The biggest bottleneck in AI today is context data, not models.

For example, you may vibe code a health app wrapping around the Gemini API. But for that app to be useful, it needs context - specifically, real-time user health data.

Many developers waste weeks "duct-taping" a standard database to a separate vector database just to help their LLMs "remember" context.

This creates a data tax: more moving parts, higher latency, and more points of failure.

If you want to build an autonomous agent that actually works - like a health pal that remembers a user's entire health history without lag - you need your data and your vectors in the same place.

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Gen AI apps are built with MongoDB Atlas

MongoDB Atlas is the developer-friendly database used to build and scale gen AI and LLM-powered apps - without needing a separate vector database. Atlas offers built-in vector search, global availability in 125+ regions, and flexible document modeling. Start building AI apps faster, all in one place.

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I asked MongoDB to partner with us today - simply because I've used them many times in my own coding life.

Why MongoDB Atlas is a cheat code for AI apps:

• Simple AI Stack: Perform RAG (Retrieval-Augmented Generation) directly where your user data lives.
• Developer Ease: You don't need to learn a niche vector-only tool. Use the JSON-like documents you already know.
• Massive Scale: It handles everything from your first "Hello World" agent to a global app in 125+ regions.

Using a unified stack is the ultimate cheat code for shipping products while others are still debugging their infrastructure.

Be on the right side of change! -- Chris

 

P.S. The agentic era is moving fast. The developers who win in 2026 are the ones who simplify their infrastructure so they can iterate daily. Start for free here and get your agent live before the weekend.

 

 

Finxter, Dr. C. Mayer
Stuttgarter Str. 106., Stuttgart Fellbach, Germany

Want out of the loop? I'm so sad to see you go.