Build Software. Build Users

Why It Matters

Understanding users before coding reduces hidden usability failures and accelerates delivery of truly valuable software.

Key Takeaways

• Traditional testing misses real user context
• LLM agents simulate user behavior for software design
• Vibe coding starts with user personas, then builds software
• Iterative loop refines both user agents and application
• Simplicity emerges from deep user understanding

Pulse Analysis

The software quality debate has long been dominated by test coverage metrics, while exhaustive unit, integration, and end‑to‑end suites catch regressions, they rarely guarantee that an application feels intuitive to its actual users. Modern large language models, trained on billions of interactions, can approximate human expectations far better than static test matrices. By leveraging LLM‑driven user agents, teams can surface hidden workflow gaps—like the bar‑explosion scenario—before they become costly failures. Consequently, organizations are rethinking quality metrics beyond line coverage.

Vibe coding formalizes this insight into a concrete workflow. Developers first define a /users directory, populate markdown profiles, happy‑path flows, and edge‑case scripts for each target segment. These artifacts are then fed to an LLM such as Claude Code or Copilot, which materializes a simulated user agent capable of navigating the UI, invoking APIs, and reporting friction points. The same LLM can concurrently generate the application code that satisfies the documented flows, creating a tight feedback loop where user intent and software implementation evolve together. This iterative loop also surfaces security edge cases early in development.

The implications for product teams are profound. Shifting the primary requirement source from abstract tickets to living user agents reduces reliance on brittle test suites and accelerates discovery of usability, accessibility, and security issues. Companies that adopt vibe coding can expect faster time‑to‑market, lower maintenance overhead, and software that aligns more closely with real‑world behavior. As LLM fidelity improves, the approach may replace traditional personas and user stories, turning every sprint into a user‑centric experiment rather than a code‑centric checklist.