The best products do one thing exceptionally well. This isn't just a nice-to-have philosophy, it's the foundation of sustainable product development. Rooted in the Unix tradition of small, sharp tools, this mindset offers a powerful alternative to the bloated, everything-and-the-kitchen-sink approach that plagues modern software.
As we move into an era of agentic AI and rapidly evolving technology, the principles behind grep, curl, and jq become more relevant than ever. These tools succeed because they focus, compose, and integrate seamlessly. Your products and AI systems should too.
The Unix way
Small, sharp tools are lightweight programs that excel at a single task. They embody Doug McIlroy's 1978 philosophy: "Write programs that do one thing and do it well." These tools share key characteristics:
- Focused scope: They solve one problem completely rather than many problems partially
- Composable design: They work together through pipes, APIs, or standard interfaces
- Clear boundaries: They avoid feature creep and unnecessary complications
Why this matters now
Products stay maintainable: When your note-taking app tries to be a calendar and task manager, everything suffers. When it focuses on fast, distraction-free writing, it excels.
Teams move faster: Small, scoped features ship quickly. You can validate assumptions and iterate without managing monolithic complexity.
Users understand value: Stripe focuses on payments, not accounting. Calendly handles scheduling, not project management. Clear value propositions win.
Systems integrate better: Products designed as focused tools integrate seamlessly through APIs and standard formats, enabling users to create custom solutions.
Agentic AI applications
This philosophy becomes crucial for AI systems:
- Specialized agents: Create focused agents that excel at specific tasks rather than generalist AIs that do everything poorly
- Composable workflows: Design agents to work together through clear interfaces, like Unix pipes for AI
- Clear failure boundaries: Narrow scope makes AI limitations predictable and manageable
How to build this way
- Define clear boundaries: Use jobs-to-be-done frameworks to identify core purpose
- Design for composition: Build with APIs and standard formats for integration
- Resist scope creep: Ask whether new features serve the core purpose
- Start minimal: Launch with the smallest viable feature that delivers value
The future belongs to products and AI systems that do one thing exceptionally well and compose beautifully with others. In a world of infinite possibilities, focus becomes your competitive advantage.
Sources: Adapted from "Small, Sharp Tools" by Brandur Leach, brandur.org, December 12, 2014. Additional insights from "The Art of Unix Programming" by Eric S. Raymond and modern Unix tool communities.
