# reduce Published articles for reduce. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Let's Reduce! A Gentle Introduction to Javascript's Reduce Method DevFeed: [Let's Reduce! A Gentle Introduction to Javascript's Reduce Method]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-07-10T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [JavaScript](), [coding](), [Redux](), [Web Development]() Tags: [array](), [arrow](), [engineering](), [filter](), [introduction](), [javascript](), [map](), [news](), [react](), [reduce](), [web-frontend]() ### AI overview A tutorial explaining JavaScript's reduce method as a foundation for understanding Redux. It describes how reduce processes array items, how each iteration depends on the previous return value, and why the method can be difficult to learn. ### Source excerpt By Josh Comeau Every summer, Khan Academy recruits a few software engineer interns. As part of their onboarding, ... Read more ## From error mitigation to fault-tolerant quantum computing DevFeed: [From error mitigation to fault-tolerant quantum computing]() Original publisher: [Read original article]() Published: 2026-09-15T14:30:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Quantum Computing](), [Hardware](), [ibm]() Tags: [compilation](), [deep-dive](), [errors](), [hardware](), [quantum](), [quantum-computing](), [quantum-error-correction-mitigation](), [quantum-software](), [real-time](), [reduce](), [resource]() ### AI overview The article describes a continuum from quantum error mitigation to quantum error correction as a path toward useful quantum computing. It reports emerging techniques with lower effective error rates, reduced sampling overhead, and lower resource requirements than conventional approaches. ### Source excerpt A spectrum of error-correcting techniques is enabling useful quantum computation, measured not by logical qubits but by the circuits you can run with them. ## EROFS Disables LZ4 Rolling Decompression Due To Data Corruption Possibility DevFeed: [EROFS Disables LZ4 Rolling Decompression Due To Data Corruption Possibility]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-13T00:33:00Z Content type: news Language: en Sources: [Phoronix]() Topics: [Filesystems](), [systems](), [Optimization](), [Linux](), [data](), [Containers]() Tags: [containers](), [data](), [desktop-linux](), [embedded](), [embedded-systems](), [kernel](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [memory](), [open-source-graphics](), [optimization](), [phoronix](), [phoronix-test-suite](), [reduce](), [systems](), [ubuntu-benchmarks](), [ubuntu-hardware](), [x86]() ### AI overview EROFS has temporarily disabled LZ4 rolling decompression because a rare interaction with the upstream LZ4 implementation could produce corrupted data. The change prioritizes data correctness in production but increases runtime memory usage. ### Source excerpt The EROFS read-only file-system popular for embedded systems, containers, and other use-cases has resorted to temporarily disabling its LZ4 rolling decompression support due to data corruption concerns... ## How to calculate DevOps platform total cost of ownership DevFeed: [How to calculate DevOps platform total cost of ownership]() Original publisher: [Read original article]() Author: GitLab Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [GitLab]() Topics: [CI/CD]() Tags: [ai](), [ci-cd](), [complexity](), [compute](), [cost](), [devops](), [devsecops](), [devsecops-platform](), [drivers](), [how-to](), [infrastructure](), [integration](), [model](), [platform](), [reduce](), [software-delivery](), [tools]() ### AI overview A guide to modeling the total cost of ownership of a DevOps platform, including subscriptions, CI/CD compute, AI usage, infrastructure, tools, and internal labor. ### Source excerpt There's nothing like budget pressure to put your DevOps platform under a microscope. But subscription fees and license costs only tell one part of the story. The total cost of ownership (TCO) for a DevOps platform also includes variable costs like CI/CD compute and AI usage, along with the infrastructure, tools, and employee time required to keep software delivery moving. That wider view matters when you're tasked with defending platform spend or comparing options with a head of finance. Designing a useful TCO model can: Make those costs transparent for stakeholders Shine a light on the reasoning (or lack thereof) behind each cost Identify areas to reduce spend without negatively impacting software delivery What total cost of ownership really includes The core challenge of calculating TCO is that DevOps platforms package and price capabilities differently. For example, one platform may bundle CI/CD or AI capabilities into a per-seat subscription, while another could price usage separately. A third may appear less expensive upfront but require additional tools and ongoing integration work. That's why list prices or pricing tiers alone won't give you a useful comparison. Start with the capabilities and workloads your organization actually needs, then calculate what it takes to support them on each platform. Use the same scope and time period for every option -- often one year -- and define which teams, applications, environments, and delivery stages are included. Separate recurring costs from one-time expenses and external spend from internal labor, so finance can audit the assumptions and forecast future years. A useful TCO model, therefore, answers two questions: What does it cost to meet our requirements today? Which variables will cause that cost to rise or fall as our usage changes? The cost categories that drive your bill Most DevOps platform costs fit into the following categories: Cost categoryWhat it includesMain cost driverPlatform accessPaid seats, role-based ## How to Confirm Phone Number Ownership with Lookup Identity Match DevFeed: [How to Confirm Phone Number Ownership with Lookup Identity Match]() Original publisher: [Read original article]() Author: Kelley Robinson Published: 2026-08-31T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [API](), [Code](), [Command-line interface](), [Serverless](), [JavaScript]() Tags: [api](), [cli](), [code](), [developer-insights](), [fake-accounts](), [fraud](), [how-to](), [javascript](), [reduce](), [request](), [serverless](), [verify]() ### AI overview This tutorial explains how to use Twilio Lookup Identity Match to confirm that a person is linked to a phone number. It covers matching submitted identity details against authoritative data, setting up a JavaScript project with the Twilio CLI and Serverless plugin, and using the result to reduce sign-up fraud. ### Source excerpt Send details like name, address, or date of birth and the Identity Match API will tell you if they match a provided phone number. Reduce fraud and verify identities quickly. ## Respond to security threats faster with Tines and Observability Pipelines DevFeed: [Respond to security threats faster with Tines and Observability Pipelines]() Original publisher: [Read original article]() Author: Zara Boddula Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Security](), [observability](), [Automation](), [log management](), [Orchestration](), [API]() Tags: [accelerate](), [api](), [automation](), [devsecops](), [logs](), [observability-pipelines](), [orchestration](), [real-time](), [reduce](), [security](), [workflows]() ### AI overview Tines and Datadog Observability Pipelines automate security-log processing by standardizing and routing logs, updating pipelines through APIs and reference tables, and applying current context in real time. The integration helps reduce alert noise, identify access-control gaps and suspicious activity, and accelerate threat investigations. ### Source excerpt Learn how Tines workflows can update Datadog Observability Pipelines to prioritize threats, reduce alert noise, and accelerate investigations. ## CircleCI Smarter Testing: Stop running tests that don't matter DevFeed: [CircleCI Smarter Testing: Stop running tests that don't matter]() Original publisher: [Read original article]() Author: Nathan Fish Published: 2026-08-26T19:00:00Z Content type: article Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [Testing](), [CI/CD]() Tags: [accelerate](), [auto-rerun-failed-tests](), [ci-cd](), [circleci](), [circleci-news](), [developer-productivity](), [dynamic-test-splitting](), [engineering-productivity](), [flaky](), [intelligent-test-selection](), [parallel](), [pipeline](), [quality](), [reduce](), [smarter-testing](), [test-impact-analysis](), [testing](), [tests](), [time]() ### AI overview CircleCI describes Smarter Testing, a set of features designed to reduce CI/CD test execution time by skipping tests unaffected by changes, balancing parallel nodes, and retrying flaky tests. The article says early users have seen test runs up to four times faster. ### Source excerpt Testing eats up to half your pipeline time. See how CircleCI Smarter Testing skips unaffected tests, rebalances parallel nodes, and retries flaky tests. ## Introducing the Admin plugin for ChatGPT Work and Codex DevFeed: [Introducing the Admin plugin for ChatGPT Work and Codex]() Original publisher: [Read original article]() Published: 2026-08-25T00:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [codex](), [Authorization](), [Monitoring](), [Slack]() Tags: [ai-adoption](), [analytics](), [chatgpt](), [codex](), [enablement](), [microsoft-teams](), [monitor](), [monitoring](), [onboarding](), [reduce](), [slack](), [tools](), [update](), [work](), [workflow]() ### AI overview OpenAI introduces an Admin plugin for ChatGPT Work and Codex that lets administrators analyze workspace usage, manage members and permissions, adjust limits, and handle requests in one conversation. It also supports recurring checks and workflows that route approvals to Slack or Microsoft Teams while keeping changes within existing roles and permissions. ### Source excerpt Use the Admin plugin for ChatGPT Work and Codex to analyze workspace usage, manage members and permissions, adjust limits, and act on admin requests. ## Vercel Sandbox is now globally available DevFeed: [Vercel Sandbox is now globally available]() Original publisher: [Read original article]() Author: Rob Herley Published: 2026-08-24T04:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Command-line interface](), [SDKs](), [Latency](), [cloud-infrastructure]() Tags: [cli](), [databases](), [documentation](), [enterprise](), [latency](), [object-storage](), [reduce](), [sdk](), [snapshots](), [vercel]() ### AI overview Vercel Sandbox is now globally available in four regions, with region selection supported on all plans and configurable failover regions for Pro and Enterprise teams. ### Source excerpt Vercel Sandbox now runs globally, starting with four regions: iad1 (Washington, D.C.), sfo1 (San Francisco), cle1 (Cleveland), and cdg1 (Paris). iad1 remains the default. Support for all Vercel regions is coming soon. Choose a region close to the databases, object storage, and other services your sandboxes access to reduce latency. Region selection is available on all plans. Pro and Enterprise teams can also configure failover regions. If the primary region is unavailable, new sandboxes start in the closest configured failover region. Failover only applies to regions you configure; without it, a sandbox starts in its selected region, or the project default. Update the SDK or CLI you use to the latest version: Configure the default region and failover regions from Settings > Sandboxes in your project, or with the Vercel CLI: Those defaults apply to every sandbox. To override them for a single sandbox, pass region and optionally failoverRegions at create time with the SDK: Or with the CLI: Learn more in the Sandbox regions documentation. Read more ## How NVIDIA scales expertise with ChatGPT Work DevFeed: [How NVIDIA scales expertise with ChatGPT Work]() Original publisher: [Read original article]() Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Nvidia](), [Artificial Intelligence]() Tags: [ai](), [ai-operations](), [chatgpt](), [events](), [global](), [nvidia](), [operations](), [partners](), [reduce](), [scale](), [tools](), [work](), [workflow](), [workflows]() ### AI overview NVIDIA uses ChatGPT Work to automate recurring operational workflows, analyze external AI developments, and help teams act on information more efficiently. A GTC planning workflow saves about 16 hours per week and can be adapted and shared across regions. ### Source excerpt NVIDIA teams use ChatGPT Work to reduce manual tasks, connect fast-moving signals, and scale successful workflows globally. ## Automatically Archive Old NetBox Branches DevFeed: [Automatically Archive Old NetBox Branches]() Original publisher: [Read original article]() Author: Rick Donato Published: 2026-08-14T08:00:15Z Content type: release Language: en Sources: [Packet Coders - Learn Network Automation]() Topics: [NetBox](), [Job](), [Database]() Tags: [automatically](), [daily](), [job](), [netbox](), [reduce](), [release](), [tips]() ### AI overview NetBox Branching v1.1.2 introduces the auto_archive_days setting, which runs a daily background job to archive merged branches older than a specified number of days. Automatic archival is disabled by default, and archived branches cannot be reverted. ### Source excerpt A common question we get from students using NetBox Branching is: How can I automatically remove stale branches from NetBox? Therefore, I thought I'd share the latest addition to NetBox Branching, introduced in v1.1.2. This release adds the auto_archive_days setting, allowing you to automatically ## Fast-tracking service deployment with Port DevFeed: [Fast-tracking service deployment with Port]() Original publisher: [Read original article]() Author: Yarden Holtzer Weiss Published: 2026-07-30T10:39:13Z Content type: tutorial Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [Deployment](), [sdlc](), [User Stories](), [code productivity](), [DevOps]() Tags: [cognitive-load](), [deployment](), [devops](), [internal-developer-portal](), [productivity](), [reduce](), [sdlc](), [user-stories]() ### AI overview A practical guide to creating a basic internal developer portal in 4-5 hours. It focuses on streamlining the SDLC by helping developers independently manage deployments, scaffold services, view runtime data, and review deployment history. ### Source excerpt How to create an efficient internal developer portal in just 4-5 hours to streamline SDLC, boost productivity, and enhance developer autonomy. ## Port's internal deployment pipeline DevFeed: [Port's internal deployment pipeline]() Original publisher: [Read original article]() Author: Matan Heled Published: 2026-07-22T12:04:29Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [Deployment](), [GitHub](), [Kubernetes](), [Microservice](), [Amazon Web Services]() Tags: [aws](), [complexity](), [deployment](), [developer-portal](), [github](), [kubernetes](), [platform](), [reduce]() ### AI overview The article explains how Port's platform team built an internal deployment pipeline using Port's developer portal and self-service actions. The approach lets developers trigger deployments directly from the portal while reducing operational tickets and standardizing procedures. ### Source excerpt Port enhances developer operations by simplifying self-service deployment, streamlining pipeline complexity, and improving log visibility. ## Write utilization now available in ISR Observability DevFeed: [Write utilization now available in ISR Observability]() Original publisher: [Read original article]() Author: Mark Knichel Published: 2026-07-16T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [observability](), [Vercel](), [Command-line interface](), [Caching]() Tags: [caching](), [cli](), [guide](), [how-to](), [observability](), [reduce](), [vercel]() ### AI overview Vercel has added a write utilization metric to the ISR Observability page for Observability Plus subscribers. The metric compares cached requests with ISR writes, helping identify routes that regenerate frequently despite receiving few requests. ### Source excerpt The ISR Observability page now shows Observability Plus subscribers a write utilization metric, helping identify routes that regenerate often but receive few requests. Write utilization is the ratio of cached requests to ISR writes. For routes with low write utilization, consider increasing the revalidation interval or switching to on-demand revalidation to reduce costs. You can also compute write utilization using the Vercel CLI, or use agents with the cdn-caching skill to investigate. Learn more about Incremental Static Regeneration or follow our guide on how to reduce ISR regenerations and costs. Read more ## Your AI is only as responsible as you are DevFeed: [Your AI is only as responsible as you are]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-07-14T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [AI Chat]() Tags: [agentic](), [ai](), [ai-governance](), [design](), [experimentation](), [microsoft](), [podcast](), [reduce](), [responsible-ai](), [se-stackoverflow](), [se-tech](), [workflow](), [workflows]() ### AI overview A podcast conversation with Microsoft's Sarah Bird on responsible AI, the NIST approach, the risks of impact-free experimentation, and human/AI workflow design that reduces unnecessary escalation. ### Source excerpt Recorded at Microsoft Build, Ryan welcomes Sarah Bird, Microsoft's Chief Product Officer for Responsible AI, about how we can build and use AI responsibly with the NIST approach, why most irresponsible AI comes from experimentation without thought of impact, and how Microsoft is researching thoughtful human/AI workflow design to reduce unnecessary escalation. ## Deadlocks and downtime DevFeed: [Deadlocks and downtime]() Original publisher: [Read original article]() Author: Simeon Griggs Published: 2026-07-08T00:00:00Z Content type: tutorial Language: en Sources: [Blog -- PlanetScale]() Topics: [Database](), [Deadlock](), [Transactions](), [Concurrency](), [Concurrent Programming]() Tags: [database](), [deadlock](), [engineering](), [errors](), [how-to](), [learn](), [postgres](), [reduce](), [transactions]() ### AI overview This article explains how Postgres deadlocks arise when concurrent transactions hold locks that each other needs, and how those deadlocks can escalate into database downtime. It examines the effects of high query volume and immediate retry loops, then recommends consistent row-update ordering, smaller transactions, and late locking to reduce the risk. ### Source excerpt Deadlocks happen when transactions block each other. Learn how they escalate into downtime, how to reduce them through better queries and retry logic, and how Traffic Control can protect your database from your application. ## The power of collaboration: How we can reduce traffic congestion DevFeed: [The power of collaboration: How we can reduce traffic congestion]() Original publisher: [Read original article]() Published: 2026-07-07T16:42:08Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [Algorithms, Complexity](), [Optimization](), [Routing (disambiguation)](), [App](), [Artificial Intelligence]() Tags: [algorithms](), [algorithms-theory](), [climate-sustainability](), [collaboration](), [data-mining-modeling](), [driving](), [google](), [optimization](), [reduce](), [routing](), [transportation](), [us]() ### AI overview Google Research describes a large-scale routing experiment in 10 major US cities. By guiding a small fraction of trips toward alternative routes, the study reports improved overall traffic conditions, faster driving speeds, and reduced emissions. ### Source excerpt Algorithms & Theory ## Variance Reduction Below the Randomization Grain DevFeed: [Variance Reduction Below the Randomization Grain]() Original publisher: [Read original article]() Author: Tilman Drerup Published: 2026-07-01T16:28:36Z Content type: article Language: en Sources: [Instacart]() Topics: [experiments]() Tags: [capacity](), [causal-inference](), [economics](), [engineering](), [experiment](), [experimentation](), [experiments](), [machine-learning](), [marketplaces](), [metrics](), [reduce](), [science](), [techniques](), [variance]() ### AI overview This article explains how marketplace experiments can reduce metric variance below the level at which treatment is randomized. It describes cluster-level randomization for containing interference and shows how fine-grained outcome predictability can improve statistical power and reduce experimentation time. ### Source excerpt Sergio Camelo, Caitlin Kearns, Matias Cersosimo, and Tilman Drerup As artificial intelligence increases the velocity of engineering and science teams, experimental throughput is set to become a bottleneck for many product decisions. Many companies can now build faster than they can experiment, with queues of good ideas running the risk of not being tested because of lack of experimental capacity. This problem is particularly severe in marketplaces, where the presence of spillover and cannibalization effects between experimental units requires cluster-level randomization techniques. That randomization, in turn, has the unfortunate tendency to substantially reduce statistical power and slow down experimentation. In this post, we show that the predictability of outcomes at fine grains can be exploited to reduce the variance of aggregate metrics, even when experiments themselves are run at a coarse level. Since statistical power depends on metric variability, this yields considerable reductions in experimentation time. The Interference Problem In marketplace settings, behavior and outcomes for individual participants are inherently intertwined. In a delivery marketplace like Instacart, for example, the dispatch system solves a bipartite matching problem between shoppers and customer orders. Since assignments are global and interdependent, matching an order to one shopper means that the same order cannot be matched to another shopper. As a result, changing the handling for a single order creates ripples that affect the orders around it. If an experimenter were to assign a treatment intervention to one of these orders while leaving neighboring orders as controls, the latter would evidently be contaminated. A common response to this problem is to randomize treatments at the level of a cluster, chosen so that interference can stay within it. In food and grocery delivery, that cluster is typically a geographical region. Since every order within a region sees the same treatme ## How Redpanda Cloud Topics rethinks Kafka compaction DevFeed: [How Redpanda Cloud Topics rethinks Kafka compaction]() Original publisher: [Read original article]() Author: Willem Kaufmann Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [cloud-infrastructure](), [Cloud](), [Streaming](), [Algorithm]() Tags: [apache](), [apache-kafka](), [architecture](), [cloud-native](), [cloud-storage](), [clusters](), [cpu](), [cycles](), [engineering](), [kafka](), [object-storage](), [reduce](), [retention](), [scale](), [storage](), [streaming]() ### AI overview The article explains how Redpanda Cloud Topics redesigns Kafka log compaction for cloud-native streaming. It describes how the architecture reduces redundant processing and CPU use, lowers cloud storage costs, and preserves Kafka behavior while addressing scaling challenges such as limited memory, tombstone removal, and rewriting large volumes of object storage data. ### Source excerpt Compaction can overwhelm poorly sized Kafka clusters, leading to full disks and maxed-out CPUs. Learn how Redpanda's Cloud Topics architecture redesigns compaction to cut redundant work, reduce cloud storage costs, and preserve the Kafka semantics you rely on. ## How we saved over $3 million in idle compute costs with Datadog Kubernetes Autoscaling DevFeed: [How we saved over $3 million in idle compute costs with Datadog Kubernetes Autoscaling]() Original publisher: [Read original article]() Author: Jacob Simonov; Danny Driscoll; Jesse Feinman; Sophie Wigmore Published: 2026-06-25T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [kubernetes autoscaling](), [autoscaling](), [Kubernetes](), [Cloud](), [migration](), [configuration](), [cpu](), [data centers]() Tags: [autoscaling](), [cloud](), [compute](), [cost](), [cpu](), [data-centers](), [guide](), [kubernetes](), [kubernetes-autoscaling](), [memory](), [migration](), [platform](), [post](), [production](), [reduce](), [resource](), [scale](), [tooling]() ### AI overview Datadog describes how its Kubernetes Autoscaling system helped a platform team automate horizontal scaling and vertical resource rightsizing across a large production fleet. The migration addressed manual pod sizing, difficult-to-maintain scaling configurations, idle compute costs, and reliability risks, contributing to more than $3 million in annualized idle compute savings across Datadog. ### Source excerpt See how how multidimensional autoscaling reduced overprovisioning and mitigated reliability risks at scale. ## Access OpenAI models and Codex through your Oracle cloud commitment DevFeed: [Access OpenAI models and Codex through your Oracle cloud commitment]() Original publisher: [Read original article]() Published: 2026-06-10T20:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [codex](), [Cloud](), [Artificial Intelligence](), [AI Strategy](), [AI Development](), [API]() Tags: [ai](), [ai-adoption](), [api](), [cloud](), [cloud-infrastructure](), [codex](), [company](), [enterprise](), [openai](), [partnership](), [production](), [reduce](), [security]() ### AI overview OpenAI and Oracle are expanding access to OpenAI models and Codex through Oracle Cloud Infrastructure and Oracle Marketplace. Eligible Oracle customers can use existing Oracle Universal Credits and procurement processes to access these offerings, initially in the United States, for building AI applications, analyzing information, automating workflows, and supporting enterprise adoption. ### Source excerpt Access OpenAI models and Codex through Oracle Cloud, using existing commitments to build and deploy AI with enterprise security and governance. ## DSO (Days Sales Outstanding): SaaS Formula, Benchmarks, and How to Cut It DevFeed: [DSO (Days Sales Outstanding): SaaS Formula, Benchmarks, and How to Cut It]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-06-06T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service]() Tags: [accounts-receivable](), [b2b](), [benchmarks](), [billing](), [collections](), [guide](), [how-to](), [metric](), [operational](), [reduce](), [revenue](), [saas](), [saas-finance]() ### AI overview A guide for SaaS founders explains how to calculate Days Sales Outstanding (DSO), interpret benchmarks across billing models, and reduce collection times. It also covers DSO's effects on working capital, growth, and bad-debt risk. ### Source excerpt DSO formula explained for SaaS founders. How to calculate days sales outstanding, what good looks like by billing model, and tactical ways to reduce it. ## 5 Tips to Save Money on Laravel Cloud DevFeed: [5 Tips to Save Money on Laravel Cloud]() Original publisher: [Read original article]() Author: Laravel Team Published: 2026-06-05T15:30:22Z Content type: article Language: en Sources: [Laravel Blog]() Topics: [Laravel](), [Cloud](), [Cache](), [Databases](), [Livewire](), [Low-Code / Internal Tools]() Tags: [app](), [apps](), [billing](), [business](), [cache](), [cloud](), [compute](), [cost](), [database](), [databases](), [developer](), [growth](), [internal-tools](), [laravel](), [operations](), [pricing](), [reduce](), [resource](), [resources](), [scale]() ### AI overview Five ways to reduce Laravel Cloud costs: enable scale to zero for Flex compute, set an organization spending limit, monitor the usage page, use managed queues, and share databases and caches across apps. ### Source excerpt Five ways to reduce your Laravel Cloud bill: enable scale to zero, set a spending cap, use the usage page, switch to managed queues, and share resources. ## Egress problems and where to find them DevFeed: [Egress problems and where to find them]() Original publisher: [Read original article]() Author: Simeon Griggs Published: 2026-05-14T00:00:00Z Content type: article Language: en Sources: [Blog -- PlanetScale]() Topics: [Databases](), [Database](), [data](), [App](), [Cloud]() Tags: [applications](), [availability](), [cloud](), [data](), [database](), [databases](), [efficiency](), [high-availability](), [performance](), [postgres](), [product](), [reduce](), [scale]() ### AI overview The article explains how reducing database egress by fetching less data and making requests less frequently can improve application performance while lowering costs. It focuses on Postgres examples, including query limits, selecting specific columns, and extracting needed values from JSONB data. ### Source excerpt Reducing the size and frequency of requests to your database has the double benefit of making your applications faster and cheaper. [Next page]()