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Teaching an agent to auto-fix bugs - Linear Now Linear writes the code, too - Linear Reviewing code in the agent era - Linear Code review should be fast - Linear Code Intelligence for Linear Agent - Linear How we hire at Linear - Linear How we use Linear Agent at Linear Post mortem on Linear security incident on March 24th, 2026 A calmer interface for a product in motion Design is more than code - Linear How our Customer Experience team works in Linear - Linear Continuous planning in Linear - Linear Designing remote work at Linear - Linear Self-driving SaaS: When software runs itself - Linear A Linear spin on Liquid Glass - Linear Best practices for designing Linear Dashboards - Linear Why we committed to a zero-bugs policy - Linear How Commure uses Dashboards to track performance and guide planning - Linear How we built Triage Intelligence - Linear Giving our team liquidity through Linear’s first tender offer - Linear How Cursor integrated with Linear for Agents - Linear Quality Wednesdays: How we trained our team to see what doesn’t work - Linear Our approach to building the Agent Interaction SDK - Linear Inside Mercury’s six-month journey building with AI agents - Linear Building our way: Announcing our Series C - Linear Why is quality so rare? - Linear Design for the AI age Building what customers need, not just what they ask for - Linear The profitable startup - Linear Why and how Scale migrated to Linear - Linear Simplifying support at scale: How Pleo uses Linear Asks - Linear How we built multi-region support for Linear How we redesigned the Linear UI (part Ⅱ) - Linear A design reset (part I) Rethinking the startup MVP: Building a competitive product | Linear Descript's internal guide for using Linear Post mortem on Linear incident from Jan 24th, 2024 | Linear Why and how we do work trials at Linear Using AI to detect similar issues Planning for unplanned work How we run projects at Linear - Linear Linear raises $35M Series B led by Accel - Linear How we think about customer experience at Linear - Linear Scaling the Linear Sync Engine - Linear Welcoming Cristina Cordova to Linear How we built Project Updates Settings are not a design failure Linear – 2021 Wrapped Fast growing startups are built on Linear Building at the early stage Linear raises $13M in Series A funding from Sequoia Capital Invisible details - Building contextual menus - Linear Practices for Building — Linear is now open for all Startups, Write Changelogs Linear’s Next Chapter: Announcing our $4.2M Seed Round
Output isn’t design - Linear
Karri Saarinen · 2026-04-17 · via Linear Blog

Design keeps being misunderstood in our industry. New tools keep promising to generate interfaces faster, move words to product instantly, or collapse design directly into code. The assumption behind them is clear: that design is the act of producing.

That is the misunderstanding. The hard part of design is rarely generating the form. It is understanding the problem well enough to know what and how something should exist at all. There is use and place for these tools, but tools are not the design process. Christopher Alexander came closer than anyone to naming this clearly. In Notes on the Synthesis of Form, he describes design as the search for a good fit between a form and its context. Context, in his sense, is not a background condition. It is the full set of forces that make a problem what it is: human needs, technical constraints, conflicting requirements, habits, edge cases, and relationships that are easy to miss until you spend time with them. Bad design appears where those forces remain unresolved. Good design appears where those misfits have been worked through carefully.

That distinction matters even more now because of how AI encourages you to work. They generate plausible outputs quickly, but they do not necessarily help you understand the underlying problem. In practice, they often do the opposite. They generate outputs, instead first trying to shape the problem or the form to the real conditions of the problem.

You can already see the result in products that look polished, ambitious, and impressive at first glance, but begin to unravel the moment you actually use them. They feel brittle, poorly integrated, and full of decisions that were never fully worked through. The form is there. The fit is not.

That is also why I still prefer designing visually over prompting. Working visually keeps me close to the problem and is slow enough gives me time to think while I work. Moving things around, testing relationships, and refining structure is not separate from the thinking. It is part of how clarity emerges.

There is something cathartic about that process, in the same way writing can be. Writing helps clarify thought because the act itself forces you to organize it. Asking AI to write for you can produce text, but it usually does not rearrange your thinking. Design works the same way for me. The value is not only in the output. It is in the gradual understanding that comes through doing the work.

AI can still be useful. It can help prototype, explore, and surprise you. But that is different from design. Design still requires judgment, conversation, tension, and time.

The risk is mistaking generated form for solved problems.

The core design is still about understanding, not output.