Balance AI and Manual Research: Orchestrating Reliable Debugging
Learn how to balance AI and manual research with an orchestration loop that mixes speed, depth, and judgment to solve ambiguous, niche engineering problems.
Accelerate Software Development with AI Without Breaking Quality
Compress feedback loops and ship small, context-rich changes so AI compounds prior decisions. Elevate architecture, testing, and review to move fast without losing production quality.
Align AI productivity with outcomes to build trusted value
AI’s 10x leverage only compounds when you align AI productivity with outcomes. This piece shows how to redesign metrics, incentives, and workflows to build durable trust.
How to Evaluate AI Content: An Impact-First Rubric for Engineers
Judge writing by impact, not authorship. This piece shows how to evaluate AI content using four quick filters—relevance, originality, quality, and discoverability—to focus on what moves engineering work forward.
Outcome-Based Review Criteria: Clarity, Correctness, and User Impact
Shift reviews from policing AI to measuring value. Use outcome-based review criteria to judge code and content by clarity, correctness, user impact, and risk.
Build production-ready AI foundations that last
AI only shines on solid engineering. This post shows how to build production-ready AI foundations with data, ops, security, and UX baked into every sprint.
Improve Decision Quality with AI: Why Slower Starts Speed Up Delivery
AI can slow the start yet speed the sprint. Learn how to improve decision quality with AI by front-loading context, stress-testing plans, and cutting rework.
Design resilient LLM workflows: from fragile chains to fault-tolerant systems
Stop duct-taping prompts and start engineering. Learn how to design resilient LLM workflows with modular steps, observability, quality gates, and safe recovery.
Scaling LLM Applications the Engineering Way: Budgets, Observability, and Architecture Over Prompt Hacks
Treat LLM apps like scalable systems, not prompt hacks. Budgets, observability, caching, model tiering, and token discipline deliver predictable cost and latency.
Manufactured Reps: How to practice high-stakes engineering
You rarely get to practice the moments that matter. This piece shows how to practice high-stakes engineering by shadowing, simulation, and rehearsal so you’re ready when pressure spikes.