# scaling Published articles for scaling. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Logarithmic auto-scaling for Laravel Horizon DevFeed: [Logarithmic auto-scaling for Laravel Horizon]() Original publisher: [Read original article]() Author: Freek Van der Herten (freek@spatie.be) Published: 2026-09-12T12:30:30Z Content type: article Language: en Sources: [freek.dev - all blogposts]() Topics: [Laravel](), [realtime](), [Development]() Tags: [horizon](), [laravel](), [performance](), [queues](), [realtime](), [scaling]() ### AI overview This article explains how logarithmic auto-scaling can be added to Laravel Horizon. Log-based weighting helps prevent large queue spikes from starving smaller realtime queues while still allocating enough workers to large backlogs. ### Source excerpt A thoughtful write-up on adding logarithmic auto-scaling to Laravel Horizon. It shows how log-based weighting keeps huge queue spikes from starving smaller realtime queues while still giving large backlogs enough workers. Read more ## Session revocations at scale DevFeed: [Session revocations at scale]() Original publisher: [Read original article]() Author: Llew Vallis Published: 2026-07-22T00:00:01Z Content type: article Language: en Sources: [Canva Engineering]() Topics: [sessions](), [Cookies](), [Authorization](), [Caching](), [MySQL](), [scaling](), [gateway](), [Redis](), [reliability]() Tags: [backend](), [caching](), [cookies](), [gateway](), [mysql](), [permissions](), [redis](), [reliability](), [scaling](), [security](), [sessions]() ### AI overview Canva describes how it manages session revocations for hundreds of millions of users. The system keeps revocations in memory for fast gateway checks, while MySQL handles refresh-time lookups; the article explains how loading the cache during deployments created database load and discusses evaluating Redis as a caching solution. ### Source excerpt How Canva keeps hundreds of millions of user sessions fast and secure ## Timers, Timeouts, and the Art of Waiting in Temporal DevFeed: [Timers, Timeouts, and the Art of Waiting in Temporal]() Original publisher: [Read original article]() Author: Joshua Smith Published: 2026-06-30T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [SDKs](), [Code](), [Processes]() Tags: [concurrent](), [crashes](), [deployment](), [developers](), [how-to](), [scaling](), [sdks](), [temporal-concepts](), [timeout](), [waiting]() ### AI overview This tutorial explains that Temporal timers provide durable business-logic waiting, while Activity and Workflow timeouts detect failures. It describes how timers survive Worker or process failures and can support large numbers of concurrent waits. ### Source excerpt Timers and timeouts look similar in Temporal but solve different problems. Learn how each one works and how to choose the right tool for durable waiting. ## Scaling a distributed cache: Why consistent hashing is mandatory DevFeed: [Scaling a distributed cache: Why consistent hashing is mandatory]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-05-17T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [consistent hashing](), [distributed-systems](), [Caching](), [sharding](), [scaling](), [Database](), [Memcached](), [Redis]() Tags: [caching](), [consistent-hashing](), [database](), [distributed](), [distributed-cache](), [distributed-systems](), [memcached](), [redis](), [scaling](), [sharding]() ### AI overview The article explains why modulo-based cache sharding can remap nearly every key when a node fails, causing a large cache miss surge and database overload. It presents consistent hashing on a fixed ring as a way to limit movement to keys assigned to the failed node, and notes that virtual nodes improve distribution across physical cache nodes. ### Source excerpt Sharding a cache with hash(key) modulo N means the denominator changes when a node dies, remapping nearly every key. Dropping from five nodes to four invalidates about 80 percent of the cache instantly and routes that miss storm straight at the primary database. Consistent hashing maps nodes and keys onto a fixed ring so only the dead node's keys move, and virtual nodes fix the uneven distribution a handful of physical nodes would otherwise produce. ## Route specialized workloads DevFeed: [Route specialized workloads]() Original publisher: [Read original article]() Author: Cecil Phillip Published: 2026-05-11T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [GPU](), [Machine learning](), [cpu](), [data analytics](), [Hardware]() Tags: [analytics](), [cost](), [cpu](), [gpu](), [ml](), [queues](), [scaling](), [separate](), [temporal-concepts](), [worker]() ### AI overview The article describes a Temporal Validated Pattern for routing Activities with different resource requirements to separate Task Queues. Dedicated Worker pools can handle GPU-intensive machine learning, CPU, high-memory analytics, and specialized hardware workloads independently. ### Source excerpt Route ML, analytics, and CPU Activities to separate Temporal Task Queues. Cut GPU costs by 60-80% while scaling each Worker pool independently. ## AI Strategy, Customer Value, and Innovation: A Conversation with Alexander Osterwalder DevFeed: [AI Strategy, Customer Value, and Innovation: A Conversation with Alexander Osterwalder]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-04-30T07:56:48Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Artificial Intelligence](), [AI Strategy](), [scaling](), [Users]() Tags: [ai](), [ai-strategy](), [models](), [scaling]() ### AI overview David Pereira summarizes a conversation with Alexander Osterwalder about AI strategy, innovation, customer value, protecting competitive moats, synthetic users, project selection, evidence, and scaling. The discussion argues that AI adoption does not replace strategic goals or conversations with real customers. ### Source excerpt AI is changing many things, not everything. ## When running your own orchestrator isn't worth it anymore -- and why EvenUp chose Temporal DevFeed: [When running your own orchestrator isn't worth it anymore -- and why EvenUp chose Temporal]() Original publisher: [Read original article]() Author: Michael Millerick Published: 2026-03-17T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [observability](), [Development]() Tags: [ai](), [community](), [metrics](), [observability](), [scaling](), [workflow]() ### AI overview EvenUp migrated workloads from a custom workflow execution system to Temporal so engineers could focus on business logic. The article describes using Temporal metrics and latency-based scaling to monitor and scale asynchronous workloads while keeping near-real-time processing delays low. ### Source excerpt Stop firefighting your workflow plumbing. Michael Millerick, Staff Software Engineer at EvenUp, explains why they migrated from a custom orchestrator to Temporal to improve scaling, observability, and engineering focus on AI-powered legal tech. ## Place good startup bets DevFeed: [Place good startup bets]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-01-14T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [scaling](), [Shopify](), [Database]() Tags: [core](), [equity](), [sales](), [scaling](), [shopify]() ### AI overview The author reflects on career and startup decisions, including regretting that he declined Shopify before its IPO. He argues that good bets limit downside, preserve upside, and are evaluated using factors such as problem interest, market access and growth, market size, unfair advantage, and early traction. ### Source excerpt You can't win every bet. Your goal is to be a little more right than wrong and keep playing ## How The New York Times is scaling Unit Test Coverage using AI Tools DevFeed: [How The New York Times is scaling Unit Test Coverage using AI Tools]() Original publisher: [Read original article]() Author: The NYT Open Team Published: 2026-01-13T18:51:55Z Content type: article Language: en Sources: [New York Times]() Topics: [Artificial Intelligence](), [test-coverage](), [unit test](), [Testing](), [Development](), [Web app](), [React]() Tags: [ai](), [ai-tools](), [code](), [coverage](), [development](), [reliability](), [scaling](), [software](), [test-coverage](), [testing](), [unit-test](), [unit-testing]() ### AI overview The New York Times describes using AI tools to expand unit-test coverage across its News site. The agents improved efficiency and coverage on critical code, but required strict human supervision and human review of the generated tests. ### Source excerpt How AI tools are helping our software engineers write better tests at scaleIllustration by Nick Little By Eric Chima and Leonardo Quixadá At The New York Times, we're all excited to build fresh new experiences that delight our users. Our product managers are driven to find new ways to get our work in front of our audience and build reader engagement. Our engineers are motivated to solve unique technical challenges. And just when you think all that work is on track, breaking news strikes and all of our plans change at once. With all that going on, who could blame us if our test coverage couldn't quite keep up? Like every engineering organization, The Times deals with routine maintenance tasks: updating dependencies, cleaning up old code, maintaining accessibility standards, and, yes, building testing into all of our products. Our engineers are committed to quality, but when you work at the speed of news, there's always a new issue that needs to be addressed. Fortunately, generative AI has arrived with the promise of tidying up after us, taking care of the busy work, and giving time back to our developers to focus on feature development. But how far can you trust it? Recently, one of our platform teams used AI tools to build out unit tests across our flagship product, the News site. This was an opportunity for our testing to catch up with our rapid development. Our goal was to improve the reliability of our web app, but also to evaluate AI products and determine how far we could push them to do work in bulk across our codebase. As expected, the agents required strict human supervision, but they improved our efficiency in writing tests and let us quickly expand coverage on some of our most critical code. AI accelerates, but humans test the tests Unit tests are a crucial part of the development process. The idea is to divide application code into small pieces and write tests for each, creating guardrails to ensure that changing one piece of the site doesn't impact the r ## Tips for running Temporal on Kubernetes DevFeed: [Tips for running Temporal on Kubernetes]() Original publisher: [Read original article]() Author: Cubby Sivasithamparam Published: 2025-12-15T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Kubernetes](), [Helm charts](), [Deployment](), [autoscaling](), [Resilience](), [Latency]() Tags: [autoscaling](), [charts](), [deployment](), [helm](), [how-to](), [kubernetes](), [performance](), [practical](), [production](), [scaling](), [temporal](), [tips]() ### AI overview This article provides practical guidance for deploying and operating Temporal on Kubernetes. It explains how Temporal adds durable state persistence, retries, and recovery, and recommends using Helm charts with external production dependencies rather than bundled development configurations. ### Source excerpt Learn practical tips for deploying and scaling Temporal on Kubernetes. Covers Helm charts, KEDA autoscaling, and production performance tuning. ## How good engineering unlocks fast scaling DevFeed: [How good engineering unlocks fast scaling]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2025-12-09T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [systems](), [Software](), [Process](), [Script]() Tags: [building](), [engineering](), [guide](), [process](), [scaling](), [software](), [tools]() ### AI overview The article explains how engineers can support faster scaling by turning recurring manual work into documented, repeatable processes. It recommends doing the work, recording the steps, creating a script, and building a tool, with SOPs and runbooks helping distribute operational knowledge. ### Source excerpt You're building systems and tools to help companies grow without drowning in work. This fuels your equity, career capital, and reputation. ## Individual contributors vs. decision makers: Same systems, different realities DevFeed: [Individual contributors vs. decision makers: Same systems, different realities]() Original publisher: [Read original article]() Author: Lauren Bennett Published: 2025-09-23T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Development](), [reliability](), [Automation](), [Orchestration](), [observability](), [Tooling]() Tags: [automation](), [community](), [complexity](), [development](), [observability](), [orchestration](), [performance](), [reliability](), [risk](), [scaling]() ### AI overview The State of Development 2025 survey compares how individual contributors and decision makers perceive operational problems. Contributors emphasize latency, fragile workflows, observability gaps, and manual recovery, while decision makers focus on security, complexity, integration, cost, scaling, and reliability. The article argues that both perspectives point toward greater reliability and less manual effort, and that tooling decisions vary by company size. ### Source excerpt ICs feel daily friction. DMs see business risk. State of Development 2025 puts both views on one page so teams share facts, cut toil, and raise reliability. ## Building, Scaling and Thinking in the Age of AI with Chip Huyen DevFeed: [Building, Scaling and Thinking in the Age of AI with Chip Huyen]() Original publisher: [Read original article]() Author: Zach Wilson Published: 2025-08-25T13:03:24Z Content type: article Language: en Sources: [DataExpert.io Newsletter]() Topics: [Artificial Intelligence](), [scaling]() Tags: [ai](), [building](), [scaling]() ### AI overview An article about building, scaling, and thinking in the age of AI, featuring Chip Huyen. ### Source excerpt Building, Scaling and Thinking in the Age of AI ## Scaling Server Sent Events - Intro to SSE and scaling guide DevFeed: [Scaling Server Sent Events - Intro to SSE and scaling guide]() Original publisher: [Read original article]() Author: Pranshu Raj Published: 2025-06-29T07:01:27Z Content type: tutorial Language: en Sources: [Pranshu Raj - blog on backend systems, performance and sidequests]() Topics: [Server-sent events (SSE)](), [scaling](), [HTTP](), [WebSocket]() Tags: [guide](), [scaling](), [sse]() ### AI overview A tutorial on Server-Sent Events (SSE), covering its HTTP-based server-to-client update model, event-stream format, browser EventSource support, and considerations for horizontal scaling and state handling. ### Source excerpt Understanding SSE and it's use cases, advantages over other realtime protocols, how to deploy it at scale. ## Rethinking Leadership in B2B SaaS: Lessons from Self-Managing Organizations DevFeed: [Rethinking Leadership in B2B SaaS: Lessons from Self-Managing Organizations]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2025-06-27T05:00:00Z Content type: opinion Language: en Sources: [Tobias Mende]() Topics: [Software as a service](), [scaling](), [structure](), [trust]() Tags: [b2b](), [collaboration](), [culture-leadership-decision-making-delegation-organizational-design](), [founders](), [leadership](), [saas](), [scaling](), [self-organizing](), [teams](), [trust]() ### AI overview An opinion article applies lessons from self-managing organizations such as Buurtzorg and Viisi to B2B SaaS teams. It argues that trust can be supported by clear roles, peer accountability, and shared expectations, and that role-based structures may help teams experiment and operate with less founder involvement. ### Source excerpt Rethinking Leadership in B2B SaaS: Lessons from Self-Managing Organizations Two months ago, I joined the Corporate Rebels Masterclass - a hands-on exploration of how pioneering companies like Buurtzorg, Haier, Viisi, and others approach leadership, structure, and collaboration. These aren't companies tinkering at the edges. They've built entire systems that challenge our default assumptions about how work should be organized. ## Announcing KEDA-based auto-scaling for Temporal Workers DevFeed: [Announcing KEDA-based auto-scaling for Temporal Workers]() Original publisher: [Read original article]() Author: Nikitha Suryadevara Published: 2025-04-12T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [autoscaling](), [event driven](), [Kubernetes]() Tags: [announce](), [automation](), [autoscaling](), [cost](), [demo](), [event-driven](), [kubernetes](), [product-news](), [scaling](), [version]() ### AI overview Temporal announces the availability of a Temporal Worker scaler in KEDA v2.17.0. The Kubernetes-based integration scales Temporal workers according to Task Queue backlog, including scaling down to zero when there is no workload. ### Source excerpt We're thrilled to announce the availability of the Temporal Worker scaler in the latest version of KEDA (v2.17.0). KEDA is a Kubernetes-based Event-Driven Autoscaling component. ## Building Scalable Mobile Projects at Android Makers 2025 DevFeed: [Building Scalable Mobile Projects at Android Makers 2025]() Original publisher: [Read original article]() Author: Cyril Mottier Published: 2025-04-10T08:37:01Z Content type: opinion Language: en Sources: [Cyril Mottier Blog]() Topics: [Mobile](), [scaling](), [builds](), [build times](), [maintenance]() Tags: [2025](), [build-times](), [builds](), [maintenance](), [mobile](), [scaling]() ### AI overview The article introduces a keynote on scaling mobile projects as codebases, contributor counts, and complexity grow. It highlights strategies for maintaining fast build times, reusable code, clear ownership, and resilient project structure. ### Source excerpt Edit - Aug 4: adding link to the video on YouTube I recently gave a keynote called "Building Scalable Mobile Projects: Fast Builds, High Reusability and Clear Ownership" at Android Makers 2025 in Paris. Here was the abstract. Every successful project eventually faces the challenges of scaling: an ever-growing codebase, an increasing number of contributors, and rising complexity. Without proper measures, this can lead to slow build times, a cluttered codebase, maintenance difficulties, code duplication, and even dead code--ultimately jeopardizing the company's ability to deliver value. ## Understanding and scaling Raft DevFeed: [Understanding and scaling Raft]() Original publisher: [Read original article]() Published: 2025-04-04T00:00:00Z Content type: tutorial Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Raft](), [consensus algorithm](), [distributed-systems](), [scaling](), [CockroachDB](), [Databases]() Tags: [cockroachdb](), [consensus-algorithm](), [databases](), [distributed-systems](), [raft](), [replication](), [scaling]() ### AI overview This tutorial explains the Raft consensus algorithm, including leader election, log replication, safety guarantees, and the network cost of consensus. It also introduces the Multi-Raft pattern for scaling distributed databases by splitting data into ranges with independent Raft groups. ### Source excerpt . [Understanding and Scaling Raft](understanding-and-scaling-raft-cover... ## How Klarna Migrated the KRED System from Mnesia to Postgres with Zero Downtime DevFeed: [How Klarna Migrated the KRED System from Mnesia to Postgres with Zero Downtime]() Original publisher: [Read original article]() Author: Onno Vos Dev Published: 2025-02-26T09:04:27Z Content type: article Language: en Sources: [Klarna Engineering]() Topics: [Databases](), [Erlang](), [Replication](), [sharding](), [Transactions](), [systems]() Tags: [cluster](), [databases](), [erlang](), [klarna](), [memory](), [postgresql](), [production](), [replication](), [scaling](), [sharding](), [software-development](), [software-engineering](), [transactions]() ### AI overview This article describes Klarna's migration of KRED, an Erlang servicing system, from Mnesia to Postgres with zero downtime. It explains the original replicated seven-node architecture, the system's scaling challenges, and a plan involving sharding across multiple clusters. ### Source excerpt How we migrated from Mnesia to Postgres with zero downtime Back in December 2004, an Erlang application was born called KRED (referring to the freshly-started company called Kreditor, now known as Klarna). KRED is one of the "servicing systems" at Klarna and keeps track of consumer debt (among other things). It was powered by Mnesia and consisted of a cluster of 7 nodes, each holding a full copy of the database on disk. The data was replicated using a custom replication mechanism built in-house by Klarna. One node was elected as the leader and its database was considered the source of truth in the system. All database transactions were executed on the leader and writes were replicated to the rest of the nodes, the so-called followers. The Mnesia database was around 15 TB and at its peak in 2018 around 1.3 TB was held in memory at all times. Considering that few suppliers were selling hardware with such specs, it's easy to claim the crown of one of the biggest Mnesia databases in terms of in-memory storage, that was running in production. The rest of the data was offloaded to disk using mnesia_eleveldb. KRED has been a stable workhorse at Klarna so why change a winning concept? Get ready, for a two part blog post where we'll first go through our journey of how we went about this and secondly, how we made Mnesia behave just like Postgres and implemented our version serializable isolation level on top of Postgres! How the journey started Three engineers, sat down in a bar in Stockholm, Sweden and asked this question: 'When Klarna truly takes off, will KRED survive? Assuming "no", and presented with a blanco check, how would we tackle this problem?' The answer quickly revolved around the issues of running Mnesia on an even larger cluster and with leveldb compaction hitting some hot tables during peak times. One can only imagine how that problem would just continue to get worse over time. Considering the three engineers had worked on KRED for a long time, scaling KRED wa ## Scaling Technology with Architectural Principles DevFeed: [Scaling Technology with Architectural Principles]() Original publisher: [Read original article]() Author: Shane Gibb Published: 2025-01-08T01:18:36Z Content type: article Language: en Sources: [REA Group]() Topics: [scaling](), [Software Engineering](), [Development](), [Continuous Deployment (CD)](), [maintenance](), [Design system](), [CI/CD]() Tags: [code](), [continuous-deployment](), [continuous-integration](), [design-system](), [engineering](), [maintenance](), [scaling](), [tech]() ### AI overview REA Group describes how architectural principles developed through 25 years of growth guide its software engineering practices, including decision-making, system resilience, team agility, platform reuse, and continuous deployment. ### Source excerpt REA Group, a global company with 1,000+ digital product builders, developed architectural principles from 25 years of growth. ## Resource-based engineering: Auto-tuning for Temporal Workers DevFeed: [Resource-based engineering: Auto-tuning for Temporal Workers]() Original publisher: [Read original article]() Author: Spencer Judge Published: 2024-11-12T07:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Workers](), [Monitoring](), [autoscaling]() Tags: [capacity](), [engineering](), [monitoring](), [performance](), [product-news](), [scaling](), [workers]() ### AI overview The article introduces resource-based auto-tuning for Temporal Workers. It explains how Workers, task queues, and task slots relate to capacity, and describes horizontal and vertical scaling based on workload backlog and available capacity. ### Source excerpt Explore how resource-based engineering powers auto-tuning for Temporal Workers, streamlining performance and management. ## How to break into data in 2024? With DataCamp's CEO, Jonathan Cornelissen. DevFeed: [How to break into data in 2024? With DataCamp's CEO, Jonathan Cornelissen.]() Original publisher: [Read original article]() Author: Meri Nova Published: 2024-10-09T17:31:09Z Content type: opinion Language: en Sources: [Meri Nova]() Topics: [Data Science](), [data analytics](), [genai](), [Artificial Intelligence](), [DataOps]() Tags: [ai](), [beginners](), [career-advice](), [data](), [data-analytics](), [data-science](), [education](), [future-of-work](), [genai](), [podcast](), [scaling](), [technical]() ### AI overview A Technical Founder podcast episode featuring DataCamp co-founder and CEO Jonathan Cornelissen. The discussion covers building DataCamp, entering data careers, data education, GenAI's impact on edtech, data literacy, and career advice for new graduates. ### Source excerpt Listen to our first episode of the "Technical Founder" podcast, where we invite AI and Data leaders to learn from their entrepreneurial and technical journey! ## Piloting Kotlin Multiplatform is Easy. Scaling is Hard. - Kevin Galligan DevFeed: [Piloting Kotlin Multiplatform is Easy. Scaling is Hard. - Kevin Galligan]() Original publisher: [Read original article]() Published: 2024-08-07T00:00:00Z Content type: article Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [Kotlin Multiplatform](), [scaling](), [Mobile](), [multiplatform](), [Development]() Tags: [development](), [kmp](), [kotlin](), [kotlin-multiplatform](), [mobile](), [multiplatform](), [scaling](), [swift](), [xcode]() ### AI overview The article explains why scaling Kotlin Multiplatform is more difficult than initial experimentation for native mobile teams. It argues that teams need to use KMP in feature development, reconsider library publishing and repository organization, and adopt an approach tailored to native Android and iOS teams. ### Source excerpt When native mobile teams explore KMP, getting started is pretty easy. The difficulty emerges when they try to scale. To get the most out of KMP for your team, and for KMP as a platform to succeed, we need to make scaling easy as well. ## KMP Team Adoption for Native Mobile: Workflow and Management Guidance DevFeed: [KMP Team Adoption for Native Mobile: Workflow and Management Guidance]() Original publisher: [Read original article]() Published: 2024-07-19T16:24:40Z Content type: opinion Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [multiplatform](), [scaling](), [Mobile](), [Development](), [Kotlin](), [cross-platform]() Tags: [kmp](), [kotlin](), [management](), [mobile](), [native](), [scaling](), [teams](), [technology](), [workflow]() ### AI overview This article introduces the challenges teams face when adopting Kotlin Multiplatform for native mobile development. It argues that although KMP is stable and capable of sharing business logic, existing tooling and sample structures are largely designed for individual developers, making team-scale adoption difficult without new approaches and guidance. ### Source excerpt KMP for native mobile is mature technology. Best practice guidance for how teams should apply it is not. KMP has amazing potential, but presents unique workflow and management problems. Understanding these problems, and having a plan, is critical. [Next page]()