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A discussion on building companies run by AI agents, where humans focus on strategy and agents handle execution, with the company acting as a shared context layer.
This article introduces the open-source AI assistant OpenHuman, which serves as a context layer for personal AI, connecting tools like Gmail, Notion, and GitHub to continuously understand the user's digital life. It turns fragmented information into task cues and project memories, applied across seven scenarios: email, meetings, creation, development, learning, team collaboration, and personal life management.
The article discusses the emergence of 'agent context engines' like Redis Iris as a runtime layer that combines retrieval, memory, data sync, and caching, enabling agents to work with live business data without custom integration per workflow.
The author critiques the lack of transparent benchmarking in emerging context layer and MCP optimizer tools that promise drastic token savings, noting that real-world tests fail to replicate claimed efficiencies. They urge developers to demand open, reproducible benchmarks and ask for recommendations of tools that actually deliver measurable results.
Unabyss is a MCP-native, self-updating context layer for AI applications, designed to maintain persistent context across interactions.