Replacing unreliable “vibe coding” with a rigorous automated evaluation loop using curated datasets, Claude judge agents, and metric tracking for production AI agents.
Many MVPs get too big because teams treat several user-facing systems and vendor-dependent workflows as one app instead of planning one complete path first.
Feature flags help teams move fast, but when they’re not cleaned up, they quietly add extra code, slow down performance, and make applications harder to maintain.
Platform engineering helps DevOps teams scale with golden paths, DevEx metrics, automation, and AI guardrails that reduce friction and improve delivery.
Most meetings waste engineering time, increase latency, and break focus. The 7 Pillars of Meeting Design help teams create efficient, outcome-driven decisions.
Distributed AI systems fail faster than humans can respond, making traditional response insufficient. Self-healing systems use telemetry and automation to recover early.
Stop "talking" to LLMs and start engineering context flows. The shift from chatbot to system component requires moving from monolithic prompts to modular agentic skills.
Egress — not compute — drives surprise cloud costs. Fix it by designing for data locality, using compression/caching wisely, and actively monitoring data flows.
Autonomous agents fail by persisting: they retry, replan, and chain tools, increasing risk, cost, and potential blast radius without strict safety controls.