For complex systems with diverse use cases (like EDI), building a comprehensive UI upfront is a failure path because you can't possibly anticipate all needs. The better approach is to first build a robust set of developer-focused APIs—like Lego blocks—that handle core functions. This allows you (and customers) to later assemble solutions without being trapped by premature UI decisions.
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For AI products, the quality of the model's response is paramount. Before building a full feature (MVP), first validate that you can achieve a 'Minimum Viable Output' (MVO). If the core AI output isn't reliable and desirable, don't waste time productizing the feature around it.
To serve both solo developers and large enterprises, GitHub focuses on creating horizontal "primitives" and APIs first. This foundational layer allows different user types to build their own specific workflows on top, avoiding the trap of creating a one-size-fits-none user experience.
Inspired by architect Christopher Alexander, a designer's role shifts from building the final "house" to creating the "pattern language." This means designing a system of reusable patterns and principles that empowers users to construct their own solutions tailored to their unique needs.
Instead of large, multi-year software rollouts, organizations should break down business objectives (e.g., shifting revenue to digital) into functional needs. This enables a modular, agile approach where technology solves specific problems for individual teams, delivering benefits in weeks, not years.
Instead of providing a vague functional description, feed prototyping AIs a detailed JSON data model first. This separates data from UI generation, forcing the AI to build a more realistic and higher-quality experience around concrete data, avoiding ambiguity and poor assumptions.
CNX discovered that its target users—backend RPG programmers—struggled with or were uninterested in modern UI/UX design. This realization led them to build a low-code tool to provide guardrails and ensure consistent, modern front-ends without requiring front-end expertise.
The founders avoid creating a rigid, atomized design system because the product is still iterating too quickly. They accept a "messy" component library and technical debt as a trade-off for speed. Formalizing a design system only makes sense once the product's UI has stabilized.
Instead of building a single-purpose application (first-order thinking), successful AI product strategy involves creating platforms that enable users to build their own solutions (second-order thinking). This approach targets a much larger opportunity by empowering users to create custom workflows.
To avoid the customization vs. scalability trap, SaaS companies should build a flexible, standard product that users never outgrow, like Lego or Notion. The only areas for customization should be at the edges: building any data source connector (ingestion) or data destination (egress) a client needs.
