Exploring the intersection of AI, automation, and indie development.
Most multi-agent setups fail because the orchestration layer consumes more tokens than the actual engineering work. Here is how deleting 17,200 redundant files and shrinking state down to three focused Markdown documents cut baseline prompt overhead by 85%.
Manual job searching is a brute-force loop with terrible returns. So I stopped searching and built a system that does it instead: autonomous scrapers feeding an LLM that scores every posting against my profile and tells me what skills I'm missing. The output isn't a job list. It's a map of the market.
The moment your agentic AI system starts repeating mistakes or forgetting decisions isn't a model failure — it's an architecture failure. Here's what context rot and token bloat actually are, why they compound as you scale, and how a non-technical Coordinator can design against them from day one.
Bield is a free, fully offline Android app for mental wellness through sound therapy. This is how an AI orchestration system — not a single chat session — shipped it in native Kotlin without me writing a line of implementation code.
Why giving up the 'learn to code' path and focusing on AI orchestration architecture is a more sustainable long-term strategy for builders with ideas but without a traditional CS background.
