Self-Hosted Brain API: Deployment, Git Review, CLI, and QA Checklist
A production-oriented plan for self-hosting a Brain API with containers, gateways, model runtimes, queues, observability, CLI operations, Git review, and release QA.
The Brain API Field Guide
Brain API architecture, implementation, and operations.
Ten original field guides for AI agents, model routing, durable memory, brain-computer interfaces, multimodal systems, observability, security, mobile AI, and self-hosted deployment.
All articles
Design the complete cognitive loop
Every guide includes a practical release checklist, internal links to adjacent topics, and one contextual source from an authoritative organization or official documentation.
On-Device Brain APIs for iOS, Android, and Mac Applications
An architecture guide to local AI inference, mobile and desktop model packaging, hybrid routing, privacy, updates, and test matrices for iOS, Android, and Mac apps.
Secure Prompt and Tool Pipelines: Threat Modeling a Brain API
A practical threat model for prompt injection, tool authorization, data exfiltration, secrets, sandboxing, output handling, and red-team tests in a Brain API.
Observability for Agentic Systems: Logs, Traces, Metrics, and Evaluations
A field guide to Brain API observability with correlated logs, traces, metrics, evaluation results, privacy controls, and incident-ready run records.
Multimodal Brain APIs for Video, AR, and VR Experiences
How to design a multimodal Brain API that aligns video, audio, spatial context, gaze, gestures, and model outputs for responsive AR and VR applications.
Brain-Computer Interface APIs: From Neural Signals to Safer Applications
A non-clinical architecture guide to neural-signal ingestion, feature pipelines, BCI API boundaries, consent, latency, simulation, and safety-oriented application design.
Building Durable AI Memory Without Creating a Vector-Database Mess
A clear architecture for working, session, semantic, and durable memory with provenance, retention, retrieval controls, and database choices that remain understandable.
LLM Model Routing: Choosing the Right AI Model for Every Request
A practical guide to routing Brain API requests by capability, latency, cost, context size, provider availability, and risk without coupling clients to one model vendor.
Agentic Brain API Design: Tools, Memory, Handoffs, and Human Approval
How to design an agentic Brain API with bounded tools, explicit state, specialist handoffs, approval gates, and evaluations that reflect real workflow outcomes.
What Is a Brain API? A Practical Architecture for Cognitive Software
A developer-focused explanation of the Brain API concept, its layers, request contracts, memory boundaries, safety controls, and a practical path from prototype to production.
New to the topic?
Start with the Brain API reference architecture.
Learn the six layers, request and response contracts, memory boundaries, safety controls, and prototype sequence.