MongoDB

Search that breaks on a typo is not it. Learn how fuzzy matching helps you find what you mean (even if you misspell it!) and how to wire it up fast with Spring Data MongoDB. Watch the video and start learning: mongodb.social/6011B6dimt

Think your MongoDB Vector Search knowledge is solid? Let's put it to the test. The Vector Search skill badge covers the essentials: from how embeddings work to why model consistency matters for relevance. Start earning now: mongodb.social/6018B6bfmG

What does it actually take to build agentic AI systems in production? On the latest episode of the MongoDB podcast, @Capgemini Cloud COE Leader Vinay Makkaji chats with Apoorva Joshi and Farid Mohammad about the architectures behind modern AI apps—from vectorization and RAG to scalable data platforms. Watch the full episode: mongodb.social/6016B6w2SE

"The best data platform is the one you don’t have to think about." That’s what @thesysdev is saying about MongoDB. It’s also why startups like @morphik_ai and @modelence are building their AI platforms on MongoDB Atlas. See how these startups are building! 👉 mongodb.social/6017B6w2Kh

🤝 81 million medical consultations. 🌐 190+ countries. 🗓️ 18 months. Heidi built their AI scribe on MongoDB Atlas and scaled without ever going down. In healthcare, that's not a nice-to-have. It's everything. Read how they're using MongoDB to give clinicians their time back → mongodb.social/6013B6Xh13

🪴 Your plants can't tell you what's wrong. So @usfca_msds Student, Moises Limon, built an app that diagnoses plant diseases from a photo. Upload a leaf image → get a diagnosis + treatment plan in seconds 96.82% accuracy. Built with PyTorch + MongoDB Atlas. Full project breakdown → mongodb.social/6012B6XUYC

You’re already switching between enough tools. Checking or updating your data shouldn’t break your flow. The MongoDB for VS Code extension now lets you: - browse collection data in a tree view - delete, copy, clone and edit documents natively - work in standard shell syntax that’s compatible to your application code 👉 mongodb.social/6019B6nMuB

Build AI Agents with Real Memory ➡️ mongodb.social/6014B6Plnp Most agentic architectures treat memory as an afterthought. This one was designed around it. The unified memory layer combines: • Real-time telemetry streaming via Change Streams • Multimodal vector search across defect images and text with Voyage AI • Short-term checkpointing so investigations survive interruption • Long-term RCA history so agents learn from past failures What this unlocks for developers: • Autonomous root cause analysis • Investigations you can resume, audit, and build on • Agents that reason across structured, unstructured, and streaming data — without crossing system boundaries Built with MongoDB Atlas, LangGraph, Amazon Bedrock, and Voyage AI. 💡 The result? Agents that investigate, correlate, and remember.

Watch @forrester’s Devin Dickerson and MongoDB’s Pete Johnson and Shilpa Kolhar talk about why your data architecture is the real foundation for AI that lasts. In episode 1 of Building the AI Era, you'll hear what actually holds teams back, like: • Why “lift and shift” often carries technical debt forward • Why flexible data models are critical in the AI era • Why leading teams are thinking data-first, not feature-first Watch Episode 1 now! 👉 mongodb.social/6012B6pudA

Something breaks in production at 2am. Your dashboard metrics confirm it. But why? To get answers, teams would often need to log into their data platform, export large log files, extract compressed archives, and manually search through thousands of lines of entries. Now, with MongoDB Atlas Log Integration, you can stream database logs directly to tools like Datadog, Splunk, cloud storage, or any OpenTelemetry-compatible backend, so your team can correlate and troubleshoot in near real time. See how it works 👇 mongodb.social/6018B6PiFO

Flexible schema. Irrational number. Perfect match. #PiDay

🧳 TUI Musement cut trip planning from 72 hours → a few minutes. The foundation? MongoDB Atlas: handling search, vector storage, and real-time AI recommendations for 15M+ customers a year. Read the full story → mongodb.social/6010B6Jht0

We won $40,000 at hackathons without writing a single line of code. A few months ago, that would’ve sounded ridiculous. Now it’s normal if the setup is good. The code wasn’t the hard part.
 The hard part was giving the agent the right tools, the right context, and an environment it could actually work in. We spent most of the hackathon on the idea, the scope, and the setup. The code came after. @MongoDB was a big part of why we could iterate that fast. I’m going to write more about the setup behind this @oscrhong @_javokhir 🚀🚀

How do you define indexes in Spring Data MongoDB? 📚 Using a library genre analogy, we break down how each works—and show how to control them directly in your code with Spring Data MongoDB, including enabling auto-index creation and using compound index. Watch the video: mongodb.social/6015B6EDfP

Build smarter AI retrieval on MongoDB Atlas 🚀 The Embedding and Reranking API gives you @VoyageAI embeddings + reranking as a standalone API. Use it with any stack or pair it with Atlas Vector Search. 200M free tokens to get started. Read more: mongodb.social/6010hA9mg Show less

Ever needed to analyze, transform, or group your data directly inside MongoDB? Allow us to take you through MongoDB's Aggregation Framework. You'll learn: $match: How to filter documents, similar to a WHERE clause in SQL $count: A simple way to count the documents that pass through the pipeline $group: How to group documents by a specific field and perform calculations like sum or average Watch the tutorial now! mongodb.social/6018hCdNG

MongoDB Atlas for unified document and vector storage. @VoyageAI for embedding models that outperform every competitor on code retrieval benchmarks. Together, they gave @FactoryAI: ✔️ A single platform replacing three disconnected systems ✔️ Billions of tokens processed daily without bottlenecks ✔️ Superior retrieval accuracy across massive codebases ✔️ The freedom to build products instead of managing infrastructure mongodb.social/6019hCUlP

Richard showed up with bolt-ons and baggage. Adam showed up with MongoDB. 👀 When your stack is stitched together with extensions and workarounds, even event registration can feel… complicated. Watch the first episode: mongodb.social/6014hArWs

Faster vector search. Less RAM. Lower costs. Vector quantization is the AI optimization most teams overlook. If you’re building RAG or vector-powered apps, this is how you keep performance high and costs under control.

📣 We’re expanding our strategic partnership with @digitalocean to make it even easier for developers to build scalable, AI-ready applications. Together, we're delivering: ✔ Fully-managed MongoDB clusters launched in minutes ✔ 16 data centers across 9 global regions ✔ Built-in scalability and security ✔ Optimized infrastructure for AI-driven workloads See what's next: mongodb.com/company/blog/i…