Taste & Craft: A Conversation with Tuomas Artman, CTO of Linear, and Gergely Orosz of The Pragmatic Engineer
◆Tuomas Artman · Gergely Orosz
AI Engineer Europe 2026 · 29:17
Product development software and AI agents
Linear builds product development software for teams to manage customer feedback, track issues, plan projects and review code in one shared workspace. Linear Agent uses issues, discussions and codebase context to investigate tasks and carry out implementation. Through Coding Sessions, teams can delegate an issue, follow the agent’s work and review the resulting pull request with Diffs. Loops extends this approach to recurring workflows triggered by schedules or events.
Founded in 2019, Linear is led by co-founders Karri Saarinen, CEO; Jori Lallo, CPO; and Tuomas Artman, CTO. Its local-first architecture keeps a database on each client, allowing issue updates and navigation without a network round trip. Delta sync retrieves only changes since a client’s last checkpoint. Its engineering team rebuilt this read path with turbopuffer to filter changes by permissions and subscriptions, while retaining PostgreSQL as the authoritative data store.
In 2026, the company reported more than 40,000 paying companies, annual recurring revenue exceeding $100 million, and positive cash flow. In August, Linear completed a $99 million employee tender offer at a $2.5 billion valuation, providing liquidity to current and former employees rather than raising new primary capital.
◆Tuomas Artman · Gergely Orosz
AI Engineer Europe 2026 · 29:17
Affiliations reflect their AIE appearances, not necessarily current employment.
Start with Moor's talk for concrete implementation examples: similar-issue detection, Slack-based issue creation, and the transition from OpenAI embeddings with pgvector to Cohere embeddings.
Tom MoorAI Engineer World's Fair 2025
Turn to Artman and Orosz's conversation for a discussion of Claude Code, Claude Opus 4.5, and Linear's reported bug-fix automation as examples of AI entering engineering work.
Tuomas Artman · Gergely OroszAI Engineer Europe 2026
Moor describes an architecture that gives agents identities as first-class workspace users, alongside behavioral expectations: communicate plans, clarify intent, and act as useful teammates.
Artman and Orosz discuss feature overload and quality-focused engineering habits, including weekly self-directed problem discovery, as practical concerns in AI-assisted development.
Affiliations reflect each recorded session, not necessarily current employment.