Over the past few months, I’ve accidentally discovered a new rhythm when coding with AI—and it has reshaped my approach significantly. It wasn’t something I planned or found in a manual. Instead, it emerged naturally through my experiments as I kept noticing consistent patterns whenever I used AI models to build new features. What started as casual exploration has evolved into a trusted process: vibe, vibe again, and then build. Each step plays a distinct role, and together they’ve transformed how I move from a rough idea to functional software.

I first noticed this pattern while developing new features for Gemini Scribe, my Obsidian plugin. I was exploring ways to visualize the file context tree for an upcoming update. Out of curiosity, I gave Cursor an open brief—virtually no guidance from me at all. I simply wanted to see how the model would respond when left entirely to its own devices. I wasn’t disappointed. The model produced a surprisingly creative user interface and intriguing visualization approaches. The first visualization was a modal dialogue showing all files in the tree with a simple hierarchy. It wasn’t ready to ship, but I vividly remember feeling genuine excitement at the unexpected creativity the model demonstrated. The wiring was messy, and there were integration gaps, but it sparked ideas I wouldn’t have reached on my own.

Encouraged by this, I initiated a second round—this time with more structure. I took insights from the initial attempt and guided the model with clearer prompts and a deliberate breakdown of the problem. Again, the model delivered: this time, a new panel on the right-hand side that displayed the hierarchy and allowed users to click directly to any note included in the file context. This feature was genuinely intriguing, closely aligning with the functional design I envisioned. Between these two experiments, I gathered valuable insights on shaping the feature, making it more useful, and improving my future interactions with the model.

These experiences have crystalized into the three phases of my workflow:

• Max vibe: Completely open-ended exploration to find creative possibilities.
• Refined vibe: Targeted experimentation guided by learnings from the first round.
• Build: Structured, focused development leveraging accumulated insights.

The first step—”max vibe and throw away”—is about unleashing the model’s creativity with maximum freedom. No constraints, no polish, just pure experimentation. It’s a discovery phase, surfacing both clever ideas and beautiful disasters. I spend roughly an hour here, take notes, then discard the output entirely. This early output is for exploration, not production.

Next comes “vibe with more detail and throw away again.” Equipped with insights from the initial exploration, I return to the model with a detailed plan, breaking the project into smaller, clearer steps. It’s still exploratory but more refined. This output remains disposable, maintaining fluidity in my thinking and preventing premature attachment to early drafts.

Only after these two rounds do I transition into production mode. At this point, experimentation gives way to deliberate building. Using my notes, I craft precise prompts and break the project into clear, manageable tasks. By now, the route forward is clear and defined. The resulting code, refined and polished, makes it to production, enriched by earlier explorations.

Interestingly, the realization of this workflow was born out of initial frustration. The first time I tried solely prompting for a specific feature set, my codebase became so tangled and problematic that I gave up on fixing it and threw it away entirely. That sense of frustration was pivotal—it highlighted how valuable it is to assume the first two tries would be disposable experiments rather than final products.

Stepping back, this workflow feels more like a structured series of experiments rather than a partnership or conversation. While I appreciate the creative input from AI models, I don’t yet see this approach as the true AI coding partner I envision. Instead, these models currently serve as tools that help me explore possibilities, challenge assumptions, and provide fresh perspectives. I discard many branches along the way, but the journey itself remains immensely valuable.

AI isn’t just writing code—it’s changing how I approach problems and explore solutions. The journey has become as valuable as the destination.

If you’ve been experimenting with AI in your projects, I’d love to hear about your rhythm and discoveries. Have you found your own version of “vibe and build”? Drop me a note—I’d love to learn how others navigate this fascinating new landscape.