Jack Wu

LiteLLM is an open-source proxy that connects your app to LLMs through one unified interface. Paired with Redis, it gives AI and ML teams a simple yet powerful way to unify access to LLMs, accelerate response times, and make AI apps real-time. Redis handles performance, memory, and data coordination that modern AI apps demand, while LiteLLM handles abstraction and routing. Our AI Product Marketing Manager Rini Vasan dives into: ▶️ What LiteLLM is ▶️ Why it works well with Redis ▶️ How LiteLLM and Redis work together Learn how to scale your LLM gateway with LiteLLM & Redis: https://lnkd.in/gqsTebF7

32

If your team is building with LLMs, you know the value of clean, retrievable data. That's why we're teaming up with Unstructured to streamline one of the hardest parts of AI development. This integration removes friction across the AI stack—from unstructured ingestion to semantic retrieval—so your teams can focus on building. Read more from our Product Marketing Managers, Rini Vasan and Jim Allen Wallace, and Unstructured’s Head of Developer Relations, Maria Khalusova here: https://lnkd.in/e98QqXAB

43

Tip: Scale large HNSW vector sets in Redis by splitting your dataset across multiple instances and querying them in parallel with multiplexing, enabling efficient parallel reads and writes that drastically improve performance and manage memory effectively. This approach leverages Redis's scalable vector support to handle hundreds of millions of vectors seamlessly. #Redis #HNSW #VectorSearch #ScalableAI #DataScience

1

We have a new Breez SDK update 👀 Want to implement atomic swaps on Spark? You now can with HTLC support. Check the release notes for more updates and the latest features: https://lnkd.in/euRXDVns

19

1 Comment

Your CI/CD bill is not exploding because of “expensive runners,” it is bleeding out through flaky tests, over-parallelized jobs, and slow feedback loops that quietly tax every deploy This piece shows you how to redesign pipelines so you cut compute waste, shrink queues, and speed up developer flow without adding yet another approval gate or YAML religion Full breakdown in the comments. #DevOps #CICD #PlatformEngineering

8

1 Comment

ClickHouse has been making headlines, from Lyft’s 100TB+ daily analytics on ClickHouse Cloud to the new 25.8 release delivering blazing-fast Parquet queries and Arrow Flight integration. In this video, Hojjat Jafarpour shows how Apache Flink’s stream processing + ClickHouse’s OLAP speed create a real-time powerhouse for live dashboards, anomaly detection, and event-driven apps. 🎥 Watch here: https://lnkd.in/gPWEEaPf #RealTimeAnalytics #StreamingData #ApacheFlink #ClickHouse #DeltaStream #OtterlyRealTime

7

1 Comment

🚨 Data streaming is due for its Iceberg moment. In this new post, our CEO, Sijie Guo, and Principal Sales Engineer, David Kjerrumgaard, explore why tightly coupled streaming architectures like Apache Kafka are hitting their limits—and what the future looks like. 👉https://hubs.ly/Q03tR4QS0 Drawing lessons from the lakehouse revolution (e.g., Apache Iceberg), they propose a three-layer architecture for streaming:
 🔹 Data – Durable, scalable object storage
 🔹 Metadata – Decoupled and authoritative
 🔹 Protocol – Stateless, multi-interface routing Streaming should be as modular, cloud-native, and vendor-neutral as modern analytics: 📉 Cut costs by up to 90%
 ⚙️ Evolve each layer independently 🔓 Enable open, multi-tool access Projects like #ApachePulsar and #StreamNativeUrsa are already paving the way!🌟 #StreamingData #Kafka #Iceberg #Lakehouse

6