Applying future-proofing principles to CTI's actual architecture — what to revisit on every model release, where the architecture should evolve, what's missing, and a concrete review cadence. The companion piece that turns the general principle into a working checklist for one production AI system.
What it actually means to 'not build to current model limitations' — the difference between stable constraints worth designing around and temporary weaknesses that age into technical debt. With concrete examples of patterns that survive model upgrades and patterns that don't.
What 'substrate' actually means in your field — a worked walkthrough across accounting, law, environmental consulting, logistics, residential construction, and wealth management. Designed so practitioners in any of these fields can finish reading already drafting their own.
How Orbital's three phases — Map, Architect, Build — were applied to CTI specifically. The case study from the inside: what got mapped, what got architected, what got built, and why the order matters.
How Orbital built an AI coaching platform whose recommendations are grounded in the athlete's own power data, fitness trajectory, and past conversations — not generic advice. The public proof of concept for the approach Orbital takes with every client.
How CTI's Performance Management Chart implementation — Critical Power, Normalized Power, TSS, CTL, ATL, TSB, plus subjective Feeling and RPE — gives the AI coach a quantitative model of fitness, fatigue, and form.
How CTI exposes its rides, fitness state, profile memory, workout generator, and coach over the Model Context Protocol — including the OAuth 2.1 + PAT auth model, the tool/resource/prompt surface, and the security plumbing that keeps it scoped per user.
How CTI's prompt versioning, skill system, admin trace review, Evalite evals, and Langfuse telemetry form a reinforcing loop that makes the AI coaching layer improve with every user interaction.
A deep dive into the intent routing, layered prompts, three-tier memory system, and hybrid search that power CTI's AI coaching layer.
Transform indoor cycling training files into an interactive, cinematic experience. Not just charts — a way to discover insights about routes, locations, and performance through exploration.
Ship useful AI products in weeks, not months. Learn the agent lab architecture, how to identify net-new work opportunities, make your data AI-ready, and execute a 4-8 week development cycle from insight to production. Focuses on outcomes over capabilities, deep integration over generic tools, and rapid iteration with real users.
Practical UX patterns for AI systems that balance autonomy and control. From pure suggestion to observation mode, learn how to design AI interfaces users actually trust—with React examples, confidence indicators, reasoning transparency, and reversible actions.
Why generic AI chatbots fail and how to build bespoke AI applications that solve real problems. Learn the three critical components of successful AI integration - deep system integration, human-in-loop UX patterns, and solving one specific problem extremely well.
Exploring three approaches to LLM state management for TypeScript applications — whole state serialization, persistent memory, or selective context. A Frontend Maximalist perspective on LLM integration architecture and the tradeoffs between simplicity, token costs, and code complexity.