From the Team
Deep dives on how we build — AI architecture, mapping, and outdoor technology.
Piling 15+ complex tools into a single LLM was causing context confusion. Here's why we bypassed top-level routing, embraced the Sub-Agent-as-a-Tool pattern, and built a generic multi-agent backend that scales.
A single always-on Cloud Run instance was costing us $160/month for compute headroom we only needed during LLM tool calls. Here's how we reshaped the system around Pub/Sub and a small dedicated worker — and what the latency numbers actually looked like.
One User. Seven Days. 1,074 AI Images.
How we built a Mapbox-compatible map compositor where the LLM orchestrates layers without ever touching GeoJSON — keeping context windows small and rendering deterministic.
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