# architecture Published articles for architecture. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How API Design Is Evolving for AI Agents, Security, Reliability, and Compliance DevFeed: [How API Design Is Evolving for AI Agents, Security, Reliability, and Compliance]() Original publisher: [Read original article]() Author: J Simpson Published: 2026-09-17T07:00:00Z Content type: article Language: en Sources: [Nordic APIs]() Topics: [API](), [Architecture & Design](), [Artificial Intelligence](), [reliability](), [Security]() Tags: [agents](), [ai-agents](), [api](), [api-architecture](), [api-as-a-product](), [api-design](), [api-discovery](), [api-security](), [architecture](), [authorization](), [blog](), [mcp](), [microservices](), [rate-limiting](), [reliability](), [security]() ### AI overview This article examines six ways API design is evolving as APIs serve AI agents and other machine consumers, with implications for security, reliability, and regulatory compliance. ### Source excerpt For over two decades, API design and architecture remained remarkably consistent. Perhaps it's the outsized influence of Roy Fielding's RESTful dissertation, but API designers have stuck to the principles of stateless architecture, resource-based endpoints, and HTTP commands to an impressive degree. That's all starting to change, now that we've radically recontextualized the way we use ... ## GPT-6 Astra Is the First Model OpenAI Classifies as Critical for Cybersecurity DevFeed: [GPT-6 Astra Is the First Model OpenAI Classifies as Critical for Cybersecurity]() Original publisher: [Read original article]() Author: Steef-Jan Wiggers Published: 2026-09-17T04:59:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Cybersecurity](), [gpt-6-astra](), [OpenAI](), [Vulnerabilities](), [Exploit](), [browser](), [Kernel](), [Chain-of-thought]() Tags: [agents](), [ai](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [azure](), [browser](), [chain-of-thought](), [cloud](), [cybersecurity](), [development](), [devops](), [exploit](), [generative-ai](), [governance](), [gpt-6-astra](), [gpt-6-astra-critical-cyber](), [kernel](), [machine-learning](), [ml-data-engineering](), [news](), [openai](), [vulnerabilities](), [zero-day]() ### AI overview OpenAI classified GPT-6 Astra as the first model to reach its Critical cybersecurity threshold. Expert-led evaluations reported previously unknown vulnerabilities in a browser and an operating-system kernel, along with working exploit chains. The system card also reported a substantial decline in chain-of-thought monitorability. ### Source excerpt OpenAI has classified GPT-6 Astra at the Critical cybersecurity threshold under its Preparedness Framework, a first. In expert-led testing the model found previously unknown vulnerabilities in a browser and an OS kernel and built working exploits. The same system card reports a substantial decline in chain-of-thought monitorability. By Steef-Jan Wiggers ## A Simple MVI Architecture Approach Using LiveData DevFeed: [A Simple MVI Architecture Approach Using LiveData]() Original publisher: [Read original article]() Author: dustin@dustn.dev (Dustin Summers) Published: 2026-09-17T04:13:15.404035Z Content type: opinion Language: en Sources: [Dustin Summers]() Topics: [Android](), [Dagger]() Tags: [architecture](), [architecture-pattern](), [error](), [livedata](), [loading](), [mvi]() ### AI overview A talk from Android Summit 2019 discusses a simple implementation of the MVI architecture pattern using LiveData and the Loading, Content, Error pattern. ### Source excerpt This was a talk provided by myself and Dan Lowe at Android Summit, 2019. In this we discuss a simple approach to the MVI (Model, View, Intent) Architecture Pattern using LiveData and the common Loading, Content, Error Pattern. Link to Dan's blog post is here which is what our talk was based around. ## A Speaker That Clips to City Poles and Leaves No Mark DevFeed: [A Speaker That Clips to City Poles and Leaves No Mark]() Original publisher: [Read original article]() Author: Ida Torres Published: 2026-09-16T22:30:15Z Content type: article Language: en Sources: [Yanko Design]() Topics: [systems]() Tags: [architecture](), [audio](), [audio-technology-concept-designs-public-speaker](), [building](), [concept-designs](), [design](), [public](), [sound](), [space](), [speaker](), [technology]() ### AI overview Mariami Kurtishvili's Orchid Sound System is a graduate thesis proposing stainless steel high-frequency horns that clip onto existing urban infrastructure without screws, drilling, or permanent marks. The project explores temporary communal sound in public spaces. ### Source excerpt A Speaker That Clips to City Poles and Leaves No Mark Walk down almost any street in an American city without your headphones in, and you will notice how little of that walk actually belongs to... ## Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads DevFeed: [Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-16T14:42:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [dropbox](), [Artificial Intelligence](), [API](), [Orchestration](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [ai-ml-data-engineering](), [apache](), [apis](), [architecture](), [architecture-design](), [asynchronous-architecture](), [backend](), [caching](), [data-pipelines](), [development](), [distributed-systems](), [dropbox](), [dropbox-riviera-ai-platform](), [embeddings](), [enterprise-content-management](), [large-language-models](), [ml-data-engineering](), [model-context-protocol-mcp](), [news](), [orchestration](), [platform-engineering](), [plugins](), [rag](), [tika]() ### AI overview Dropbox has expanded Riviera from an internal file-preview service into a content-processing platform supporting more than 300 file formats and over 100 transformation capabilities. The platform supports Dropbox products including Search, Replay, Sign, and Dash, and provides APIs for asynchronous document conversion, media transcription, and structured metadata extraction for AI and RAG workflows. ### Source excerpt Dropbox has evolved Riviera from a file preview service into a universal content processing platform supporting more than 300 file formats and over 100 transformation capabilities. Processing hundreds of thousands of transformations per second, Riviera now supports Search, Replay, Sign, and Dash, while its APIs enable asynchronous content extraction for AI and RAG workflows. By Leela Kumili ## How Engineering Principles Can Help You Scale DevFeed: [How Engineering Principles Can Help You Scale]() Original publisher: [Read original article]() Author: Khan Academy Published: 2019-08-21T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Development](), [Code](), [Processes]() Tags: [architecture](), [communication](), [decision-making](), [engineering](), [growth](), [news](), [process](), [professional-development](), [scale](), [velocity]() ### AI overview Marta Kosarchyn describes how Khan Academy's growing engineering team outgrew its initial principles. The article argues that evolving engineering principles can support scaling while preserving culture and engagement, and explains how the original approach eventually contributed to slower delivery, code clutter, and implicit architectural decisions. ### Source excerpt By Marta Kosarchyn Our engineering team has grown a lot over the past couple of years, and we're ... Read more ## Babel in Khan Academy's i18n Toolchain DevFeed: [Babel in Khan Academy's i18n Toolchain]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-06-22T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Babel](), [Internationalization (i18n)](), [toolchain](), [es6](), [JavaScript](), [Parser](), [Universal Syntax Tree.](), [Open Source]() Tags: [api](), [architecture](), [babel](), [build](), [build-system](), [code](), [engineering](), [es6](), [eslint](), [frontend](), [javascript](), [news](), [plugin](), [source](), [structure](), [syntax](), [tests](), [toolchain](), [types](), [web-frontend](), [workflow]() ### AI overview Khan Academy describes switching its ES6 and JSX frontend compilation from jstransform to Babel. The article explains a custom Babel plugin for preserving the project's internationalization behavior and discusses matching generated output, line numbers, whitespace, and the existing build chain. ### Source excerpt By Kevin Barabash We've been using ES6 (along with JSX) for sometime at Khan Academy. Right now, we're ... 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 ## Khan Academy Uses Local Storage and Request Queues to Handle Offline Actions DevFeed: [Khan Academy Uses Local Storage and Request Queues to Handle Offline Actions]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-08-17T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [LocalStorage](), [client](), [Code](), [Internet](), [servers]() Tags: [architecture](), [change](), [code](), [engineering](), [function](), [hints](), [linear](), [local](), [news](), [offline](), [queue](), [request](), [server](), [web-frontend]() ### AI overview This article explains how Khan Academy addressed offline hint cheating by changing the client's request architecture. Actions are stored in local storage and placed in a queue for retry when connectivity returns, with linear backoff to avoid repeated requests during outages. ### Source excerpt By Phillip Lemons The problem Recently, a number of students on Khan Academy found a way to cheat ... Read more ## What do software architects at Khan Academy do? DevFeed: [What do software architects at Khan Academy do?]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-05-14T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Software](), [Development](), [coding]() Tags: [architecture](), [article](), [collaboration](), [decision-making](), [engineering](), [infrastructure](), [news](), [platforms](), [process](), [software](), [standards](), [tools]() ### AI overview Kevin Dangoor explains how software architects at Khan Academy view their role: as product managers for the system in which software is built. The article emphasizes improving coding standards, tools, platforms, and processes by collaborating with engineers and engineering management, with DACI used to structure architecture-change decisions. ### Source excerpt By Kevin Dangoor "Architect" is a new role in Khan Academy's engineering team this year, and my colleague, ... Read more ## Mainframe modernization options and migration strategies for 2026 DevFeed: [Mainframe modernization options and migration strategies for 2026]() Original publisher: [Read original article]() Author: nkondo Published: 2026-09-16T00:35:16Z Content type: opinion Language: ja Sources: [VMware Blogs]() Topics: [Mainframe](), [Cloud]() Tags: [architecture](), [cloud](), [international](), [mainframe](), [modernization](), [tag-78060198c513](), [tag-b333ed7fe4f9](), [vmware-cloud-foundation]() ### AI overview This article examines mainframe modernization in 2026, comparing on-premises, public cloud, sovereign cloud, and dedicated VMware infrastructure options. It recommends assessing performance, availability, security, data sovereignty, cost, and migration risks, with staged migration and current-state assessment as key practices. ### Source excerpt 第一回：2026年、メインフレーム・モダナイゼーションの現在地 メインフレーム（汎用コンピュータ）は、日本の社会インフラや主要企業の基幹業務システムにおいて、長年にわたり高い堅牢性と信頼性を提供してきました。IBMのz/OS、富士通のXSP/OSIV、NECのACOSなど、各社が独自に発展させたプラットフォームは、現在も重要インフラを支えています。しかし2026年現在、主要メーカーによるメインフレーム事業の撤退・サポート終了方針の提示、および保守を担うベテラン技術者の定年退職によるスキル継承の限界から、事業継続上の実害を伴う課題が顕在化しています。 こうした状況下において企業に求められるのは、単なるシステムの延命ではなく、自社のデータに対する統制（データ主権）を維持し、将来的なデータ活用を見据えたシステムアーキテクチャの再設計です。 本稿では、メインフレームのモダナイゼーションにおける選択肢として、オンプレミス環境や各種クラウド基盤の特性およびリスクを比較・整理することで、失敗しない移行への第一歩を考えます。 1. 確実性を高めるアプローチとインフラの選択肢 メインフレーム脱却において重要な検討事項は、移行後におけるシステム性能、可用性、セキュリティなどの非機能要件の維持です。 プログラム言語の変換のみでは、基幹システムとしての要求水準を満たすことはできません。この課題に対して、VMwareのTanzuアーキテクチャチームが公開している技術指針（An Architecture Path to Mainframe Modernization）では、一括刷新（ビッグバン移行）に伴う処理結果の不一致や障害リスクを回避するため、段階的な移行手法が推奨されています。具体的には、アプリケーション層において、現行システムを稼働させながら、重要な機能やデータを単位ごとに新環境へ段階的に移行する手法（Stranglerパターン）を適用します。そして、それを受け止めるインフラ層として、自社統制が可能なVMware環境上のLinux基盤を採用することにより、既存のビジネスロジックを活用しつつ、メインフレームと同等の応答性や可用性を維持することが可能となります。 一方で、パブリッククラウド各社が公開している情報をもとに直接パブリッククラウドのネイティブ環境へ移行するルートを検討する企業も少なくありません。パブリッククラウドは最新のAI機能や外部サービスと手軽に連携できるメリットがありますが、基幹システムをそのまま持ち込む際には把握しておくべき現実的な注意点があります。 共有環境（マルチテナント）における非機能要件の課題 他社と物理リソースを共有する構成では、ミリ秒単位の低遅延処理や、他システムの負荷影響を排除したリソース隔離の再現が困難です。 専用（占有）環境におけるコストおよび為替リスク 上記の課題を回避するために占有環境を採用した場合、初期費用および運用費用が高騰しやすくなります。また、サービス特有のドル建て課金体系により、為替変動（ドル高・円安）や国際情勢の影響を受けて中長期的なインフラコストが不透明化するリスクがあります。 データ主権に関わるリスク 米国クラウド法（CLOUD Act）に基づく国外法執行機関からのデータアクセス要求の可能性や、暗号鍵の管理権限を事業者に依存することによるデータ閲覧リスクなど、データ統制上の課題が生じます。 現在では、オンプレミスの堅牢性とクラウドの柔軟性を両立させるアプローチとして、国内事業者によるソブリンクラウドや、ハイパースケーラー上で展開される専有VMware基盤（VMC on AWS, AVS, OCVS等）も有力な選択肢となっています。コスト・性能・法制度リスクを多角的に評価した上で、適材適所の配置を行うことが不可欠です。 2. 2026年における移行戦略と仕分け 規約や為替リスクを回避し、自社主導でデータを管理・運用するための移行パターンとして、代表的な3つのアプローチを整理します。 移行パターン 具体的なアプローチ 2026年現在の評価とリスク 1. リプレイス （全面手組み刷新） システムを解体し、パッケージ導入またはゼロから再構築します。 数千万行規模のビジネスロジックの再設計を伴うため、膨大なコストと期間を要します。現行システムとの処理結果の相違等により、長期化・頓挫リスクが高くなります。 2. リホスト （クラウド直接移行） 現行プログラムをパブリッククラウドの仮想マシンへそのまま移行します。 移行期間の短縮は見込めるものの、共有環境における処理遅延、占有環境採用時のコスト高騰・為替リスク、および国外法制度に伴うデータ統制上のリスクが存在します。 3. リファクター （自動リライト＋次世代 ## CISA's Logging Reference Architecture for OMB M-26-14: What federal agencies should do next DevFeed: [CISA's Logging Reference Architecture for OMB M-26-14: What federal agencies should do next]() Original publisher: [Read original article]() Author: Woody Walton Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [cisa](), [Logging](), [cybersecurity and infrastructure security agency](), [log management](), [Architecture & Design](), [implementation](), [audit](), [Security]() Tags: [architecture](), [audit](), [cisa](), [cybersecurity-and-infrastructure-security-agency](), [cybersecurity-open-source-standards-log-management-government](), [government](), [implementation](), [logging](), [logs](), [public-sector](), [security]() ### AI overview This Elastic blog explains CISA's Logging Reference Architecture for OMB M-26-14 and the actions federal civilian executive branch agencies must take. It highlights planning and maturity deadlines, recommends assessing existing capabilities and documenting gaps, and discusses storage tiers, retrieval, immutability, and audit requirements. ### Source excerpt Now that CISA has released its Logging Reference Architecture (LRA) for OMB M-26-14, federal agencies tasked to complete their logging plans. In this blog, we walk you through the most important aspects of the LRA guidance. ## Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each DevFeed: [Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T17:00:11Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Mixture of Experts (MoE)](), [Nemotron](), [Routing (disambiguation)]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [architecture](), [cost](), [data-center-cloud](), [deployment](), [llms](), [memory](), [mixture-of-experts-moe](), [models](), [moe](), [nemo](), [nemotron](), [performance](), [router](), [routing](), [tokens]() ### AI overview This article explains how dense and Mixture-of-Experts models activate parameters, compares their effects on throughput, memory cost, and serving complexity, and discusses when each architecture fits different deployment constraints. It uses Nemotron 3.5 Lightning as an example of an MoE model. ### Source excerpt How can a 30B-parameter model activate only 3B parameters per token, and still use the capacity of the larger model? Nemotron 3.5 Lightning illustrates the... ## Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE DevFeed: [Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Federated Learning](), [Docker](), [Kubernetes](), [Nvidia](), [Server](), [dataset](), [Deployment]() Tags: [architecture](), [compute](), [configuration](), [container](), [data-analytics-processing](), [data-science](), [dataset](), [deployment](), [docker](), [docker-container](), [federated-learning](), [gpu](), [job](), [kubernetes](), [nvidia](), [nvidia-flare](), [server]() ### AI overview This article explains how NVIDIA FLARE scales federated learning across sites with different infrastructure, including Docker, Kubernetes, and Slurm. Its two-layer architecture separates persistent federation services from on-demand job execution, while allowing each site to retain local control over compute, data, secrets, and scheduling. ### Source excerpt Federated learning (FL) projects often begin with a straightforward setup: one server, a few clients, and one dataset at each site. As those projects grow, the... ## How Solaris' Turnstile Influenced the Modern System Designs of Web Browsers and Language Runtimes DevFeed: [How Solaris' Turnstile Influenced the Modern System Designs of Web Browsers and Language Runtimes]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-09-15T06:06:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [systems](), [Programming](), [Kernel](), [observability](), [browsers](), [web browsers]() Tags: [architecture](), [architecture-design](), [architectures](), [blocking](), [concurrency](), [development](), [efficiency](), [kernel](), [locking](), [memory](), [news](), [observability](), [operating-systems](), [solaris](), [storage](), [systems](), [turnstile-system-design]() ### AI overview The article examines how Solaris innovations influenced modern systems engineering. It discusses the Slab Allocator, OpenZFS, DTrace, Zones, and turnstiles, focusing on how turnstiles reduce per-lock memory overhead while supporting priority inheritance for contended locks. ### Source excerpt Sun's Solaris influenced modern systems engineering, with key innovations like the Slab Allocator for efficient memory management, OpenZFS for advanced storage, and DTrace for observability. Its turnstile mechanism addressed issues with mutexes, promoting lean locking designs that are reflected in contemporary programming languages and web browsers, enhancing performance and memory efficiency. By Olimpiu Pop ## Amazon Linux 2027 Enters Public Preview with SELinux Enforcing by Default DevFeed: [Amazon Linux 2027 Enters Public Preview with SELinux Enforcing by Default]() Original publisher: [Read original article]() Author: Steef-Jan Wiggers Published: 2026-09-15T03:40:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Linux](), [SELinux](), [migration](), [Amazon Web Services](), [Kernel](), [amazon]() Tags: [amazon](), [amazon-linux-2027-preview](), [architecture](), [architecture-design](), [aws](), [cloud](), [development](), [devops](), [ec2](), [infrastructure](), [kernel](), [linux](), [migration](), [news](), [operating-systems](), [preview](), [selinux](), [x86-64]() ### AI overview AWS has released Amazon Linux 2027 in public preview with SELinux enforcing by default. The change may require application and migration work because policies that only logged violations on AL2023 can block applications on AL2027. The release has no announced AL2023 end-of-support date, general-availability date, or in-place migration path. ### Source excerpt AWS has released Amazon Linux 2027 in public preview, built on the AL2023 baseline with kernel 7.1 and SELinux in enforcing mode by default. Applications that pass on AL2023's permissive mode may fail under enforcing. The announcement gives no AL2023 end-of-support date, no GA date, and no in-place migration path. By Steef-Jan Wiggers ## Closing the Resilience Gap with Native Splunk in Cisco Nexus One DevFeed: [Closing the Resilience Gap with Native Splunk in Cisco Nexus One]() Original publisher: [Read original article]() Author: Murali Gandluru Published: 2026-09-14T19:55:29Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [Resilience](), [telemetry](), [Network](), [dashboards](), [Architecture & Design](), [data](), [Artificial Intelligence]() Tags: [2026](), [ai](), [architecture](), [business](), [cisco](), [cisco-data-fabric](), [dashboards](), [data](), [data-center](), [infrastructure](), [network](), [nexus-dashboard](), [nexus-one](), [operational](), [resilience](), [telemetry]() ### AI overview Cisco describes expanding Native Splunk in Cisco Nexus One from a single-node to a multi-node architecture. The integration brings Splunk search, dashboards, alerting, and analytics together with authoritative network context and distributed application, infrastructure, and security data through Cisco Data Fabric, helping teams investigate incidents and move from telemetry to root cause faster. ### Source excerpt See how Native Splunk and Cisco Nexus One bring analytics closer to network data, strengthen resilience, and connect teams across operational domains. ## AWS Security Reference Architecture: A deep dive into PCI DSS compliance DevFeed: [AWS Security Reference Architecture: A deep dive into PCI DSS compliance]() Original publisher: [Read original article]() Author: Avik Mukherjee Published: 2026-09-14T17:46:56Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Amazon Web Services](), [Security](), [Architecture & Design](), [Network Segmentation](), [Encryption](), [Logging](), [Access Control](), [Cloud]() Tags: [access-control](), [amazon-web-services](), [announcements](), [architecture](), [compliance](), [encryption](), [foundational-100](), [logging](), [network-segmentation](), [pci-dss](), [security](), [security-blog](), [security-identity-compliance]() ### AI overview AWS announces the AWS Security Reference Architecture (AWS SRA) PCI DSS Deep Dive, a guide that extends the core AWS SRA with prescriptive architecture-level guidance for organizations handling cardholder data on AWS. It explains how AWS SRA patterns address PCI DSS concerns including account scoping, network segmentation, encryption, logging, and access control. ### Source excerpt Amazon Web Services (AWS) is excited to announce the publication of the AWS Security Reference Architecture (AWS SRA) Payment Card Industry (PCI) Data Security Standard (DSS) Deep Dive. This new guide extends the core AWS SRA to provide prescriptive, architecture-level guidance for organizations that store, process, or transmit cardholder data on AWS. Organizations subject to [...] ## How to Use Differential Testing During a Legacy Migration DevFeed: [How to Use Differential Testing During a Legacy Migration]() Original publisher: [Read original article]() Author: Hugo Teijiz Published: 2026-09-14T16:25:50Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [migration](), [Testing](), [TypeScript](), [Integration testing](), [API](), [observability](), [Artificial Intelligence]() Tags: [ai](), [api](), [architecture](), [artificial-intelligence](), [code](), [how-to](), [integration](), [legacy-code](), [legacy-modernization](), [migration](), [modernization](), [observability](), [software-architecture](), [software-testing](), [testing](), [tests](), [tutorial](), [typescript]() ### AI overview This tutorial explains how to use differential testing during a legacy migration by running old and new implementations with identical inputs and comparing their behavior. It covers equivalence rules, output normalization, nondeterministic values, errors, side effects, automated testing, mismatch analysis, production shadow traffic, and using AI to classify divergences without allowing it to determine correctness. ### Source excerpt The most dangerous moment in a legacy migration isn't necessarily when you start writing the new implementation. It's when the new implementation looks finished. The code compiles, the tests pass, the ## Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 - Understanding row lock contention in PostgreSQL DevFeed: [Troubleshooting row lock contention in Amazon Aurora PostgreSQL: Part 1 - Understanding row lock contention in PostgreSQL]() Original publisher: [Read original article]() Author: Sameer Kumar Published: 2026-09-14T16:02:08Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [PostgreSQL](), [Concurrency](), [Monitoring](), [Transactions](), [Amazon CloudWatch](), [Amazon RDS](), [Architecture & Design](), [Extension]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-rds](), [architecture](), [availability](), [concurrency](), [database](), [database-performance](), [extension](), [locks](), [monitoring](), [postgresql](), [production](), [rds-for-postgresql](), [technical-how-to](), [transactions](), [troubleshooting]() ### AI overview This first part of a two-part series explains row lock contention in PostgreSQL and Amazon Aurora PostgreSQL. It covers how concurrent transactions competing for the same rows can reduce throughput and cause timeouts despite healthy CPU and I/O, then introduces PostgreSQL locking internals and monitoring techniques using system views, functions, the pgrowlocks extension, and log_lock_waits. The article notes that the same behavior and investigation approach apply to Amazon RDS for PostgreSQL. ### Source excerpt Row lock contention can collapse database throughput during a flash sale even when CPU and I/O look healthy. In Part 1 of this series, learn how PostgreSQL row locking works and how to monitor lock contention in Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL using system views, the pgrowlocks extension, and the log_lock_waits parameter. ## How to Implement Feature Flags for Safe and Gradual Rollouts DevFeed: [How to Implement Feature Flags for Safe and Gradual Rollouts]() Original publisher: [Read original article]() Author: Abisoye Alli-Balogun Published: 2026-09-14T14:58:55Z Content type: article Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [feature flags](), [CI/CD](), [Deployment](), [Development](), [Tech Debt](), [Testing]() Tags: [architecture](), [best-practices](), [ci-cd](), [deployment](), [development](), [experimentation](), [feature-flags](), [how-to](), [tech-debt](), [testing]() ### AI overview This article explains how to implement feature flags for safe, gradual rollouts. It covers separating deployment from release, boolean toggles, percentage-based rollouts, user segmentation, experimentation, kill switches, lifecycle management, and common anti-patterns. ### Source excerpt Feature flags are one of the most powerful tools in a team's deployment arsenal. They decouple deployment from release, meaning your CI/CD pipeline can push code to production servers on every merge, ## Inside LuBot's database-per-tenant architecture DevFeed: [Inside LuBot's database-per-tenant architecture]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2026-09-14T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Database](), [Software as a service](), [Supabase](), [Provisioning](), [stripe](), [API]() Tags: [api](), [architecture](), [billing](), [case-studies](), [checkout](), [database](), [infrastructure](), [product](), [provisioning](), [saas](), [scale](), [stripe]() ### AI overview This article explains LuBot's database-per-tenant architecture. LuBot uses Supabase for shared administrative data such as organizations, users, and billing state, while each paying customer receives an isolated Postgres database. Stripe checkout triggers a webhook that provisions a Neon branch through the Neon API, with deterministic naming to prevent duplicate tenants. Neon branches provide independent compute and can scale idle tenants to zero. ### Source excerpt It took six days to go from zero to isolated Postgres per tenant, provisioned the moment someone pays. A traditional Postgres setup would have been a full quarter of work. ## Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills DevFeed: [Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills]() Original publisher: [Read original article]() Author: Alex Porcelli Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Agent Skills](), [Artificial Intelligence](), [Large Language Model](), [NeMo]() Tags: [agent](), [agent-skills](), [agentic](), [agentic-ai-architecture](), [agents](), [ai](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [architectures](), [artificial-intelligence](), [business](), [decision-models-agentic-ai](), [development](), [enterprise](), [enterprise-architecture](), [governance](), [infoq](), [large-language-models](), [llms](), [ml-data-engineering](), [models](), [nemo](), [presentation](), [production](), [qcon-ai-boston-2026](), [qcon-software-development-conference](), [skills](), [transcripts]() ### AI overview Alex Porcelli explains how DMN decision models can be integrated with LLMs, agent skills, and NeMo guardrails to create auditable and deterministic agentic architectures for high-stakes enterprise decisions. ### Source excerpt Alex Porcelli discusses the critical gap in enterprise AI: non-deterministic output and lack of accountability in high-stakes decisions. He shares how integrating DMN decision models with LLMs, agent skills, and NeMo guardrails creates auditable, deterministic agentic architectures - allowing business leaders to own decision logic while engineers maintain robust architectural governance. By Alex Porcelli ## Podcast: How Will We Train Developers If AI Does the Routine Work: A Conversation with Scott Hanselman DevFeed: [Podcast: How Will We Train Developers If AI Does the Routine Work: A Conversation with Scott Hanselman]() Original publisher: [Read original article]() Author: Scott Hanselman Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [AI Development](), [Architecture & Design](), [coding]() Tags: [agentic-ai](), [agents](), [ai](), [ai-agents](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [artificial-intelligence](), [code](), [developers](), [infoq](), [junior-developers](), [ml-data-engineering](), [podcast](), [software-engineering](), [the-infoq-podcast](), [train-developers-ai-routine-work]() ### AI overview The podcast discusses how to train software engineers when AI agents perform much of the routine work traditionally assigned to junior developers. Scott Hanselman advocates a preceptorship model, experienced engineers overseeing AI-generated work, and long-term investment in mentorship and human connection. ### Source excerpt In this podcast, Michael Stiefel spoke to Scott Hanselman about developing new software engineers when artificial intelligence agents are doing most of the work on which junior developers were trained. Hanselman suggests the software industry should adopt a preceptorship model similar to the nursing profession. By Scott Hanselman [Next page]()