:00:00 -0500 https://speakerdeck.com/cweichen/learning-fast-experimentation-and-open-research-at-spotify https://speakerdeck.com/cweichen/learning-fast-experimentation-and-open-research-at-spotify