Redis

Redis University covers every role that touches Redis, not just developers writing queries. Developers get hands-on labs building a RAG chatbot, running vector search with Redis VL, and working through context engineering directly, plus full learning paths to get started and go deeper. Software operators get dedicated paths for standing up and running Redis Software. Cloud operators get the same for Redis Cloud. And for teams working on specific capabilities, there are focused tracks for Redis AI and vector, and search and query. Whatever your role, there's a path built for it. Become a Redis expert: https://lnkd.in/gYkS2mpw

New capabilities just shipped: unified visibility (Radar), real time sync (Redis Data Integration), and seamless scaling, now with search on Flex. See it, sync it, scale it. 💥 https://lnkd.in/eHdBXKeX

Redis is sponsoring Interrupt NYC! Stop by their booth during the event and go hands-on with their data platform, built for caching, AI, and real-time apps.

Context. That's the difference between an AI demo and a reliable production agent. The fix? Bringing fragmented context into one place, then adding meaning so an intelligent system can actually use it. That's what Redis Iris does. More in The state of context engineering report: https://lnkd.in/eSsdvpaJ

Models get better every few months but context debt gets worse every month it goes unaddressed. Redis CTO Benjamin Renaud, breaks down the fix into two solutions: someone owning unified context infrastructure as a real job, not an afterthought, and a real-time platform built to support it. Redis Iris is that platform, built so context does not decay while the models around it improve. Get the full breakdown in The state of context engineering report: https://lnkd.in/eSsdvpaJ

Your agents should be getting smarter. Build them on fresh data and context that improve over time with Redis Iris: https://redis.io/iris/

🚨 We've launched a plugin that brings Redis engineering guidance into ChatGPT Work and Codex. Agents building on Redis now get skill-specific instructions loaded only when the work calls for it: data structure selection, connection pooling and pipelining, vector search and hybrid retrieval, semantic caching with LangCache, clustering, security configuration, and Redis Agent Memory provisioning through Redis Iris. The gap this closes is specific. Ask an agent for a cache, and it knows Redis. Ask it to configure Agent Memory or a hybrid retrieval pipeline, and it often reaches for outdated patterns because that guidance isn't in its training data yet. The plugin puts current Redis engineering knowledge directly into the agent's context. This builds on the direction we set with Redis Iris back in May: Redis as the memory and context layer for agentic AI, now extended into the tools agents use to build. Install Redis Development through the plugin browser in ChatGPT Work or Codex, or run ‘npx skills add redis/agent-skills’ for other supported agents. Read more: https://lnkd.in/e5NyvpJQ

A recommendation is useless if the user has already clicked away. ⏱️ That's why Databricks and Redis have partnered to deliver in-session personalization by combining Databricks Real-Time Mode (RTM) for continuous processing with Redis for sub-millisecond serving, without managing a separate streaming engine. Explore the joint architecture, performance benchmarks, and code snippets: https://lnkd.in/e93DwTGs

Building AI-native teams isn’t about adding an AI tool to an existing process. Redis Engineer and former CEO at Decodable, Eric Sammer treats AI as a default way of working, not something bolted on later. At Redis, that means pods of 3-4 engineers with a single feature lead, daily stand-ups built around alignment instead of status, and roadmap decisions that now happen every few hours instead of every few weeks. It also means a no-slop policy: engineers own the quality of anything an AI agent generates. The same judgment that runs through our engineering culture is what goes into building Redis Agent Memory, discipline applied to what actually holds up in production. Check out the full Engineering Leadership newsletter with Gregor Ojstersek: https://lnkd.in/eDY5-myb

Two systems, one job: continuous compute and instant serving. Databricks Real-Time Mode computes state as events happen. Redis serves it in sub-millisecond time. No separate streaming engine required. In production testing, the pattern held 100,000 events per second with recommendations served in under 160ms at p99. The same architecture is what lets multiple agents share state and coordinate without stepping on each other, proof that Redis is built for agentic workloads at production scale. Full pattern and code walkthrough here: https://lnkd.in/e93DwTGs