Rivu Chakraborty

20 NOVEMBER 2026

BENGALURU

( SPEAKER )

Google Developer Expert, Android | Mobile & Desktop Apps

Sarvam AI

Rivu Chakraborty is an Android GDE (Google Developer Expert) and India's first Kotlin GDE, a community person, & one of the early adopters of Kotlin. With overall 13+ years of experience, he is currently working at Sarvam AI, developing their native Mobile and Desktop apps.
He's also building FretPractice and Dhun by FretPractice, AI native Music practice and production platform.

He has previously worked with the biggest organisations from India and South-East-Asia, his last stint was working at JioHotstar / JioCinema (India's biggest OTT+Streaming platform) as Mobile Architect / Principal Engineer, he also previously worked at Meesho (a leading unicorn E-Commerce Startup from India), Gojek (one of South-East Asia's biggest startups), Paytm (India's biggest Fintech startup), and Byju's (one of India's biggest Edtech startup).

He has contributed to multiple Kotlin and Android Development books including authoring Reactive Programming in Kotlin, co-authoring Functional Kotlin the first-ever book to help Kotlin developers learn Functional Programming and use Arrow-kt in their projects, and co-authoring Hands-On Data Structures and Algorithms with Kotlin.

He has been using Kotlin since December 2015. Rivu formed KotlinKolkata User Group, the first active Kotlin User Group in India. Before he had to move out of Kolkata, he was organizing meetups and events for both KotlinKolkata and GDG Kolkata. Along with organizing community & events, he also speaks at events/conferences and local meetups.

Session

From Trace to Triage: Reading Perfetto, Then Automating It

Every team has one person who is designated and actually have to read a Perfetto trace - a queue of everyone else waiting on them. This session fixes both halves of that problem. First we walk the senior-engineer mental model live on a real trace: is the main thread running or waiting, which frame missed its 16.6 ms deadline and was it the app or SurfaceFlinger, where cold-start time actually goes, and where binder/IPC contention hides. Then we do something new , we encode that exact checklist into an AI agent that runs PerfettoSQL against a trace programmatically, ranks hotspots, and maps slice names back to the functions in your codebase. The result: you'll leave able to read a trace yourself, and able to hand that expertise to an agent so trace triage stops depending on one person. Whether you're chasing jank, slow cold starts, or ANRs, you'll have both the human method and a way to scale it across your team.