Building always-on, business-critical AI applications or agents on a constantly updating and growing volume of unstructured data requires resilient and fast data infrastructure.  I am super excited to finally announce @tensorlake's open-source, real-time data framework, Indexify. ​ Real-time processing: Optimized for tasks like summarization, extraction, embedding, and parsing, Indexify works well with frequently updated data. It can ingest any data modality at scale, with incremental updates that don't require re-processing entire documents. ​ Reliability, Multi-Cloud and Hardware Acceleration: Indexify reliably processes data even during transient infrastructure failures, ensuring high availability . Extracted data is automatically stored in storage systems. Pipelines can run on GPUs, CPUs, and across multiple clouds for flexibility and resilience. ​ Observability: Fully observable, Indexify allows you to identify bottlenecks in extraction pipelines and retrieval APIs for semantic searches and SQL queries. ​ Indexify has been tested on AWS with hundreds of thousands of documents and images to ensure production-readiness. ​ It comes with retrieval APIs for RAG applications, autonomous agents or any AI application. It's fully extensible, allowing you to bring any model into pipelines. ​ Blog Post: medium.com/tensorlake-ai/ann… GitHub: github.com/tensorlakeai/inde… Website: getindexify.ai Discord Community: discord.gg/vxQPZpp7bV

Every single time someone explains me the K8s networking architecture, I loose track of the story 60 seconds in. And, I have written NAT64 routers, ILA routers, etc using XDP/eBPF, etc. Not a dig on K8s but when running things at scale less moving parts are better.