Treat LLM apps like scalable systems, not prompt hacks. Budgets, observability, caching, model tiering, and token discipline deliver predictable cost and latency.
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.
Demos wow, but launches fail without production-grade process and clear ownership. Here’s how to make AI demos production-ready and ship reliably without fire drills.
Treat AI as leverage: offload coding to AI so you can focus on architecture, integration, and business outcomes. Ship smarter by orchestrating systems, not grinding boilerplate.
Engineers can turn messy systems into assets by hunting leverage points in complex systems—using rules, timing, and exceptions to unlock asymmetric results. This post shows how to run a 30-day experiment for a real win.
Ratings compress nuance. Learn how to read mid-tier reasoning, map trade-offs to your priorities, and choose tools that truly fit—across coffee gear to LLMs.
Stop equating price and stars with better outcomes. This piece applies satisficing vs optimizing to tools, models, and trips so fit, slack, and lower stakes create more joy.
Engineers judge competence by your scaffolding, not your story. Learn how to build technical credibility with engineers by making assumptions, constraints, interfaces, and risks visible first.
Outages shouldn’t freeze your work. Learn how to reduce dependency on AI tools by keeping human baselines, portable prompts, and clear fallback paths so momentum endures.
Effective reviews prioritize impact over volume. This code review prioritization framework uses four lenses to decide what to block now, what to defer, and keep momentum.
