Hanson Ho
where else to find them
what they talk about
all 5 in the filters
where they keep going back
no series more than once — all 6 in the filters
on a stage with
- Andrew Tunallonce
- David Rifkinonce
who talks about the same things
- Jason Plumb6 talks
- Liudmila Molkova7 talks
- David González14 talks
- Ted Young23 talks
- Enrique López Mañas22 talks
- Ishan Khanna8 talks
| title | conference | date |
|---|---|---|
| User-Focused Observability 101 For Android Devs | droidcon Berlin 2025 | 2025-09-24 |
User-focused observability uses context and relationships to explain production telemetry changes, not just surface metrics. Example: a 25% drop in checkout… | ||
| Lightning Talk: Beyond Good Enough: Why We Want a Kotlin API and SDK | Open Observability Summit + OTel Community Day 2025 | 2025-06-26 |
Embrace wants an idiomatic Kotlin path because Java APIs are merely workable on Android and break for Kotlin Multiplatform and fragile mobile runtimes. A pure… | ||
| Transmissions From the Tweet Factory: How Observability Transformed Mobile Performance at Twitter | Observability Day 2025 | 2025-04-01 |
Twitter builds on PerfTown logs then develops Production Client Tracing (PCT) optimized for mobile (spans, durable storage, minimal overhead) and integrates… | ||
| “How’s the App Doing?” Bringing Mobile Into Your Reliability Picture | SREcon25 Americas | 2025-03-25 |
Argues that server-side SLOs miss a huge slice of failures unless apps send user-centric telemetry and shows concrete gaps: requests stuck in client queues… | ||
| OpenTelemetry for Mobile Apps: Challenges and Opportunities in Data Mob... | OTel Community Day North America 2024 | 2024-06-25 |
Embrace describes rebuilding native Android and iOS SDKs to OpenTelemetry standards, confronting concrete issues: 72% of digital transactions happen on mobile… | ||
| Combating sampling bias in production How to collect and interpret performance data to drive growth | droidcon San Francisco 2023 | 2023-06-08 |
Production perf metrics suffer three concrete sampling biases: overrepresentation of heavy users (data points ≠ users), underrepresentation of churned or… | ||
| nothing matches | ||