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Documentation Index

Fetch the complete documentation index at: https://docs.folksbase.joselito.dev/llms.txt

Use this file to discover all available pages before exploring further.

You can’t talk about software engineering today without addressing the neural network in the room. Given how AI is fundamentally reshaping our industry, I felt it was essential to dedicate a specific section to how it influenced folksbase. For me, this isn’t just about using a trendy tool; it’s about my philosophy on where AI fits into a professional workflow and how it should—or shouldn’t—show up in a product built for humans.

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Invisible AI in Product

I have a very clear take on AI: unless your product is fundamentally an “AI tool,” the technology itself should remain invisible. It should be a silent facilitator, not the main character. In folksbase, I treat AI as a layer of high-level assistance that disappears into the background to respect the user’s time. You’ll find AI working in two specific places here, though I never explicitly label it as such:
  • Smart Column Mapping: When you upload a CSV, the system suggests which headers match our schema (like mapping “E-mail Address” to email).
  • Humanized Summaries: The notification emails sent via Resend after an import aren’t just a list of raw numbers; they are brief, human-sounding summaries of the results.
The user doesn’t need to know there’s a Large Language Model involved; they just need the system to “magically” understand their data. It’s about functionality over marketing jargon.

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My Engineering Workflow

As a developer, I don’t let AI drive the car; it’s my high-performance co-pilot. My development process for this challenge was a deliberate “pass-the-baton” between different models and my own expertise:
  • Strategic Planning (Claude Opus): I used the Claude app directly for the initial architecture and the “Master Plan”. In my experience, Opus still handles high-level, complex reasoning and long-term planning better than any other model.
  • Core Development and writing commits (Amazon Kiro): This was my “vibe coding” partner. Kiro handled the heavy lifting and micro-features, allowing me to focus on the high-level logic.
  • Code Review & Analysis (Claude Sonnet): I used Sonnet specifically through Claude Code. It has demonstrated superior performance for CLI-based tasks and code analysis, plus the tokens are more cost-effective for the volume of code being reviewed.
  • QA & Final Refinement (Amazon Kiro): The final stretch involved squashing bugs and polishing the UI to get it as close to “pixel perfect” as possible.
  • Documentation & Context Refining (Gemini): For the final documentation and the “human” side of the project, I brought in Gemini. While other models handled the raw code, Gemini acted as my specialized co-pilot for the docs. Because it understands the full context of how I write, my personal preferences, and the specific soul I wanted to give to folksbase, it was the best tool to ensure that this documentation sounds exactly like a conversation with me—balancing technical depth with my authentic personality.
In the end, while the AI wrote a significant portion of the code, the “soul” of the project—the design decisions, the UX trade-offs, and the final polish—came from somenohe who truly loves building for the web.