# Redis Redis is an open-source framework and data technology providing caching, streaming, session management, search, and data-integration capabilities. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Dragonfly 2.0 adds Redis and Valkey compatibility and reduces processing overhead DevFeed: [Dragonfly 2.0 adds Redis and Valkey compatibility and reduces processing overhead]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-17T10:02:19Z Content type: release Language: en Sources: [Phoronix]() Topics: [in-memory database](), [Memcached](), [Redis](), [data]() Tags: [compatibility](), [desktop-linux](), [in-memory-database](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [memcached](), [open-source-graphics](), [overhead](), [phoronix](), [phoronix-test-suite](), [redis](), [release](), [serialization](), [ubuntu-benchmarks](), [ubuntu-hardware](), [valkey](), [version]() ### AI overview Dragonfly 2.0 is a new release of the in-memory database that expands Redis and Valkey compatibility, adds Valkey 9 RDB loading support, and reduces connection processing and memory accounting overhead. ### Source excerpt Released on Wednesday was a new version of the Dragonfly database that continues to promote itself as "the world's most efficient in-memory data store" and a modern replacement to the likes of Redis and Memcached while being fully compatible with their APIs... ## Local and distributed cache coherence: stale data caused by missed invalidation messages DevFeed: [Local and distributed cache coherence: stale data caused by missed invalidation messages]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-17T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Caching](), [Redis](), [data](), [Network]() Tags: [cache](), [cache-coherence](), [caching](), [consistency](), [data](), [distributed-cache](), [distributed-systems](), [jvm](), [network](), [redis](), [ttl]() ### AI overview The article explains that adding an in-process cache in front of a distributed cache can improve latency but creates independently stale copies across service instances. It focuses on Redis pub/sub invalidation, which provides no persistence, acknowledgement, retry, or replay, allowing instances to miss invalidation messages and serve stale data until their TTL expires. ### Source excerpt An in-process cache in front of a distributed cache removes a network hop and adds one independent copy of the data per instance, each of which can be stale on its own schedule. Invalidation is normally broadcast over pub/sub, which is fire and forget, so any instance that is restarting, garbage collecting or briefly disconnected simply misses the message and serves stale data until its TTL expires. That TTL is not a performance setting, it is the maximum duration of incorrectness. ## Why your Redis is slow: it is single threaded and you sent it KEYS DevFeed: [Why your Redis is slow: it is single threaded and you sent it KEYS]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Redis](), [Latency](), [Concurrency]() Tags: [blocking](), [caching](), [command](), [latency](), [observability](), [performance](), [redis](), [single-threaded]() ### AI overview The article explains that Redis executes commands one at a time on a single thread, so expensive commands can block every client and cause latency spikes across all operations. It identifies KEYS as a key offender and also discusses large DEL operations, unbounded LRANGE, SMEMBERS on large sets, and FLUSHALL. Redis 6 threaded I/O helps with socket handling but does not change single-threaded command execution. ### Source excerpt Redis executes commands one at a time on a single thread, so any command that takes 200ms makes every other client wait 200ms regardless of how trivial their request was. KEYS scans the entire keyspace and is the obvious offender, but DEL on a large collection, unbounded LRANGE, SMEMBERS on a big set and FLUSHALL are all linear in the size of the data and block identically. The tell is a latency spike across all operations at once while CPU and network look normal. ## Security updates for Monday DevFeed: [Security updates for Monday]() Original publisher: [Read original article]() Author: jzb Published: 2026-09-14T13:18:14Z Content type: news Language: en Sources: [LWN.net]() Topics: [Security](), [Debian](), [Fedora](), [Firefox](), [Git](), [nginx](), [Python](), [cURL](), [Elixir](), [F#](), [Redis](), [Rust]() Tags: [curl](), [debian](), [elixir](), [firefox](), [git](), [nginx](), [python](), [redis](), [rust](), [security](), [updates]() ### AI overview Security updates were issued across AlmaLinux, Debian, Fedora, Gentoo, Mageia, Oracle, and SUSE. The affected software includes operating-system components, browsers, developer tools, programming-language packages, servers, libraries, and cloud-related utilities. ### Source excerpt Security updates have been issued by AlmaLinux (389-ds-base, apr-util, coreutils, freerdp, git-lfs, glib2, gstreamer1-plugins-base, kernel, libkcapi, nginx, nodejs:22, nodejs:24, osbuild-composer, perl-YAML-Syck, postgresql16-postgis, ruby, ruby4.0, ruby:3.3, and vim), Debian (jbig2dec, kamailio, nginx, spip, and xorg-server), Fedora (baresip, bind, bluez, bubblewrap, chirp, chromium, cockpit, composer, corosync, darktable, dokuwiki, elixir, exiv2, expat, firefox, freerdp, freerdp2, gdk-pixbuf2, gegl04, golang-x-perf, grpcurl, kernel, kernel-headers, libevent, libmongocrypt, libpcap, libre, libsoup3, memcached, mingw-expat, mingw-openexr, mongo-c-driver, mrtg, nagios-plugins, nsd, nss, openssl, openvpn, PackageKit, pdns-recursor, perl-Net-OAuth, perl-XML-Bare, php-pecl-mongodb2, python-asteval, python-pip, rclone, rest, rust-hickory-net, rust-hickory-proto, rust-hickory-resolver, rust-ppmd-rust, rust-webbrowser, srt, syncthing, tar, tkimg, and valkey), Gentoo (Chromium, Google Chrome, Microsoft Edge, Opera, Vivaldi and Ruby), Mageia (bind, ffmpeg, glibc, java-17-openjdk, java-21-openjdk, librabbitmq, perl-Catalyst-Plugin-Static-Simple, perl-Imager, tor, and xz), Oracle (389-ds:1.4, ansible-core, apr-util, coreutils, freerdp, git-lfs, glib2, gstreamer1-plugins-base, gzip, httpd:2.4, image-builder, java-21-openjdk, kernel, mrtg, nginx, osbuild-composer, perl-DBI, postgresql16-postgis, python-lxml, python3.12-lxml, redis:6, and vim), SUSE (389-ds, ansible-core, ansible-creator, azure-storage-azcopy, cargo-audit, chromedriver, chromium, clamav, containerized-data-importer1.65, containerized-data-importer1.66, curl, dracut, ffmpeg-4, google-guest-agent, google-osconfig-agent, helm, java-1_8_0-ibm, jupyter-nbconvert, kernel, libpng16, libusb-1_0, libvirt, multipath-tools, NetworkManager, opensc, openssl-3, perl-Authen-SASL, perl-HTML-FormHandler, perl-Mojolicious, perl-Protocol-HTTP2, python-jwcrypto, python-sqlparse, python-tornado6, python313-geopy, python313-modelscope ## Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more DevFeed: [Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more]() Original publisher: [Read original article]() Author: Bosmat Tuvel Published: 2026-09-14T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Redis](), [Data structures](), [Data Management](), [Open Source]() Tags: [data-management](), [improvements](), [memory](), [new-features](), [operations](), [performance](), [redis](), [streams](), [tech](), [time-series]() ### AI overview Redis 8.10 in Redis Open Source introduces compact hashes, incremental backup and restore, JSONPath syntax extensions, more flexible Stream consumption, new Set cardinality operations, atomic movement of multiple List elements, and enhanced Time Series capabilities. The release also improves memory efficiency, throughput, and operational reliability at scale. ### Source excerpt Redis 8.10 in Redis Open Source is now available, delivering improvements that make Redis more memory efficient, expressive, and easier to operate at scale. Highlights include compact hashes with up to 50% lower memory usage and 2x higher hash loadin... ## Choosing among write-through, write-behind, and write-around caching strategies DevFeed: [Choosing among write-through, write-behind, and write-around caching strategies]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Caching](), [Databases](), [Redis](), [consistency](), [Latency]() Tags: [cache](), [caching](), [consistency](), [database](), [latency](), [orders](), [postgres](), [redis](), [strategies](), [system-design](), [write](), [write-behind](), [write-through]() ### AI overview This article compares write-through, write-behind, and write-around caching strategies. It explains their trade-offs among database latency, cache hit rates, consistency, and the risk of losing acknowledged writes when a process fails. ### Source excerpt The three write strategies differ in what happens between the cache write and the database write, and that gap is where correctness lives. Write-through updates both before acknowledging, so it is safe and pays the full database latency. Write-behind acknowledges after the cache write and flushes later, which is fast and can lose acknowledged writes if the process dies. Write-around skips the cache entirely on write, which avoids caching data nobody reads and costs a miss on the next read. ## Caching Responses for Repeated Questions in Production LLM Applications DevFeed: [Caching Responses for Repeated Questions in Production LLM Applications]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-10T21:34:26Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [Caching](), [Redis](), [Embeddings](), [Large Language Model](), [Inference]() Tags: [cache](), [caching](), [embeddings](), [inference](), [llm](), [redis]() ### AI overview Production LLM applications can reduce repeated model calls by caching generated responses and matching new questions to prior questions using embeddings. The article discusses Redis LangCache, including similarity thresholds, expiration, data isolation, monitoring, and an example where a cache hit was roughly 6x faster than direct inference. ### Source excerpt What production agents must preserve between turns, and what they must reset. ## Cache stampede: how one expired key takes down the database DevFeed: [Cache stampede: how one expired key takes down the database]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-05T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Cache](), [Caching](), [Database](), [consistent hashing](), [Redis]() Tags: [cache-stampede](), [caching](), [connection-pool](), [consistent-hashing](), [distributed-systems](), [jitter](), [query](), [redis](), [reliability](), [thundering-herd](), [traffic](), [ttl]() ### AI overview This article explains how a cache stampede occurs when a hot key expires and many requests simultaneously recompute the same value against the database. It recommends TTL jitter to prevent synchronized expirations and request coalescing so only one caller recomputes while others wait or serve stale data. It also discusses cache warming and consistent hashing for broader cache-failure scenarios. ### Source excerpt A cache TTL is a scheduled simultaneous failure: every request being served from one key misses at the same instant and goes to the origin together. If the recompute takes two seconds, every request arriving during those two seconds also misses, so the pileup grows faster than it drains. Jitter on the TTL stops keys expiring in lockstep, and request coalescing so only one caller recomputes while the rest wait or serve stale is what stops a single expensive key from saturating the database. ## Why Gusto hired Evil Martians for Sidekiq infrastructure DevFeed: [Why Gusto hired Evil Martians for Sidekiq infrastructure]() Original publisher: [Read original article]() Author: Irina Nazarova (inazarova@evilmartians.com) Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [Evil Martians]() Topics: [Sidekiq](), [Rails](), [Redis](), [Ruby]() Tags: [ai](), [case-study](), [engineering](), [infrastructure](), [rails](), [redis](), [ruby](), [scale](), [sidekiq]() ### AI overview This case study examines why Gusto hired Evil Martians to work on the Sidekiq and Redis infrastructure supporting background jobs in its large Rails monolith. It discusses the operational demands of running payroll-related jobs at scale and the tradeoffs involved when a company uses consultants instead of waiting for a perfect hire. ### Source excerpt Gusto runs payroll for 500,000+ businesses on one of the largest Rails monoliths anywhere. Why a team this strong hired Evil Martians for Sidekiq at scale, what running background jobs at that size actually takes, and when to stop waiting for the perfect hire. ## 8 Redis Use Cases Engineers Should Know DevFeed: [8 Redis Use Cases Engineers Should Know]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-01T12:54:52Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Redis](), [Data structures](), [Caching](), [real-time](), [Server](), [systems]() Tags: [caching](), [data-structure](), [real-time](), [redis](), [server](), [use-cases]() ### AI overview This article explains that Redis is more than a caching system. It describes how Redis data structures support real-time application patterns such as counters, sessions, queues, leaderboards, rate limiting, coordination, event logs, notifications, and expiring state. ### Source excerpt Most engineers know Redis for caching. But that's only the beginning... ## OAuth token refresh has a race condition. Fix it with a conditional write, not a distributed lock. DevFeed: [OAuth token refresh has a race condition. Fix it with a conditional write, not a distributed lock.]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-31T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [OAuth](), [race-condition](), [Authorization](), [Auth0](), [Redis]() Tags: [auth0](), [authorization](), [concurrent](), [oauth](), [race-condition](), [redis](), [the-result]() ### AI overview This article explains how concurrent OAuth token refreshes can cause two distinct failures: stale tokens from lost updates and user disconnection when refresh-token rotation treats concurrent reuse as replay. It recommends layered defenses, including conditional writes, and explains why a Redis lock does not address both problems. ### Source excerpt Concurrent refreshes don't just fail. They can disconnect the user entirely. Here are four layers of defense, cheapest first, and why the Redis lock everyone reaches for isn't the one keeping you safe. ## How Redis Builds AI-Native Engineering Teams DevFeed: [How Redis Builds AI-Native Engineering Teams]() Original publisher: [Read original article]() Author: Gregor Ojstersek Published: 2026-08-27T12:35:46Z Content type: opinion Language: en Sources: [Engineering Leadership]() Topics: [Redis](), [Artificial Intelligence]() Tags: [ai](), [engineering](), [insights](), [redis](), [teams]() ### AI overview The article presents insights from a conversation with Redis Distinguished Engineer Eric Sammer about how Redis builds AI-native engineering teams. The supplied evidence does not provide specific practices or findings beyond that subject. ### Source excerpt Insights from my conversation with Eric Sammer, Distinguished Engineer at Redis. ## Investigating Redis Client Failures After a Node Loss at hh.ru DevFeed: [Investigating Redis Client Failures After a Node Loss at hh.ru]() Original publisher: [Read original article]() Author: nicholasgribanov (hh.ru) Published: 2026-08-26T05:30:49Z Content type: article Language: ru Sources: [HeadHunter RU]() Topics: [Redis]() Tags: [consul](), [downtime](), [java](), [jedis](), [redis](), [sre](), [tag-1d4e49052118](), [tag-68e701e78517](), [tag-b0a411324cb6](), [tag-b92bf5906bbd](), [tag-dfac9042ce7b](), [timeout]() ### AI overview A Platform team at hh.ru investigates incidents in its internal Redis client, including a 57-minute outage after a Redis node was lost. The team used controlled-load testing, Jedis updates, timeout and pool experiments, new metrics, and logs to trace the failures to a code issue and improve client stability. ### Source excerpt Пару лет назад мы устроили настоящее расследование серии инцидентов в поисках скрытого дефекта нашего Redis-клиента. Команда воспроизводила сбои под контролируемой нагрузкой, проверяла одну гипотезу за другой, обновляла Jedis, экспериментировала с таймаутами и размерами пулов -- но ничего не помогало. А помогли новые метрики и логи, настойчивость команды, ночные эксперименты и готовность разбирать поведение системы до последнего соединения. Получилась история с неожиданными поворотами, ложными следами и одной лишней строчкой кода в роли главного подозреваемого -- а её итогом стал Redis-клиент, который оказался устойчивее, чем был до начала расследования. Меня зовут Коля Грибанов, я тимлид команды "Платформа" в hh.ru. В статье расскажу, почему потеря одной ноды Redis вызывала шторм из десятков тысяч соединений, и как мы шаг за шагом искали причину инцидентов. Читать далее ## How Sprig Replaced Postgres, ClickHouse & Redis...with 4-8x Better Latency DevFeed: [How Sprig Replaced Postgres, ClickHouse & Redis...with 4-8x Better Latency]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-08-24T13:30:56Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [Latency](), [Low Latency](), [Databases](), [PostgreSQL](), [clickhouse](), [Redis](), [real-time](), [Back end](), [recommendation systems](), [Artificial Intelligence]() Tags: [backend](), [clickhouse](), [data](), [databases](), [engineering](), [latency](), [low-latency](), [postgres](), [postgresql](), [real-time](), [recommendation-systems](), [redis](), [user-stories]() ### AI overview Sprig outgrew PostgreSQL as its AI-powered product research platform scaled to more than 1.3 trillion events, 75 billion attributes, and high-volume real-time processing. The article describes its database challenges and the path toward lower-latency data infrastructure involving PostgreSQL, ClickHouse, and Redis. ### Source excerpt With ScyllaDB, a small engineering team could focus on building their product instead of battling their databases. ## Efficient Bulk Hash Insertion with Redis 8.10's HIMPORT DevFeed: [Efficient Bulk Hash Insertion with Redis 8.10's HIMPORT]() Original publisher: [Read original article]() Author: David Maier Published: 2026-08-24T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [Redis](), [Ruby](), [Code](), [Command-line interface]() Tags: [article](), [cli](), [code](), [make](), [redis](), [ruby](), [tech]() ### AI overview This article explains how to use Redis 8.10's HIMPORT command with redis-rb to perform more efficient bulk hash insertion in Ruby. It covers connection setup, prepared field sets, automatic preparation, reconnection behavior, the demo CLI, and pipelined imports. ### Source excerpt I'm not a Ruby developer, and my code samples might make that clear. However, there are two reasons I'm using Ruby for the examples in this article: A new release of redis-rb. Its support for a new Redis command: HIMPORT. Getting started Establish... ## Chaining Activities -- from text to vectors DevFeed: [Chaining Activities -- from text to vectors]() Original publisher: [Read original article]() Author: Houman Kargaran Published: 2026-08-12T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [data](), [Embeddings](), [on-prem](), [pii](), [Redis](), [retry](), [Sequences]() Tags: [article](), [community](), [data-pipeline](), [embeddings](), [on-prem](), [pii](), [redis](), [retries](), [storage]() ### AI overview This guest post explains a Temporal workflow that chains discrete Activities to process complaints while maintaining a PII boundary. It uses an on-premises all-MiniLM-L6-v2 model for embeddings, caches vectors in Redis, stores them externally, and relies on deterministic workflow sequencing and retry policies. ### Source excerpt Learn how to build a durable, PII-conscious data pipeline with Temporal using on-prem embeddings, idempotent Activities, external vector storage, and retries. ## How we migrated the database behind every Vercel build DevFeed: [How we migrated the database behind every Vercel build]() Original publisher: [Read original article]() Author: Andrew Healey Published: 2026-08-11T22:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [migration](), [Redis](), [Vercel](), [Containers](), [Deployment](), [Database](), [Cache]() Tags: [cache](), [containers](), [database](), [deployment](), [latency](), [migration](), [production](), [redis](), [vercel]() ### AI overview Vercel describes migrating the database behind its build warm pool from Redis to DynamoDB. The migration moved important billing mappings and other pool state to durable storage while running live under production traffic, in phased rollouts with feature flags and rollback plans. ### Source excerpt Every build on Vercel starts in the build warm pool, which is a set of standby containers that let builds begin without waiting for new compute. The pool runs on state that tracks which containers are ready, the tokens each one uses to authenticate, and the mapping that ties every running build back to the deployment that gets billed for it. When we built the pool, we put all of that in Redis, which was fast and made sense at the time. Over the years, though, that state turned into a liability. Tokens and container statuses can be rebuilt if they get lost, but the billing mappings can't, and all of it was sitting in a store that we ran as an ephemeral cache. That state needed to live somewhere durable, which is why we decided to migrate it to DynamoDB. The problem is that the pool never stops. Containers are coming up, polling, picking up work, and expiring around the clock, which meant we couldn't pause the world, copy the data, and restart. The migration had to happen live, under production traffic, in phases, each one behind a flag with a rollback ready if we needed it. Durable state in an ephemeral store Redis was a good home for it at first. It was fast, familiar, and efficient for the access patterns the pool started with. But over time the state became more important than the store holding it. If Redis became unavailable or lost data, the pool could no longer reliably authenticate containers, track which of them were ready, or resolve the work in flight. Lose a token and the pool rebuilds it within about ten minutes. Lose a mapping and the build is never billed, because nothing else records which deployment it belonged to. We wanted it in durable storage and landed on DynamoDB. On-demand scaling fits bursty deployment traffic, TTL is native, and there are no connections to manage at high concurrency. But what it did not promise was Redis's latency. What Redis made cheap Inside Redis, the pool's state looked like this: Tokens lived in a set so we could check m ## Reciprocal rank fusion: why combining search results is harder than it looks DevFeed: [Reciprocal rank fusion: why combining search results is harder than it looks]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-08-09T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [AI search](), [Redis](), [real-time]() Tags: [guide](), [redis](), [retrieval](), [search](), [tech-de](), [vector]() ### AI overview A guide to reciprocal rank fusion (RRF), a method for combining keyword and vector search rankings without directly adding their raw scores. It explains the formula, the role of the constant k, why agreement between retrievers matters, and RRF's unsupervised nature. ### Source excerpt You run a keyword search and get back a ranked list with Best Matching 25 (BM25) scores. You run a vector search over the same documents and get a second list with cosine similarities. You want to merge them into a single ranking that surfaces the mos... ## How Redis brings persistent memory to Snowflake Cortex Agents DevFeed: [How Redis brings persistent memory to Snowflake Cortex Agents]() Original publisher: [Read original article]() Author: Mike Moss Published: 2026-08-06T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Redis](), [Artificial Intelligence](), [AI Development]() Tags: [agentic](), [agents](), [ai](), [ai-agents](), [applications](), [memory](), [redis](), [tech]() ### AI overview Redis Agent Memory is now available on the Snowflake Marketplace for Snowflake Cortex AI agents. It provides persistent long-term memory across conversations, allowing agents to retain business context, preferences, events, and reusable knowledge instead of losing relevant context when a session ends. ### Source excerpt AI agents can reason and act, but without memory, every interaction starts from zero. Intelligent short-term memory and persistent context across conversations are what turns a capable model into a truly useful agent. It should remember the useful det... ## How Slate replaced a Redis bottleneck with Tinybird for real-time prices and P&L DevFeed: [How Slate replaced a Redis bottleneck with Tinybird for real-time prices and P&L]() Original publisher: [Read original article]() Author: Tinybird Published: 2026-08-05T13:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [Redis](), [real-time]() Tags: [customer-stories](), [infrastructure](), [real-time](), [redis]() ### AI overview This case study describes how Slate replaced a Redis bottleneck with Tinybird to provide real-time prices and P&L for thousands of investors. ### Source excerpt How Slate replaced a Redis bottleneck with Tinybird to unlock real-time prices and P&L for thousands of investors, and stopped saying no to growth. ## Semantic memory search for AI agents DevFeed: [Semantic memory search for AI agents]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-08-04T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [AI Agent](), [Redis](), [Embeddings](), [Language models]() Tags: [ai](), [ai-agents](), [data](), [embeddings](), [redis](), [tech-de]() ### AI overview Semantic memory search gives AI agents durable recall by storing facts outside the language model and retrieving them by meaning. The article explains how vector embeddings and similarity search supply relevant context, with Redis Iris combining memory, live data, and retrieval for fast agent context. ### Source excerpt Your AI agent handles a long onboarding conversation. The next day, it asks the same user for their name. That's not a bug. A language model keeps no memory of earlier calls, so without an external memory layer, each request starts fresh and the agent... ## Connect AI agents to data sources with Redis DevFeed: [Connect AI agents to data sources with Redis]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-08-03T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [AI Agent](), [Redis](), [data](), [Model Context Protocol](), [Retrieval Augmented Generation (RAG)](), [API](), [Embeddings](), [JSON Schema](), [Large Language Model]() Tags: [agents](), [ai](), [ai-agents](), [api](), [data](), [embeddings](), [integration](), [json](), [llm](), [model-context-protocol](), [rag](), [redis](), [retrieval](), [tech-de]() ### AI overview This guide explains how to connect AI agents to production data sources and provide runtime context. It covers RAG, tool and function calling, Model Context Protocol, and custom API connectors, with Redis Iris presented as a real-time context engine for production workloads. ### Source excerpt An AI agent that can't reach your data is just a chatbot with opinions. Without runtime context, a model only knows its training data and whatever sits in the current prompt. So your app has to feed it production-specific facts at runtime: your produc... ## Why CRUD APIs Stop Teaching New Lessons After Basic Production Problems Are Solved DevFeed: [Why CRUD APIs Stop Teaching New Lessons After Basic Production Problems Are Solved]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-02T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [CRUD](), [REST API](), [Back end](), [SQL](), [Latency](), [Network](), [Redis]() Tags: [api](), [backend](), [cache-invalidation](), [career](), [crud](), [latency](), [learning](), [network](), [schema](), [schema-design](), [system-design]() ### AI overview A small CRUD API teaches backend fundamentals such as validation, schema design, migrations, query behavior, connection pooling, and cache invalidation. After those issues are understood, developers may need more complex failure domains involving cross-service contracts, event ordering, and slow downstream calls to continue learning. ### Source excerpt A todo CRUD API teaches real things, including validation, schema design, the N+1 query, index selection, pool sizing, and cache invalidation, and then it goes quiet. Boredom arriving right after you fix those once is not laziness; it is the signal that the problem stopped presenting decisions you have not already seen. The move is up the stack, to a failure domain with cross-service contracts, event ordering, and downstream calls that hang. ## Introducing Enterprise Flexible Commitment for Vercel Marketplace DevFeed: [Introducing Enterprise Flexible Commitment for Vercel Marketplace]() Original publisher: [Read original article]() Author: Hedi Zandi Published: 2026-07-30T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Supabase](), [MongoDB](), [Redis]() Tags: [cost](), [customers](), [documentation](), [enterprise](), [infrastructure](), [integrations](), [mongodb](), [pricing](), [redis](), [vercel]() ### AI overview Vercel now allows Enterprise customers to use part of their Flexible Commitment for eligible resources purchased through the Vercel Marketplace. The initial supported partners include Neon, Supabase, MongoDB, and Redis, with more providers planned. ### Source excerpt Enterprise customers can now apply a portion of their Flexible Commitment toward eligible resources purchased through the Vercel Marketplace. With Flex Commit support, eligible Marketplace cost can draw directly from your existing commitment making it easier to provision the infrastructure and services your applications need. We're rolling this out with an initial set of Marketplace partners, including Neon, Supabase, Mongodb and Redis, with support for more providers coming. Eligible integrations are marked with a Flexible Commitment Eligible badge on their Marketplace page. With Marketplace Flex Commit you can: Use committed Enterprise spend for eligible Marketplace resources Eliminate separate credit card or procurement workflows Provision and manage third-party services directly from the Vercel Marketplace Track Marketplace usage alongside the rest of your Enterprise commitment This is the first step toward making the Marketplace a seamless part of the Enterprise experience. To learn more, read our pricing page documentation and reach out to your Account Manager. Read more [Next page]()