Which made me think about the conference 1990s 2000s vibe. . . I am proposing a wine, food, and data takeover of a restaurant for a day in the NoVa DC area Bring and open mind sticky notes? Dry erase markers, and be prepared to work and fix things Check my linkedin for more

Google just opened the floodgates. They've released their Data Commons through MCP—billions of verified data points from governments, the UN, authoritative sources. This isn't just about better training data for AI, this is about fundamentally different AI capabilities that will separate winners from losers. https://developers.googleblog.com/en/datacommonsmcp/ Here's what most business leaders are missing while they're focused on model selection and prompt engineering: The AI reliability crisis has potentially been solved—but only for organizations that understand how to leverage authoritative data sources. You now have: Billions of verified data points from authoritative sources including governments and the UN Data Commons MCP Server providing direct access to structured, context-rich datasets Universal data integration through Anthropic's Model Context Protocol standard Hallucination reduction through grounding AI outputs in verified sources Competitive advantage for enterprises that integrate authoritative data into AI systems But here's what makes this strategically transformative: This isn't just about accessing better data. It's about building AI systems that deliver reliable, verifiable outputs that create sustainable competitive advantages. Want to learn by doing and get practical experience with live expert help and support? Looking to learn more about Agentic AI? Consider joining us in the AI Leadership Institute. Our next masterclass in just a few weeks away!

Just wondering if anyone else in the community is developing a product with AI's help, or is an AI product? If so, I would love to hear your experiences. If I seem to be heads-down, that's what I'm doing right now! I'm working on getting the IP registered, presently (trademark and copyright). Patent is also a possibility once I generate some revenue from it. I can't publicly disclose the product yet, but I'm happy to share my high-level experiences with others who are developing products with/integrating AI.

While the goal is to include everyone, the focus sometimes narrows to a specific demographic in pursuit of higher sales. However, true inclusivity in tech must go beyond personas—it's about making sure every voice is heard. Visit my AI Leadership Institute LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:7231714285389819905

Happy Reading! You can be sure we will have a video walk through of each of these! Agentic AI patterns and workflows on AWS: 🔗 https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/introduction.html Foundations of agentic AI on AWS 🔗 https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-foundations/introduction.html Writing best practices for RAG: 🔗 https://docs.aws.amazon.com/prescriptive-guidance/latest/writing-best-practices-rag/introduction.html Agentic AI frameworks, protocols, and tools on AWS: 🔗 https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-frameworks/introduction.html Building architectures for agentic AI on AWS: 🔗 https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-multitenant/introduction.html Creating RAG solutions on AWS for healthcare: 🔗 https://docs.aws.amazon.com/prescriptive-guidance/latest/rag-healthcare-use-cases/introduction.html Choosing an AWS vector database for RAG use cases: 🔗 https://docs.aws.amazon.com/prescriptive-guidance/latest/choosing-an-aws-vector-database-for-rag-use-cases/introduction.html Building serverless architectures for agentic AI using AWS: 🔗 https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-serverless/introduction.html