See how fast feels

Joined January 2014
6,237 Photos and videos
Jun 12
"A little memory and access to actual data." That's all @cassidoo wanted for her agent. What she got instead: retrieval, sync pipelines, permissions, caching, and a full taxonomy of what agents should remember, forget, and never touch. Her piece in TLDR is one of the most honest (and funniest) accounts we've seen of why agent context is real infrastructure, and exactly why Redis Iris exists.
1
1
2
1,031
Redis retweeted
Most AI agents don't fail because they're dumb. They fail because they can't find the right data. The model is smart. But the data it needs is spread out. Some sits in your database. Some in a ticket system. Some in an old doc no one has opened in months. So the agent guesses. And you get a bad answer. If you've built an app like this, you know the pain. The data lives in five places. None of it talks to each other. And now we're asking AI to make sense of all of it, live. Here's what I keep coming back to: this is a tooling problem, not a brains problem. If we want AI to work at scale, we need better plumbing under it. That's why Redis Iris caught my eye. It's a "context engine". Think of it as a layer that sits between your agent and your data. Its one job is to feed the agent the right context, fast. A few things it does: - Pulls live data from your systems, so nothing goes stale - Gives the agent a clean path to your data, so it stops guessing - Remembers past chats, so it doesn't start from zero each time - Caches repeat answers to save time and tokens What I like most: it's built on Redis. The same tool a lot of us already trust for caching. So it's not one more strange thing to learn. We're still early in all of this. But better tools like this are how AI agents go from "cool demo" to "I trust it in production". Curious how it works? Take a look here: fandf.co/3Qan56b Thanks to @Redisinc for sponsoring this post.
5
4
37
2,516
Big congrats to Jin Choi, Vinay Bhagavath, Vijay Sithambaram, and Christin Philip — the winners of Best Use of Redis at WeaveHacks 4 this weekend! Their project, Rezn, didn't just use Redis as infrastructure. It made Redis part of the creative loop. rezn-ai generates music candidates, lets a producer approve, reject, or refine them, then uses that feedback to make the next generation smarter. The more a producer interacts, the more the system learns their taste.
1
3
1,030
Redis handled both real-time coordination and long-term memory. The result: an agentic music workflow that was responsive, stateful, and genuinely self-improving.
1
1
206
This is exactly where Redis Iris shines, giving agents memory that outlasts a single prompt. This is exactly where Redis Iris shines, giving agents memory that outlasts a single prompt. More about Rezn: 💻 Repo: github.com/gorajing/rezn-ai 🎥 Demo: youtube.com/watch?v=G7IgUwm7…
229
Redis retweeted
what a blast at WeaveHacks this weekend! thanks for hosting @wandb🎉🎉 & congrats to EmailSignal for winning best use of @CopilotKit🪁! saw so many dope projects, ranging from AG-UI in VR to cardiology. it’s exciting to see what people can hack together in <48 hours always great to co-sponsor alongside our friends @Redisinc, @OpenAI, & @cursor_ai. huge shoutout to Alex & Anna for running the event
Biggest turnout we've had at @wandb hackathon ever (Sorry for folks who couldn't get in) The energy is insane! Looking forward to incredible hacks over the weekend, hackers are pumped! Thank you sponsors @Redisinc @OpenAIDevs @cursor_ai and @CopilotKit
4
4
56
8,213
Redis retweeted
Biggest turnout we've had at @wandb hackathon ever (Sorry for folks who couldn't get in) The energy is insane! Looking forward to incredible hacks over the weekend, hackers are pumped! Thank you sponsors @Redisinc @OpenAIDevs @cursor_ai and @CopilotKit
2
7
36
11,114
Building agents? Check out this hour-long masterclass by @SonnySangha on breaking through the context bottleneck so you can build agents that actually work. Watch here: youtube.com/watch?v=PoMEGxNL…
1
2
7
1,849
➡️ Working memory vs. long-term memory, and when an agent should write to each ➡️ How Redis Context Retriever auto-generates business entities and exposes them as MCP tools ➡️ Why semantic caching with LangCache turns paraphrased repeat queries into cache hits
1
332
➡️ How Redis Data Integration keeps agent context fresh from your source databases without manual stitching ➡️The cold-cache vs. warm-cache latency difference, measured live. If you're building agents, or agent infrastructure, watch this today.
164
Redis 8.8 is here — free to download for anyone on Redis Open Source. New data structure, a built-in rate limiter, faster streams, and throughput gains across the board. Read all about it here: redis.io/blog/announcing-red… 🧵
1
5
20
1,723
More upgrades: ➡️Hash subkey notifications — subscribe to field-level events ➡️Multiple time series aggregators in one command (hello, candlestick charts) ➡️Explicit JSON float storage: BF16/FP16/FP32/FP64 ➡️New COUNT aggregator for sorted set unions/intersections
1
203