# batch Published articles for batch. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A Bootiful Podcast: Spring Tools lead Martin Lippert DevFeed: [A Bootiful Podcast: Spring Tools lead Martin Lippert]() Original publisher: [Read original article]() Author: joshlong Published: 2026-09-17T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [developer tooling](), [Development](), [Java](), [AI-assisted coding](), [Model Context Protocol (MCP)](), [vs-code]() Tags: [ai-coding-agents](), [batch](), [boot](), [cloud](), [developer-tooling](), [development](), [event-driven](), [java](), [mcp](), [microservices](), [podcast](), [reactive](), [serverless](), [spring](), [vs-code](), [web-applications]() ### AI overview A podcast conversation with Spring Tools lead Martin Lippert examines the evolution of developer tooling from Eclipse to VS Code and beyond. It discusses Spring Tools architecture, language servers, MCP, developer flow, and how AI coding agents can better understand Spring projects. ### Source excerpt Hi, Spring fans! In this conversation, Spring Tools lead Martin Lippert joins us for a fascinating look at how developer tooling has evolved from Eclipse to VS Code and beyond. We dig into the architecture behind Spring Tools, language servers, MCP, and how the same ideas that once helped developers stay in flow are now helping AI coding agents understand Spring projects better. If you care about the future of Java, IDEs, and AI-assisted development, this one's packed with insight. ## New data pipeline management platform at Khan Academy DevFeed: [New data pipeline management platform at Khan Academy]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-04-30T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [data](), [DataOps](), [systems]() Tags: [batch](), [bigquery](), [cloud-dataflow](), [data](), [data-pipelines](), [debugging](), [engineering](), [infrastructure](), [news](), [pipeline](), [scheduling]() ### AI overview Khan Academy developed Khanalytics to manage its growing collection of data pipelines. The platform provides a sandboxed environment for batch jobs, a web interface, automatic parallelization, centralized logs, and pipeline scheduling with dependencies. ### Source excerpt By Ragini Gupta Data is very crucial to Khan Academy and is itself an internal product for the ... Read more ## Spring Office Hours Podcast: S5E23 - Java 27 Release Party with Billy Korando DevFeed: [Spring Office Hours Podcast: S5E23 - Java 27 Release Party with Billy Korando]() Original publisher: [Read original article]() Author: danvega Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Spring]() Topics: [Java](), [java-27](), [JDK 27]() Tags: [batch](), [boot](), [cloud](), [developer](), [ecosystem](), [event-driven](), [java](), [java-27](), [live](), [live-stream](), [microservices](), [podcast](), [reactive](), [release](), [serverless](), [spring](), [web-applications]() ### AI overview A Spring Office Hours podcast episode discusses Java 27 with Billy Korando and highlights related launch updates and selected JDK enhancement proposals. ### Source excerpt Join Dan Vega and DaShaun Carter for the latest updates from the Spring Ecosystem. In this episode, Dan and DaShaun are joined by Kansas City JUG organizer and Java Developer Advocate, Billy Korando. In this episode, we celebrate the release of Java 27. You can participate in our live stream to ask questions or catch the replay on your preferred podcast platform. Show Notes JDK 27 Java 27 Launch Stream JEP 534: Compact Object Headers by Default JEP 523: Make G1 the Default Garbage Collector in All Environments JEP 533: Structured Concurrency (Seventh Preview) JEP 531: Lazy Constants (Third Preview) JEP 536: JFR In-Process Data Redaction Transitioning Java to more frequent security updates Project Babylon S5E22 - Live from KCDC Billy Korando on LinkedIn The show: springofficehours.io - episodes, schedule, community Spring Developer on YouTube - join us live every Monday Dan Vega DaShaun Carter The Spring Blog - the news we cover each week ## This Week in Spring - September 15th, 2026 DevFeed: [This Week in Spring - September 15th, 2026]() Original publisher: [Read original article]() Author: joshlong Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [Spring AI](), [Java](), [Security](), [OAuth 2.0](), [Spring Boot](), [JavaFX](), [IntelliJ IDEA](), [debugging]() Tags: [batch](), [boot](), [cloud](), [debugging](), [event-driven](), [intellij-idea](), [java](), [learn](), [microservices](), [oauth-2-0](), [reactive](), [security](), [serverless](), [spring](), [spring-ai](), [web-applications]() ### AI overview A September 15, 2026 roundup of Spring-related developer content, covering Java memory management, Spring AI chat-memory summarization, JavaFX application security with Spring Security and OAuth 2.0, OAuth 2.1 with Spring Authorization Server, Spring and Gemini video understanding, Java AI libraries, Netflix's Java and Spring story, Spring Batch, and Spring Boot debugging. ### Source excerpt Hi, Spring fans! Welcome to another rip-roarin' installment of This Week in Spring! I'm writing this to you from sun-kissed San Francisco, CA, sipping some coffee and watching the bay from my breakfast nook. What a wonderful day! A wonderful day in which to learn about the latest and greatest in Spring, even! Let's dive right in! I really loved this presentation by Oracle's Ron Pressler on the principles of memory management in Java More Craig Walls Spring AI goodness! Here's a nice recipe on summarizing chat memory Over the last few weeks, I've done some content on securing JavaFX applications with Spring Security and OAuth 2.0. That work has landed, and our friends at JFX-central.com have taken that video and the resulting code and transcribed the content into this lovely tutorial - check it out! I loved this post on securing modern applications with OAuth 2.1 and Spring Authorization Server This is a really cool video on understanding video with Spring and Gemini I just learned about this amazing new Java library called Quixotic.ai (what a name! LOL) that provides all sorts of cool stuff that might make your Java-based AI workloads even better. I wonder if there are amazing integration possibilities for Spring AI, too... In last week's A Bootiful Podcast, I was delighted to sit down and chat with Netflix's Paul Bakker on their Java and Spring story, scaling the system, and more. Huh! There's a new Spring Batch IntelliJ IDEA plugin, but search me for what's new! No release notes. Either way, get it while it's hot! Speaking of Spring Batch, there's a nice post here on scaling to millions of rows with Spring Batch that just dropped Speaking of Spring and IntelliJ, there's a nice article over on Baeldung on debugging Spring Boot-based workloads with the Spring debugger ## Monitor TAS and gang scheduling for AI training in Kubernetes DevFeed: [Monitor TAS and gang scheduling for AI training in Kubernetes]() Original publisher: [Read original article]() Author: David Lentz; Kathy Lin Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Kubernetes](), [kueue](), [distributed-training](), [GPU](), [Monitoring](), [datadog]() Tags: [ai](), [batch](), [containers](), [datadog](), [distributed-training](), [gpu](), [gpu-monitoring](), [kubernetes](), [kueue](), [monitoring](), [scheduler]() ### AI overview This article explains why Kubernetes scheduling is insufficient for distributed AI training workloads and how topology-aware scheduling and gang scheduling address hardware placement and simultaneous startup requirements. It discusses implementing these capabilities with Kueue and the Coscheduling plugin, and monitoring and troubleshooting them with Datadog GPU Monitoring. ### Source excerpt Learn how Datadog helps you correlate Kueue, Coscheduling, GPU, and training framework signals to validate gang scheduling and topology-aware scheduling. ## 🍔🧠 Pinterest's Fix for the Hardest Problem in ML Infra DevFeed: [🍔🧠 Pinterest's Fix for the Hardest Problem in ML Infra]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-09-14T15:31:30Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [recommendation systems](), [Feature Engineering](), [Inference](), [Streaming](), [configuration](), [Framework](), [data](), [Low Latency]() Tags: [batch](), [configuration](), [data](), [feature-engineering](), [low-latency](), [ml](), [pinterest](), [ranking](), [recommendation-systems](), [streaming]() ### AI overview Pinterest redesigned its user-sequence platform for ranking, retrieval, and recommendation systems by defining signals once and instantiating them consistently across streaming, batch, and serving workloads. The approach uses Python configuration with validated schemas, a shared execution engine, cooperating streaming and batch paths, and columnar time-partitioned storage to improve freshness, completeness, consistency, and operational efficiency. ### Source excerpt PLUS: OpenAI agents beat math 🧮, Test techniques for agents ⚡, Postgres survival guide 📖 ## Speeding Up Azure Durable Functions with Fan-Out/Fan-In and a Higher Concurrency Limit DevFeed: [Speeding Up Azure Durable Functions with Fan-Out/Fan-In and a Higher Concurrency Limit]() Original publisher: [Read original article]() Author: Michael Li Published: 2026-09-14T12:00:32Z Content type: tutorial Language: en Sources: [Atomic Object]() Topics: [Azure](), [Concurrent Programming](), [Code]() Tags: [apis](), [azure](), [azure-functions](), [batch](), [concurrency](), [development-practices](), [file](), [storage]() ### AI overview An Azure Durable Functions batch job was accelerated by combining fan-out/fan-in orchestration with a higher function-host concurrency limit. Together, the changes reduced a representative run from about 710 seconds to about 240 seconds. ### Source excerpt For one of our projects, we run a nightly job that generates a batch of files. It's a long process: pulling records from an upstream system, calling several APIs to gather info about each one, generating a file per record, and writing the results back to storage. For a small batch, this works fine, but [...] The post 2 Changes Made Our Azure Durable Functions 3X Faster appeared first on Atomic Spin. ## A Bootiful Podcast: Netflix's Paul Bakker DevFeed: [A Bootiful Podcast: Netflix's Paul Bakker]() Original publisher: [Read original article]() Author: joshlong Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [Concurrency](), [Concurrent Programming](), [Tooling]() Tags: [ai](), [batch](), [boot](), [cloud](), [complexity](), [concurrency](), [event-driven](), [java](), [microservices](), [netflix](), [podcast](), [reactive](), [serverless](), [spring](), [tooling](), [web-applications]() ### AI overview A podcast interview with Netflix's Paul Bakker on scaling Java, adopting newer JDKs to reduce costs, and using virtual threads and structured concurrency instead of reactive complexity. It also previews AI-powered tooling and Project Valhalla's future. ### Source excerpt Hi, Spring fans! This week's interview is a must-watch if you care about where Java is headed next. This week, I talk to Netflix's Paull Bakker! We dig into Netflix's real-world playbook for scaling Java, cutting costs with newer JDKs, and replacing reactive complexity with virtual threads and structured concurrency. Plus, there's a sneak peek at the exciting future of Java, from AI-powered tooling to the upcoming Valhalla era. ## Announcing 90-minute function timeout on AWS Lambda Managed Instances DevFeed: [Announcing 90-minute function timeout on AWS Lambda Managed Instances]() Original publisher: [Read original article]() Author: Tarun Rai Madan Published: 2026-09-09T19:08:14Z Content type: release Language: en Sources: [AWS Compute Blog]() Topics: [data-processing](), [Transcodings](), [Amazon EC2]() Tags: [ai-inference](), [announcements](), [aws](), [aws-lambda](), [batch](), [data-processing](), [event](), [event-driven](), [foundational-100](), [serverless]() ### AI overview AWS Lambda Managed Instances now support a 90-minute timeout for asynchronous and event source mapping invocations, increasing the previous 15-minute limit by six times. The update targets longer-running data processing, media transcoding, AI inference, and batch workloads. ### Source excerpt AWS Lambda now supports a 90-minute function timeout for asynchronous and event source mapping (ESM) invocations on Lambda Managed Instances, a 6x increase from the previous 15-minute limit. Data processing, media transcoding, AI inference, and batch workloads can now run on Lambda without re-architecting. ## Automate user-level custom permissions for Amazon Quick DevFeed: [Automate user-level custom permissions for Amazon Quick]() Original publisher: [Read original article]() Author: Ashok Dasineni Published: 2026-09-09T15:45:24Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Authorization](), [AWS Lambda](), [IAM Identity Center](), [Script]() Tags: [amazon-eventbridge](), [amazon-quick-suite](), [api](), [automation](), [aws-iam-identity-center](), [aws-lambda](), [batch](), [cli](), [intermediate-200](), [python](), [technical-how-to]() ### AI overview This tutorial presents four ways to automate user-level custom permissions in Amazon Quick: setting permissions during user registration, applying account or role defaults, using EventBridge and Lambda for group-based logic, and running retroactive batch updates. ### Source excerpt Amazon Quick custom permissions let you enforce least-privilege access by toggling features per user. This post walks through four patterns to automate custom permissions across the user lifecycle: a RegisterUser API parameter, account and role defaults, event-driven Amazon EventBridge and AWS Lambda automation, and a retroactive batch update script. ## Spring Tools 5.4.0 released DevFeed: [Spring Tools 5.4.0 released]() Original publisher: [Read original article]() Author: martinlippert Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [Spring]() Topics: [releases](), [Claude](), [Model Context Protocol](), [Visual Studio Code]() Tags: [batch](), [boot](), [claude](), [claude-code](), [cloud](), [event-driven](), [java](), [mcp](), [microservices](), [performance](), [reactive](), [release](), [release-notes](), [serverless](), [spring](), [tools](), [updates](), [validation](), [web-applications]() ### AI overview Spring Tools 5.4.0 has been released for several development environments, with new validations and quick fixes, Claude Code and MCP enhancements, and stability and performance improvements. ### Source excerpt On behalf of the team and everyone who has contributed, I am pleased to announce the 5.4.0 release of the Spring Tools for Visual Studio Code, Cursor, Eclipse, Theia - and Claude Code. Hightlights New Validations & Quick Fixes: Added validations and quick fixes to convert to @ApplicationModuleListener, @SpringJUnitConfig, @RestController, and specific @Scope annotations Claude Code / MCP Enhancements: Enabled the Claude Code plugin to render a project's logical structure Stability & Performance: Significant speedups around various quick fixes and repository-based version validation + improved reliability while indexing source code Updates to the Spring Tools for Eclipse distribution updated to the latest Eclipse 2026-09 release (new and noteworthy) Detailed changes can be found in the release notes: https://github.com/spring-projects/spring-tools/releases/tag/5.4.0.RELEASE Downloads To download the distribution for Eclipse and find links to the marketplace entries for Visual Studio Code, Cursor, and Theia, please go visit: Spring Tools: https://spring.io/tools/ Next up is the 5.5.0 release, currently scheduled for mid December 2026. ## Spring Office Hours Podcast: S5E22 - Live from KCDC DevFeed: [Spring Office Hours Podcast: S5E22 - Live from KCDC]() Original publisher: [Read original article]() Author: danvega Published: 2026-09-09T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [Spring AI](), [Java](), [JDK 27]() Tags: [ai](), [batch](), [boot](), [cloud](), [conference](), [event-driven](), [events](), [java](), [jdk-27](), [live-stream](), [microservices](), [podcast](), [reactive](), [serverless](), [spring](), [web-applications]() ### AI overview Spring Office Hours Podcast episode S5E22 features Dan Vega and DaShaun Carter broadcasting from the Kansas City Developer Conference, with conference highlights, attendee and speaker conversations, and news from the Spring and Java ecosystems. ### Source excerpt Join Dan Vega and DaShaun Carter for the latest updates from the Spring Ecosystem. In this episode, Dan and DaShaun broadcast live from the Kansas City Developer Conference, one of the largest community driven developer events in the country. Expect conference highlights, hallway track conversations with speakers and attendees, and the latest news from the Spring and Java worlds. You can participate in our live stream to ask questions or catch the replay on your preferred podcast platform. Show Notes KCDC Spring AI Recipes Spring Modulith Spring AI Community Project Babylon JDK 27 The show: springofficehours.io - episodes, schedule, community Spring Developer on YouTube - join us live every Monday Dan Vega DaShaun Carter The Spring Blog - the news we cover each week ## Kubernetes v1.37: Advancing Workload-Aware Scheduling DevFeed: [Kubernetes v1.37: Advancing Workload-Aware Scheduling]() Original publisher: [Read original article]() Published: 2026-09-08T18:30:00Z Content type: release Language: en Sources: [Kubernetes Blog]() Topics: [releases]() Tags: [api](), [batch](), [go](), [integration](), [kubernetes](), [release]() ### AI overview Kubernetes v1.37 advances workload-aware scheduling by promoting Workload and PodGroup APIs, gang scheduling, workload-aware preemption, and shared DRA ResourceClaims for PodGroups to Beta. It also introduces CompositePodGroup and controller integration APIs for complex distributed and batch workloads. ### Source excerpt AI/ML and complex batch workloads continue to push the boundaries of Kubernetes scheduling. Following the foundational workload-centric enhancements introduced in previous releases, Kubernetes v1.37 delivers the next major milestone in the Workload-Aware Scheduling (WAS) journey. In this release, the core Workload and PodGroup APIs--enabling gang scheduling--along with Workload-Aware Preemption (WAP) and shared DRA ResourceClaims for PodGroups, all graduate to Beta, solidifying their role in the Kubernetes ecosystem. To address the hierarchical scheduling requirements of modern high-performance distributed workloads, v1.37 introduces the new CompositePodGroup API. This new API allows expressing multi-level topology constraints, gang scheduling, and preemption policies for complex, heterogeneous groups of Pods. Crucially, this architectural expansion unlocks native scheduling support for advanced workload structures commonly managed by higher-order extension APIs such as JobSet and LeaderWorkerSet (LWS). Alongside these API additions, v1.37 focuses on streamlining adoption by introducing a new set of controller integration APIs and the workloadbuilder Go library. These provide standardized building blocks that significantly simplify how out-of-tree controllers can integrate with WAS capabilities. Utilizing these new tools, the native Job controller integration has been upgraded to fully consume the expanded WAS APIs--enabling advanced scheduling policies, flexible disruption modes, and topology-aware scheduling for standard batch workloads. Gang scheduling and Workload / PodGroup APIs Kubernetes v1.37 delivers a major milestone: Workload / PodGroup APIs and gang scheduling are officially graduating to Beta. This graduation signals that native, "all-or-nothing" scheduling for workloads is solidifying for wider adoption. Key updates to the API and gang scheduling algorithm in this release include: Beta graduation and API versioning changes The core Workload and PodGroup A ## This Week in Spring - September 8th, 2026 DevFeed: [This Week in Spring - September 8th, 2026]() Original publisher: [Read original article]() Author: joshlong Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [Artificial Intelligence](), [Large Language Model](), [Electron](), [App](), [IntelliJ IDEA]() Tags: [ai](), [applications](), [article](), [batch](), [boot](), [cloud](), [event-driven](), [idea](), [intellij](), [intellij-idea](), [java](), [linux](), [llm](), [llms](), [microservices](), [oauth](), [reactive](), [serverless](), [spring](), [spring-ai](), [spring-boot](), [video](), [web-applications](), [windows]() ### AI overview This weekly Spring roundup highlights Spring AI content about running LLMs in the JVM and locally, building a Spring AI starter for agents.md, and improving tool use. It also covers Spring Boot and JavaFX desktop applications, native images, Spring Security OAuth clients, secure application images, external configuration, Spring Cloud AWS, and a Spring Boot Analyzer. ### Source excerpt Bonjour a tout le monde! Welcome to another rip-roarin' installment of This Week in Spring! It's a fabulous and fun day here in Paris, France, as I wait to board the train to Amsterdam for the IntelliJ IDEA conference! It's going to be amazing. We've got another incredible week's roundup to dive into, so let's do it! I feel like some of the best content over the past several months in this weekly roundup has been Craig Walls' Spring AI Recipes section. Fantastic stuff! This latest one looks at running an LLM in-JVM This is a nice article on using vertical slices in Spring Boot on Solodev.sk, by Dominik. Well done! Another amazing installment from Craig Walls, this one looking at running against local LLMs My friend and colleague DaShaun Carter talks about his Spring AI starter for agents.md Craig also has this lovely post on efficient tool use in Spring AI Last week, I did two videos on using Spring Boot, JavaFX, GraalVM native images, and Spring Security (and PKCE) to build native, lightning-fast, dynamic, efficient, reusable desktop applications that run well on Mac, Windows, and Linux, and look amazing, while taking small fractions of the RAM of a similar Electron-based application. Here's the first one, showing how to use Spring Boot and JavaFX together, so that you get the component model, event dispatch subsystem, internationalization, lifecycle management, and, of course, the entire and very rich ecosystem of Spring components and can use them to manage JavaFX components, too. We also look at native image compilation. Here's the second video, which looks at using Spring Security's OAuth client in the context of a desktop application, which can not, by definition, hold a client secret. In last week's installment of A Bootiful Podcast, I was delighted to chat with BellSoft's Catherine Edelveis about trusted and secure images for your Spring Boot applications This is a nice post on managing external configurations with Spring Cloud Config A nice recap of some of ## Delivering Real-Time Personalization with Databricks and Redis DevFeed: [Delivering Real-Time Personalization with Databricks and Redis]() Original publisher: [Read original article]() Author: Philip Laussermair, Anant Pingle Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [data]() Tags: [analytics](), [apache-flink](), [batch](), [data](), [databricks](), [e-commerce](), [performance](), [personalization](), [real-time](), [redis](), [spark](), [streaming](), [tech]() ### AI overview The article explains how Databricks Real-Time Mode and Redis support low-latency personalization by continuously processing event streams and serving fresh results quickly. ### Source excerpt Why real-time matters A customer is browsing an e-commerce site. They search for running shoes, open a product, read reviews, and add an item to the cart. Every one of those actions is a signal about what they want right now. If the homepage they lan... ## How we built a benchmarking framework to horizontally accelerate transaction model research DevFeed: [How we built a benchmarking framework to horizontally accelerate transaction model research]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-03T13:53:30Z Content type: article Language: en Sources: [Nubank]() Topics: [benchmarking](), [Benchmark](), [Transformer](), [scaling](), [data](), [experiments]() Tags: [batch](), [benchmarking](), [data-science-machine-learning](), [distributed](), [evaluation](), [performance](), [research](), [training](), [workflow]() ### AI overview Nubank describes a benchmarking framework for horizontally evaluating transformer-based transaction representation models across more than 20 benchmarks. The automated, reproducible workflow tests data, architecture, and training changes across multiple tasks and trials, helping identify improvements that generalize. It increased the team's experimentation capacity to roughly five times more experiments per month. ### Source excerpt The framework that transformed weeks of manual experimentation into automated pipelines for horizontal transaction model research The post How we built a benchmarking framework to horizontally accelerate transaction model research appeared first on Building Nubank. ## A Bootiful Podcast: BellSoft's Catherine Edelveis on hardened runtime images, container security, and more DevFeed: [A Bootiful Podcast: BellSoft's Catherine Edelveis on hardened runtime images, container security, and more]() Original publisher: [Read original article]() Author: joshlong Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [container-security]() Tags: [batch](), [boot](), [cloud](), [container-security](), [event-driven](), [java](), [microservices](), [podcast](), [reactive](), [security](), [serverless](), [spring](), [spring-boot](), [web-applications]() ### AI overview A podcast conversation about using buildpacks and hardened images to ship Spring Boot applications with stronger security, cleaner defaults, and less Dockerfile work. ### Source excerpt Hi, Spring fans! I chat with BellSoft's Catherine Edelweiss about using buildpacks and hardened images to ship Spring Boot apps with stronger security, cleaner defaults, and far less Dockerfile pain. #BellSoft #Docker #Java #JRE #SpringBoot ## Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference DevFeed: [Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-02T16:04:19Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference](), [Large Language Model]() Tags: [accelerate](), [agentic-ai-generative-ai](), [ai](), [ai-inference](), [ai-models](), [batch](), [data-center-cloud](), [developer-tools-techniques](), [how-to](), [inference](), [inference-performance](), [llm](), [llms](), [nvfp4](), [training-ai-models]() ### AI overview The article explains speculative decoding as a way to speed up LLM inference while preserving standard-decoding outputs. A smaller draft model proposes several tokens, which the larger target model verifies in parallel; it also defines draft and acceptance lengths and gives a speedup formula. ### Source excerpt This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and... ## Async Programming in Python: From Generators to asyncio DevFeed: [Async Programming in Python: From Generators to asyncio]() Original publisher: [Read original article]() Author: Doug Farrell Published: 2026-08-31T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Python](), [Programming](), [Concurrent Programming](), [IO](), [Code](), [Python 3.14]() Tags: [automation](), [batch](), [code](), [learning](), [programming](), [python](), [tutorial]() ### AI overview A tutorial on Python asynchronous programming, explaining how event loops, coroutines, async and await enable a single-threaded program to handle slow I/O without blocking. It progresses from synchronous code and generators to concurrent task execution with asyncio, using examples compatible with Python 3.11 and later. ### Source excerpt Learn how Python async programming works. Write async functions with async and await, and run slow I/O operations concurrently with asyncio. ## 【etcd】写入路径深读：Txn、mod revision 与 quota/alarm DevFeed: [【etcd】写入路径深读：Txn、mod revision 与 quota/alarm]() Original publisher: [Read original article]() Author: Liao Tonglang Published: 2026-08-26T00:00:00Z Content type: tutorial Language: zh Sources: [土法炼钢 - 系统与基础设施]() Topics: [etcd](), [Back end](), [Raft](), [Remote Procedure Call (RPC)](), [maintenance]() Tags: [alarm](), [apiserver](), [applied-index](), [apply](), [batch](), [bbolt](), [committed-index](), [compaction](), [defrag](), [distributed](), [errcompacted](), [etcd](), [follower](), [k8s](), [kubernetes](), [mod-revision](), [nospace](), [quota](), [revision](), [txn](), [v3-5](), [v3-5-33]() ### AI overview This tutorial explains the etcd v3.5.33 write path from gRPC requests through Raft proposal, quota checks, MVCC apply, and ModRevision assignment. It also describes proposal backpressure, backend quota accounting, and how a NOSPACE alarm blocks writes while reads, deletes, and maintenance operations can continue. ### Source excerpt 走读 etcd v3.5.33 写入路径：raftRequest 背压、Txn compare/mod 与 ModRevision 分配、backend quota 与 NOSPACE alarm 如何把集群推入只读。 ## 【etcd】bbolt 后端：mmap、batch 与单写者约束 DevFeed: [【etcd】bbolt 后端：mmap、batch 与单写者约束]() Original publisher: [Read original article]() Author: Liao Tonglang Published: 2026-08-26T00:00:00Z Content type: tutorial Language: zh Sources: [土法炼钢 - 系统与基础设施]() Topics: [etcd](), [Databases](), [Raft]() Tags: [apply](), [backend](), [batch](), [bbolt](), [commit](), [distributed](), [etcd](), [kubernetes](), [mmap](), [mvcc](), [raft](), [v3-5](), [v3-5-33]() ### AI overview This article explains how etcd v3.5.33 maps its MVCC storage model onto the bbolt backend. It covers bucket layout, mmap behavior, buffered batch commits, read and write transaction concurrency, and how bbolt's single-writer constraint shapes apply and linearizable reads. ### Source excerpt 钉清 etcd v3.5.33 的 bbolt 后端：bucket 布局、mmap 预映射、100ms/10000 条 batch commit、ConcurrentReadTx 与单写者如何约束 MVCC 读写并发。 ## Optimizing Redshift Write Patterns: Tackling Tombstones and Ghost Rows DevFeed: [Optimizing Redshift Write Patterns: Tackling Tombstones and Ghost Rows]() Original publisher: [Read original article]() Author: Harichandan Pulagam Published: 2026-08-24T20:18:12Z Content type: article Language: en Sources: [Wealthfront]() Topics: [Amazon Redshift](), [dashboards](), [Data Quality]() Tags: [amazon-redshift](), [batch](), [dashboards](), [data-quality](), [latency](), [load](), [performance](), [redshift](), [space](), [wealthfront-engineering]() ### AI overview This Wealthfront engineering post examines how Redshift tables grew to nearly 10 times the size of their useful data because deleted rows remained on disk as ghost rows. It describes the resulting read and write latency and the write strategies adopted to control table size. ### Source excerpt Amazon Redshift is a core part of our analytics platform, powering dashboards, data quality checks, ad-hoc analytical workloads, and downstream reporting on a shared cluster. Because everything runs on the same cluster, the size and health of our tables directly affects every workload. At Wealthfront, data drives every decision we make, which means any performance... Read more ## GPU-Accelerated Clustering for Financial Instruments at Scale DevFeed: [GPU-Accelerated Clustering for Financial Instruments at Scale]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-21T16:21:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [Algorithm](), [Matrix]() Tags: [batch](), [communication](), [data-analytics-processing](), [data-science](), [financial-services](), [gb200](), [gpu](), [memory](), [nemo](), [nemotron](), [nvidia](), [post](), [scale](), [simulation-modeling-design](), [workflow]() ### AI overview A GPU-accelerated workflow uses rolling correlation and tail-dependence matrices to cluster financial instruments for portfolio construction, risk aggregation, statistical arbitrage, and trade surveillance. Its adaptive SymNMF-based solver supports soft factor loadings and hard cluster labels, while memory-efficient and distributed implementations scale from single GPUs to one million instruments across multiple nodes. ### Source excerpt Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters, soft factor... ## Human judgment doesn't leave the software factory. It relocates. DevFeed: [Human judgment doesn't leave the software factory. It relocates.]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Addy Osmani]() Topics: [Software](), [coding](), [event driven](), [implementation](), [GitHub](), [Claude Code](), [codex]() Tags: [agent](), [architecture](), [automated](), [batch](), [build](), [claude-code](), [codex](), [event-driven](), [github](), [linear](), [maintainability](), [mutation-testing](), [pull-requests](), [quality](), [scanners](), [slack](), [software](), [testing](), [verification]() ### AI overview A field guide to building a repeatable software factory while keeping humans responsible for product intent, system design, quality standards, code review, and final merge decisions. It recommends early and continuous quality checks, deliberate constraints, and event-driven automation when ordinary coding workflows are no longer sufficient. ### Source excerpt A field guide to building a software factory that still has an owner. [Next page]()