Spiral's redesign was driven by the principle that "good writing is downstream of good thinking." Instead of just generating content, the tool focuses on helping users explore and clarify their own ideas through an interactive, question-based process, making the AI a partner in thought.
Most AI writing tools produce generic content. Spiral was rebuilt to act as a partner. It first interviews the user to understand their thoughts and taste, helping them think more deeply before generating drafts. This collaborative process avoids "slop" and leads to more authentic writing.
Unlike many AI tools that hide the model's reasoning, Spiral displays it by default. This intentional design choice frames the AI as a "writing partner," helping users understand its perspective, spot misunderstandings, and collaborate more effectively, which builds trust in the process.
The new Spiral app, with its complex UI and multiple features, was built almost entirely by one person. This was made possible by leveraging AI coding agents like Droid and Claude, which dramatically accelerates the development process from idea to a beautiful, functional product.
When building Spiral, a single large language model trying to both interview the user and write content failed due to "context rot." The solution was a multi-agent system where an "interviewer" agent hands off the full context to a separate "writer" agent, improving performance and reliability.
Using AI to code doesn't mean sacrificing craftsmanship. It shifts the craftsman's role from writing every line to being a director with a strong vision. The key is measuring the AI's output against that vision and ensuring each piece fits the larger puzzle correctly, not just functionally.
To codify a specific person's "taste" in writing, the team fed the DSPy framework a dataset of tweets with thumbs up/down ratings and explanations. DSPy then optimized a prompt that created an AI "judge" capable of evaluating new content with 76.5% accuracy against that person's preferences.
The GM of Spiral felt demotivated and his product stagnated because he didn't personally use it or believe in its vision. The breakthrough came when he pivoted to solve a problem he genuinely cared about—making AI a tool for better thinking, not just faster content production.
Earlier AI models would praise any writing given to them. A breakthrough occurred when the Spiral team found Claude 4 Opus could reliably judge writing quality, even its own. This capability enables building AI products with built-in feedback loops for self-improvement and developing taste.