I work with frontier models-ChatGPT, Claude, Perplexity, Grok-integrating them into production systems where they solve actual problems. Building systems that are reliable and useful is the goal, while keeping them scalable and secure.
For work, we use frontier models as tools-the same way we'd use any API. The skill isn't knowing what a model can do in a chat window. It's understanding how to make them reliable, cost-effective, and useful inside infrastructure that has to work.
Integrated ChatGPT, Claude, and other frontier models into production systems using OpenRouter and direct API access. Handles model selection, fallback logic, and cost optimization.
Rather than parsing freeform text, use structured prompts and JSON schema to guarantee consistent, predictable output. Essential for infrastructure that depends on it.
Models integrated as part of larger data pipelines: pulling context from databases, APIs, and external services, then processing through frontier models with Supabase, webhooks, and serverless functions.
Practical system that combines Bitcoin node data, news sources, and structured databases with LLM-powered analysis. Tests integration patterns, cost management, and reliability in a live environment.
Not a portfolio of prompts. Not a collection of ChatGPT screenshots. It's experience making frontier models work as reliable building blocks inside infrastructure-where correctness, latency, and cost matter.
Treat frontier models like any other external system: APIs, databases, caches. Plan for failure, cost, and latency. Design around constraints.
Expect JSON, not poetry. Use schemas. Validate. Retry. Systems that depend on AI need predictable input and output.
Know which model to use for which task. Route based on complexity. Cache when you can. Every API call has a cost and a latency budget.
Frontier models solve specific problems well. They're not magic. Integrate them where they add measurable value, not everywhere.
I work best with companies that have a long-term vision on Bitcoin and AI.