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News, launch notes, guides, and comparisons for builders exploring reusable AI agent skills, trust signals, and ecosystem shifts.
Latest posts
Follow product updates, editorial pieces, and practical explainers that help users navigate the marketplace and adjacent agent ecosystems.
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Build local vector retrieval indexes with Faiss
Use Faiss to create and query local vector indexes for agent retrieval and RAG workflows before adding heavier managed vector infrastructure.
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Expose data catalog context to AI workflows with DataHub
Use DataHub metadata, ownership, schema, and lineage context to ground agent-assisted data discovery and governance workflows.
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Optimize agent development loops with Coze Loop
Use Coze Loop to evaluate, debug, and improve agent prompts and workflows across development iterations.
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Xquik X Data API Skill
Use Xquik from coding agents to search X posts, inspect profiles, run bulk extractions, monitor accounts or keywords, verify HMAC webhooks, and call the remote MCP server or REST API with an XQUIK_API_KEY.
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Scale agent retrieval workloads with Milvus
Use Milvus to create vector collections, ingest embeddings, and serve filtered similarity search for RAG and agent retrieval workloads.
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Run agent-generated code in local microVM sandboxes with Microsandbox
Use Microsandbox to create local microVM-backed sandboxes for isolated execution of untrusted agent-generated code and tool calls.
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Build enterprise RAG and agent workflows with Bisheng
Use Bisheng to assemble, evaluate, and publish enterprise RAG and agent workflows across internal data, models, and business users.
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Store Python project task runners in pyproject.toml with taskipy
Define short Python project commands in pyproject.toml so agents and maintainers can run the same test, lint, docs, and release tasks without hunting through shell notes.
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Add temporal context memory to agent workflows with Zep
Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.
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Run RAG chatbot workflows with Verba
Ingest a bounded document set into Verba, configure retrieval and model providers, then review grounded chatbot answers before handing context to an agent workflow.