Archive Note
We Built LIO in 72 Hours. Here's How.
This build note described a fast prototype cycle for testing company visibility across major AI systems.
The Build
The project set out to test whether a product or company appeared in model-generated recommendations. The system used multiple AI tools for prompt refinement, front-end generation, debugging, and orchestration.
The important moment was not any single tool. It was the cross-agent workflow: one system encountered a problem, another diagnosed it, and the implementation loop kept moving.
The original build targeted visibility across OpenAI, Claude, Gemini, and Perplexity. The practical question was whether a company could see where it did and did not appear in AI-mediated discovery.
The stack mixed human direction with AI-assisted build tools: prompt refinement, a front-end workflow canvas, Supabase-backed functionality, GitHub integration, and agentic debugging.
One memorable failure involved a Perplexity API error caused by an invalid model name. The point of the story was that the system recovered through cross-agent diagnosis and implementation rather than waiting for a conventional escalation path.
The Lesson
The note captured a broader operating shift. Work that might once have required a full team, a sprint plan, and multiple handoffs could be explored quickly when the human supplied clear intent and agents handled bounded execution tasks.
That does not remove the need for judgment. It increases the importance of judgment because the loop can now move much faster.
The build note also made a personal operating point: a non-programmer could participate directly in product creation when agents handled enough of the scaffolding and implementation path.
That was not a claim that expertise no longer matters. It was a claim that the bottleneck moved. Clear intent, inspection, taste, and correction became more valuable because execution could happen quickly.
Why It Still Matters
This note captured the archive's practical energy: not just theorizing about agents, but using them to build and learn in public.
The current site no longer sends readers to the old LIO surface, but the build story remains useful as an example of compressed learning loops.