AI Implementation
AI Agent Architectures: Patterns and Trade-offs
A practical map of AI agent architecture patterns — workflows, autonomous agents, multi-agent frameworks, memory, and error handling — and the trade-offs behind each…
Engineering
AI Implementation
AI Agent Observability: Tracing and standards
AI agent observability explained: OpenTelemetry GenAI tracing, MCP instrumentation, platform choices, and the eval gates that catch quality regressions before they ship.
Blockchain
Tokenization of Real-World Assets: A Technical and Business Guide
Real-world asset tokenization explained for product and engineering leaders: how it works, token standards, compliance architecture, and how to ship it to production.
Product Engineering
Smart Contracts: What They Are and How to Build Them for Your Business
Imagine a business agreement that executes itself: no intermediaries, no paperwork delays, no risk of someone not holding up their end of the deal.…
Product Engineering
From Senior to Staff Engineer: The Skill That Changes Everything
Most engineers assume the path to staff is paved with better code. Better architecture. Deeper mastery of patterns and systems. For a long time,…
AI Implementation
Harness Engineering: Why “Done” Isn’t the Agent Saying So
In November 2025, Justin Young at Anthropic published a post on what they had learned running long-running coding agents. One observation in that post…
AI Implementation
Skills vs. Subagents: The Layer Most Teams Confuse
I had a conversation with a tech lead at a client last quarter that crystallized why this article needed to exist. They had spent…
AI Implementation
Spec-Driven Development: How to Capture Intent Before You Burn Tokens
In February 2025, Andrej Karpathy tweeted a phrase that stuck: “There’s a new kind of coding I call vibe coding, where you fully give…
AI Implementation
Plan Mode is the Deal-Breaker: Why Direct Mode Burns Tokens
A few weeks ago I sat with one of our senior engineers while he debugged a project that had gone sideways. The team was…