# Serverless Published articles for Serverless. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation DevFeed: [Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation]() Original publisher: [Read original article]() Author: Samantha Stuart Published: 2026-09-16T15:17:37Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [pii](), [Serverless](), [AWS Lambda](), [AWS Step Functions](), [Artificial Intelligence](), [Amazon Web Services](), [Ansible]() Tags: [advanced-300](), [amazon-bedrock](), [amazon-bedrock-data-automation](), [aws](), [aws-lambda](), [aws-step-functions](), [how-to](), [pii-redaction](), [pipeline](), [precision](), [serverless](), [technical-how-to]() ### AI overview This tutorial presents a serverless AWS pipeline for detecting and redacting personally identifiable information in scanned documents and images. It uses Amazon Bedrock Data Automation with a custom blueprint, AWS Step Functions, and AWS Lambda, with a token-matching quality check to improve recall on degraded and handwritten documents. ### Source excerpt Learn how to automate end-to-end PII detection and redaction from scanned documents at scale using Amazon Bedrock Data Automation with a custom blueprint, AWS Step Functions, and AWS Lambda. A custom blueprint redacts sensitive fields with field-level precision, and a token matching quality check raises recall across degraded and handwritten documents. ## The Original Serverless Architecture is Still Here DevFeed: [The Original Serverless Architecture is Still Here]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-05-31T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [serverless architecture](), [Kubernetes](), [Orchestration](), [Docker](), [Cloud](), [Firebase](), [DynamoDB]() Tags: [amazon](), [cloud](), [containers](), [docker](), [dynamodb](), [engineering](), [firebase](), [infrastructure](), [kubernetes](), [news](), [orchestration](), [serverless](), [serverless-architecture]() ### AI overview This commentary compares Kubernetes-based architectures with serverless approaches. It explains that Kubernetes offers flexibility through containers, Helm, ingress controllers, monitoring tools, and service meshes, but requires substantial configuration and maintenance. Serverless platforms such as Firebase and Amazon Lambda abstract away server infrastructure so developers can focus on applications and stateless functions. ### Source excerpt By Kevin Dangoor This month, my colleague Dave Rosile and I went to GlueCon 2018 in sunny Denver, ... 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 ## Build a WhatsApp AI agent with Appwrite Functions and TablesDB DevFeed: [Build a WhatsApp AI agent with Appwrite Functions and TablesDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI Agent](), [Messaging](), [Serverless](), [API](), [Database]() Tags: [ai](), [ai-agent](), [api](), [app](), [build](), [data](), [database](), [messages](), [messaging](), [meta](), [serverless](), [support](), [tutorial](), [tutorials](), [whatsapp]() ### AI overview This tutorial shows how to build a WhatsApp AI support agent with Appwrite Functions and TablesDB. One function receives and stores incoming messages, while a second function retrieves conversation history, uses an order-lookup tool, sends replies through Meta's WhatsApp Cloud API, and stores the responses. Conversation summaries help keep long threads within the model's token budget. ### Source excerpt Turn a WhatsApp number into an AI support agent. Two Appwrite Functions receive messages and reply, TablesDB keeps the conversation history, and a compaction step keeps the context small. ## Elastic announces GA of cross-project search on Serverless, enabling teams to query across all linked projects without moving a byte DevFeed: [Elastic announces GA of cross-project search on Serverless, enabling teams to query across all linked projects without moving a byte]() Original publisher: [Read original article]() Author: Jordi Mon Companys,Lucy Wang Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Serverless](), [Elastic Cloud](), [Cloud](), [observability](), [Security](), [SIEM, Security, Observability]() Tags: [architecture-cloud-migration-migrating-cloud-native-scaling-search-analytics-search-applicatio](), [cloud](), [cloud-elasticsearch-platform](), [customers](), [elastic](), [elastic-cloud](), [observability](), [observability-security-search](), [security](), [serverless]() ### AI overview Elastic announced the general availability of cross-project search for Elastic Cloud Serverless. It lets teams query multiple linked projects in a single session across regions, project types, and cloud providers, while keeping data in place and avoiding certificates, remote-cluster configuration, and per-connection authentication. ### Source excerpt Cross-project search is now GA on Elastic Cloud Serverless. Link multiple projects and query all of them from a single Discover or ES|QL session: no certificates, no remote cluster config, and three clicks to set up. Available from September 1, 2026. ## 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 ## What Managed Postgres Services Handle for Teams DevFeed: [What Managed Postgres Services Handle for Teams]() Original publisher: [Read original article]() Author: Databricks Staff Published: 2026-09-14T23:36:59Z Content type: article Language: en Sources: [Databricks]() Topics: [PostgreSQL](), [Database](), [Serverless](), [databricks](), [AI Development]() Tags: [data-plus-ai-foundations](), [database](), [databricks](), [developer-tooling](), [migration](), [postgres](), [postgresql](), [recovery](), [security](), [serverless]() ### AI overview The article defines managed Postgres by the operational responsibilities a provider assumes, including patching, scaling, failover, and backups. It discusses how these responsibilities can vary between providers and describes Lakebase Postgres as a serverless offering with automatic scaling, PostgreSQL compatibility, recovery, and Databricks integrations. ### Source excerpt Every Postgres vendor calls itself "managed." Few of them agree on what that word... ## Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale DevFeed: [Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale]() Original publisher: [Read original article]() Author: Aswin Vasudevan Published: 2026-09-14T21:22:45Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [threat detection](), [Amazon Bedrock](), [Security](), [Serverless](), [API](), [Amazon Web Services](), [AWS CloudTrail](), [Amazon CloudWatch](), [Amazon S3]() Tags: [advanced-300](), [agentic](), [agents](), [amazon-bedrock-agentcore](), [amazon-cloudwatch](), [amazon-s3](), [analysis](), [api](), [aws](), [aws-cloudtrail](), [code](), [customer-solutions](), [data](), [data-processing](), [security](), [serverless](), [threat-detection]() ### AI overview Abnormal AI uses Amazon Bedrock AgentCore Code Interpreter as an ephemeral, serverless compute scratch pad for real-time inline email threat detection. The article describes its sandbox isolation, networking and file-handling options, preloaded Python capabilities, observability integrations, and use at billion-message scale. ### Source excerpt Learn how Abnormal AI deployed Amazon Bedrock AgentCore Code Interpreter as an ephemeral compute scratch pad for the agents behind its real-time email threat detection at billion-message scale, plus the sandbox design decisions and practical lessons for builders deploying Code Interpreter in production. ## Scaling AI Agent Infrastructure with the MCP Stateless updates DevFeed: [Scaling AI Agent Infrastructure with the MCP Stateless updates]() Original publisher: [Read original article]() Author: Kurtis Van Gent; Alan Blount Published: 2026-09-12T11:04:33.891311Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [networking](), [SDKs]() Tags: [agentic](), [ai-agent](), [c-sharp](), [go](), [http](), [infrastructure](), [mcp](), [python](), [routing](), [sdks](), [serverless](), [typescript](), [updates]() ### AI overview The MCP release candidate replaces transport-level session management with a stateless core for HTTP load balancing, cloud-native scaling, and serverless deployments. It also introduces routing headers, caching controls, MRTR, and beta SDKs for Python, TypeScript, Go, and C#. ### Source excerpt The 2026-07-28 Model Context Protocol (MCP) specification replaces legacy stateful constraints with a fully stateless core, enabling cloud-native horizontal scaling, serverless deployments, and standard round-robin load balancing. This architectural shift introduces standardized HTTP headers for efficient routing without deep packet inspection, caching controls, and Multi Round-Trip Requests (MRTR) to handle interactive and long-running tasks without blocking connections. Developers can immediately begin migrating their agentic applications to this highly scalable infrastructure using the newly available beta SDKs for Python, TypeScript, Go, and C#. ## Model routing with Google Cloud API Gateway DevFeed: [Model routing with Google Cloud API Gateway]() Original publisher: [Read original article]() Author: Mak Ahmad; Sanjay Pujare Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Amazon API Gateway](), [Model Routing](), [Google](), [Cloud](), [OpenAPI Specification](), [Serverless](), [Claude](), [OpenAI]() Tags: [ai-gateway](), [api-gateway](), [claude](), [cloud](), [google](), [model-routing](), [models](), [openai](), [openapi](), [serverless]() ### AI overview Google Cloud API Gateway adds model routing in Public Preview, providing a serverless, OpenAI-compatible endpoint that dynamically routes requests to Gemini, Claude, or OpenAI OSS-GPT. Developers can configure routing in OpenAPI specifications, centralize model changes, and separate application authentication from backend model credentials. ### Source excerpt Google Cloud API Gateway now offers a model routing feature in Public Preview, allowing developers to dynamically route traffic to models like Gemini, Claude, or OpenAI OSS-GPT without hardcoding endpoints or managing open-source proxies. Developers can easily configure these routing rules directly within their OpenAPI 3.x specifications by mapping virtual model names to specific backend targets on a shared host. Once deployed, the Gateway acts as a serverless ingress layer that accepts standard OpenAI-compatible requests, automatically transcodes the payload to the native schema of the target model, and routes the traffic on the fly. ## Lambda SnapStart Comes to Container Images, Ending a Packaging Tradeoff DevFeed: [Lambda SnapStart Comes to Container Images, Ending a Packaging Tradeoff]() Original publisher: [Read original article]() Author: Steef-Jan Wiggers Published: 2026-09-12T10:09:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Deployment](), [NumPy]() Tags: [announcement](), [architecture](), [architecture-design](), [aws](), [aws-lambda](), [cloud](), [containers](), [development](), [devops](), [lambda-snapstart-container-image](), [news](), [performance](), [python](), [serverless](), [startup]() ### AI overview AWS has extended Lambda SnapStart to container-image functions, allowing teams to combine larger dependency packages with sub-second startup times. The change removes a packaging tradeoff previously faced by workloads such as pandas- and NumPy-based Lambda functions. ### Source excerpt AWS has extended Lambda SnapStart to container image functions, which hold up to 10 GB against 250 MB for zip archives. Teams previously chose between dependency headroom and sub-second startup. A Reddit thread from a month earlier shows what that cost: stripping whitespace and docstrings from installed packages to stay under the limit. By Steef-Jan Wiggers ## The Architecture for Serving 100 Fine-Tuned Models on One GPU DevFeed: [The Architecture for Serving 100 Fine-Tuned Models on One GPU]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-11T21:25:15Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [GPU](), [vllm](), [Fine-tuning](), [lora](), [Serverless](), [Deployment](), [Code]() Tags: [code](), [deployment](), [fine-tuning](), [gpu](), [lora](), [memory](), [models](), [production](), [serverless](), [vllm](), [workers]() ### AI overview This tutorial compares architectures for serving 100 fine-tuned 7B model variants on GPUs. It explains how separate merged models increase storage, GPU memory use, scaling pools, cold starts, and idle capacity, while a shared base model with LoRA adapters enables adapter reuse through vLLM. The article plans to test merged, unmerged startup-loaded, request-time adapter loading, and hosted-per-tenant deployments on Runpod Serverless. ### Source excerpt ...explained with code. ## Build interactive MCP Apps using Amazon Bedrock AgentCore DevFeed: [Build interactive MCP Apps using Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Dantis Stephen Published: 2026-09-11T18:23:17Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI Chat](), [Web Development]() Tags: [amazon-bedrock-agentcore](), [chatgpt](), [claude](), [how-to](), [html](), [intermediate-200](), [mcp](), [model-context-protocol](), [serverless](), [technical-how-to](), [ui]() ### AI overview Tutorial on building and deploying an MCP App with interactive HTML widgets on Amazon Bedrock AgentCore for use across supported AI hosts. ### Source excerpt Learn how to build and deploy an MCP App with interactive HTML widgets on Amazon Bedrock AgentCore. Because MCP Apps is a host-agnostic standard, the same server delivers the same rich experience across AI hosts like ChatGPT and Claude that support the extension. ## How AWS Lambda logs every flow across thousands of microVMs per host with eBPF and Rust DevFeed: [How AWS Lambda logs every flow across thousands of microVMs per host with eBPF and Rust]() Original publisher: [Read original article]() Author: Prashant Kumar Singh Published: 2026-09-11T12:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [AWS Lambda](), [Architecture & Design](), [SIEM, Security, Observability](), [telemetry](), [Amazon EC2](), [Amazon S3](), [VPC]() Tags: [architecture](), [aws](), [aws-lambda](), [aws-marketplace](), [compliance](), [ebpf](), [firecracker](), [incident](), [incident-response](), [logs](), [monitoring](), [observability](), [post-contributed](), [rust](), [s3](), [scale](), [security](), [server](), [serverless](), [sponsor-aws-marketplace](), [sponsored-post-contributed](), [vpc]() ### AI overview AWS Lambda describes replacing an aging network-capture system with an eBPF and Rust pipeline that records network flows across short-lived, tenant-isolated microVMs. The system prioritizes complete, correctly attributed records with minimal overhead for security investigation, metering, audit, observability, and monitoring. ### Source excerpt On any compute platform, when a security alert fires, the question is always the same. Which workload talked to that The post How AWS Lambda logs every flow across thousands of microVMs per host with eBPF and Rust appeared first on The New Stack. ## 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. ## Threat matrix: Mapping threats across cloud web applications DevFeed: [Threat matrix: Mapping threats across cloud web applications]() Original publisher: [Read original article]() Author: Microsoft Security Research and Lior Leizerovich Published: 2026-09-09T21:30:00Z Content type: article Language: en Sources: [Microsoft Security Blog]() Topics: [web applications](), [cloud-infrastructure](), [Microsoft]() Tags: [cloud](), [framework](), [mapping](), [microsoft](), [security](), [serverless](), [web-applications]() ### AI overview Microsoft introduces a MITRE ATT&CK-aligned threat matrix for cloud-hosted web applications and serverless platforms. The framework maps attack techniques to help security teams identify visibility gaps, prioritize hardening, and investigate threats across application and cloud layers. ### Source excerpt Microsoft introduces the Cloud Web Applications Threat Matrix, a MITRE ATT&CK-aligned framework that helps defenders understand, prioritize, and mitigate threats to cloud-hosted web apps and serverless platforms. The post Threat matrix: Mapping threats across cloud web applications appeared first on Microsoft Security Blog. ## 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. ## Building async Python applications with Tortoise ORM and Amazon Aurora DSQL DevFeed: [Building async Python applications with Tortoise ORM and Amazon Aurora DSQL]() Original publisher: [Read original article]() Author: Lasita Bhattacharya Published: 2026-09-09T15:27:43Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [DSQL](), [Concurrency](), [AWS IAM](), [Database](), [CRUD]() Tags: [advanced-300](), [amazon-aurora](), [concurrency](), [database](), [dsql](), [how-to](), [iam](), [orm](), [postgresql](), [python](), [rideshare](), [serverless](), [technical-how-to]() ### AI overview A tutorial for building a high-concurrency asynchronous Python rideshare application with Tortoise ORM and Amazon Aurora DSQL. It covers UUID-based models, IAM-authenticated asyncpg connections, OCC retry handling, and asynchronous CRUD operations. ### Source excerpt Build a high-concurrency async Python rideshare application with Tortoise ORM and Amazon Aurora DSQL. This post walks through the key adaptations: UUID primary keys, IAM-authenticated asyncpg connections with a connection-pool patch, individual DDL execution, and optimistic concurrency control (OCC) retry logic. ## From dedicated Workers to Serverless Workers: Migrating Temporal Workflows to AWS Lambda DevFeed: [From dedicated Workers to Serverless Workers: Migrating Temporal Workflows to AWS Lambda]() Original publisher: [Read original article]() Author: Garima Gupta Published: 2026-09-09T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [AWS Lambda](), [Serverless](), [Amazon Web Services](), [Deployment](), [Testing](), [SDKs]() Tags: [aws](), [aws-lambda](), [deployment](), [go](), [java](), [kubernetes](), [net](), [python](), [serverless](), [temporal](), [temporal-voices](), [testing](), [typescript](), [versioning]() ### AI overview This guide explains how to migrate Temporal Workers from long-lived processes on Kubernetes to Temporal Cloud Serverless Workers running on AWS Lambda. It focuses on AWS Lambda with Python examples and covers lifecycle changes, invocation scaling, deployment, versioning, and testing. ### Source excerpt Learn to migrate Temporal Workers from dedicated infrastructure to AWS Lambda with Serverless Workers, with guidance on deployment, versioning, and testing. ## 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 ## Measuring real-time performance per dollar under continuous load: CostBench's first end-to-end results DevFeed: [Measuring real-time performance per dollar under continuous load: CostBench's first end-to-end results]() Original publisher: [Read original article]() Author: Tom Schreiber; Lionel Palacin Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [AI, ML & Data Engineering](), [Amazon Redshift]() Tags: [analytics](), [architecture](), [benchmark](), [bigquery](), [clickhouse](), [cloud](), [cost](), [data](), [performance](), [real-time](), [serverless](), [streaming](), [systems](), [tracing](), [work]() ### AI overview CostBench benchmarks the cost and performance of real-time cloud data warehouses under continuous ingestion and query load. It compares ClickHouse Cloud with Snowflake, BigQuery, and Redshift Serverless, reporting better end-to-end performance per dollar for ClickHouse Cloud in the tested workload. ### Source excerpt CostBench puts cloud data warehouses under continuous load. Across the complete path from fresh data to fast answers, ClickHouse Cloud delivers 412-1,996x better performance per dollar. ## 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 [Next page]()