# Caching Published articles for Caching. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## September 2026 newsletter DevFeed: [September 2026 newsletter]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-17T13:42:49Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [releases](), [Replication](), [Grafana Cloud Metrics]() Tags: [2026](), [caching](), [clickhouse](), [events](), [news](), [newsletter](), [real-time](), [release](), [replication](), [september-2026]() ### AI overview The September 2026 ClickHouse newsletter covers the 26.8 release, including custom HTTP handlers, pipelined SQL, dynamic query filtering, tokenizer support, and other features. It also summarizes replication-queue diagnostics, CostBench results, preview releases, community contributions, and upcoming events. ### Source excerpt Welcome to the September 2026 ClickHouse newsletter, featuring ClickHouse 26.8, PromQL, On-Demand Compute, CostBench results, and the latest community news and events. ## 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. ## Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads DevFeed: [Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-16T14:42:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [dropbox](), [Artificial Intelligence](), [API](), [Orchestration](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [ai-ml-data-engineering](), [apache](), [apis](), [architecture](), [architecture-design](), [asynchronous-architecture](), [backend](), [caching](), [data-pipelines](), [development](), [distributed-systems](), [dropbox](), [dropbox-riviera-ai-platform](), [embeddings](), [enterprise-content-management](), [large-language-models](), [ml-data-engineering](), [model-context-protocol-mcp](), [news](), [orchestration](), [platform-engineering](), [plugins](), [rag](), [tika]() ### AI overview Dropbox has expanded Riviera from an internal file-preview service into a content-processing platform supporting more than 300 file formats and over 100 transformation capabilities. The platform supports Dropbox products including Search, Replay, Sign, and Dash, and provides APIs for asynchronous document conversion, media transcription, and structured metadata extraction for AI and RAG workflows. ### Source excerpt Dropbox has evolved Riviera from a file preview service into a universal content processing platform supporting more than 300 file formats and over 100 transformation capabilities. Processing hundreds of thousands of transformations per second, Riviera now supports Search, Replay, Sign, and Dash, while its APIs enable asynchronous content extraction for AI and RAG workflows. By Leela Kumili ## The User Write Lock: an Alternative to Transactions for Google App Engine DevFeed: [The User Write Lock: an Alternative to Transactions for Google App Engine]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-06-20T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Transactions](), [Databases](), [Google]() Tags: [caching](), [databases](), [engineering](), [google](), [infrastructure](), [news](), [processes](), [retry](), [transactions]() ### AI overview This developer article explains how transactions in Google App Engine's datastore provide atomicity for single-entity and multi-entity writes, but can introduce frozen datastore state, retries, idempotence requirements, and restrictions on datastore queries. It presents the User Write Lock as an alternative, although the supplied text ends before describing that alternative. ### Source excerpt By Craig Silverstein Transactions are the standard method in Google App Engine's datastore -- as in many databases ... Read more ## How Khan Academy Scaled to 2.5x Traffic During the Coronavirus Pandemic DevFeed: [How Khan Academy Scaled to 2.5x Traffic During the Coronavirus Pandemic]() Original publisher: [Read original article]() Author: Khan Academy Published: 2020-05-09T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Scalability](), [Architecture & Design](), [Serverless](), [Caching](), [Google Cloud Platform (GCP)](), [Load Balancing]() Tags: [architecture](), [caching](), [cdn](), [engineering](), [gcp](), [google-cloud](), [load-balancing](), [news](), [scalability](), [serverless](), [web]() ### AI overview Marta Kosarchyn explains how Khan Academy handled site usage reaching 2.5 times the previous year's level during the coronavirus pandemic. The article attributes the scalability to Google Cloud services, serverless infrastructure, Datastore, Memcache, Fastly CDN caching, and advance preparation. ### Source excerpt By Marta Kosarchyn Talk about rapid scaling... A few months ago I posted some thoughts on scaling and ... Read more ## Prompt Caching Support in Spring AI with Anthropic Claude DevFeed: [Prompt Caching Support in Spring AI with Anthropic Claude]() Original publisher: [Read original article]() Author: Stelios Anastasakis Published: 2026-09-16T07:49:56Z Content type: tutorial Language: en Sources: [Baeldung]() Topics: [Spring AI](), [Anthropic Claude](), [Caching]() Tags: [anthropic](), [anthropic-claude](), [artificial-intelligence](), [artificial-intelligence-anthropic-spring-ai-chatclient](), [caching](), [how-to](), [spring-ai](), [spring-ai-chatclient]() ### AI overview This tutorial explains how prompt caching works in Spring AI with Anthropic Claude. It covers dependencies, model-specific requirements and limitations, configuration options, caching hierarchy, and practical considerations. Prompt caching can reduce latency and input-token costs when prompt prefixes are reused. ### Source excerpt Learn how prompt caching works, the limitations for different Claude models, and how to use it in Spring AI. The post Prompt Caching Support in Spring AI with Anthropic Claude first appeared on Baeldung. ## Optimizing cost and latency with Amazon Bedrock prompt caching DevFeed: [Optimizing cost and latency with Amazon Bedrock prompt caching]() Original publisher: [Read original article]() Author: Daniel Abib Published: 2026-09-15T16:18:19Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [Caching](), [Latency](), [API](), [foundation-models](), [LangChain](), [Multi-tenancy]() Tags: [advanced-300](), [amazon-bedrock](), [api](), [caching](), [foundation-models](), [integration](), [langchain](), [latency](), [pricing](), [technical-how-to](), [tokens]() ### AI overview This AWS post explains how Amazon Bedrock prompt caching can reduce repeated input-token costs by up to 90 percent and lower time to first token when requests reuse the same context. It presents six scenarios using the Converse API, including document, system prompt, tool definition, mixed TTL, tenant-isolated, and LangChain caching. ### Source excerpt Prompt caching in Amazon Bedrock can cut input token costs by up to 90% when you repeatedly send the same context to foundation models. This post walks through six practical prompt caching scenarios using the Converse API: message content, system prompt, tool definition, mixed TTL, tenant isolation, and LangChain integration. ## Laravel MCP 1.0 Is Released DevFeed: [Laravel MCP 1.0 Is Released]() Original publisher: [Read original article]() Author: Eric L. Barnes Published: 2026-09-15T14:27:51Z Content type: release Language: en Sources: [Laravel]() Topics: [Laravel](), [Model Context Protocol (MCP)](), [OAuth](), [pkce](), [Caching](), [servers]() Tags: [caching](), [context-window](), [laravel](), [mcp](), [news](), [oauth](), [pkce](), [servers]() ### AI overview Laravel MCP 1.0 is the first stable release of Laravel's package for building Model Context Protocol servers. It adds protocol revision 2026-07-28 support, searchable tool catalogs, cache hints, stateless request handling, OAuth changes requiring PKCE, and Client ID Metadata Documents. ### Source excerpt Laravel MCP 1.0 ships protocol revision 2026-07-28, stateless servers, searchable tool catalogs, cache hints, and reworked OAuth with required PKCE. The post Laravel MCP 1.0 Is Released appeared first on Laravel News. Join the Laravel Newsletter to get Laravel articles like this directly in your inbox. ## 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. ## Agoda Replaces 72-Shard SQL Server Price Cache with DragonflyDB DevFeed: [Agoda Replaces 72-Shard SQL Server Price Cache with DragonflyDB]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-14T13:48:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Cache](), [Caching](), [migration](), [sql-server](), [Latency](), [Prometheus]() Tags: [a-b-testing](), [agoda-price-cache-dragonflydb](), [architecture-design](), [cache](), [caching](), [development](), [devops](), [distributed-cache](), [distributed-data](), [distributed-systems](), [latency](), [microservices](), [migration](), [news](), [prometheus](), [redis](), [sql-server]() ### AI overview Agoda migrated its 1.5 TB hotel Price Cache from 72 Microsoft SQL Server shards to DragonflyDB. The staged migration used workload testing, dual reads, parity metrics, and gradual traffic shifting; Agoda reported substantially lower P99 read latency. ### Source excerpt Agoda migrated its 1.5 TB hotel Price Cache from 72 SQL Server shards to DragonflyDB to handle growing read and write volumes. The migration used staged dual reads, parity validation, gradual traffic shifting, and decentralized failover detection. Agoda reports an approximately eightfold reduction in P99 read latency, with two DragonflyDB clusters providing high availability. By Leela Kumili ## Using Exact-Match Response Caching to Reduce LLM Costs DevFeed: [Using Exact-Match Response Caching to Reduce LLM Costs]() Original publisher: [Read original article]() Author: Abhilash Rao Mesala Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [Caching](), [Large Language Model]() Tags: [ai-engineering](), [ai-infrastructure](), [caching](), [contributed](), [cost](), [finops](), [generation](), [hash](), [llm](), [token]() ### AI overview The article explains how to reduce LLM costs by fingerprinting requests, context, model settings, and underlying data to create exact-match cache keys. Valid cached responses can be reused without calling the model. It distinguishes response caching from provider prompt caching, where only eligible prompt computation is reused. ### Source excerpt An LLM can answer the same question a thousand times and charge you each time. Before paying for another answer, The post Why an old caching trick is your secret to lower LLM costs appeared first on The New Stack. ## Cloudflare Tests Cache Transcoding to Reduce Storage Requirements DevFeed: [Cloudflare Tests Cache Transcoding to Reduce Storage Requirements]() Original publisher: [Read original article]() Author: Renato Losio Published: 2026-09-13T10:35:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Caching](), [Transcodings](), [cloud-infrastructure](), [Pingora]() Tags: [architecture-design](), [cache](), [caching](), [cdn](), [cloud](), [cloudflare](), [cloudflare-cache-transcoding](), [compression](), [cpu](), [data-centers](), [development](), [edge-computing](), [news](), [pingora](), [rust](), [storage]() ### AI overview Cloudflare is testing Cache Transcoding, a prototype that Zstandard-compresses eligible uncompressed text before it is stored in cache. The approach aims to increase effective cache capacity and reduce inter-data-center transfer, with configurable CPU and storage trade-offs. ### Source excerpt Cloudflare recently described a prototype called Cache Transcoding that compresses eligible cache content, mainly uncompressed text such as HTML, JSON, CSS, and JavaScript, using Zstandard before storing it on disk. The hyperscaler estimates that the approach could provide petabytes of additional effective cache capacity, although broader testing is still needed. By Renato Losio ## Cache invalidation is a distributed systems problem DevFeed: [Cache invalidation is a distributed systems problem]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-13T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Cache](), [Caching](), [distributed-systems]() Tags: [cache](), [cache-invalidation](), [caching](), [consistency](), [distributed](), [distributed-systems](), [ordering](), [race-conditions](), [redis](), [ttl]() ### AI overview Cache invalidation coordinates updates between a database and a cache without a shared transaction. The article explains how operation ordering can let a concurrent reader repopulate stale data and recommends writing to the database before invalidating the cache, followed by a second invalidation to close the remaining race. ### Source excerpt A cache and a database are two stores that must agree, which makes every invalidation a distributed transaction without a coordinator. The ordering matters more than the mechanism: invalidating before the database write leaves a window where a concurrent reader repopulates the cache with the old value and it stays wrong until the TTL expires. Deleting the key rather than writing the new value removes a whole class of ordering bug, because two concurrent deletes commute and two concurrent writes do not. ## Claims About RubyGems Caching and RubyDoc Code Execution DevFeed: [Claims About RubyGems Caching and RubyDoc Code Execution]() Original publisher: [Read original article]() Published: 2026-09-12T00:02:11Z Content type: opinion Language: en Sources: [Aaron Patterson]() Topics: [rubygems](), [Caching](), [vulnerability](), [Docker](), [Web Scraping]() Tags: [caching](), [docker](), [rubygems](), [scraping](), [vulnerability]() ### AI overview An opinion post discusses alleged RubyGems activity involving a caching vulnerability and web scraping. It also describes how published gems can cause RubyDoc.info to run YARD-loaded code in a networked Docker container. ### Source excerpt Today Reuters and the Wall Street Journal both reported about rogue AI agents at OpenAI attacking RubyGems.org. https://www.rubyhack.ai/ has an amazing writeup, and you should read it. I just wanted to make a quick post about it because it's wild. TL;DR: It seems like OpenAI Bots knew about the RubyGems caching vulnerability, tried to take advantage of it, and at the same time ran some weird web scraping code on RubyDoc.info. Back in May, socket.dev reported about a "GemStuffer Campaign" where someone (I guess OpenAI) was uploading tons of junk gems to RubyGems.org. For some reason, the gems would scrape UK government websites, then repackage the data as gems, and attempt to upload them to RubyGems. I honestly didn't think much about this (or even look into it) until Sydney Von Arx and Spencer Kitts (both co-authors on https://www.rubyhack.ai) contacted me asking about RubyGems. I thought the claims they were making were completely outlandish until I actually read the code in these "GemStuffer" gems. After reading the code in these gems, a couple things stood out to me. YARD Documentation First, the gems leverage YARD documentation to execute arbitrary code on host machines. In most of the examples you'll see a .yardopts file that looks like this: --load ./script.rb README.md lib/**/*.rb Here's a link to an example. If you have YARD installed, and you install this gem, then YARD will load and run whatever is in ./script.rb from inside the gem. I think it's pretty common knowledge that C extensions will execute extconf.rb (so you basically have an RCE vector), but I was surprised to find out that a documentation tool would do that too. Nobody is going to install a gem named slnleaker5 though, so why would this matter? Well, any time a Gem is published RubyDoc.info will download the gem and process the YARD documentation. RubyDoc.info will execute the arbitrary code inside a Docker container. The Docker container still has network access though, so these gems could ha ## 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. ## Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference DevFeed: [Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference]() Original publisher: [Read original article]() Author: Kareem Syed-Mohammed Published: 2026-09-10T21:58:09Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [Large Language Model](), [Low-Latency Inference](), [Caching](), [Inference](), [Inference Performance]() Tags: [advanced-300](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [announcements](), [benchmarks](), [caching](), [inference](), [latency](), [llama](), [llm](), [routing](), [tensorrt-llm](), [vllm]() ### AI overview Amazon SageMaker Inference introduces prefix-aware routing for LLM requests. By consistently sending requests with matching prompt prefixes to the same instance, it improves reuse of cached KV computations and can reduce time to first token. ### Source excerpt Amazon SageMaker Inference now offers prefix-aware routing, a routing strategy that sends requests sharing the same prompt prefix to the same instance so the KV cache stays warm. In benchmarks on Llama 3.1 70B, it reduced P50 time-to-first-token by up to 77% and raised KV cache hit rates from about 25% to over 80%. ## Reduce inference cold starts on Amazon SageMaker HyperPod with model caching DevFeed: [Reduce inference cold starts on Amazon SageMaker HyperPod with model caching]() Original publisher: [Read original article]() Author: Kareem Syed-Mohammed Published: 2026-09-10T21:37:49Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Caching](), [Inference](), [model-deployment](), [Large Language Model](), [autoscaling](), [Kubernetes](), [Amazon S3]() Tags: [amazon](), [amazon-s3](), [amazon-sagemaker](), [amazon-sagemaker-hyperpod](), [announcements](), [autoscaling](), [benchmark](), [caching](), [expert-400](), [inference](), [kubernetes](), [llm]() ### AI overview Amazon SageMaker HyperPod model caching preloads model weights and container images onto cluster nodes, reducing inference-pod cold starts by serving assets from local NVMe storage. ### Source excerpt Amazon SageMaker HyperPod now supports model caching for inference, which pre-loads model weights and container images onto cluster nodes so pods read from local NVMe storage instead of downloading over the network. Learn how model caching cuts cold starts from tens of minutes to seconds, how it works, and how to enable it. ## 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. ## Improving Lakebase Postgres compute cache DevFeed: [Improving Lakebase Postgres compute cache]() Original publisher: [Read original article]() Author: David Wein; Sunil Kamath; Haoyu Huang Published: 2026-09-10T13:47:03Z Content type: article Language: en Sources: [Databricks]() Topics: [Caching](), [databricks](), [Databases](), [autoscaling](), [Amazon S3](), [Filesystems](), [cloud-infrastructure]() Tags: [autoscaling](), [cache](), [caching](), [data](), [data-engineering](), [databricks](), [engineering](), [filesystem](), [latency](), [low-latency](), [memory](), [s3](), [storage]() ### AI overview This Databricks article describes improvements to compute-side caching for Lakebase Postgres, a disaggregated storage system backed by object storage such as Amazon S3. It explains PostgreSQL shared buffers, the operating system page cache, and the planned use of dynamically autoscaling shared buffers consuming up to 75% of compute memory. It also introduces a local file cache as an incremental solution for fixed compute instances. ### Source excerpt The disaggregated storage model of Lakebase Postgres provides a feature rich, flexible... ## Why You're Getting 429s in Production Using Auth0 and How to Stop Them DevFeed: [Why You're Getting 429s in Production Using Auth0 and How to Stop Them]() Original publisher: [Read original article]() Author: Carla Urrea Stabile Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Auth0 Blog]() Topics: [Auth0](), [API](), [Authorization](), [Caching](), [JSON Web Tokens](), [Latency]() Tags: [apis](), [auth0](), [authorization](), [caching](), [errors](), [how-to](), [jwt](), [latency](), [production](), [token]() ### AI overview This tutorial explains why Auth0 API requests return 429 rate-limit errors in production. It identifies per-request Management API calls and repeatedly fetching machine-to-machine tokens as common causes, and recommends putting suitable authorization data in JWT claims, caching reusable tokens, logging rate-limit headers, and using short-lived tokens or live checks when changes must take effect immediately. ### Source excerpt Getting Auth0 429 status code errors in production? They almost always trace back to APIs rate limit. Here are the two causes and how to fix them. ## ICYMI: What landed for AI builders in August 2026 DevFeed: [ICYMI: What landed for AI builders in August 2026]() Original publisher: [Read original article]() Author: Tanvi Girinath Published: 2026-09-09T20:01:03Z Content type: news Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [Inference](), [Caching](), [SDKs](), [Deployment]() Tags: [agents](), [ai](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [announcements](), [caching](), [deployment](), [govcloud](), [inference](), [intermediate-200](), [latency](), [open](), [robots](), [updates]() ### AI overview A recap of August 2026 Amazon Bedrock, AgentCore, and Strands updates for AI builders, including expanded context windows, inference, long-running agents, GovCloud availability, and robot deployment. ### Source excerpt A recap of August 2026 launches for AI builders across Amazon Bedrock, Amazon Bedrock AgentCore, and Strands: million-token context for OpenAI models, cross-Region inference, agents that run for up to 14 days on dedicated compute, expanded AWS GovCloud availability, and Strands Robots for physical deployment. ## Negative caching protects databases from repeated lookups for nonexistent keys DevFeed: [Negative caching protects databases from repeated lookups for nonexistent keys]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-09T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Caching](), [Cache](), [Database](), [Security](), [Latency]() Tags: [cache](), [cache-penetration](), [caching](), [database](), [negative-caching](), [protection](), [redis](), [reliability](), [security](), [ttl]() ### AI overview Negative caching prevents repeated database queries for nonexistent keys by storing a distinguishable marker for negative results. The article explains how partner integrations, scrapers, stale clients, migrations, or attackers can exploit this gap and recommends using a shorter TTL for negative entries. ### Source excerpt A cache that stores only found values gives you no protection against lookups for things that do not exist, and a miss on a nonexistent key costs the full origin query every single time. If the key is user supplied, an attacker can generate unlimited unique misses and bypass the cache entirely, which is cache penetration. Caching the negative result fixes it, with a shorter TTL than positive entries because a value appearing is a much more likely event than one disappearing. ## DeepSeek V4.1 Flash now available on AI Gateway DevFeed: [DeepSeek V4.1 Flash now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [deepseek](), [Artificial Intelligence](), [API](), [Language models](), [AI-assisted coding](), [Claude Code](), [codex](), [cursor]() Tags: [ai](), [ai-gateway](), [api](), [caching](), [codex](), [context-window](), [cursor](), [deepseek](), [responses](), [tool](), [vercel](), [vision]() ### AI overview DeepSeek V4.1 Flash is now available through Vercel's AI Gateway, offering native image understanding, a 1 million token context window, responses up to 384,000 tokens, reasoning, tool use, and prompt caching. Developers can use it in Claude Code, Codex, Cursor, and other coding agents with the model name deepseek/deepseek-v4.1-flash. ### Source excerpt DeepSeek V4.1 Flash is now available on AI Gateway with native image understanding. V4.1 Flash has vision support and accepts text and images in the same request, so you can ask questions about screenshots, read charts, and extract information from visual content. The model has a 1 million token context window and supports responses up to 384,000 tokens, along with reasoning, tool use, and prompt caching. Its new architecture processes input and generates output with separate components, reducing the active computation needed for each stage. Use deepseek/deepseek-v4.1-flash as the model name: To use it in Claude Code, Codex, Cursor, and more, install the latest Vercel CLI and run setup: Then select deepseek/deepseek-v4.1-flash in the agent. See the coding agents guide for details. Try DeepSeek V4.1 Flash in the model playground. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. You can view all language models available on AI Gateway. Read more ## Architecting for 6 Billion Daily Requests: Inside Wix's Media Platform DevFeed: [Architecting for 6 Billion Daily Requests: Inside Wix's Media Platform]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-09-08T08:24:05Z Content type: article Language: en Sources: [Wix Engineering]() Topics: [Caching](), [Go Language](), [Latency](), [Compression](), [data](), [C]() Tags: [c](), [cache](), [caching](), [compression](), [cpu](), [data](), [go](), [latency](), [technical]() ### AI overview Wix describes engineering challenges in its media platform, which processes images, video, and audio at large scale. The supplied text explains how the team used unsafe pointers to reduce Go/C memory-copy overhead for AVIF encoding and a tiered Masters cache to reduce CPU work during image resizing. ### Source excerpt At Wix, "Media" isn't just about storing files. It's about processing streams of information - images, video, and audio - at an immense scale. We host over 300 million websites and serve more than 6 billion media requests every single day. To handle dozens of petabytes of data while keeping costs low and latency minimal, we had to treat media handling as a complex engineering discipline. Below are five specific technical challenges we faced and the architectural solutions we implemented to... [Next page]()