Google Photos

Google Photos Magic Editor: GenAI Under the Hood of a Billion-User App - Kelvin Ma, Google Photos

Jul 19, 2025 · 20:28

Kelvin Ma, an engineer on Google Photos' editing team, explains how the billion-user app built the Magic Editor by integrating generative AI with on-device computational photography. He traces the evolution from earlier ML features like post-capture segmentation (UNet model, 10 MB) and Magic Eraser (a system of models) to the new server-side generative AI experience, which handles tasks like relocating objects and reimagining backgrounds. Key challenges include managing model size (now hundreds of MB), client-server latency, ambiguous problem scoping (e.g., moving from 5% to 80% reliability), and trust and safety. Ma advocates using evals, reducing ambiguity through product-research collaboration, and iterating from large general models to smaller, faster ones for production. He also highlights that Google Photos serves 1.5 billion monthly active users and processes hundreds of millions of edits per month, and the editor is being rebuilt as AI-first.

Personality Driven Development: Exploring the Frontier of Agents with Attitude

Feb 17, 2025 · 18:00

Ben, from Perpetual, argues that anthropomorphizing AI agents—giving them personalities, forms, and preferences—transforms user adoption and engagement, despite significant challenges. He presents examples like Tech Lead, an artichoke recruiter, and hamster teams, showing how form factors create instant understanding and enable price anchoring (e.g., 1/20th the cost of a junior employee). Benefits include easy problem decomposition (specialized agents outperform generalized), branding, and fun. However, downsides include reinforcing stereotypes (100% of generated software engineers appear male), raised performance expectations, difficulty rebranding, distraction from core value, and stark reminders of job replacement—illustrated by a CEO introducing an IC as the one who 'knows what they're doing.' Ben highlights personality-driven development where every instance is bespoke, with prompt-driven preferences (e.g., 'I like Ivy League schools'), making debugging uniquely challenging.