Kortshut
Designing a context-first operating system.
An ongoing exploration into AI-native workflows — and what computing looks like when context matters more than applications. This documents how the thinking evolves, not a finished product.
Focus areas
- Product Strategy
- AI Workflow Design
- macOS Product Design
- Information Architecture
- Human–AI Interaction
- Design Systems
- AI-Assisted Development
The shift
LLMs didn't eliminate work — they shifted where work happens.
The bottleneck was no longer writing. It became assembling enough context for AI to produce meaningful results.
Every design review, engineering task, research session, or strategy doc followed the same pattern: capture information, screenshot, copy text, switch apps, reconstruct context, prompt an AI, return to work — and repeat.
The models improved rapidly. The workflow did not. Kortshut began as an exploration into reducing that friction.
Observation
Computers organize work around files and applications. People organize work around context.
A designer isn't thinking about Figma — they're thinking about redesigning onboarding. A developer isn't thinking about Finder — they're thinking about solving a bug using notes, screenshots, logs, and previous discussions.
Current operating systems understand where files live. They don't understand why they belong together. That observation became the foundation for every subsequent product decision.
Early hypothesis
“If we make AI easier to access, people will work faster.”
Early concepts focused on faster prompting, keyboard shortcuts, AI launchers, and clipboard improvements. They improved speed — but failed to address the larger issue.
The real bottleneck wasn't opening AI. It was rebuilding context before every conversation.
Research & continuous discovery
Kortshut evolved through continuous observation and dogfooding — not a fixed philosophy.
The same patterns kept surfacing: screenshots became temporary memory; clipboard contents disappeared despite remaining valuable; users repeatedly rebuilt the same prompt context; AI quality depended more on context than prompt wording; and keyboard shortcuts only mattered when attached to repeatable workflows.
That shifted the product away from “AI utilities” toward workflow orchestration.
Why context, not prompts
Early versions emphasized prompts. Research suggested users cared far less about prompts than outcomes. The better question became: how quickly can someone gather everything AI needs without breaking their flow? This principle continues to shape the roadmap.
Exploration 01 — Persistent context
What if copied information wasn't disposable, but persistent working memory?
Instead of viewing the clipboard as transient, we explored it as persistent working memory. Historical clipboard items, screenshots, and captured references became reusable pieces of context rather than temporary artifacts.
The goal wasn't to remember what had been copied. It was to recover thinking.
Exploration 02 — Workflow shortcuts
Keyboard shortcuts were never the destination — they became vehicles for repeatable workflows.
The design challenge shifted from reducing clicks to preserving cognitive flow.
The workflow, compressed
We stopped designing around prompts.
- Initial belief
- Prompts were the primary unit of interaction.
- Observation
- Users repeated workflows more consistently than prompts.
- Current direction
- Design around reusable workflows that naturally contain prompting.
The clipboard became working memory.
- The shift
- Copied content stopped being transient and became searchable, referenceable, and context-aware.
- Status
- This remains an active area of product exploration.
What we chose not to build
Kortshut intentionally avoids becoming another chatbot, another launcher, another note-taking app, or another prompt marketplace. Those categories already exist. The opportunity lies in connecting them through context.
Designed while adopting AI
Kortshut was designed while actively adopting AI throughout the process. Claude Code, Cursor, Codex, Google Stitch, and Figma AI accelerated prototyping, implementation, and experimentation — shortening the feedback loop between hypothesis and validation, rather than replacing design thinking.
How my thinking changed
Open questions
- Should context be assembled manually, or inferred automatically?
- Is the clipboard the right primitive — or is context itself the primitive?
- When does automation become invisible enough to feel natural?
- How should AI balance initiative with user control?
An active exploration
Kortshut remains an active exploration into that problem. Rather than documenting a finished product, this case study documents an evolving way of thinking about human–computer interaction in an AI-native world.
The product remains alive. The thinking continues.