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Anam Hira

Anam Hira

CTO & Co‑founder at Revyl; ex‑Uber ML engineer; UBC CS & Statistics

anam
San Francisco, California
Joined October 2024
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Summary

Machine‑learning + mobile testing expert: Anam built DragonCrawl while at Uber—an LLM‑based mobile testing system that adapts to UI changes and reportedly blocked multiple high‑priority bugs and delivered large cost savings—then used that experience to co‑found Revyl, an AI‑native platform that links resilient E2E tests to telemetry for proactive observability. uber+2
Startup CTO & operator: As Revyl's co‑founder and CTO, Anam leads the technical direction (agentic LLMs, test‑to‑telemetry linking, mobile adapters) for a YC‑backed company that raised a pre‑seed round in 2024 and is operating out of San Francisco while maintaining Canadian/UBC roots. revyl+2
Active open‑source and engineering contributor: Maintains public GitHub repos and contributed to Revyl's Mobileadapt/CogniSim tooling that encodes accessibility trees and UI structure for LLM agents, indicating strengths in applied ML, mobile automation, and engineering for production observability. github+1
Research & learning orientation: Public profiles and personal site position Anam as focused on 'learning how to learn,' with interests in reward structures, memory for agents, and agentic LLMs — a mix of applied research and product engineering. anamhira+1

Work

Education

Projects

Writing

Proactive Observability in Testing with Anam Hira (TestGuild podcast)

March 1, 2025

Interview/podcast where Anam discusses proactive observability, applying LLMs to end‑to‑end testing, and Revyl's approach connecting tests with telemetry.

Favicon imagetestguild.com

Canadian‑founded, YC‑backed Revyl closes $1.5 million CAD to help companies catch bugs with AI

October 1, 2024

Press coverage describing Revyl's founding, funding, Canadian/UBC roots, and background on the DragonCrawl work at Uber that inspired the startup.

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DragonCrawl: Generative AI for High‑Quality Mobile Testing

April 1, 2024

Uber engineering blog post describing DragonCrawl: motivations, architecture, modeling and evaluation of an LLM‑based mobile testing system.

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Hobbies

Agentic LLMs, learning algorithms, and tools for agentic/autonomous learning anamhira

Public coding and open‑source projects (multiple repos on GitHub) github