# scale Published articles for scale. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## OpenTelemetry everywhere: Migrating a metrics platform at scale DevFeed: [OpenTelemetry everywhere: Migrating a metrics platform at scale]() Original publisher: [Read original article]() Author: Iris Grace Endozo, Farzad Vazirnia and Albert Kerr, Atlassian Published: 2026-09-17T11:00:00Z Content type: article Language: en Sources: [Cloud Native Computing Foundation]() Topics: [OpenTelemetry](), [migration](), [observability](), [telemetry](), [atlassian](), [Open Source]() Tags: [atlassian](), [blog](), [collector](), [metrics](), [migration](), [observability](), [opentelemetry](), [pipeline](), [scale](), [sidecar]() ### AI overview Atlassian describes replacing a large-scale gostatsd metrics pipeline with OpenTelemetry while preserving the existing StatsD interface for service teams. The migration uses purpose-built OpenTelemetry Collector distributions across collection, ingest, aggregation, and forwarding stages, with support for both StatsD and OTLP during the transition. ### Source excerpt Why we did this at all For most of the last decade our metrics pipeline ran on gostatsd, the open-source StatsD implementation we maintain. It primarily did two jobs: as sidecar on every host and the... ## 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 ## 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. ## How We Built Automated Capacity Testing for Kafka Consumers DevFeed: [How We Built Automated Capacity Testing for Kafka Consumers]() Original publisher: [Read original article]() Author: Kaan Karakaya Published: 2026-09-14T09:46:34Z Content type: tutorial Language: en Sources: [Booking.com Development - Medium]() Topics: [Kafka](), [Testing](), [HTTP]() Tags: [automated](), [capacity](), [health-checks](), [kafka](), [load](), [load-balancer](), [parallelism](), [partition](), [recovery](), [scale](), [site-reliability-engineer](), [sre](), [testing]() ### AI overview This article describes Booking.com's extension of an existing capacity-testing platform for Kafka consumers. It explains how changing partition assignment can provide a controlled, measurable way to test consumer throughput and whether remaining consumers can absorb reassigned work after an instance or failure domain disappears. ### Source excerpt Photo by GuerrillaBuzz on Unsplash Kafka makes it easy to distribute work across consumer instances. It is much harder to prove, safely and repeatedly, how those instances behave when the distribution changes and one of them has to carry more than its usual share. For teams that run Kafka at scale, this is a practical reliability question: how much load can a consumer instance actually handle? We had automated capacity testing for HTTP services, but Kafka consumers were still tested with manual drills. Those drills could tell us something, but they were disruptive, difficult to reproduce, and risky precisely when the system was close to its limit. We wanted a controlled way to answer three questions: What is the maximum sustainable throughput of a consumer instance? If an instance or failure domain disappears, can the remaining consumers absorb the reassigned work? Are we overprovisioning resources because we do not know the real limit? The result was an extension to our capacity-testing platform that turns Kafka partition assignment into a safe, measurable load-control mechanism. Why HTTP capacity testing did not translate Our existing platform was designed for request-response services behind a load balancer. A scheduled test selects one instance, routes an increasing share of traffic to it, runs health checks after each step, and records the highest ratio the instance can sustain. After the test, traffic returns to its normal distribution and the result is reported to the service owner. Kafka has no equivalent traffic knob. Consumers pull records, and the unit of parallelism is the partition. Within a consumer group, each partition is owned by one consumer at a time. If a topic has 12 partitions and four equally loaded instances, each instance owns about three. When one instance disappears, a rebalance gives the survivors more partitions -- and the extra work arrives as a step change, not as a smooth increase from a load balancer. The key translation: for an HTTP ## Athena spotlight: Black Duck on the importance of flagging zero-days at scale DevFeed: [Athena spotlight: Black Duck on the importance of flagging zero-days at scale]() Original publisher: [Read original article]() Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Vulnerabilities](), [Artificial Intelligence](), [Frontier AI](), [Frontier Model](), [Security](), [Open Source]() Tags: [ai](), [anthropic](), [athena](), [attacks](), [chainguard](), [code](), [container-images](), [frontier-ai](), [frontier-ai-models](), [frontier-model](), [management](), [mythos](), [open-source](), [osv](), [project-glasswing](), [scale](), [security](), [source](), [vulnerabilities]() ### AI overview The article explains how Black Duck and the Athena coalition address the growing volume of AI-discovered open source zero-day vulnerabilities. Athena members use frontier models to scan sandboxed applications, while Chainguard triages, validates, and remediates findings and shares artifacts and OSV data. Black Duck uses that feed to alert customers and provide mitigation and remediation guidance. ### Source excerpt AI can find zero-days faster than teams can fix them. See how Black Duck and Athena work together to turn findings into actionable protection. ## How to scale Alloy as a central telemetry gateway: capacity planning, load testing, and production lessons DevFeed: [How to scale Alloy as a central telemetry gateway: capacity planning, load testing, and production lessons]() Original publisher: [Read original article]() Author: Fatjon Nebiu Published: 2026-09-12T11:22:06.456390Z Content type: tutorial Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana]() Tags: [analytics](), [auth](), [best-practices](), [cloud](), [cost](), [data](), [deployment](), [enterprise](), [grafana](), [grafana-alloy](), [grafana-cloud](), [how-to](), [kubernetes](), [logs](), [metrics](), [monitoring](), [observability](), [opentelemetry](), [platform](), [production](), [prometheus](), [scale](), [telemetry](), [testing](), [traces]() ### AI overview A practical guide to scaling Grafana Alloy as a centralized telemetry gateway. It covers capacity planning, load testing, and production considerations for collecting metrics, logs, and traces and forwarding them to Grafana Cloud. ### Source excerpt Running Alloy as a single-instance sidecar is simple. Running it as a centralized gateway that absorbs the full telemetry stream of an enterprise platform--tens of millions of active series, terabytes of logs per day, and tens of thousands of trace spans per second--is a different challenge altogether. To get it right, you need deliberate capacity planning, honest load testing, and a monitoring setup that doesn't rely on the very thing you're testing. As part of the Professional Services team here at Grafana Labs, we've seen this firsthand working with customers. In this post, we'll walk you through the best practices we follow to help them find success, and we'll do so using real, anonymized data from a recent engagement. We'll cover how we sized and load tested a production Alloy central collector deployment on Kubernetes, what the numbers looked like under real stress, and how the cluster behaves today handling the full production telemetry workload for a large enterprise platform. By the end, you should have a better sense for how you can create your own central gateway for collecting telemetry in Grafana Cloud. Why a central gateway? Before diving into numbers, it's worth explaining the pattern. In a central gateway setup, all telemetry from application teams--metrics, logs, and traces--flows to a shared Alloy fleet via OTLP or native Prometheus/Loki write protocols. Alloy buffers, processes, batches, and forwards everything to Grafana Cloud. This gives you several things that per-team sidecar deployments struggle to provide: A single control plane: Auth, rate limiting, and routing in one place so application teams don't need to manage Grafana Cloud credentials Centralized buffering: Ensure a transient Grafana Cloud slowdown doesn't immediately cause data loss at the source Cost visibility: Configure the gateway to only accept telemetry data containing the label or attribute that is mandatory for cost-attribution Protocol normalization: Send OTLP, Prometheus Remote ## How to use Google microbenchmarks for evaluating TPU performance DevFeed: [How to use Google microbenchmarks for evaluating TPU performance]() Original publisher: [Read original article]() Author: Junjie Qian; Chi Shuen Lee; Yu-Hsuan (Amy) Lin; Haixiong (Sean) Wang Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [Google AI](), [Inference Performance]() Tags: [architecture](), [benchmarks](), [compute](), [developers](), [google](), [guides](), [hardware](), [how-to](), [kernel](), [machine-learning](), [memory](), [mesh](), [metrics](), [model](), [open](), [open-source](), [performance](), [scale](), [software](), [tpu]() ### AI overview A tutorial on using Google's TPU microbenchmark suite to measure network, compute, memory, host-transfer, and attention performance. The results can establish a Roofline baseline and guide workload-specific optimization. ### Source excerpt Google's open-source TPU microbenchmark suite provides developers with granular performance metrics across Network, Compute, HBM, Host Transfer, and Attention components to validate real-world hardware capabilities. By leveraging these benchmarks to establish a Roofline model, engineers can accurately diagnose whether their machine learning workloads are compute-, memory-, or network-bound. This empirical baseline directly guides targeted software optimizations--such as kernel tuning, mesh sharding, and rematerialization--to maximize hardware utilization for large-scale model deployments. ## 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. ## Rapidly scaling online storage to serve over 1 billion ChatGPT users DevFeed: [Rapidly scaling online storage to serve over 1 billion ChatGPT users]() Original publisher: [Read original article]() Published: 2026-09-11T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [cloud-infrastructure](), [Database](), [data]() Tags: [chatgpt](), [data](), [database](), [engineering](), [global](), [infrastructure](), [openai](), [platform](), [python](), [scale](), [storage]() ### AI overview OpenAI describes scaling Habitat from a Python client-side library into a distributed online storage platform for global product traffic. The article focuses on reliability, performance, and capacity decisions made during rapid growth. ### Source excerpt Learn how OpenAI evolved Habitat from a Python library into a globally distributed storage platform serving 1 billion ChatGPT users and 22M requests per second. ## Scality Maestro 1.1 Manages ARTESCA Fleets From One Console With No VPN Into Customer Sites DevFeed: [Scality Maestro 1.1 Manages ARTESCA Fleets From One Console With No VPN Into Customer Sites]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-10T20:17:01Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [cloud-infrastructure](), [Monitoring](), [Tooling](), [Deployment](), [Server](), [Firewall](), [Network](), [Disk image]() Tags: [data-protection](), [deployment](), [enterprise](), [firewall](), [infrastructure](), [monitoring](), [network](), [platform](), [production](), [scale](), [server](), [software](), [tooling](), [updates](), [upgrade]() ### AI overview Scality has generally released Maestro 1.1, a fleet management platform for service providers operating many Scality ARTESCA object storage deployments. It provides centralized monitoring, software and server firmware updates, capacity tracking, and automated pay-as-you-go billing without requiring VPN tunnels or customer-side network reconfiguration. Each deployment connects outbound to Maestro, which does not connect back into customer networks. ### Source excerpt Scality has made Maestro 1.1 generally available, a fleet management platform for service providers running large numbers of Scality ARTESCA object storage deployments on behalf of their customers. The target is the backup-as-a-service business that starts as a handful of ARTESCA clusters and grows to tens or hundreds, each sitting inside a different customer's network The post Scality Maestro 1.1 Manages ARTESCA Fleets From One Console With No VPN Into Customer Sites appeared first on StorageReview.com. ## Kubernetes v1.37: Scheduler Preemption for In-Place Pod Resize (Alpha) DevFeed: [Kubernetes v1.37: Scheduler Preemption for In-Place Pod Resize (Alpha)]() Original publisher: [Read original article]() Author: Natasha Sarkar Published: 2026-09-10T18:30:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [Containers]() Tags: [containers](), [cpu](), [feature](), [kubernetes](), [memory](), [node](), [resource](), [scale]() ### AI overview Kubernetes v1.37 adds an alpha scheduler-preemption feature for in-place Pod resizes. It can preempt lower-priority workloads to make capacity available for valid, higher-priority resize requests that are deferred on full nodes. ### Source excerpt In Kubernetes, resource allocation has historically been a static decision made during a Pod's initial scheduling and placement. With the graduation of the core in-Place Pod resize feature to General Availability in v1.35, application developers and cluster operators gained the powerful ability to dynamically adjust CPU and memory allocations of running containers without incurring disruptive restarts or application downtime. However, in-place resizing introduced a unique resource scheduling gap: if a running Pod requested a resource scale-up that exceeded the host node's allocatable headroom, the Kubelet was forced to mark the request as Deferred. The Pod would remain parked in this state indefinitely, waiting for resources on the node to naturally free up. To bridge this scheduling gap, Kubernetes v1.37 introduces scheduler preemption for in-place Pod resize (Alpha), behind the InPlacePodVerticalScalingSchedulerPreemption feature gate. This feature allows the Kubernetes scheduler to actively free up capacity on a fully-utilized node by preempting lower-priority workloads, enabling the pending in-place resizes of critical, higher-priority applications to succeed. The "deferred" resize challenge To understand why this preemption mechanism is needed, it is helpful to look at how Kubernetes handles running Pod resizing. When a user or controller (such as the Vertical Pod Autoscaler) updates the resource requests of an active container, the Kubelet evaluates whether the underlying node has enough spare allocatable capacity to fulfill the increase. If the node's resources are fully utilized and cannot satisfy the new limits, the Kubelet sets the container's resizeStatus (reported in the Pod's status.containerStatuses[]) to Deferred. Unlike an Infeasible resize request (which is immediately rejected because it exceeds physical machine boundaries, namespace limit ranges, or admission quotas) a Deferred status indicates that the request is valid but is temporarily unable t ## Redefining Digital Resilience in the AI Era DevFeed: [Redefining Digital Resilience in the AI Era]() Original publisher: [Read original article]() Author: Kamal Hathi Published: 2026-09-10T17:00:15Z Content type: news Language: en Sources: [Cisco Blogs]() Topics: [Resilience](), [Artificial Intelligence](), [data](), [Architecture & Design](), [Security](), [SOC](), [monitor]() Tags: [agentic-ai-security](), [agentic-ops](), [agentic-security](), [ai](), [ai-agents](), [autonomous](), [cost](), [digital-resilience](), [executive-platform](), [governance](), [monitor](), [observability](), [resilience](), [scale](), [security](), [soc](), [speed](), [workflows]() ### AI overview Splunk is evolving its data platform for the AI era, focusing on digital resilience, trusted autonomous agents, machine-speed security operations, and economically governed AI workflows. ### Source excerpt Splunk is evolving for the AI era - removing constraints to help customers achieve digital resilience and leverage AI with speed, scale and trust. ## High-Throughput Structure Prediction with BioNeMo Inference Runtime DevFeed: [High-Throughput Structure Prediction with BioNeMo Inference Runtime]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering]() Tags: [bionemo](), [cuda](), [cuda-graphs](), [data-center-cloud](), [data-science](), [drug-discovery](), [gpu](), [healthcare-life-sciences](), [how-to](), [hpc-scientific-computing](), [inference](), [integration](), [kernels](), [model](), [models](), [node](), [nvidia](), [python](), [pytorch](), [resource](), [scale](), [simulation-modeling-design](), [tokenization](), [torch](), [tutorial](), [workflow]() ### AI overview A tutorial on using NVIDIA BioNeMo Inference Runtime to accelerate biomolecular structure-prediction models on GPUs. It covers the end-to-end Boltz2 workflow, PyTorch integration, input requirements, and Ray-based single-node throughput scaling. ### Source excerpt Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA... ## Announcing On-Demand Compute: Instant compute for your most intensive workloads DevFeed: [Announcing On-Demand Compute: Instant compute for your most intensive workloads]() Original publisher: [Read original article]() Author: Melvyn Peignon Published: 2026-09-10T14:22:58Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [autoscaling](), [cloud-infrastructure]() Tags: [autoscaling](), [clickhouse](), [cloud](), [compute](), [cost](), [infrastructure](), [production](), [scale](), [workers]() ### AI overview ClickHouse announces a private preview of On-Demand Compute, which allocates shared workers to eligible individual queries so intensive workloads can run without competing with a service's primary compute. ### Source excerpt ClickHouse On-Demand Compute lets you scale individual queries with additional workers, run intensive workloads without disrupting production, and use compute when you need it. ## Introducing Neki DevFeed: [Introducing Neki]() Original publisher: [Read original article]() Author: Nick Van Wiggeren Published: 2026-09-10T12:00:00Z Content type: release Language: en Sources: [Blog -- PlanetScale]() Topics: [Database](), [MySQL]() Tags: [database](), [mysql](), [neki](), [platform](), [postgres](), [product](), [routing](), [scale](), [workflow]() ### AI overview PlanetScale announces Neki in platform preview, a sharded Postgres offering designed to scale a database across multiple machines while retaining Postgres compatibility. ### Source excerpt Neki, sharded Postgres by PlanetScale, is now available in platform preview. ## The lifecycle of a sharded Postgres query DevFeed: [The lifecycle of a sharded Postgres query]() Original publisher: [Read original article]() Author: PlanetScale Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Blog -- PlanetScale]() Topics: [Database](), [networking]() Tags: [architecture](), [auth](), [database](), [engineering](), [neki](), [postgres](), [routing](), [scale](), [server](), [sql]() ### AI overview An overview of how a SQL query moves through a sharded Postgres database, from authentication and routing to execution across shards. ### Source excerpt Follow a SQL query through the router, across four Postgres shards, and back. ## Build with OpenAI Agents API on Vercel DevFeed: [Build with OpenAI Agents API on Vercel]() Original publisher: [Read original article]() Author: Allen Zhou Published: 2026-09-10T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Bots]() Tags: [agent](), [agents](), [api](), [architecture](), [code](), [guide](), [integration](), [openai](), [sandbox](), [scale](), [tool](), [vercel]() ### AI overview Vercel now supports building and deploying long-running, tool-using agents with the OpenAI Agents API. OpenAI manages agent loops and session state, while Vercel provides hosted applications, Sandbox execution, persistent workspaces, webhook and queue integration, and scale-to-zero operation. ### Source excerpt You can now build and deploy long-running, tool-using agents with the OpenAI Agents API on Vercel. OpenAI manages the agent loop and session state, while Vercel hosts the application and connects each session to Vercel Sandbox for code execution and file access. With this integration, you get: An OpenAI-managed agent loop and session state Reliable Sandbox creation and reconnection through signed OpenAI webhooks and Vercel Queues An isolated execution environment for every agent session A persistent workspace that retains files across follow-up instructions A scale-to-zero architecture without an always-on worker Follow the step-by-step guide to build and deploy an agent, or explore the sample application. Read more ## HPE Alletra Storage MP B10000 10.6.0 Arrives With Six-Node Scale-Out and Agentic Support Automation DevFeed: [HPE Alletra Storage MP B10000 10.6.0 Arrives With Six-Node Scale-Out and Agentic Support Automation]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-09T16:17:13Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [releases](), [Software](), [Automation](), [ransomware](), [real-time](), [data]() Tags: [agentic](), [architecture](), [automation](), [data](), [energy](), [enterprise](), [enterprise-storage](), [hpe](), [performance](), [products](), [ransomware](), [real-time](), [release](), [scale](), [software](), [storage](), [update]() ### AI overview HPE has generally released version 10.6.0, also called Release 6, for the Alletra Storage MP B10000. The update expands disaggregated block-and-file storage from four to six controller nodes, adds agent-based support automation and built-in real-time ransomware detection, and increases the StoreMore Guarantee to a 5:1 effective capacity ratio. ### Source excerpt HPE has made the 10.6.0 software release for the Alletra Storage MP B10000 generally available, landing inside the Q3 2026 window the company set when it previewed the release in May. HPE is also calling it Release 6 in its channel materials. The update takes the B10000's disaggregated block-and-file architecture from four controller nodes to The post HPE Alletra Storage MP B10000 10.6.0 Arrives With Six-Node Scale-Out and Agentic Support Automation appeared first on StorageReview.com. ## Evaluation-First AI Agents: How Zepto Scales Customer Support on Databricks and MLflow DevFeed: [Evaluation-First AI Agents: How Zepto Scales Customer Support on Databricks and MLflow]() Original publisher: [Read original article]() Author: Gireesh Sreedhar KP; Deepak Dhankani; Eash Sharma Published: 2026-09-09T03:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [AI Agent](), [databricks](), [AI Infrastructure](), [real-time]() Tags: [agents](), [ai](), [ai-engineering](), [architecture](), [best-practices](), [blog](), [company](), [customer](), [customers](), [data-science-and-ml](), [data-strategy](), [databricks](), [efficiency](), [engineering](), [evaluation](), [india](), [industries](), [operations](), [platform](), [product](), [production](), [real-time](), [retail-consumer-goods](), [scale](), [workflows]() ### AI overview This Databricks and MLflow case study describes how Zepto uses an evaluation-first, multi-agent AI system to operate customer support at more than 100,000 tickets per day. It focuses on the system architecture, evaluation framework, quality gate, and development and production loops used to improve reliability as volume, product categories, languages, and failure modes expand. ### Source excerpt Zepto's Push for Reliable, Real-Time Customer SupportZepto is one of India's fastest-growing... ## Built for Reliability: How American Express Processes Payments at Scale DevFeed: [Built for Reliability: How American Express Processes Payments at Scale]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-08T18:31:03Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [Architecture & Design](), [Microservice](), [Microservices](), [Network](), [Server]() Tags: [account-balance](), [architecture](), [bank](), [card](), [cell-based-architecture](), [microservices](), [network](), [payments](), [scale](), [server](), [services]() ### AI overview The article explains how American Express processes payments through a cell-based architecture. Transactions move through independent processing units and microservices, with isolation designed to limit disruption when services fail. ### Source excerpt In this article, we will try to understand how the transaction runs through such a cell-based architecture and how the payments are processed even when some services are failing. ## Cloud Native Computing Foundation Announces Karmada Graduation DevFeed: [Cloud Native Computing Foundation Announces Karmada Graduation]() Original publisher: [Read original article]() Author: Haley White Published: 2026-09-08T02:06:17Z Content type: news Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Kubernetes](), [Orchestration](), [Cloud Native Ecosystem](), [Inference](), [autoscaling](), [GPU](), [cloud-infrastructure]() Tags: [ai](), [ai-infrastructure](), [announcements](), [autoscaling](), [china](), [cloud](), [critical-infrastructure](), [ecosystem](), [enterprise](), [global](), [gpu](), [inference](), [infrastructure](), [kubernetes](), [open-source](), [orchestration](), [production](), [pytorch](), [release](), [resilience](), [scale]() ### AI overview Karmada has graduated from the CNCF after reaching production maturity as an open-source engine for orchestrating applications across multiple Kubernetes clusters, clouds, and regions. Its v1.19 release adds multi-component scheduling for distributed AI training jobs, while the project supports hybrid-cloud capacity, multi-region resilience, and multi-cluster AI infrastructure. ### Source excerpt Multi-cluster, multi-cloud Kubernetes orchestration project reaches production maturity as global enterprises scale AI training and inference across hybrid infrastructure Key Highlights SHANGHAI, China - KubeCon + CloudNativeCon + OpenInfra Summit + PyTorch Conference China 2026 -... ## Outbid.lol's Growth and Lessons for Payment Infrastructure DevFeed: [Outbid.lol's Growth and Lessons for Payment Infrastructure]() Original publisher: [Read original article]() Author: Rishabh Goel Published: 2026-09-07T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [Website](), [Internet]() Tags: [build](), [competition](), [newsletter](), [payments](), [platform](), [product](), [scale](), [small-ideas]() ### AI overview The article examines how Outbid.lol grew from a simple paid public leaderboard into a widely discussed launch, citing reported figures of more than a million visitors and over $220K in bids or revenue. It also discusses the importance of payment infrastructure during sudden growth and announces Dodo Payments v1.113.6 improvements. ### Source excerpt Outbid.lol turned a 3-hour build into $220K and a million visitors. Why small ideas scale, plus v1.113.6: subscription pause, PPP, and Charm Pricing. ## Introducing Scheduled Upgrades in ClickHouse Managed Postgres DevFeed: [Introducing Scheduled Upgrades in ClickHouse Managed Postgres]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-09-03T16:22:48Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [configuration](), [Operating system]() Tags: [clickhouse](), [enterprise](), [platform](), [postgres](), [scale](), [security](), [storage](), [updates]() ### AI overview ClickHouse Managed Postgres introduces configurable maintenance windows for routine platform upgrades. Scale and Enterprise organizations can schedule when maintenance occurs, with Enterprise users also able to select days of the week. ### Source excerpt ClickHouse Managed Postgres now supports scheduled upgrade windows, giving Scale and Enterprise users more control over when routine platform maintenance occurs. ## Cursor Cloud Agents can now run in Vercel Sandbox DevFeed: [Cursor Cloud Agents can now run in Vercel Sandbox]() Original publisher: [Read original article]() Author: Allen Zhou Published: 2026-09-03T15:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [cursor](), [Vercel](), [Agent Harness](), [Firecracker]() Tags: [agents](), [apis](), [architecture](), [cursor](), [enterprise](), [firecracker](), [guide](), [sandbox](), [scale](), [vercel](), [virtual-machines](), [workflows]() ### AI overview Cursor Cloud Agents can run in Vercel Sandbox, using isolated Firecracker microVMs as their execution environment. The architecture combines Cursor's agent harness and inference loop with Vercel Functions and Workflows for durable request handling, worker provisioning, monitoring, retries, cleanup, and short-lived user-scoped credentials. ### Source excerpt Cursor Cloud Agents can now run in Vercel Sandbox instead of Cursor's hosted machines. Cursor manages the agent harness and inference loop. Its Self-Hosted Machines APIs let you supply the execution environment where agents clone repositories, edit files, and run commands and tests. Self-Hosted Machines requires a Cursor Enterprise plan. Vercel Sandbox provides that execution environment as an isolated Firecracker microVM for each agent request. Vercel Functions and Vercel Workflows form a durable control plane that claims queued agent requests, provisions workers, monitors sessions, and cleans up automatically. With this architecture, you get: A scale-to-zero worker pool without long-lived virtual machines A dedicated, isolated Sandbox for every agent request Durable retries when a worker or session fails Short-lived, user-scoped credentials inside each Sandbox Follow the step-by-step guide to deploy the reference implementation to your own Vercel account, or learn more in the Vercel Sandbox documentation. Read more [Next page]()