MongoDB
24.7K posts
MongoDB
@MongoDB
/*Think outside rows and columns.*/
Joined December 2008
Most banks aren't failing at AI because of the model. They're failing because of the architecture around it. You can't build next-generation financial services on a fragmented, legacy foundation. To unlock true speed-to-market and real-time AI capabilities, banks need an
- Introducing the Industry Solutions blog channel! Whether you operate in Financial Services, Telecommunications, Retail, or Manufacturing, you can now explore specific use cases and data architectures to accelerate time-to-market and drive competitive advantage. Explore the
- MongoDB is now available in the Grok Build Plugin Marketplace! You get: - Agent Skills for MongoDB workflows and best practices - MCP Server for real-time, context-aware guidance See it in action.👇
00:00 - ☁️ @imagineagi's @skygodkingdom and @neo_lky work with some of the world's biggest companies, and their edge comes down to one thing: knowing exactly who to reach and having the data infrastructure to act on it fast. Their relationship graph spans people, companies, and
00:00 - What shipped last month? "Between Two Leaves" is back with a very special MongoDB.local London edition. Thibaut Gourdel and Nic Raboy take you through MongoDB 8.3, automated vector embeddings, massive infrastructure updates, and even a live demo. Watch the full episode 👉
00:00 - The software the world runs on has to be secure. MongoDB has joined Project Glasswing, @AnthropicAI's initiative to secure the world's most critical software for the AI era, working alongside organizations like Apple, Google, Microsoft, and NVIDIA to strengthen the security and
Learn more about MongoDB’s security investment in the age of AI: mongodb.social/6014B8tyso - Arek Borucki started with a MongoDB University course and ended up as an ML Engineer at @huggingface. 🧑💻 What he shared about scaling MongoDB in production applies to your stack right now.👇
00:00 - Most agent failures are actually data failures. Teams keep tuning prompts, swapping models, and adding guardrails when the real issue is data that isn't retrieval ready, stale context, or workflows that can't maintain state. MongoDB Field CTO Pete Johnson breaks down the three
- "No matter what AI workload you run, you always need LLMs, a harness, and a data layer." That's MongoDB President and CEO @cj_mongodb on @Bloomberg Open Interest, making the case that the data layer is where agentic AI lives or dies, and that it has to scale as those workloads
