Make your Redis 17,000X Faster
1.21 ns. 190,909x faster than Redis alone. 827M ops/sec.
What it does
Cachee AI: Predictive caching layer that eliminates Redis roundtrips. 2µs L1 hits vs 5-20ms Redis = 2,500-10,000x speedup. 99.96% latency reduction. Cache infrastructure that learns your patterns using AI for the lowest possible latency. ⚡ 6.62x throughput improvement 🧠 99% L1 hit rate, 0.02ms latency 📈 1M+ patterns learned per hour 💰 85%+ fewer Redis calls Validated: 4.5M requests, zero errors, 5/5 production tests passed. Cold start to 80% hit rate in 29 seconds.One SDK.15 minute start
Does the same job
all alternatives →
- ACAgent-cache – Multi-tier LLM/tool/session caching for Valkey and RedisApr 2026 · ▲18
Multi-tier exact-match cache for AI agents backed by Valkey or Redis. LLM responses, tool results, and session state behind one connection. Framework adapters for LangChain, LangGraph, and Vercel AI SDK. OpenTelemetry and Prometheus built in. No modules required - works on vanilla Valkey 7+ and Redis 6.2+. Shipped v0.1.0 yesterday, v0.2.0 today with cluster mode. Streaming support coming next. Existing options locked you into one tier (LangChain = LLM only, LangGraph = state only) or one framework. This solves both. npm:…
- ABA bi-directional, persisted KV store that is faster than Redis2025 · hpkv.io · ▲79
we've been working on a KV store for the past year or so which is 2-6x faster than Redis (benchmark link below) yet disk persisted! so you get the speed of in-memory KV stores but with disk persistence. To achieve this we've created our custom filesystem that is optimized for our special usecase and we're doing smart batching for writes and predictive fetching for reads. In addition to basic operations, it also provides atomic inc/dec, atomic json patch, range scans and a unique key monitoring mechanism (pub-sub) over WebSockets which essentially allows you to receive notification on…
- RCReduce ChatGPT costs 10x with distributed cache for LLMs2024 · edgematic.dev · ▲22
Hello HN! We're building a caching solution for LLMs (ChatGPT, Claude). By combining cutting-edge approaches, such as edge computing, prompt compression, vectorization, and others - it can reduce your AI bills by up to 10x and significantly lower response times. Key Features: - cost efficiency: our system stores frequent queries, reducing the number of upstream (paid) API calls - fast responses: with various nodes globally, we reduce latency by serving data from the nearest location - scalability: designed to handle increasing loads and data sizes without degrading performance. The cache…
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More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, March 2026
the whole month →
Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com
