# incremental Published articles for incremental. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Migrating Doximity's iOS Newsfeed to SwiftUI Without Disrupting Users or Development DevFeed: [Migrating Doximity's iOS Newsfeed to SwiftUI Without Disrupting Users or Development]() Original publisher: [Read original article]() Author: Doximity Published: 2026-09-04T10:50:00Z Content type: tutorial Language: en Sources: [Doximity]() Topics: [iOS](), [SwiftUI](), [Development](), [App]() Tags: [app](), [architecture](), [bridge](), [development](), [incremental](), [ios](), [migration](), [swiftui]() ### AI overview This article describes Doximity's incremental migration of its iOS Newsfeed from UIKit to SwiftUI. The team moved one section at a time, kept UIKit and SwiftUI compatible, continuously measured behavior, and made each step reversible. The feed's server-driven card and section architecture, with state and logic managed in The Composable Architecture, provided the migration boundary. ### Source excerpt The Newsfeed is one of the most-used features in the Doximity iOS app. It is the first thing many users see when they open the app. For doctors checking in between patients or residents catching up during a break, it has to be fast and efficient. Those expectations defined success for the migration. Feature work had to continue, analytics had to remain reliable, and the experience had to remain familiar to users even as the underlying implementation changed. We approached the migration less like a rewrite and more like a controlled rollout. We moved one section at a time, kept the UIKit and SwiftUI implementations compatible, measured behavior continuously, and made each step reversible. The technical bridge enabled the migration, but this incremental approach reduced the risk to the product. The Newsfeed brings several types of content together in a single scrolling experience. Here are a few examples of the different cards users may encounter: The Architecture Behind the Newsfeed Before discussing the migration, it helps to understand the architecture that made our incremental approach possible. The Newsfeed is not one large custom view. It is a server-driven list built from cards, with each card assembled from smaller sections. The server controls the composition of the feed. It tells the app which cards should appear, which sections belong inside each card, and the order in which those sections should render. The iOS app owns the rendering and behavior of each known section type. This separation makes the feed flexible. Rather than relying on a fixed set of hardcoded card types, the server can compose cards from reusable sections such as headlines, authors, summaries, media, polls, and social actions. By combining and ordering those sections differently, the server can create different card layouts without requiring a new implementation in the app. This is roughly what the model looks like: Server-driven feed response Newsfeed ├─ Card │ ├─ Section: author │ ├─ S ## Incremental backup on AWS Glacier with rustic DevFeed: [Incremental backup on AWS Glacier with rustic]() Original publisher: [Read original article]() Published: 2026-08-19T21:04:01Z Content type: tutorial Language: en Sources: [Personal blog of Anurag Bhatia]() Topics: [backups](), [restic](), [Amazon Web Services](), [Amazon S3](), [Rust](), [Open Source](), [Feathers]() Tags: [aws](), [aws-glacier-deep-archive](), [backblaze-b2](), [backup](), [backups](), [client](), [config](), [cost](), [data](), [encryption](), [encryption-at-rest](), [incremental](), [lifecycle](), [open-source](), [password](), [restic](), [restore](), [rust](), [rustic](), [s3](), [snapshots]() ### AI overview A practical guide to using rustic, a Rust implementation compatible with restic, for incremental backups with AWS Glacier cold storage. It explains keeping backup data in cold storage while maintaining metadata in S3 Standard or Backblaze B2 so snapshots, incremental backups, and targeted restores remain possible. ### Source excerpt I have been a heavy restic user for several years. In Jan of this year, I tried making restic work with AWS Glacier deep archive but it did not work out. AWS Glacier Deep Archive, for those who may not know, is one of the cheapest storage options, with costs as low as $1/TB, but with very expensive retrieval due to high egress charges from AWS, in addition to retrieval fees, API charges, etc. It can act as a backup of last resort, kind of like insurance if everything else fails, since it costs roughly 1/7th of what Backblaze B2 charges for the same data ($6.95/TB/month). Restic by design is not compatible with Glacier because it stores a mix of data blobs and tree blobs inside /data. Thus, as AWS lifecycle rules move data from S3 Standard to Glacier, basic repo operations start failing. They do have some experimental support as per FAQ, but it did not work out for me when I tried in Jan. A few days back, I came across an open-source project named rustic -- it's a restic implementation in Rust and supports cold storage. The rustic client is compatible with a traditional restic repo. Rustic and cold storage Rustic supports AWS Glacier as well as OVH cold storage. The way it works is: they keep all data in cold storage and a copy of the metadata in hot storage. This additional copy of metadata in hot storage allows operations like incremental backups, viewing snapshots, and requesting restores where only the specific data blobs needed for the restore are pulled from Glacier. This additional hot storage can sit on S3 standard or even Backblaze B2 (which I prefer due to zero API cost). Demo 1) Create repo Let's create two buckets - one in S3 (to store data in Glacier) and one on Backblaze B2 to hold an additional copy of metadata. > rclone mkdir s3:rustic-demo-cold > rclone mkdir b2:rustic-demo-hot 2) Create rustic config profile This file will define the cold and hot repo locations, along with the password file location, restoration command syntax, etc. [repository] repo ## How to take incremental steps towards data democratisation DevFeed: [How to take incremental steps towards data democratisation]() Original publisher: [Read original article]() Author: Andy Scotland Published: 2026-08-17T13:09:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [data](), [trust](), [decision-making](), [Artificial Intelligence]() Tags: [agent-factory](), [ai](), [ai-agents](), [artificial-intelligence](), [compliance](), [conway-s-law](), [data](), [data-democratisation](), [data-engineering](), [data-platform](), [data-products](), [governance](), [incremental](), [trust]() ### AI overview This article explains how organisations can pursue data democratisation incrementally while preserving control, risk management and governance. It describes potential benefits in financial services and discusses how trusted data, data products, platforms and AI agents could support innovation, decision-making and customer service. ### Source excerpt Organisations increasingly recognise the value of making data more accessible, but concerns around control, risk and governance often stand in the way. In this post, I explore why data democratisation doesn't require organisations to sacrifice oversight, and how data products, platforms and agent factories can unlock innovation while maintaining trust, compliance and accountability. ## Dynamic Derivations + Dynamic build graphs: next steps DevFeed: [Dynamic Derivations + Dynamic build graphs: next steps]() Original publisher: [Read original article]() Author: tomberek Published: 2026-08-07T18:58:35Z Content type: opinion Language: en Sources: [Announcements - NixOS Discourse]() Topics: [Nix](), [build performance](), [systems](), [build times](), [bazel]() Tags: [announcements](), [bazel](), [build](), [ci](), [incremental](), [performant](), [ux]() ### AI overview A proposal for a Dynamic Derivations working group in the Nix ecosystem outlines collaboration goals, user-experience refinement, infrastructure needs, and adoption efforts. The post also summarizes the feature's development history and discusses potential benefits such as faster rebuilds and fine-grained incremental builds. ### Source excerpt After the long-history (see below) of Dynamic Derivations we are at a point where there are many groups exploring the feature and I am proposing a working group to: share the experiences of the implementors refine the user experience based on usage of the various PoCs determine infrastructure needs deduplicate work raise awareness build momentum for adoption ... anything else relevant I believe this feature has the potential to be a game-changer. Faster rebuilds, easier development, reduced pressure on CI systems, cacheable and alternative forms of instantiation, less need for "lang2nix", close the gap with Bazel, and fine-grained incremental builds. Along with the existing work on better schedulers, RBE-style remote builders, bytecode evaluation, parallel eval, memory optimizations (thanks @xokdvium !), and so much more - this is an exciting time for Nix and I can see DynDrvs as a way to fix some of the outstanding UX issues Nix has in day-to-day usage. There is even work to make IFD more performant, there is lots of design space to explore here. This should be a part of what Nix has to offer the world. @Ericson2314 has devoted years to this, and it deserves the payoff of becoming a mature feature. This is a call to action. Let's consider collaboration and a semimonthly meeting to at least say hello + consider next steps: Dynamic Derivations Working Group - Crab Fit History Dynamic Derivations have been in-progress for a long time. This has much of the history that I can put together. Let me know if this can be more complete. (and if anyone can help move/migrate into the wiki?) Build Systems à la Carte : 2018 RFC started Apr 2021 by @Ericson2314 and @L-as with [RFC 0092] Computed derivations by Ericson2314 - Pull Request #92 - NixOS/rfcs - GitHub (after reworking RFC 0040). Based on Initial example PoC by @L-as GitHub - L-as/nix-build.nix: nix-build.nix with RFC 92 - GitHub Content-addressed Nix Dec 2021: Implementing a content-addressed Nix by @thufschmitt. RFC Shep ## Strategies for Migrating from a Monolith to Microservices DevFeed: [Strategies for Migrating from a Monolith to Microservices]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-06-23T07:55:12Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [Architecture & Design](), [microservices architecture](), [migration](), [legacy systems](), [gateway]() Tags: [api-gateway](), [architecture](), [automated](), [big-bang](), [feature-flags](), [incremental](), [legacy-systems](), [microservices](), [migration](), [monolith](), [parallel](), [rewrite](), [strategies](), [testing]() ### AI overview This tutorial outlines patterns for gradually moving from a monolithic architecture to microservices. It describes the Strangler Fig Pattern, Parallel Run Pattern, and Collaborator Pattern, covering techniques such as API gateways, incremental migration, traffic splitting, feature flags, and automated comparison testing. ### Source excerpt Top Strategies ## Improving performance in the layers panel DevFeed: [Improving performance in the layers panel]() Original publisher: [Read original article]() Author: Shannen Wu; Peter Hayes Published: 2026-06-11T17:30:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Caching](), [ui](), [data](), [JavaScript]() Tags: [architecture](), [caching](), [caching-strategies](), [compute](), [data](), [figma](), [incremental](), [javascript](), [performance](), [ui]() ### AI overview Figma explains how it rebuilt the layers panel architecture to improve performance in very large, complex files. The changes use two-pass data gathering and caching strategies, producing 30-50% faster interactions in some cases. ### Source excerpt The layers panel is the central blueprint of a Figma file. We rearchitected it with new computation and caching strategies, resulting in 30-50% faster interactions in some of the largest, most complex files. ## Strategies for Moving from a Monolith to Microservices DevFeed: [Strategies for Moving from a Monolith to Microservices]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-05-26T07:58:51Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [microservices architecture](), [migration](), [legacy systems](), [proxy]() Tags: [architecture](), [big-bang](), [feature-flags](), [incremental](), [monolith]() ### AI overview This tutorial describes patterns for gradually moving from a monolithic architecture to microservices. It covers the Strangler Fig Pattern, Parallel Run Pattern, and Collaborator Pattern in the supplied text, emphasizing incremental migration, traffic splitting, behavior comparison, feature flags, and reduced rewrite risk. ### Source excerpt Strategies worth knowing ## Achieving Fast Inner Dev Loops with Gradle: Configuration Cache and Beyond DevFeed: [Achieving Fast Inner Dev Loops with Gradle: Configuration Cache and Beyond]() Original publisher: [Read original article]() Author: Alex Semin Published: 2026-05-20T04:00:00Z Content type: article Language: en Sources: [The Gradle Blog]() Topics: [Gradle](), [code productivity](), [build performance](), [build times](), [Development]() Tags: [build](), [build-tool](), [compilation](), [context](), [developer-productivity](), [flow](), [gradle](), [incremental](), [kotlinconf](), [parallelism](), [productivity]() ### AI overview This article explains how Gradle is working to accelerate inner developer loops. It describes the shift from execution time to configuration time as the main build bottleneck in large codebases, while highlighting configuration cache, incremental builds, build cache, and parallelism as approaches to reduce waiting and preserve developer flow. ### Source excerpt How many engineers in your organization have to sit idle for 10 or more minutes just to rebuild the app locally after a small change? If you're an engineering leader, build engineer, or part of a developer productivity team, you're not alone in facing this challenge. Building complex software is inherently demanding, but the time your developers spend waiting has a profound impact beyond just the literal minutes lost. It fundamentally disrupts their psychological flow. "There are like these time scales of psychological flow, right? So if something takes longer than a minute, you already start planning for it... you suffer from context switch as well. If it's 1 second, that's where we want to be right in that seamless state of flow. And if it's less than one second, then you feel it's like magic... productive developers are happy developers." -- Rodrigo Oliveira When developers have to wait, their attention drifts, and the cost of context switching destroys their momentum. Our goal as enablers of developer productivity should be to eliminate this friction, bringing that feedback loop as close as possible to the "magical one second". In a recent presentation at KotlinConf 2025, Gradle engineers Rodrigo Oliveira and Alex Semin detailed how Gradle Build Tool is evolving to keep developers in that productive, happy "flow state". By tackling the most significant bottlenecks in modern software builds, Gradle is paving the way for incredibly fast inner developer loops. If you prefer to watch the presentation recording, you can find it here. The shifting bottleneck: From execution to configuration On every invocation, Gradle goes through three distinct phases: initialization, configuration, and execution. Historically, when we optimized Gradle builds, we focused almost exclusively on the execution phase. This is when the majority of work expected by a developer happens: production sources compilation, test execution, etc. For years, execution was the longest phase. We tackled thi ## Incremental Maintenance DevFeed: [Incremental Maintenance]() Original publisher: [Read original article]() Published: 2026-05-14T01:53:09Z Content type: opinion Language: en Sources: [Blogs on Ryan Harter]() Topics: [maintenance](), [Dependency injection](), [implementation](), [screen](), [App]() Tags: [core](), [dependencies](), [dependency-injection](), [incremental](), [maintenance](), [patterns](), [product](), [screen]() ### AI overview An opinion article about incremental maintenance in a large, aging application. It argues that small refactoring choices can reduce entrenched patterns, using dependency injection to keep feature-gating details out of a monolithic tab-host screen. ### Source excerpt I recently reviewed some code at work that made me pause. The change affected a part of the codebase that we affectionately call "The Monolith", which is some of the oldest code in our 16 year old app. While there are reasonable arguments that it's hard to tease apart this older hairball of code, there are often small choices that can either move you closer to the decoupled promised land, or further entrench you in bad patterns. ## Metro 1.0.0 Is Stable as a Kotlin Multiplatform Compile-Time Dependency Injection Framework DevFeed: [Metro 1.0.0 Is Stable as a Kotlin Multiplatform Compile-Time Dependency Injection Framework]() Original publisher: [Read original article]() Author: Zac Sweers Published: 2026-04-27T21:07:22Z Content type: release Language: en Sources: [Zac Sweers]() Topics: [Kotlin](), [Dependency injection](), [multiplatform](), [Compiler](), [Framework](), [gradle-plugin]() Tags: [build-performance](), [build-times](), [compilation](), [compiler](), [dependency-injection](), [experimental](), [gradle](), [gradle-plugin](), [incremental](), [kotlin](), [metro](), [multiplatform](), [open-source](), [perfetto](), [stable]() ### AI overview Metro 1.0.0 is now stable. The Kotlin multiplatform compile-time dependency injection framework uses a compiler plugin and provides API-stable runtime APIs, MetroX artifacts, and a Gradle plugin unless marked experimental. The article also describes build-performance improvements and compile-time validation features. ### Source excerpt New here? Metro is a multiplatform, compile-time dependency injection framework for Kotlin implemented as a compiler plugin. Metro 1.0.0 is out now and stable. This means that its runtime APIs (runtime, MetroX artifacts, Gradle plugin, etc.) are now API-stable unless annotated with an experimental annotation. This ## Full Refresh vs Incremental Pipelines DevFeed: [Full Refresh vs Incremental Pipelines]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-03-17T20:40:38Z Content type: article Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data](), [datasets]() Tags: [data](), [incremental](), [pipelines]() ### AI overview An article about the tradeoffs between full-refresh and incremental pipelines for data teams. ### Source excerpt Tradeoffs Every Data Team Should Know ## The 5 Processes Every Business Should Automate First DevFeed: [The 5 Processes Every Business Should Automate First]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2026-01-30T05:00:00Z Content type: article Language: en Sources: [Tobias Mende]() Topics: [Automation](), [n8n](), [Processes]() Tags: [automate](), [automation](), [automation-productivity-n8n-business-process-efficiency](), [crm](), [email](), [incremental](), [n8n](), [risk](), [slack](), [workflows]() ### AI overview This article recommends starting business automation with small, frequent, rule-based, low-risk processes rather than a large transformation project. It presents five candidate processes and illustrates lead response automation using a contact form, CRM, confirmation email, and Slack or email notification. ### Source excerpt The 5 Processes Every Business Should Automate First Most businesses I work with know they should automate more. They have heard the promises. They might even have an n8n instance running somewhere with one or two small workflows that someone set up a while ago. ## Lumos: Inside Dream11's Leap from Task-Based Models to Foundational Intelligence DevFeed: [Lumos: Inside Dream11's Leap from Task-Based Models to Foundational Intelligence]() Original publisher: [Read original article]() Author: Dream Blog Published: 2026-01-22T06:40:39Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [foundation-models](), [Sports](), [Machine learning](), [Large language models (LLMs)]() Tags: [analysis](), [competition](), [context](), [dream11](), [fragmentation](), [incremental](), [llm](), [ml](), [models](), [notifications](), [personalisation](), [scale](), [sports](), [systems](), [tech]() ### AI overview Dream11 describes Lumos, a foundation model for personalisation that connects user behaviour, context, and changing interests across sports experiences. The article reports a 2.5% lift in ROC AUC and a 4.6% reduction in MAPE across key tasks, while replacing dozens of task-specific systems with a single scalable foundation. ### Source excerpt By Dhruv Nigam At Dream11, our mission to 'make every match more exciting' starts with a simple truth: every fan experiences sport differently. Some users show up for marquee matches, while others engage consistently across the season. Some enjoy deep analysis; others come for emotion, banter, and shared moments. Even how fans prefer to be spoken to -- through in-app communication or notifications -- varies, from playful and expressive to direct and informational. In sports, context changes everything. A quiet weekday feels very different from the eve of a knockout match, and behaviour shifts with formats, rivalries, and the stage of competition. Personalisation at Dream11 therefore goes beyond surface-level customisation -- it's about understanding fans in motion and how their interests evolve. We've long recognised this challenge, but understanding and acting on these signals across millions of users, each with their own patterns and preferences, is far from easy. Over time, it became clear that small, incremental ML enhancements wouldn't get us where we needed to go. To stay truly user-first, we needed a system that could connect behaviour, context, and past, present, and future moments, all at once. That realisation led us to a ground-up rethink of how we build models at Dream11, and eventually, to Lumos -- our foundation model for personalisation. Lumos helped deliver a 2.5% lift in ROC AUC (Area Under the Receiver Operating Characteristic Curve) and a 4.6% reduction in MAPE (mean absolute percentage error) across key tasks, significantly improving personalisation, while replacing dozens of task-specific systems with a single, scalable foundation.The Problem: When Task-Based Models Stop Scaling For a long time, our personalisation stack relied on 50+ small, specialised models, each designed to understand a narrow aspect of user behaviour. Some models focused on sports affinity, others on language preferences or communication style. While these were effective in iso ## Apollo Client 4.1 Adds @stream Support and GraphQL Incremental Delivery Improvements DevFeed: [Apollo Client 4.1 Adds @stream Support and GraphQL Incremental Delivery Improvements]() Original publisher: [Read original article]() Author: Jerel Miller Published: 2026-01-21T12:36:16Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [apollo-client](), [GraphQL](), [client]() Tags: [announcement](), [apollo-client](), [cache](), [compatibility](), [fragments](), [graphql](), [incremental](), [performance](), [release]() ### AI overview Apollo Client 4.1 adds full @stream directive support, compatibility with the latest GraphQL incremental delivery specification, and APIs for watching multiple fragments. The release also includes cache fixes, deduplication improvements, and other updates. ### Source excerpt Apollo Client 4.1 introduces full @stream directive support, compatibility with the latest GraphQL incremental delivery spec, and powerful new APIs for working with fragments. ## Backups incrementales con rsync en Linux DevFeed: [Backups incrementales con rsync en Linux]() Original publisher: [Read original article]() Author: Antonio Pérez Published: 2026-01-16T00:00:00Z Content type: tutorial Language: es Sources: [tengoping.com]() Topics: [backups](), [rsync](), [Linux](), [Script](), [ssh](), [systemd]() Tags: [backup](), [backups](), [filesystem](), [incremental](), [linux](), [nas](), [rsync](), [ssh](), [systemd]() ### AI overview A tutorial on implementing incremental backups with rsync and hardlinks on Linux servers. It explains how unchanged files are hardlinked to save space, warns that hardlinks are not independent copies, discusses filesystem compatibility and destructive synchronization risks, and covers SSH transfers, exclusions, systemd timers, and integrity checks. ### Source excerpt Cómo implementar una estrategia de backups incrementales usando rsync y hardlinks para ahorrar espacio y tiempo en tus servidores. ## Introducing Real-Time Data with Materialized Views in Turso DevFeed: [Introducing Real-Time Data with Materialized Views in Turso]() Original publisher: [Read original article]() Author: Glauber Costa Published: 2025-10-09T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Turso](), [real-time](), [Databases](), [SQLite](), [Streaming](), [Kafka](), [SQL]() Tags: [database](), [experimental](), [incremental](), [kafka](), [real-time](), [sql](), [sqlite](), [streaming](), [turso]() ### AI overview Turso is introducing experimental Live Materialized Views that keep query results current as base tables change. The feature uses Incremental View Maintenance and the DBSP framework, updating results in proportion to the size of incoming changes rather than the size of the underlying tables. It is intended for streaming and real-time systems, including systems connected to Kafka queues and CDC logs. ### Source excerpt Turso now supports Live Materialized Views with Incremental View Maintenance, keeping query results fresh in time proportional to changes, not table size. ## Building a Scalable AWS Backup System for S3 Data Warehouses DevFeed: [Building a Scalable AWS Backup System for S3 Data Warehouses]() Original publisher: [Read original article]() Author: Oleh Motrunych Published: 2025-09-22T00:00:00Z Content type: article Language: en Sources: [Scribd Tech]() Topics: [Amazon S3](), [Amazon Web Services](), [AWS Lambda](), [parquet](), [cloud-infrastructure]() Tags: [aws](), [aws-lambda](), [backup](), [backups](), [cleanup](), [compute](), [cost](), [data](), [data-lake](), [databases](), [datasets](), [deltalake](), [efficiency](), [featured](), [incremental](), [lambda](), [lifecycle](), [parquet](), [pipeline](), [reduce](), [retention](), [s3](), [terraform]() ### AI overview This article describes a monthly backup system for S3 data warehouses that uses incremental copying, S3 Inventory validation, parallel processing, and Glacier retention. It routes smaller workloads to AWS Lambda and larger workloads to ECS Fargate, with lifecycle-based cleanup for eligible files. ### Source excerpt We designed and implemented a scalable, cost-optimized backup system for S3 data warehouses that runs automatically on a monthly schedule. The system handles petabytes of data across multiple databases and uses a hybrid approach: AWS Lambda for small workloads and ECS Fargate for larger ones. At its core, the pipeline performs incremental backups -- copying only new or changed parquet files while always preserving delta logs -- dramatically reducing costs and runtime compared to full backups. Data is validated through S3 Inventory manifests, processed in parallel, and stored in Glacier for long-term retention. To avoid data loss and reduce storage costs, we also implemented a safe deletion workflow. Files older than 90 days, successfully backed up, and no longer present in the source are tagged for lifecycle-based cleanup instead of being deleted immediately. This approach ensures reliability, efficiency, and safety: backups scale seamlessly from small to massive datasets, compute resources are right-sized, and storage is continuously optimized. ## State of the Configuration Cache - On the Road to Gradle 9 DevFeed: [State of the Configuration Cache - On the Road to Gradle 9]() Original publisher: [Read original article]() Author: Mikhail Lopatkin Published: 2025-03-14T04:00:00Z Content type: article Language: en Sources: [The Gradle Blog]() Topics: [Gradle](), [configuration](), [build performance](), [Caching](), [Developer experience]() Tags: [build](), [caching](), [configuration](), [developer-experience](), [gradle](), [incremental](), [performance]() ### AI overview This article explains the state of Gradle's Configuration Cache ahead of Gradle 9.0. It describes how the feature reduces repeated configuration-phase work, improves build performance and developer experience, and is planned to become the preferred execution mode before being enabled by default in Gradle 10.0. ### Source excerpt Introduction As Gradle 9.0 approaches, we're sharing updates on the Configuration Cache--a key feature that significantly improves configuration time for large projects. In this major release, we plan to make the Configuration Cache the preferred execution mode, with the goal of enabling it by default in Gradle 10.0. The Configuration Cache is one of Gradle's most anticipated features; progress has been substantial. As Gradle Fellow Tony Robalik noted: "I'm excited about all the work Gradle has done to make the Configuration Cache stable and the preferred mechanism for running builds. At work, we've observed that enabling Configuration Cache globally will recover roughly 4 years of lost engineering time annually. The configuration cache also lays the foundation for Isolated Projects, which has been an eagerly awaited feature since Gradle first announced it several years ago!". Read this post to learn more about recent Configuration Cache performance and compatibility improvements, how to adopt it, and our plans for future releases. A Bit of History Development often happens in small increments--you write some code, run tests, fix failures, and repeat. In a build tool, this means executing the same tasks repeatedly. Gradle's execution model consists of three phases: Initialization - Discovers the project structure. Configuration - Builds the task graph. Execution - Runs tasks to perform the actual work. In an incremental workflow, where the same tasks are requested without changes to the build scripts, the configuration phase typically produces an identical task graph each time. Gradle excels at build caching, which caches the execution phase. This allows even massive projects and mono repositories to build in just a few seconds when no significant changes occur. However, generating the task graph for a complex project during the configuration phase can take a non-trivial amount of time. In some cases--like the project shown in the screenshot below--this overhead may eve ## Fireside Chats: Gong's Approach to Software Documentation DevFeed: [Fireside Chats: Gong's Approach to Software Documentation]() Original publisher: [Read original article]() Author: Tiffany Chen Published: 2025-02-18T00:00:00Z Content type: opinion Language: en Sources: [Mintlify Blog]() Topics: [Documentation](), [Software](), [legacy]() Tags: [ai-trends](), [documentation](), [incremental](), [legacy](), [quality](), [structure](), [technical-writing]() ### AI overview Jeffrey Aftel of Gong discusses rebuilding legacy software documentation through an incremental, bottoms-up process. The approach focuses on improving sections of content over time, adapting to different audience goals, treating documentation as part of the product, and hiring technical writers who understand the product and challenge assumptions. ### Source excerpt We sat down with Jeffrey Aftel, Director of Product Writing at Gong, to discuss his approach to rebuilding legacy software documentation, his principles for impactful content, and the traits he looks for in technical writers. Jeff's background also includes technical writing leadership roles at HP and Velo by Wix. ## The pursuit of fast feedback loops DevFeed: [The pursuit of fast feedback loops]() Original publisher: [Read original article]() Author: Saket Narayan Published: 2024-11-27T17:46:56Z Content type: opinion Language: en Sources: [Saket Narayan]() Topics: [Mobile](), [Compose](), [ui](), [unit tests](), [jetbrains]() Tags: [android](), [compose](), [hot-reloading](), [incremental](), [jetbrains](), [layout](), [mobile](), [screenshot](), [tests]() ### AI overview This opinion argues that fast feedback loops for validating software changes matter more than hot reloading itself. It discusses the reliability history of hot-reload tools and describes using screenshot tests and unit tests to shorten UI and presentation/backend iteration cycles. ### Source excerpt Sebastian Sellmair from JetBrains recently showed off a prototype of hot-reloading Compose UI code for deploying changes without rebuilding the project. It looks exciting and I want it to succeed. At the same time, I've learned to be cautiously optimistic about hot-reload solutions. Google has tried this multiple times: their first attempt (Instant Run) was [...] The post The pursuit of fast feedback loops appeared first on Saket Narayan. ## Incremental builds in Nix and garnix DevFeed: [Incremental builds in Nix and garnix]() Original publisher: [Read original article]() Published: 2024-11-16T00:00:00Z Content type: article Language: en Sources: [Garnix Blog]() Topics: [Nix](), [build performance](), [build times]() Tags: [builds](), [compilation](), [incremental]() ### AI overview The article announces incremental compilation in garnix, reviews prior work on incremental compilation in Nix, and describes garnix's design. ### Source excerpt We've added incremental compilation to garnix. In this blog, we discuss prior art on incremental compilation in Nix, and describe our own design. ## What the interns have wrought, 2024 edition DevFeed: [What the interns have wrought, 2024 edition]() Original publisher: [Read original article]() Author: Yaron Minsky Published: 2024-08-26T00:00:00Z Content type: article Language: en Sources: [Jane Street]() Topics: [OCaml](), [data](), [Concurrency](), [build times](), [Rust](), [Programming]() Tags: [build-times](), [concurrency](), [data](), [incremental](), [internship](), [ocaml](), [programming](), [rust]() ### AI overview Jane Street's 2024 internship review highlights projects involving an OCaml dataframe library, a faster but less compact binary-serialization protocol, and improvements to a time-travel debugger. It also explains why the company is exploring OCaml alternatives to Polars for safer parallelism and better build performance. ### Source excerpt We're once again at the end of our internship season, and it's time do our annual review of what the interns achieved while they were here. ## From Monolith to Federation DevFeed: [From Monolith to Federation]() Original publisher: [Read original article]() Author: Daljit Summan Published: 2023-08-09T11:33:08Z Content type: tutorial Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [API](), [Back end]() Tags: [architecture](), [back-end](), [backend](), [data](), [gateway](), [graphql](), [incremental]() ### AI overview This article discusses using GraphQL Federation and the Strangler fig pattern to incrementally migrate a monolith. It describes placing a facade in front of the monolith, splitting it by domain, decoupling clients through a Federation interface, and routing data through an API Gateway. ### Source excerpt Introduction Many organizations own a Monolith (GraphQL or Non-GraphQL) and migrating away always seems an impossible task, most organizations fall into a seemingly endless amount of effort with very little progress to show for it. At Apollo we have seen many different patterns of migrating away from a monolith. ## This Week at Zed Industries: #10 DevFeed: [This Week at Zed Industries: #10]() Original publisher: [Read original article]() Author: Julia Risley Published: 2023-07-07T00:00:00Z Content type: article Language: en Sources: [Zed Industries - Blog]() Topics: [ui](), [Code](), [TypeScript](), [Rust](), [bug](), [Database](), [Network](), [macOS](), [Vim]() Tags: [bug](), [code](), [collaboration](), [crash](), [database](), [incremental](), [macos](), [network](), [refactor](), [rust](), [typescript](), [ui](), [vim]() ### AI overview A weekly Zed development update covering collaboration UI and native calling, theme code work, assistant fixes, GPUI and Vim improvements, and a macOS crash fix for multiple instances competing for local database access. ### Source excerpt A quiet week of rest and incremental improvement. [Next page]()