# Cache Published articles for Cache. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## JavaScript bundling and HTTP performance trade-offs DevFeed: [JavaScript bundling and HTTP performance trade-offs]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-11-23T23:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [JavaScript](), [HTTP](), [Caching](), [browserify](), [Webpack]() Tags: [browserify](), [cache](), [engineering](), [http](), [infrastructure](), [js](), [news](), [webpack]() ### AI overview The article examines trade-offs between bundling JavaScript files, serving them separately, and using semantically related bundles. It explains how these approaches balance HTTP request overhead, browser fetch limits, cacheability, and unused downloaded code. ### Source excerpt By Craig Silverstein Bundling JavaScript files The traditional advice for web developers is to bundle the JavaScript files ... Read more ## Ensuring transaction-safety in Google App Engine DevFeed: [Ensuring transaction-safety in Google App Engine]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-06-27T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Transactions](), [Databases](), [Code](), [Google]() Tags: [cache](), [code](), [database](), [engineering](), [google](), [hooks](), [infrastructure](), [news](), [tracing](), [transactions]() ### AI overview The article explains how Khan Academy uses transactions and user-write locks with Google App Engine to prevent database corruption caused by concurrent requests overwriting one another. It describes hooks that detect transaction-safety violations and highlights caching and code-path mistakes that can cause stale data to be used inside transactions. ### Source excerpt By Craig Silverstein In last week's exciting post, I described an alternative to transactions that we use at ... Read more ## Memcached-Backed Content Infrastructure DevFeed: [Memcached-Backed Content Infrastructure]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-05-15T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Caching](), [Back end](), [data](), [Front end]() Tags: [backend](), [cache](), [engineering](), [infrastructure](), [memcached](), [news](), [profiling]() ### AI overview Khan Academy describes testing and rolling out a Memcached-backed content-serving backend. The article explains the existing content versioning and storage model, the performance limitations of fetching many individual items, and the prior approach of loading a compressed site-wide content bundle at server startup. ### Source excerpt By Ben Kraft Last post, I wrote about how we did profiling on App Engine's Memcached service to ... Read more ## Replica-aware routing public beta DevFeed: [Replica-aware routing public beta]() Original publisher: [Read original article]() Author: Amy Chen; Jan Mensch Published: 2026-09-15T13:15:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Routing (disambiguation)](), [Replication](), [Caching]() Tags: [cache](), [clickhouse](), [consistency](), [replication](), [routing]() ### AI overview ClickHouse introduces replica-aware routing in Public Beta for Enterprise customers. The feature routes requests to the same replica, allowing continued access to temporary tables and named sessions and supporting read-after-write consistency over HTTP or the native protocol. ### Source excerpt Temporary tables and named sessions live on a single ClickHouse replica, so a follow-up query routed elsewhere can't see them. Replica-aware routing pins your requests to the same replica over HTTP or the native protocol -- and here's how we built it. ## What's New in Apollo Client 4.3 DevFeed: [What's New in Apollo Client 4.3]() Original publisher: [Read original article]() Author: Jerel Miller Published: 2026-09-15T12:01:05Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [apollo-client](), [GraphQL](), [TypeScript](), [Cache](), [Code generation]() Tags: [apollo-client](), [cache](), [codegen](), [graphql](), [native](), [typescript]() ### AI overview Apollo Client 4.3 adds native custom scalar support, type-safe cache access, and improved TypeScript handling for incremental GraphQL data. The release also updates generated types and data-state narrowing for deferred fields. ### Source excerpt Apollo Client 4.3 is here: native custom scalar support, type-safe cache access, and more consistent @defer and @stream behavior. See what's new and upgrade today. ## Apollo Client 4.3 adds native custom scalar support and safer TypeScript types DevFeed: [Apollo Client 4.3 adds native custom scalar support and safer TypeScript types]() Original publisher: [Read original article]() Author: Jerel Miller Published: 2026-09-15T12:01:05Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [TypeScript](), [Cache](), [Caching](), [JSON]() Tags: [cache](), [graphql](), [json](), [typescript](), [upgrade]() ### AI overview Apollo Client 4.3 adds native custom scalar integration, type-safe cache access, and improved TypeScript handling for incremental GraphQL data. ### Source excerpt Apollo Client 4.3 is here: native custom scalar support, type-safe cache access, and more consistent @defer and @stream behavior. See what's new and upgrade today. ## Performance improvements in Percona Server 8.4.11-11 DevFeed: [Performance improvements in Percona Server 8.4.11-11]() Original publisher: [Read original article]() Author: Bogdan Degtyariov Published: 2026-09-15T11:52:54Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [Percona Server for MySQL](), [Percona](), [MySQL](), [Cache](), [Scalability](), [Database]() Tags: [benchmark](), [benchmarks](), [cache](), [io](), [mysql](), [percona](), [percona-server-for-mysql](), [performance](), [scalability]() ### AI overview This article describes performance and scalability improvements in Percona Server for MySQL 8.4.11-11, focusing on changes to the InnoDB buffer pool and page flushing. It explains how narrowing mutex coverage and using finer-grained latching allows physical reads to proceed more in parallel, particularly for read-heavy, I/O-bound workloads. ### Source excerpt Focusing on Percona Server 8.4.11-11 My previous post (Performance Progression of Percona Server for MySQL 8.4) did a brief review of the performance changes in Percona Server for MySQL 8.4 released in 2026. I recommend reading it first to better understand the material in this post. Version 8.4.11-11 includes patches that deliver significant improvements in ... Continued The post Performance improvements in Percona Server 8.4.11-11 appeared first on Percona. ## Symfony 6.4.46 released DevFeed: [Symfony 6.4.46 released]() Original publisher: [Read original article]() Author: Fabien Potencier Published: 2026-09-15T07:08:49Z Content type: release Language: en Sources: [Symfony Blog]() Topics: [Symfony](), [Security](), [OpenID connect (OIDC)](), [Cache]() Tags: [bug](), [cache](), [oidc](), [release](), [security](), [symfony](), [upgrade](), [versioning]() ### AI overview Symfony 6.4.46 is released with security, bug, translation, and component updates across the framework, including changes to Messenger, Form, HttpFoundation, OIDC handling, Cache, and other components. ### Source excerpt Symfony 6.4.46 has just been released. Read the Symfony upgrade guide to learn more about upgrading Symfony and use the SymfonyInsight upgrade reports to detect the code you will need to change in your project. Tip... ## Lightbits Inferra KV Cache Engine Claims 16x Session Density and 10M-Token Contexts DevFeed: [Lightbits Inferra KV Cache Engine Claims 16x Session Density and 10M-Token Contexts]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-14T16:23:21Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Inference](), [Inference Performance](), [Orchestration](), [GPU](), [Multi-tenancy](), [sglang](), [TensorRT](), [vllm](), [Security]() Tags: [ai](), [cache](), [concurrent](), [enterprise](), [gpu](), [inference](), [latency](), [nvme](), [orchestration](), [security]() ### AI overview Lightbits Labs is introducing Inferra, a KV cache orchestration engine for AI inference. It virtualizes GPU memory across DRAM and NVMe storage, preserving attention states for long-context and multi-session workloads. Lightbits claims up to 16 times more concurrent sessions, more than 100 times lower latency than recomputation, and context windows of up to 10 million tokens. Inferra supports vLLM, TensorRT, and SGLang and includes tiering, predictive prefetching, tenant isolation, and encrypted data transfer. ### Source excerpt Lightbits Labs, the company that invented NVMe over TCP, is moving into inference software with Inferra, a KV cache orchestration engine that makes its public debut tomorrow, September 15, at the AI Infra Summit in Santa Clara. The software virtualizes GPU memory across DRAM and NVMe storage tiers and turns the KV cache into a The post Lightbits Inferra KV Cache Engine Claims 16x Session Density and 10M-Token Contexts appeared first on StorageReview.com. ## 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 ## 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 ## A Week of Symfony #1028 (September 7-13, 2026) DevFeed: [A Week of Symfony #1028 (September 7-13, 2026)]() Original publisher: [Read original article]() Author: Javier Eguiluz Published: 2026-09-13T07:05:00Z Content type: news Language: en Sources: [Symfony Blog]() Topics: [Code](), [OAuth 2.0](), [OpenID connect (OIDC)](), [Security]() Tags: [cache](), [code](), [development](), [docs](), [http](), [pull-requests](), [security](), [symfony]() ### AI overview Symfony's weekly roundup covers standalone framework bundles, Docker-style console subcommands, expanded OAuth2 and OpenID Connect support, and SymfonyCon Warsaw 2026 announcements. It also reports merged pull requests, closed issues, and selected development fixes. ### Source excerpt This week, Symfony introduced standalone bundles for the framework features (MessengerBundle, HttpClientBundle, PropertyInfoBundle, etc.), added Docker-style console subcommands, and expanded OAuth2 and OpenID Connect support. In addition, we announced the... ## 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. ## How to call external APIs from Webflow without leaking API keys DevFeed: [How to call external APIs from Webflow without leaking API keys]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [API](), [API keys](), [Authentication](), [Back end](), [Automation](), [Caching](), [Next.js](), [Node.js](), [GitHub]() Tags: [api-keys](), [apis](), [authentication](), [automation](), [backend](), [cache](), [github](), [guides](), [how-to](), [next-js](), [node-js]() ### AI overview A practical guide to integrating external APIs with Webflow through a Webflow Cloud server-side proxy, client-side fetch for public APIs, or no-code automation. It emphasizes keeping API credentials out of the browser and explains why the proxy is preferred for authenticated production integrations. ### Source excerpt Learn three ways to connect external APIs to Webflow (server-side proxy, client-side fetch, and no-code automation). ## How we shipped 15 Tbps for OpenAI in 90 days (Session 2 of 3) DevFeed: [How we shipped 15 Tbps for OpenAI in 90 days (Session 2 of 3)]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-09-12T00:00:00Z Content type: article Language: en Sources: [Sridhar Rajarao]() Topics: [OpenAI](), [Network](), [GPU](), [Server](), [Cache](), [API](), [Oracle Database]() Tags: [api](), [architecture](), [build](), [cache](), [capacity](), [database](), [debugging](), [execution](), [gateway](), [gpu](), [network](), [object](), [openai](), [performance](), [platform-engineering](), [server](), [servers](), [sre](), [storage](), [testing](), [warp]() ### AI overview The second session describes turning an architecture for OpenAI's 15 Tbps system into a delivery plan. It covers coordinated capacity planning across network, gateway, server, storage, and database teams; caching object names through the Inventory API; delivery tracking; and performance validation. Early WARP testing found packet drops caused by an unsuitable MTU of 1500, which was changed to 9100. ### Source excerpt Architecture was only the first week. Session 2 is about the build: capacity, execution discipline, and the first signs that performance would be the real test. ## New Cache Aware Scheduling Fixes Out For Testing DevFeed: [New Cache Aware Scheduling Fixes Out For Testing]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-11T18:00:14Z Content type: news Language: en Sources: [Phoronix]() Topics: [Cache](), [Linux](), [cpu](), [intel](), [Latency]() Tags: [cache](), [cpu](), [desktop-linux](), [feature](), [intel](), [kernel](), [latency](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [processors](), [series](), [testing](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview New fixes to Linux kernel Cache Aware Scheduling are available for testing on Intel hybrid CPUs. The patches address tasks being stranded or moved away from their preferred last-level cache, along with a use-after-free issue, while known interference with Intel Turbo Boost Max Technology remains unresolved. ### Source excerpt Since being merged back during the Linux 7.2 cycle, the Cache Aware Scheduling code continues seeing more fixes as users test out this scheduling enhancement on different processors and system configurations... ## DeepSeek's new model sets a template for powerful LLMs that run lean DevFeed: [DeepSeek's new model sets a template for powerful LLMs that run lean]() Original publisher: [Read original article]() Author: Tobias Mann Published: 2026-09-11T07:15:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [deepseek](), [Large Language Model](), [Caching](), [Latency](), [Google]() Tags: [ai-and-ml](), [cache](), [compute](), [cost](), [datacenter](), [deepseek](), [embedding](), [flash](), [google](), [gpu](), [latency](), [llm](), [llms](), [model](), [release]() ### AI overview DeepSeek V4.1 Flash is a larger LLM whose architectural changes aim to reduce serving memory and compute needs. The article highlights lower KV-cache consumption, improved prompt processing, and an N-gram-based conditional memory module. ### Source excerpt DeepSeek V4.1 Flash proves that just because you build a bigger model doesn't mean you need more GPUs to serve it ## 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. ## Building Pinterest's VLM Serving Stack on NVIDIA Dynamo DevFeed: [Building Pinterest's VLM Serving Stack on NVIDIA Dynamo]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-09-10T23:08:16Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [vlm](), [Inference](), [model-deployment](), [Orchestration](), [Latency](), [Caching](), [Open Source Models & Datasets]() Tags: [blackwell](), [cache](), [dynamo](), [embeddings](), [engineering](), [inference](), [latency](), [llm](), [multimodal](), [multimodal-ai](), [nvidia](), [nvidia-blackwell](), [open-source](), [orchestration](), [performance](), [pinterest](), [vllm](), [vlm](), [vlm-serving]() ### AI overview Pinterest describes its VLM serving stack built on NVIDIA Blackwell GPUs and NVIDIA Dynamo. The stack addresses multimodal inference demands such as image processing, variable prefill costs, KV-cache pressure, routing, and cache offloading. ### Source excerpt Lei Pan | Senior Software Engineer; Salina Wu | Senior Software Engineer; Cristian Lopez | Software Engineer I; Guangtong Bai | Staff Software Engineer; Soam Acharya | Principal Engineer; Saurabh Vishwas Joshi | Principal Engineer; Chia-Wei Chen | Staff Software Engineer; Ambud Sharma | Principal Engineer Why VLM Serving Matters at Pinterest Pinterest is a visual search and discovery platform, so its AI systems must reason over both language and visual content. Vision-language models (VLMs), which can interpret images, compare visual candidates, and respond naturally to user intent, are becoming the foundation for the next generation of Pinterest experiences: Pinterest Assistant, hybrid search, multimodal reranking, content understanding, signal generation, content safety, and more. This direction also reflects Pinterest's broader strategy to customize open-source models to meet its product & scale needs. Pinterest Assistant is a standout example. This multi-turn conversational experience covers both user language and visual content. Serving it requires low-latency VLM inference over rich multimodal context as well as reworking Qwen3-VL with proprietary multimodal embeddings to cut runtime cost while improving performance. Serving VLMs, however, introduces more challenges compared to text-only LLM workloads. Requests may carry multiple images, require extra vision encoder computation, incur larger and more variable prefill cost, and create higher KV cache pressure. To support this new class of models & product experiences, we built Pinterest's VLM serving stack on top of NVIDIA Blackwell GPUs and NVIDIA Dynamo. Blackwell GPUs incorporate many architectural innovations that are uniquely positioned for today's most demanding AI workloads -- including higher BF16/FP8 compute throughput, increased memory bandwidth, and larger HBM memory capacity -- that enable dramatically higher performance for inference. Dynamo provides a distributed inference orchestration layer that g ## 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. ## How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra DevFeed: [How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T16:55:32Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference Performance](), [Language models]() Tags: [agentic-ai-generative-ai](), [benchmark](), [build-ai-agents](), [cache](), [developer-tools-techniques](), [gpu](), [inference](), [inference-performance](), [latency](), [mamba](), [mixture-of-experts](), [nemotron](), [nim](), [nvidia-blackwell](), [optimization]() ### AI overview The article explains how NVIDIA NIM bundles serving optimizations to improve throughput for Nemotron 3 Ultra while meeting latency targets on GPU infrastructure. ### Source excerpt Deploying a large language model is only the first step toward production-ready serving. Production teams also need to serve as many concurrent users as... ## 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... ## Qualcomm Talks Next-Gen Oryon CPU, Adreno GPU, and Hexagon NPU DevFeed: [Qualcomm Talks Next-Gen Oryon CPU, Adreno GPU, and Hexagon NPU]() Original publisher: [Read original article]() Author: Vic A Published: 2026-09-10T13:05:36Z Content type: article Language: en Sources: [ServeTheHome]() Topics: [cpu](), [GPU](), [Artificial Intelligence](), [Cache]() Tags: [adreno](), [architecture](), [cache](), [cpu](), [gpu](), [hexagon](), [memory](), [mobile](), [npu](), [oryon](), [performance](), [qualcomm](), [unity](), [unreal-engine]() ### AI overview Qualcomm disclosed details of the next-generation mobile Snapdragon platform, including an Oryon CPU with 5 GHz Prime cores and FlexCache, an Adreno GPU with Matrix Cores and local high-performance memory, and a Hexagon NPU with transformer acceleration, expanded shared memory, and support for context lengths up to 32K. ### Source excerpt Qualcomm disclosed more on its next-gen Oryon CPU, Adreno GPU, and Hexagon NPU that will power next-gen devices The post Qualcomm Talks Next-Gen Oryon CPU, Adreno GPU, and Hexagon NPU appeared first on ServeTheHome. 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