Notes from building AI systems in production — agents, retrieval, and the engineering around them.
Demo days hide regressions. Here's the golden-set harness, score thresholds, and release gate I require before an agent touches production traffic.
Buyers searching conversational AI for customer service usually get a chatbot demo. Here's the channel router, CRM grounding, tool contracts, and escalation path I actually ship — and when a rule bot is enough.
The real architecture for a customer support AI agent — LangGraph, RAG, safe tool-calling, and the production problems most guides leave out.
Production agents should dry-run every write against a shadow executor before they touch live state. Here's the mode router, diff gate, and promote path I use.
Production AI agents fail less when every tool call goes through a contract layer instead of another prompt rewrite. Here's the schema, allowlist, and budget system I use.
A demo that answers three cherry-picked questions is not an eval. Here's the small golden-set harness I use before I trust a retrieval stack.
I spent a weekend on Huddle, a meeting copilot on Gemini 3.5 Transcribe. The interesting part was not the happy path. It was three things the API will not do at the same time.
Not every automation needs an LLM. Here's when a rules engine, a query, or a script is the better call — and when an agent actually earns its complexity.
I built a pose-conditioned diffusion model from scratch in PyTorch — feed it a skeleton, get a 64×64 game sprite in that pose. Here's how the conditioning and training work.
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A Claude Code-powered multi-agent pipeline that analyzes a job, scores legitimacy and fit, retrieves evidence from your past projects, drafts a proposal, and self-critiques it — all before you read a single word.
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