n8n’s Post

🚀 Leveraging Deep Learning to solve real retail challenges. Using YOLO for object detection, combined with a carefully labeled dataset, we’ve trained a model capable of identifying stock gaps directly on supermarket shelves. This solution doesn’t stop at detection — it’s supported by a robust backend: ✅ FastAPI for high-performance APIs ✅ Redis for efficient caching and scalability ✅ Database integration to track stock variations and trigger alerts in real time The result: an end-to-end system that enhances efficiency, reduces manual auditing, and provides actionable insights for smarter inventory management. 🛒 From shelf to system, AI is transforming the way we monitor and respond to stock availability. #DeepLearning #YOLO #ComputerVision #FastAPI #Redis #AI #RetailTech #MachineLearning

Smarter search starts with smarter integration. See how MongoDB, Voyage AI, and Pureinsights are redefining AI-powered search. https://lnkd.in/gbWMpf4f #AISearch #VectorSearch #EnterpriseSearch #MongoDB #Pureinsights

Diving deep into the world of AI agents! 🚀 I've been busy architecting a robust RAG (Retrieval Augmented Generation) agent on n8n. This setup integrates Google Gemini for advanced language understanding, Redis for efficient memory management, and Pinecone as a high-performance vector store to ensure highly contextual and accurate responses. It's incredible to see how these components come together to create intelligent systems. What are your favorite tools for building AI workflows? #AI #ArtificialIntelligence #RAG #n8n #Automation #DeepLearning #VectorDatabases #GoogleGemini #Redis #Pinecone

Introducing MySQL AI: A New Era of Intelligence In a transformative move, we’re excited to announce MySQL AI—a suite of generative AI, machine learning and vector processing capabilities of MySQL HeatWave into MySQL Enterprise Edition. This leap enables developers to build rich AI/ML applications with MySQL in the development environment of their choice and deploy the applications on-premises or cloud. Whether you're building chatbots, enhancing customer experiences, streamlining internal operations, detecting financial fraud, or powering predictive maintenance, MySQL AI makes advanced AI accessible using data that is already in MySQL or enterprise data in file systems. #MySQL #AI #Innovation #MySQLHeatWave https://lnkd.in/dXYuSEQf

𝐖𝐢𝐥𝐥 𝐀𝐦𝐚𝐳𝐨𝐧 𝐒3 𝐕𝐞𝐜𝐭𝐨𝐫𝐬 𝐊𝐢𝐥𝐥 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 𝐨𝐫 𝐒𝐚𝐯𝐞 𝐓𝐡𝐞𝐦? A few weeks ago, during a late-night debugging session, I realized that half of our team’s cloud bill came from something unexpected not LLM calls, but vector search. That hit me. The infrastructure powering retrieval was actually more expensive than the model generating responses. Then AWS announced 𝐒3 𝐕𝐞𝐜𝐭𝐨𝐫𝐬, and everyone started asking: Is this the end of vector databases? Let’s be real. S3 Vectors is impressive: affordable storage, massive scalability, native AWS integration. For small-scale RAG prototypes or internal tools, it’s almost unbeatable. But if you’re running high-performance search, recommendation engines, or multi-tenant AI services, its limitations become clear. Slow writes, capped collection sizes, no hybrid search, and recall that flatlines around 85%. Still, the big picture isn’t about replacement it’s about 𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧. We’re entering a new era of 𝐭𝐢𝐞𝐫𝐞𝐝 𝐯𝐞𝐜𝐭𝐨𝐫 𝐬𝐭𝐨𝐫𝐚𝐠𝐞. Hot data stays in fast, specialized databases; warm and cold data move to cheaper object storage like S3. This hybrid approach mirrors what happened with traditional databases and data warehouses years ago. Amazon didn’t just build a competitor they validated an entire market. Their move signals that vector data is no longer niche. It’s foundational. If you’re exploring how to scale GenAI systems sustainably, the takeaway is clear: don’t think in terms of “killing” or “saving” technologies. Think in 𝐥𝐚𝐲𝐞𝐫𝐬. The smartest architectures balance speed, cost, and access because in the end, the future isn’t about one database to rule them all. It’s about the right tool for each layer of the stack. #MachineLearning #MLOps #ModelDeployment #AI #Ensemble #AIEngineering #Deeplearning #DataScience #LLM #GenAI #NLP

LLMs don’t use tables, but they do store knowledge—and they’re changing how we think about data access. Are they just fuzzy, probabilistic databases? Watch this quick explainer and see why Azure Cosmos DB is the perfect partner for intelligent AI apps that need low-latency storage, vector search, and global scale. https://msft.it/6042s7TlA #AzureCosmosDB

LLMs don’t use tables, but they do store knowledge—and they’re changing how we think about data access. Are they just fuzzy, probabilistic databases? Watch this quick explainer and see why Azure Cosmos DB is the perfect partner for intelligent AI apps that need low-latency storage, vector search, and global scale. https://msft.it/6049s7TlN #AzureCosmosDB

Purpose-built rerankers, such as rerank-2.5 (Voyage AI by MongoDB), are up to 60x cheaper, 48x faster, and achieve up to 15% better reranking accuracy (NDCG@10) than state-of-the-art LLMs. https://lnkd.in/eJFFA9jP

Purpose-built rerankers, such as rerank-2.5 (Voyage AI by MongoDB), are up to 60x cheaper, 48x faster, and achieve up to 15% better reranking accuracy (NDCG@10) than state-of-the-art LLMs. https://lnkd.in/eJFFA9jP

How to Use an AI Agent to Create AWS Infrastructure Using Natural Language. We release a new UI in version v0.0.3 #AI #AWS #Agent