In the Yogiyo PE pilot, code generation has sped up, but verification and processes remain bottlenecks.
The delivery marker ad feature launched in June 2026 exceeded exposure and click targets by over 200%.
In environments where AI writes code, designing verification methods that allow humans to be confident in 'deployability' has emerged as a core role.
Yogiyo validated the Product Engineer model, where a frontend developer leveraged AI coding agents to fulfill PO, BE, and FE roles.
Yogiyo is piloting a Product Engineer model that dissolves role boundaries in live services to address organizational productivity stagnation despite the adoption of AI coding tools.
By automating crash and app review analysis with AI and n8n, on-call time was reduced.
Yogiyo cut UX research lead time by 25% using ChatGPT, Gemini Pro, and NotebookLM.
Yogiyo was integrated into ChatGPT as an order-oriented UI using MCP and Apps SDK widgets.
YDS v2 reduced bundle size through ESM, sideEffects, and maintaining module boundaries, and went beyond Next.js limitations to directly address legacy browsers.
500ms Pod latency effectively paralyzed the service, while 250ms only increased latency but held up.
A TF formed a month earlier completed the 2025 R&D Center Year End Party as a people-centered year-end event.
Using Map Projection to arrange overlapping markers in a circular layout improved map explorability.
Yogiyo built a DDL review bot with Amazon Bedrock, improving review speed by about 20%.
The YDS components were rebuilt with Radix Primitives and Panda CSS to achieve both accessibility and flexibility.
Using Live Activities and APNs, order status is now reflected in real time even outside the app.
Yogiyo summarized the issues it encountered while starting chaos experiments on membership-yo with AWS FIS.
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