# Architecture & Design Published articles for Architecture & Design. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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 ## 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 ## Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review DevFeed: [Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review]() Original publisher: [Read original article]() Author: Sarah Deitke Published: 2026-09-16T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [Code review](), [Pull Request](), [Tooling](), [engineering-culture](), [observability]() Tags: [adoption](), [agile](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [artificial-intelligence](), [automated](), [automation](), [autonomous](), [case-study](), [code-review](), [code-reviews](), [culture](), [culture-methods](), [developer](), [developer-experience](), [development](), [duolingo-ai-literacy-code-review](), [infoq](), [metrics](), [ml-data-engineering](), [observability](), [pairing](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [transcripts]() ### AI overview Sarah Deitke presents Duolingo's approach to cultural AI adoption through internal AI literacy workshops, observability dashboards, and safe AI guardrails. The presentation includes a case study on redesigning code review with an automated PR risk-assessment bot and reports faster delivery without increased defect rates. ### Source excerpt Sarah Deitke discusses how Duolingo drives cultural AI adoption beyond tooling access. She explains their internal AI literacy workshops and observability dashboards, then shares a case study on redesigning code review using an automated PR risk-assessment bot. Deitke demonstrates how pairing targeted developer education with safe AI guardrails speeds up delivery without increasing defect rates. By Sarah Deitke ## Lyft Moves Streaming Fleet to Apache Flink Kubernetes Operator DevFeed: [Lyft Moves Streaming Fleet to Apache Flink Kubernetes Operator]() Original publisher: [Read original article]() Author: Mark Silvester Published: 2026-09-16T11:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [flink](), [apache-flink](), [Kubernetes](), [autoscaling](), [Deployment](), [legacy](), [Testing]() Tags: [apache-flink](), [architecture-design](), [autoscaling](), [deployment](), [development](), [devops](), [flink](), [kubernetes](), [kubernetes-operator](), [legacy](), [lyft-flink-k8s-operator](), [news](), [testing](), [upgrade]() ### AI overview Lyft moved hundreds of production Apache Flink jobs from its in-house Kubernetes operator to the Apache Flink Kubernetes Operator. The change enabled last-state upgrades, in-place autoscaling, and resource autotuning, while Lyft adapted legacy deployment specifications through its deploy API and contributed a fix for a configuration-renaming bug. ### Source excerpt Lyft has moved hundreds of production Flink jobs from a 2020 in-house Kubernetes operator to the Apache Flink Kubernetes Operator, unlocking last-state upgrades, in-place autoscaling and resource autotuning across the fleet. By Mark Silvester ## Java 27 Delivers Post-Quantum Cryptography, Future Language Innovation, Helidon 27, JavaFX 27 DevFeed: [Java 27 Delivers Post-Quantum Cryptography, Future Language Innovation, Helidon 27, JavaFX 27]() Original publisher: [Read original article]() Author: Michael Redlich Published: 2026-09-16T10:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Java](), [java-27](), [Post-quantum cryptography](), [Cryptography](), [Security](), [Programming](), [releases]() Tags: [application-security](), [architecture-design](), [cryptography](), [development](), [digital-signature](), [helidon](), [java](), [java-27](), [java27-released](), [javafx](), [jdk-27](), [jdk-28](), [lattice](), [module](), [news](), [oracle](), [portfolio](), [post-quantum-cryptography](), [preview](), [project-valhalla](), [quantum-computing](), [release](), [security](), [structured-concurrency](), [vector]() ### AI overview Oracle released Java 27, the second non-LTS release since JDK 25. Its final feature set contains nine JEPs, including post-quantum hybrid key exchange for TLS 1.3, structured concurrency, compact object headers, JFR data redaction, the Vector API, and PEM encodings for cryptographic objects. Five features remain in preview or incubator stages. ### Source excerpt Oracle has released version 27 of the Java programming language and virtual machine. As the second non-LTS release since JDK 25, the final feature set includes nine JEPs, five of which are still progressing through the preview and incubator stages. This release focuses on strengthening security, future language innovation, and projects under the auspices of the Java Verified Portfolio. By Michael Redlich ## Pinterest's Manas Search Platform Uses Quantization and SSD-Based Serving DevFeed: [Pinterest's Manas Search Platform Uses Quantization and SSD-Based Serving]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-09-16T06:06:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [quantization](), [AI search](), [webgpu]() Tags: [algorithms](), [architecture-design](), [development](), [infrastructure](), [news](), [optimization](), [performance](), [pinterest-search](), [quantization](), [search](), [ssd](), [vector]() ### AI overview Pinterest Engineering enhanced its Manas distributed search platform with scalar and product quantization, SSD-based serving, and late-interaction retrieval. The reported evaluations describe trade-offs among index size, recall, throughput, latency, and serving cost. ### Source excerpt Pinterest Engineering has enhanced its Manas search platform to manage vast data, improving efficiency in search and discovery functions. By applying Scalar and Product Quantization, memory usage decreased significantly while maintaining high recall rates. The platform utilizes SSDs for optimized performance, and it is transitioning to multi-vector models for refined relevance matching. By Olimpiu Pop ## 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 ## Java News Roundup: New OpenJDK JEPs, CDI 5.0, Spring, Open Liberty, RefactorFirst, ADK for Kotlin DevFeed: [Java News Roundup: New OpenJDK JEPs, CDI 5.0, Spring, Open Liberty, RefactorFirst, ADK for Kotlin]() Original publisher: [Read original article]() Author: Michael Redlich Published: 2026-09-14T20:15:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Java](), [Concurrency](), [JDK 27](), [releases](), [Jakarta EE](), [Gradle](), [Groovy]() Tags: [2026](), [architecture-design](), [concurrency](), [development](), [gradle](), [groovy](), [jakarta-ee](), [java](), [java-news-roundup-sep07-2026](), [jdk-27](), [jdk-28](), [kotlin](), [micronaut](), [news](), [open-jdk](), [open-liberty](), [refactorfirst](), [releases](), [spring-framework](), [tornadovm]() ### AI overview A Java news roundup for September 7, 2026, covers new OpenJDK proposals for ahead-of-time compilation and structured concurrency, Jakarta CDI 5.0 and ADK for Kotlin 1.0, Open Liberty, TornadoVM, RefactorFirst, Micronaut, Groovy 6.0, and Gradle 9.8. It also reports current JDK 27 and JDK 28 early-access builds and describes improvements in TornadoVM 6.1.0. ### Source excerpt This week's Java roundup for September 7th, 2026, features news highlighting: new JEPs for ahead-of-time compilation and structured concurrency; GA releases of Jakarta CDI 5.0 and ADK for Kotlin 1.0; the September 2026 edition of Open Liberty; point releases of TornadoVM and RefactorFirst; a maintenance release of Micronaut; and first releases candidates of Groovy 6.0 and Gradle 9.8. By Michael Redlich ## Agoda Replaces 72-Shard SQL Server Price Cache with DragonflyDB DevFeed: [Agoda Replaces 72-Shard SQL Server Price Cache with DragonflyDB]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-14T13:48:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Cache](), [Caching](), [migration](), [sql-server](), [Latency](), [Prometheus]() Tags: [a-b-testing](), [agoda-price-cache-dragonflydb](), [architecture-design](), [cache](), [caching](), [development](), [devops](), [distributed-cache](), [distributed-data](), [distributed-systems](), [latency](), [microservices](), [migration](), [news](), [prometheus](), [redis](), [sql-server]() ### AI overview Agoda migrated its 1.5 TB hotel Price Cache from 72 Microsoft SQL Server shards to DragonflyDB. The staged migration used workload testing, dual reads, parity metrics, and gradual traffic shifting; Agoda reported substantially lower P99 read latency. ### Source excerpt Agoda migrated its 1.5 TB hotel Price Cache from 72 SQL Server shards to DragonflyDB to handle growing read and write volumes. The migration used staged dual reads, parity validation, gradual traffic shifting, and decentralized failover detection. Agoda reports an approximately eightfold reduction in P99 read latency, with two DragonflyDB clusters providing high availability. By Leela Kumili ## Article: Implementing Durable Workflows on Postgres Without an External Orchestrator DevFeed: [Article: Implementing Durable Workflows on Postgres Without an External Orchestrator]() Original publisher: [Read original article]() Author: Raman Varma Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Databases](), [SQL](), [Provisioning](), [incident](), [CI/CD](), [Kubernetes](), [AWS Step Functions](), [Artificial Intelligence]() Tags: [ai](), [ai-ml-data-engineering](), [architecture-design](), [article](), [automation](), [aws-step-functions](), [database](), [durable-workflows-postgres](), [incident-response](), [infrastructure](), [kubernetes](), [ml-data-engineering](), [orchestration](), [postgres](), [queue](), [relational-databases](), [sql](), [workflows]() ### AI overview This article explains how to implement durable workflows on Postgres without an external orchestrator. It describes using row-level locking as a concurrent work queue, primary-key checkpoints for idempotency, and leases with a sweeper for crash recovery. Workflow state, sleeps, and human approvals can persist in the database and survive process restarts. ### Source excerpt Postgres can serve as the durable state store and coordination layer for workflows, eliminating the need for an external orchestrator. SKIP LOCKED enables concurrent work processing, primary-key checkpoints enforce idempotency, and leases support crash recovery. Workflow sleeps and human approvals can also be persisted as database state and survive restarts. By Raman Varma ## 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 ## Cloudflare Tests Cache Transcoding to Reduce Storage Requirements DevFeed: [Cloudflare Tests Cache Transcoding to Reduce Storage Requirements]() Original publisher: [Read original article]() Author: Renato Losio Published: 2026-09-13T10:35:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Caching](), [Transcodings](), [cloud-infrastructure](), [Pingora]() Tags: [architecture-design](), [cache](), [caching](), [cdn](), [cloud](), [cloudflare](), [cloudflare-cache-transcoding](), [compression](), [cpu](), [data-centers](), [development](), [edge-computing](), [news](), [pingora](), [rust](), [storage]() ### AI overview Cloudflare is testing Cache Transcoding, a prototype that Zstandard-compresses eligible uncompressed text before it is stored in cache. The approach aims to increase effective cache capacity and reduce inter-data-center transfer, with configurable CPU and storage trade-offs. ### Source excerpt Cloudflare recently described a prototype called Cache Transcoding that compresses eligible cache content, mainly uncompressed text such as HTML, JSON, CSS, and JavaScript, using Zstandard before storing it on disk. The hyperscaler estimates that the approach could provide petabytes of additional effective cache capacity, although broader testing is still needed. By Renato Losio ## Presentation: From Retrieval to Reasoning: Building Production-Ready Agentic AI Systems with Knowledge Graphs DevFeed: [Presentation: From Retrieval to Reasoning: Building Production-Ready Agentic AI Systems with Knowledge Graphs]() Original publisher: [Read original article]() Author: Cassie Shum Published: 2026-09-12T11:00:00Z Content type: tutorial Language: en Sources: [InfoQ]() Topics: [Graphs](), [Architecture & Design](), [Retrieval Augmented Generation (RAG)](), [Code]() Tags: [agentic-ai](), [agentic-ai-architecture](), [agents](), [ai](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [artificial-intelligence](), [code](), [development](), [engineering](), [infoq](), [knowledge-graph](), [knowledge-graphs-agentic-systems-patterns](), [large-language-models](), [ml-data-engineering](), [presentation](), [production](), [qcon-ai-boston-2026](), [qcon-software-development-conference](), [rag](), [reasoning](), [retrieval-augmented-generation](), [transcripts]() ### AI overview A presentation on using knowledge graphs as a foundation for production-ready agentic AI systems. It covers architectural patterns for context bundling, decision provenance, code as truth, and agent visibility, along with a graph-based engineering harness for feedback loops, token optimization, and reliability. ### Source excerpt Cassie Shum discusses why knowledge graphs serve as a critical foundation for agentic systems. Moving beyond basic RAG, she explains 4 practical architectural patterns: context bundling, decision provenance, code as truth, and agent visibility. She demonstrates an engineering harness built on a knowledge graph to streamline feedback loops, optimize token usage, and maintain system reliability. By Cassie Shum ## 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 ## NVIDIA Personal AI Router Distributes AI Tasks across Local Compute DevFeed: [NVIDIA Personal AI Router Distributes AI Tasks across Local Compute]() Original publisher: [Read original article]() Author: Sergio De Simone Published: 2026-09-11T15:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Local AI](), [Inference](), [GPU](), [Hardware]() Tags: [agent](), [agents](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [compute](), [demo](), [development](), [gpu](), [inference](), [large-language-models](), [local](), [ml-data-engineering](), [news](), [node](), [nvidia](), [nvidia-pair-ai-task-router](), [open-source](), [orchestration](), [performance]() ### AI overview NVIDIA has introduced PAIR in beta, a local router that distributes inference requests across compatible computers for multi-agent AI workloads. It works with local inference services such as Ollama and LM Studio and selects a node based on model and engine requirements. ### Source excerpt NVIDIA Personal AI Router (PAIR), now available in beta, lets you combine the inference capacity of multiple computers on your local network and automatically distribute AI requests among them. It is primarily designed for local multi-agent AI workloads, where multiple independent model calls can otherwise overwhelm one GPU. By Sergio De Simone ## Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows DevFeed: [Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-11T14:17:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Orchestration](), [Amazon S3](), [Kafka](), [Apache Iceberg]() Tags: [amazon-s3](), [apache-iceberg](), [apache-kafka](), [architecture-design](), [asynchronous-architecture](), [cassandra](), [cloud-architecture](), [concurrency](), [development](), [devops](), [distributed-systems](), [elasticsearch](), [java-operator-sdk](), [kafka](), [latency](), [microservices](), [netflix](), [netflix-conductor-4-workflow](), [news](), [orchestration](), [s3](), [scalability](), [windows-workflow-foundation](), [workflow](), [workflow-bpm](), [workflow-foundation]() ### AI overview Netflix reworked Conductor 4.0 to scale workflow orchestration to roughly 200,000 definitions and 420 million monthly executions. The redesign raises supported workflow size to 30,000 tasks and reports a roughly 40% reduction in p99 evaluation latency by loading only task data needed for each decision. ### Source excerpt Netflix has reworked its Conductor workflow orchestration engine to handle larger workloads, increasing supported workflow size from about 2,500 to 30,000 tasks and reducing p99 workflow evaluation latency by about 40%. Conductor 4.0 separates workflow metadata from task data, moves evaluation to asynchronous processing, and introduces dynamic worker allocation and concurrency controls. By Leela Kumili ## Presentation: How To Run on Three Clouds at Once, and When Not To DevFeed: [Presentation: How To Run on Three Clouds at Once, and When Not To]() Original publisher: [Read original article]() Author: Ross McFarlane, Kevin Holditch Published: 2026-09-11T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [networking](), [Databases](), [Kubernetes]() Tags: [amazon](), [architecture](), [architecture-design](), [availability](), [aws](), [cloud](), [cloud-architecture](), [cloud-computing](), [cloud-networking](), [cockroach-labs](), [cockroachdb](), [containers](), [databases](), [devops](), [disaster-recovery](), [engineering](), [europe](), [financial-applications](), [fintech](), [form3-multicloud-architecture](), [infoq](), [kubernetes](), [microservices](), [networking](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [resilience](), [transcripts](), [us]() ### AI overview A presentation on Form3's move from one cloud to a triple-active multi-cloud architecture, covering cross-cloud networking, distributed databases, Kubernetes operators, and regional disaster-recovery expectations. ### Source excerpt Ross McFarlane and Kevin Holditch discuss Form3's evolution from a single-cloud setup to a triple active multi-cloud architecture. They share key engineering strategies for cross-cloud networking, distributed databases with CockroachDB and NATS, custom Kubernetes operators, and navigating distinct regional disaster recovery expectations across the UK, Europe, and US financial markets. By Ross McFarlane, Kevin Holditch ## Building production-ready agentic systems: Lessons from Shopify Sidekick DevFeed: [Building production-ready agentic systems: Lessons from Shopify Sidekick]() Original publisher: [Read original article]() Author: Andrew McNamara Published: 2025-08-26T10:00:00Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [AI Engineering](), [Shopify](), [Artificial Intelligence](), [Architecture & Design](), [Large Language Model](), [grpo](), [JIT](), [data]() Tags: [ai](), [ai-engineering](), [architecture-design](), [evaluation](), [grpo](), [jit](), [llm](), [platform](), [production](), [shopify]() ### AI overview This article describes how Shopify evolved Sidekick from a tool-calling system into a production-oriented agentic platform. It discusses the agentic loop, scaling challenges caused by a growing tool inventory, architecture design, evaluation, GRPO training, and the use of just-in-time instructions to reduce system-prompt complexity. ### Source excerpt Learn how we evolved our AI assistant architecture and built robust evaluation frameworks for real-world deployment. ## Top Meta Software Engineer System Design & Product Architecture Interview Questions 2024 DevFeed: [Top Meta Software Engineer System Design & Product Architecture Interview Questions 2024]() Original publisher: [Read original article]() Author: Evan King Published: 2024-05-16T00:34:59Z Content type: tutorial Language: en Sources: [Evan King]() Topics: [Architecture & Design](), [Software](), [coding]() Tags: [architecture](), [architecture-design](), [blog-post](), [interview](), [interview-questions](), [interview-tips](), [meta](), [software-engineer](), [software-engineering](), [system-design-interview]() ### AI overview A former Meta Staff engineer and interviewer presents commonly asked Meta system design and product architecture interview questions. The article distinguishes the two interview types and advises readers to study the underlying concepts and problem-solving frameworks rather than memorize answers. ### Source excerpt I'll cut to the chase, my names Evan, I am a former Meta Staff engineer and interviewer. I've been putting together breakdowns of common problems asked across the top FAANGs. When it comes to the Meta design interview, they let you choose between a System Design interview and a Product Architecture interview. Not sure what the difference is? Checkout this blog post I wrote on the topic. But anyway, I know what you're here for. In my experience, here are the questions that are most commonly asked for each interview type. Read the linked breakdowns, definitely, but these aren't interviews where you can just memorize an answer key, so don't treat them as such. Use them to learn the underlying concepts and frameworks for tackling these types of problems. System Design Design an Ad Click Aggregator Design a Top K Service Design Memcached Design FB Live Comments Design TAO Design Uber Design Ticketmaster Design a Distributed Job Scheduler Product Architecture Design Ticketmaster Design Uber Design IG Auction Design Dropbox Design Newsfeed Design Facebook i18n Design Just the UX and APIs for Newsfeed Design Leetcode Design Messenger ## Data Center Fabric Designs: Size Matters DevFeed: [Data Center Fabric Designs: Size Matters]() Original publisher: [Read original article]() Published: 2024-03-14T07:55:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [data centers](), [Architecture & Design](), [Networks](), [vendor lock-in](), [Streaming]() Tags: [architecture-design](), [data-center](), [design](), [evpn](), [fabric](), [networks](), [streaming](), [vendor-lock-in]() ### AI overview The article argues that the appropriate data center fabric design depends on network size and operational complexity. Mixing vendors may help large streaming services avoid vendor lock-in, reduce prices, and address supply-chain issues, but it is usually impractical for enterprise data centers because the operational costs of multiple operating systems, support centers, and tooling can outweigh acquisition savings. ### Source excerpt The "should we use the same vendor for fabric spines and leaves?" discussion triggered the expected counterexamples. Here's one: I actually have worked with a few orgs that mix vendors at both spine and leaf layer. Can't take names but they run fairly large streaming services. To me it seems like a play to avoid vendor lock-in, drive price points down and be in front of supply chain issues. As always, one has to keep two things in mind: Read more ... ## ViewModels in Jetpack Compose: Their Uses, Alternatives, and Setup DevFeed: [ViewModels in Jetpack Compose: Their Uses, Alternatives, and Setup]() Original publisher: [Read original article]() Author: Alex Styl Published: 2023-05-13T00:00:00Z Content type: tutorial Language: en Sources: [Composables - Concise, no-fluff video tutorials for Android developers]() Topics: [Jetpack Compose](), [Android](), [API](), [business logic](), [Architecture & Design](), [configuration](), [Library]() Tags: [android](), [api](), [architecture-design](), [business-logic](), [configuration](), [jetpack-compose](), [tutorial](), [viewmodel]() ### AI overview A tutorial explaining Android ViewModels in Jetpack Compose, including how they preserve data across configuration changes, hold UI-related business logic, and emit updated state. It also discusses when ViewModels may not be necessary, alternatives outside Android, and the dependencies used to access them in composables. ### Source excerpt Everything you need to know about ViewModels in Jetpack Compose ## Review of 97 Things Every Software Architect Should Know DevFeed: [Review of 97 Things Every Software Architect Should Know]() Original publisher: [Read original article]() Author: Federico Published: 2018-06-06T23:06:00Z Content type: opinion Language: en Sources: [Federico Cargnelutti]() Topics: [Architecture & Design](), [Software Engineering](), [Software](), [Development]() Tags: [advice](), [architecture-design](), [book](), [personal-experience](), [software-architecture](), [software-engineering]() ### AI overview A personal review of 97 Things Every Software Architect Should Know, a compilation of advice from more than 40 software architects. The article highlights lessons about communication, complexity, developer empowerment, and balancing technical and organizational concerns, while noting that the book is not an in-depth theoretical work. ### Source excerpt I've finally had a chance to read a book I bought a while ago called "97 Things Every Software Architect Should Know - Collective Wisdom from the Experts". Not the shortest title for a book, but very descriptive. I bought this book at the OSCON Conference in Portland last year. It's an interesting book and [...] [Next page]()