# Architecture & Design Software engineering discipline concerned with the structural design of software systems. 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 ... ## Architecting a secure landing zone in the AWS European Sovereign Cloud DevFeed: [Architecting a secure landing zone in the AWS European Sovereign Cloud]() Original publisher: [Read original article]() Author: Pablo Pagani Published: 2026-09-16T21:20:48Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Amazon Web Services](), [Cloud](), [Architecture & Design](), [AWS IAM](), [Infrastructure as code](), [CI/CD](), [SIEM, Security](), [Incident response]() Tags: [advanced-300](), [aws](), [aws-identity-and-access-management-iam](), [ci-cd](), [cloud](), [eu](), [europe](), [incident-response](), [infrastructure-as-code-iac](), [partition](), [security](), [security-blog](), [security-identity-compliance](), [technical-how-to]() ### AI overview This article explains how to architect a secure, scalable landing zone in the AWS European Sovereign Cloud. It describes the aws-eusc partition boundary and covers governance, identity, logging, data protection, network design, CI/CD, artifact distribution, and incident response. ### Source excerpt The AWS European Sovereign Cloud is a new, independent cloud for Europe, physically and logically separate from existing AWS Regions and operated within the European Union (EU). It provides the same services, features, and APIs as AWS commercial Regions, but runs as a distinct AWS partition (aws-eusc), with its own control plane, AWS Identity and [...] ## Constraining AI agents with Red Hat AI: Containment, identity, and governance DevFeed: [Constraining AI agents with Red Hat AI: Containment, identity, and governance]() Original publisher: [Read original article]() Author: Grace Ableidinger Published: 2026-09-16T13:01:59Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Artificial Intelligence](), [Security](), [Zero Trust](), [Containers](), [Kubernetes](), [Architecture & Design]() Tags: [ai](), [ai-agents](), [containers](), [deployment](), [kubernetes](), [prompt-injection](), [security](), [trust]() ### AI overview This tutorial explains how to secure AI agents running on Red Hat OpenShift using containment, verifiable identity, and governance. It covers namespace isolation, quotas, sandboxing, workload identity, and admission control, with OpenClaw used in the demo. ### Source excerpt When an agent process runs on your laptop, it typically inherits anything your user has access to. Often this includes the full network stack, the file system, and the credentials sitting in memory. When integrating with GitHub, Slack, or a cloud provider, you could be one faulty permission or well-crafted prompt injection away from a security incident. The post Constraining AI agents with Red Hat AI: Containment, identity, and governance appeared first on Red Hat Developer. ## 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 ## 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. ## The AI Hurricane Is Here DevFeed: [The AI Hurricane Is Here]() Original publisher: [Read original article]() Author: Manoj Nair Published: 2026-09-15T04:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [AI Development](), [Security](), [Vulnerabilities](), [Architecture & Design](), [audit trail]() Tags: [agentic-development](), [agents](), [ai](), [application-security](), [audit-trail](), [awareness](), [blog](), [code-security](), [developer](), [executive](), [security](), [security-labs](), [snyk-platform](), [snyk-security-intel](), [supply-chain-security](), [tech](), [validation](), [vulnerabilities](), [vulnerability-insights]() ### AI overview The article argues that AI is accelerating software creation and cyberattacks, widening the gap between machine-speed development and slower validation. It calls for securing agentic development, enforcing runtime controls, maintaining inventories and audit trails for production AI applications, and using independent validation. ### Source excerpt AI is accelerating software creation and cyberattacks alike. Leaders must secure agents and code at inception, enforce controls at runtime, and validate defenses independently. ## 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 [...] ## Resolve Amazon Aurora PostgreSQL lock contention with Database Insights: Part 2 DevFeed: [Resolve Amazon Aurora PostgreSQL lock contention with Database Insights: Part 2]() Original publisher: [Read original article]() Author: Sameer Kumar Published: 2026-09-14T16:02:16Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [Amazon CloudWatch](), [PostgreSQL](), [Concurrency](), [Monitoring](), [Database](), [Amazon EC2](), [Architecture & Design](), [Amazon Web Services](), [AWS CloudFormation]() Tags: [advanced-300](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-ec2](), [aws](), [aws-cloudformation](), [concurrency](), [monitoring](), [performance](), [postgresql](), [rds-for-postgresql](), [simulation](), [technical-how-to]() ### AI overview This tutorial explains how to diagnose and resolve lock contention in Amazon Aurora PostgreSQL using Amazon CloudWatch Database Insights. It demonstrates Lock Analysis and the Lock Tree visualization for identifying blocking sessions, then covers immediate fixes, configuration changes, optimistic concurrency control, asynchronous processing, SKIP LOCKED, and row splitting. ### Source excerpt Part 1 showed how row lock contention degrades Amazon Aurora PostgreSQL throughput. In Part 2, use Amazon CloudWatch Database Insights and its Lock Tree to pinpoint blocking sessions, then resolve contention with query termination, timeout parameters, and architectural patterns such as SKIP LOCKED and row splitting that restore throughput. ## 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 Ninth Wave built AI-powered open finance onboarding on Amazon Bedrock DevFeed: [How Ninth Wave built AI-powered open finance onboarding on Amazon Bedrock]() Original publisher: [Read original article]() Author: Shawn Kelly Published: 2026-09-14T15:58:57Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [Artificial Intelligence](), [API](), [Architecture & Design](), [Security](), [Finance](), [Amazon Web Services](), [data]() Tags: [advanced-300](), [ai](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [apis](), [customer-solutions](), [fintech](), [onboarding](), [pci-dss](), [security](), [soc-2]() ### AI overview The article describes how Ninth Wave built Compass, a multi-agent AI onboarding assistant powered by Amazon Bedrock AgentCore. Compass helps financial institutions validate bank APIs, map fields to Financial Data Exchange standards, and score production readiness while supporting secure, compliant open finance connectivity. ### Source excerpt Learn how Ninth Wave built Compass, a multi-agent AI onboarding assistant on Amazon Bedrock AgentCore that validates bank APIs against Financial Data Exchange (FDX) standards, scores compliance, and compresses open finance onboarding from weeks to minutes while meeting SOC 2 and PCI DSS requirements. ## 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 ## Infineon RISC-V for Automotive at Hot Chips 2026 DevFeed: [Infineon RISC-V for Automotive at Hot Chips 2026]() Original publisher: [Read original article]() Author: Vic A Published: 2026-09-13T21:58:44Z Content type: article Language: en Sources: [ServeTheHome]() Topics: [RISC-V](), [Microcontroller](), [real-time](), [Low Latency](), [Architecture & Design](), [Security](), [IO](), [Inference Performance]() Tags: [2026](), [architecture](), [automotive](), [compute](), [cost](), [efficiency](), [infineon](), [latency](), [low-latency](), [microcontrollers](), [mobile](), [performance](), [posix](), [processors](), [real-time](), [risc-v](), [security]() ### AI overview Infineon presents RISC-V automotive processors for next-generation vehicle architectures. The article covers real-time control, low latency, power efficiency, security, zone controllers, central car computers, and heterogeneous workloads including DSP, AI inference, audio, and POSIX-based services. ### Source excerpt At Hot Chips 2026, Infineon presented a case for using RISC-V in various automotive processors in next-generation cars The post Infineon RISC-V for Automotive at Hot Chips 2026 appeared first on ServeTheHome. ## Java Weekly, Issue 663 DevFeed: [Java Weekly, Issue 663]() Original publisher: [Read original article]() Author: baeldung Published: 2026-09-13T11:00:00Z Content type: article Language: en Sources: [Baeldung]() Topics: [Architecture & Design](), [coding](), [releases]() Tags: [agent](), [agentic](), [ai](), [architecture](), [java](), [kotlin](), [no-ads](), [no-after-post](), [no-before-post](), [no-optins](), [releases](), [spec-driven-development](), [spring](), [spring-boot](), [weekly-review](), [weekly-review-no-ads-no-after-post-no-before-post-no-optins]() ### AI overview A Java-focused weekly roundup featuring spec-driven development, agent memory and AI-agent articles, technical presentations, and software release announcements. ### Source excerpt A better spec-driven development and the brain as example architecture. The post Java Weekly, Issue 663 first appeared on Baeldung. ## Multi-Agent Architecture and Microservices: What Still Works and Where It Breaks DevFeed: [Multi-Agent Architecture and Microservices: What Still Works and Where It Breaks]() Original publisher: [Read original article]() Author: Byte-Sized Design Published: 2026-09-12T17:56:36Z Content type: opinion Language: en Sources: [Byte-Sized Design]() Topics: [Microservice](), [Architecture & Design]() Tags: [agent](), [architecture](), [microservices](), [multi-agent]() ### AI overview An opinion article comparing multi-agent architecture with microservices. It discusses what still works, three places the approach breaks, and one current heuristic. ### Source excerpt The parts that still work, the three places it breaks, and one heuristic for right now ## Why Go is an Ideal Language for AI-Assisted Software Engineering DevFeed: [Why Go is an Ideal Language for AI-Assisted Software Engineering]() Original publisher: [Read original article]() Author: Cameron Balahan; Richard Seroter Published: 2026-09-12T11:04:33.891311Z Content type: opinion Language: en Sources: [Google Developers Blog]() Topics: [Go Language](), [Software Engineering](), [AI Development](), [Architecture & Design](), [Tooling]() Tags: [ai](), [ai-coding](), [compatibility](), [developer-tools](), [engineering](), [go](), [software-development](), [software-engineering]() ### AI overview The article argues that AI-assisted software engineering shifts developers' work from writing boilerplate toward reviewing, verifying, maintaining, and architecting systems. It presents Go as well suited to this model because its simplicity, readability, tooling, compatibility guarantees, and team-oriented design provide consistency and guardrails for AI-generated code. ### Source excerpt As AI coding assistants shift the developer's primary role from writing boilerplate to reviewing and maintaining systems, language choice becomes critical for long-term architectural integrity. Go directly addresses this new paradigm by utilizing its strict compiler, integrated toolchain, and uncompromising readability to provide deterministic guardrails that help AI models self-correct and generate highly standardized code. By enforcing ecosystem-wide consistency and strict backward compatibility, the Go platform empowers engineering teams to efficiently verify, optimize, and maintain high-velocity, AI-generated output in production environments. ## Build zero-trust AI agents with Google's Agent Development Kit DevFeed: [Build zero-trust AI agents with Google's Agent Development Kit]() Original publisher: [Read original article]() Author: Shubham Saboo; Eric Dong Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [AI Agent](), [Security](), [Zero Trust](), [Google](), [prompt injection](), [Architecture & Design](), [Code](), [Python](), [Open Source]() Tags: [agent](), [agents](), [ai](), [architecture](), [code](), [google](), [open-source](), [prompt-injection](), [python](), [sandboxes](), [security](), [zero-trust]() ### AI overview This article explains how to build zero-trust AI agents with Google's Agent Development Kit (ADK). It presents an autonomous customer support and returns agent as an example and recommends hard infrastructure-level controls: cryptographic signatures for database writes, kernel-level sandboxing with gVisor for dynamic code execution, and deterministic gateways for validating I/O, business logic, and data leakage. These controls address prompt injection, unauthorized state changes, credential leakage, and host compromise. ### Source excerpt Building autonomous AI agents that mutate production state requires moving beyond soft system prompts to a robust zero-trust architecture. To secure Google Agent Development Kit (ADK) workflows against prompt injections and malicious execution, developers must implement hardware-backed cryptographic signatures for database writes, kernel-level sandboxing with gVisor for dynamic code, and deterministic semantic gateways for I/O validation. By enforcing these hard security boundaries at the infrastructure level, you can safely deploy multi-tool AI agents without risking unauthorized data manipulation or server compromise. ## 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 ## When to Split a Single AI Agent into Multiple Agents DevFeed: [When to Split a Single AI Agent into Multiple Agents]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-09-12T05:19:14Z Content type: opinion Language: en Sources: [The T-Shaped Dev]() Topics: [Artificial Intelligence](), [Architecture & Design](), [Microservices]() Tags: [agent](), [ai](), [ai-agents](), [microservices](), [multi-agent]() ### AI overview The article argues that multi-agent systems can be worthwhile when a single agent suffers from expanding tool choices, conflicting system instructions, and excessive blast radius. It recommends starting with one agent and using measurable signs to determine when to split into specialized agents managed by a supervisor. ### Source excerpt When one agent should become many, when it shouldn't, and the supervisor pattern that makes the split work. (8 min) ## Evolving Pinterest's Embedding Retrieval Platform DevFeed: [Evolving Pinterest's Embedding Retrieval Platform]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-09-11T15:01:03Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [Architecture & Design](), [Algorithms](), [Scalability](), [Latency](), [IO]() Tags: [ann](), [cost](), [embedding](), [embeddings](), [engineering](), [infrastructure](), [latency](), [memory](), [models](), [pinterest](), [platform](), [quantization](), [real-time](), [retrieval](), [scalability](), [search]() ### AI overview Pinterest describes evolving its Manas embedding-retrieval platform to address the cost, scale, and flexibility challenges of serving billions of embeddings. The excerpt covers ANN search, vector quantization, and SSD-based serving. ### Source excerpt Authors: Bowen Zhou | Staff Software Engineer; Shan Gao | Senior Software Engineer; Jingwen Hu | Software Engineer II; Wenjiang Chu | Staff Software Engineer The Billion-Embedding Challenge At Pinterest, the "signal" is our lifeblood. Whether it's a home decor enthusiast finding the perfect rug or a fashion seeker discovering a new aesthetic, our discovery engine relies on understanding deep semantic relationships to help our users find inspirations. Over the last few years, the explosive growth of embedding-based retrieval has fundamentally transformed how we surface these signals -- and at the heart of that transformation is Manas, Pinterest's in-house distributed search platform. Embedding Retrieval is one of the core capabilities of Manas, supporting multiple approximate nearest neighbor search algorithms, hybrid queries with both token and embedding clauses, as well as real-time updates to ensure fresh contents become searchable within seconds. Deployed on over 80 clusters and serving billions of embeddings, Manas embedding retrieval powers all major product surfaces at Pinterest including Home Feed, Search, Related Pins, Ads, and Notifications. However, as our corpus scales toward tens of billions of embeddings and our models capture increasingly complex interactions, we face mounting challenges around cost efficiency, scalability, and flexibility. On the infrastructure side, traditional ANN algorithms like HNSW are notoriously memory-hungry -- they require the entire index to reside in RAM to maintain low query latency, making cost grow linearly with corpus size. On the modeling side, the classic two-tower retrieval paradigm is too restrictive: it reduces each candidate to a single embedding and scores relevance through a simple dot product, leaving little room to express richer, context-dependent notions of similarity. To tackle these challenges, our team has been evolving Manas's embedding retrieval stack across three fronts: Quantization. We reduce the memor ## Britain's technology brief is now everyone's job and nobody's responsibility DevFeed: [Britain's technology brief is now everyone's job and nobody's responsibility]() Original publisher: [Read original article]() Author: Lindsay Clark Published: 2026-09-11T13:12:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Machine learning](), [Architecture & Design](), [Security & Privacy](), [Vibe coding](), [NVLink](), [Aeternum]() Tags: [ai](), [cybersecurity](), [government](), [government-of-the-united-kingdom](), [open-source](), [opinion](), [public-sector](), [science](), [space](), [spacex](), [systems](), [technology](), [whitehall]() ### AI overview This technology news roundup examines how Britain's science, AI, and digital-government responsibilities are spread across competing ministerial portfolios. It also covers security incidents, AI companies, semiconductor infrastructure, open-source software, operating systems, and developer tools. ### Source excerpt Whitehall has scattered science, AI, and digital government across a thicket of competing ministerial portfolios ## Red Hat edge platforms: Choosing the right one for your use case DevFeed: [Red Hat edge platforms: Choosing the right one for your use case]() Original publisher: [Read original article]() Author: Daniel Froehlich Published: 2026-09-11T13:01:48Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Edge](), [Containers](), [Kubernetes](), [Architecture & Design](), [Hardware](), [Availability](), [Microservice]() Tags: [containers](), [deployment](), [edge](), [edge-computing](), [hardware](), [high-availability](), [kubernetes](), [management-tools](), [microservices](), [red-hat](), [virtual-machines]() ### AI overview This article introduces a series about choosing Red Hat edge platforms for specific deployment needs. It compares Red Hat Enterprise Linux and Red Hat OpenShift across workload types, platform sizes, hardware requirements, Kubernetes use cases, availability, storage, and management needs. ### Source excerpt Choosing the right platform for an edge deployment is one of the most consequential decisions an organization makes--and one of the most confusing. The options range from a single-board computer running a handful of containers to a full Kubernetes cluster with high availability, software-defined storage, and centralized management. Pick too small, and you hit a wall when requirements grow. The post Red Hat edge platforms: Choosing the right one for your use case appeared first on Red Hat Developer. ## 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. ## AI cybersecurity is a cat and mouse game DevFeed: [AI cybersecurity is a cat and mouse game]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-09-11T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [Cybersecurity](), [Vulnerabilities](), [Code](), [Architecture & Design]() Tags: [agentic-ai](), [ai](), [ai-cybersecurity](), [applications](), [architecture](), [code](), [cybersecurity](), [infrastructure](), [podcast](), [se-stackoverflow](), [se-tech](), [security](), [vulnerabilities]() ### AI overview A podcast conversation about AI's role in cybersecurity, application-adjacent protections, vulnerability probing, and resilient code infrastructure. ### Source excerpt Ryan chats with Sam Curry, CSO at Zscaler, about where human intelligence sits in the new security landscape with AI, why shifting security protections closer to applications helps limit probes for vulnerabilities, and why building more resilient code infrastructure is the best way to address the vulnerabilities AI does discover. [Next page]()