Performance optimization

By Greg Nowak, 30 March, 2017

Optimizing Drupal: A Practical Performance Checklist

A practical Drupal performance checklist for faster pages, healthier caching, stable Core Web Vitals, and an optimization backlog your team can ship.

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Your AI model has an expiry date: build the migration lane now

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GitHub’s repo-level Copilot metrics show where AI is active, but not whether it adds value. This scorecard connects usage with delivery, quality, and cost.

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Move delay-tolerant AI work into dependable batch queues to cut processing costs without compromising quality, data controls, or urgent workflows.

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Shorter TLS lifetimes leave less room for manual handoffs and faulty deploy hooks. Build a renewal path that protects service availability.

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A timed-out AI action may already have succeeded. Stable keys, durable ledgers, queues and stored results prevent a routine retry from duplicating real work.

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A practical framework for measuring AI-search visibility with fixed prompts, repeated tests, separate metrics, retained evidence, and honest reporting.

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New AI admin APIs can automate access and spend controls, but reliable governance still starts with authoritative directory data and clear ownership.

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Before an AI agent can take on real work, its workflow needs clear scope, permissions, handoffs, evaluation cases, and production monitoring.

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Chatbot transcripts spread across providers, logs and support tools. Here is how to map each copy, redact sensitive data and test deletion properly.