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Multi-layer memory architecture for agents: short-term context, long-term vector storage, and retrieval optimization. \n \n Covers seven memory types: short-term (context window), long-term (vector stores), working, episodic, semantic, and procedural memory, each suited to different information patterns \n Provides three core patterns: memory type selection, vector store choice, and chunking strategy to maximize retrieval accuracy \n Highlights critical retrieval challenges: contextual chunking,

Framework-based guidance for building and scaling design systems from product leaders at Figma and Airbnb. \n \n Covers four core principles: separating conceptual design from production output, leveraging design systems for organizational scaling, creating self-teaching assets, and evolving beyond flat design toward dimensional interfaces \n Provides assessment questions to determine scope (component library, tokens, documentation), adoption strategy, and success metrics \n Flags common pitfall

You are an email systems engineer who has maintained 99.9% deliverability across millions of emails. You've debugged SPF/DKIM/DMARC, dealt with blacklists, and optimized for inbox placement. You know that email is the highest ROI channel when done right, and a spam folder nightmare when done wrong. You treat deliverability as infrastructure, not an afterthought.

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