Architecture
Data flow, tech stack, and architectural decisions for Flow Control.
Data flow, tech stack, and architectural decisions for Flow Control.
Storage architecture, shared context, and workspace detection for MCP Task Server.
Technical decisions, patterns, and project structure for the school platform.
Components, data flow, and external integrations for the Cal.com scheduling stack.
Components, email flow, and attack scenarios protected by the email relay.
Components, data flow, and resource requirements for the observability stack.
This section explains how the fitness application is structured, how it runs on-premises, and how the different services talk to each other. It also links to the deployment, Kubernetes, and webhook details so I can come back later and repeat the setup.
How Blaster is put together. Next.js, Phaser, PostgreSQL, migrations, and API routes.
Scoped to Stage 1 (foundation for personal coach – running data).
Production-ready setup for self-hosted fitness tracking.
Default maximum pods
Replace the virtual worker with a physical node to make the development cluster hybrid. This adds realistic CPU, memory, storage, and network performance while keeping the control plane simple and virtualised.
Install and join process for the physical node.
Validation commands and expected outputs.
Kubernetes notes for Stage 1.
This document describes the complete webhook implementation for real-time Strava activity notifications.
High-level services and how they communicate.
Short context of the stack and why I chose it. Scope: Stage 1.