Findyet
Hundreds of thousands of jobs, one search. Listings come straight from company career pages, and your CV decides which ones rise to the top.
#01 The product
While job hunting, listings were scattered across dozens of sites, the same posting kept showing up again and again, and most AI job tools came with steep subscriptions. Findyet started as the tool I wanted for my own search, and I now use it to manage my own applications.
It collects public listings straight from company career pages, removes duplicates, and makes them searchable in seconds. Search can be narrowed by role, experience, location, salary, language, and relocation. After a CV is added, recommendations are ranked against the skills, seniority, and preferences on the profile, and each match shows why it fits and what is missing.
The web app is live with an English and Turkish interface. An iOS app is coming soon, and more AI features, including auto-apply, are in progress.
#02 Engineering
Findyet is built on an open-source project that collects public listings from company career boards. On top of it I added new sources, a Turkish interface, recommendations, and application flows, and I run it on my own infrastructure.
The backend is Go over PostgreSQL with pgvector, with Meilisearch for full-text search and Redis for caching. The web frontend is Svelte, and the iOS app is built with Expo. An MCP server exposes job search to AI agents.
It was also a deliberate step outside my usual stack: Go and Svelte instead of PHP, Node.js, and React.
$ ls ./screenshots
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