# evolution Published articles for evolution. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The Evolution of HTTP, Clearly Explained DevFeed: [The Evolution of HTTP, Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-04T16:39:02Z Content type: article Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [HTTP](), [client](), [servers](), [Caching](), [Web]() Tags: [browser](), [caching](), [encoding](), [evolution](), [http](), [http-3](), [protocol](), [request](), [responses](), [server](), [tcp]() ### AI overview An explanatory article traces HTTP from versions 0.9 through 1.1, describing how headers, status codes, persistent connections, caching, and chunked transfer encoding addressed the web's growing scale and performance needs. It introduces HTTP/3 as part of that broader evolution. ### Source excerpt From 0.9 to 3.0: What changed and why? ## Schema Evolution: Changing the Contract Without Breaking What Runs DevFeed: [Schema Evolution: Changing the Contract Without Breaking What Runs]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-08-20T15:32:18Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [schema-evolution]() Tags: [article](), [evolution](), [schema](), [schema-evolution]() ### AI overview The article examines schema evolution and strategies for managing changes to data schemas. ### Source excerpt In this article, we will look at schema evolution and strategies for the same. ## Эволюция данных: генетический алгоритм в задаче классификации текстов DevFeed: [Эволюция данных: генетический алгоритм в задаче классификации текстов]() Original publisher: [Read original article]() Author: evgeneration (red\_mad\_robot) Published: 2026-04-23T19:05:42Z Content type: article Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [Natural language processing](), [Large Language Model]() Tags: [ai](), [evolution](), [llm](), [ml](), [nlp](), [prompt](), [tag-1cd610c0e518](), [tag-1f12060c781a](), [tag-2d1b71a8786f](), [tag-7bc388df28ed](), [tag-9bf5e01ce62e](), [tag-9d8cf70dc46c]() ### AI overview The article describes using a genetic algorithm to evolve prompts that generate training data for text-classification models. The approach was motivated by poor results from synthetic datasets and manual prompt iteration, with prompt fitness evaluated by classifier performance on a fixed test set. ### Source excerpt Привет! Меня зовут Женя Андриевская. Я -- NLP-инженер в лаборатории R&D red_mad_robot. Сегодня расскажу, как мы призвали на помощь эволюцию, чтобы улучшить качество данных в задачах классификации текстов. Да-да, ту самую эволюцию, с мутациями и скрещиваниями -- только в мире данных, промптов и LLM. Читать далее ## Beyond Prompting: How Algorithmic Evolution Doubled our Training Speed DevFeed: [Beyond Prompting: How Algorithmic Evolution Doubled our Training Speed]() Original publisher: [Read original article]() Author: Rex Lin Published: 2026-03-30T12:47:12Z Content type: article Language: en Sources: [Klarna Engineering]() Topics: [Artificial Intelligence](), [Optimization](), [Code](), [Google](), [Transformer](), [systems]() Tags: [ai](), [artificial-intelligence](), [code](), [engineering](), [evolution](), [google](), [machine-learning](), [optimization](), [software-engineering](), [speed](), [training]() ### AI overview Klarna describes using Google's AlphaEvolve to explore code optimizations for a large transformer-model training pipeline. Across nearly 6,000 candidate programs over three weeks, the system reportedly doubled training speed and produced a better model. Engineers defined the sandbox, metrics, constraints, hints, and error handling while AlphaEvolve generated, tested, and scored candidates. ### Source excerpt By Rex Lin and Valeria Verzi (Klarna Engineering), with Anant Nawalgaria (Google) We knew our training pipeline could be faster -- significantly faster. At Klarna, one of our largest models (a transformer trained on vast streams of payment events and shared like infrastructure across many internal systems) is on a tight training loop. Speed is money at our scale. The opportunity for improvement was not in the hyperparameters, but in the plumbing: the way numbers moved between processors, the way memory was allocated, the way the model performed its most basic mathematical operations. The challenge was scale. An engineer might try five or ten structural rewrites. A particularly ambitious one, armed with an AI coding assistant, might push to a hundred. But the full search space, the universe of possible combinations of precision formats, data pipelines, attention mechanisms, and gradient strategies, numbered in the thousands. We partnered with Google to apply a different kind of tool. Instead of trying to prompt our way to a solution, we handed the problem to AlphaEvolve, which treats code optimization the way evolution treats organisms: generate candidates, test them, keep the fittest, repeat. Over three weeks and nearly 6,000 candidate programs, it doubled our training speed and, unexpectedly, produced a better model in the process. The Machine That Writes Machines AlphaEvolve is not a chatbot for code. You don't interact with it through prompts. Instead, you build a sandbox around it. The engineer's job is to define what can change in the code and what cannot (passing only structural code snippets to the system, never customer data) to specify the metric that matters, and to set the constraints that must never be violated. You write hints in the form of code comments. You craft error messages that help the system learn when it fails. Then you step back. The system takes over from there. It generates a candidate program, runs it, measures the result, scores it. The b ## Different Types of Database Management Systems DevFeed: [Different Types of Database Management Systems]() Original publisher: [Read original article]() Author: Sumit Malviya Published: 2026-03-20T10:30:23Z Content type: article Language: en Sources: [Backend Archives | Talent500 blog]() Topics: [Databases](), [Database](), [data](), [Software](), [Scalability](), [NoSQL](), [MongoDB](), [MySQL](), [Oracle Database](), [sql-server]() Tags: [applications](), [backend](), [backup](), [big-data-and-dbms](), [choosing-the-right-dbms-for-your-needs](), [cloud-based-dbms](), [cloud-native](), [comparison-of-dbms-types](), [conclusion](), [consistency](), [data](), [database](), [definition-and-importance-of-dbms](), [different-types-of-database-management-systems](), [emerging-types-in-database-management-systems](), [evolution](), [evolution-of-database-management-systems](), [examples](), [hierarchical-database-management-systems](), [industries](), [integrity](), [management](), [mysql](), [network-database-management-systems](), [nosql](), [nosql-databases](), [object-oriented-database-management-systems-oodbms](), [recovery](), [relational-database-management-systems-rdbms](), [relational-databases](), [scalability](), [systems](), [types](), [types-of-database-management-systems]() ### AI overview This article explains what database management systems are, why they matter, how they support storage, queries, security, integrity, backup, and recovery, and how major DBMS categories have evolved. It also introduces examples including MySQL, Oracle Database, Microsoft SQL Server, and MongoDB. ### Source excerpt Data is one of the most valuable assets for organizations in this AI-powered digital era. From banking systems and e-commerce [...] The post Different Types of Database Management Systems appeared first on Talent500 blog. ## Humans in AI Podcast - Matt Butcher on Cloud, Edge and AI DevFeed: [Humans in AI Podcast - Matt Butcher on Cloud, Edge and AI]() Original publisher: [Read original article]() Author: Matt Butcher Published: 2025-08-21T12:00:00Z Content type: article Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [Networks](), [Cloud](), [Edge]() Tags: [ai](), [akamai](), [ceo](), [cloud](), [edge](), [evolution](), [networks](), [podcast]() ### AI overview A Humans in AI podcast episode featuring Matt Butcher discusses the evolution of content delivery networks, with particular attention to Akamai's innovations. ### Source excerpt In the latest podcast episode of Humans in AI our CEO, Matt Butcher discusses the evolution of content delivery networks, particularly focusing on Akamai's innovations ## CPython Performance Evolution: From Specialization to JIT DevFeed: [CPython Performance Evolution: From Specialization to JIT]() Original publisher: [Read original article]() Author: Abhimanyu Saharan Published: 2025-05-24T00:00:00Z Content type: article Language: en Sources: [Abhimanyu Saharan]() Topics: [JIT]() Tags: [3](), [adaptive](), [bytecode](), [evolution](), [jit](), [performance](), [python](), [python-3-14]() ### AI overview This article explains CPython's performance evolution from adaptive bytecode in version 3.11 to JIT execution in version 3.14, improving performance without requiring an interpreter rewrite. ### Source excerpt Explore how CPython evolved from adaptive bytecode in 3.11 to JIT execution in 3.14, boosting performance without rewriting the interpreter. ## The Ethereum Foundation's Vision DevFeed: [The Ethereum Foundation's Vision]() Original publisher: [Read original article]() Author: Aya Miyaguchi; Vitalik Buterin Published: 2025-04-28T00:00:00Z Content type: article Language: en Sources: [Ethereum Foundation Blog]() Topics: [Ethereum](), [Finance]() Tags: [blog](), [ecosystem](), [ethereum](), [evolution](), [identity](), [infrastructure](), [organizational](), [platform](), [vision]() ### AI overview The Ethereum Foundation outlines its vision, role, and priorities as a steward of the Ethereum ecosystem. It emphasizes decentralization, support for ecosystem initiatives, and strategically focusing on areas where the Foundation can contribute uniquely while enabling others to assume responsibilities over time. ### Source excerpt _Ethereum's strength lies in its decentralization -- not just technically, but socially and structurally. This blog articulates the EF's vision, its role, and priorities as one of the ecosystem's stewards. We use this moment as an opportunity to restate our vision, principles and values which we continue to uphold as... ## Teleport's Evolution: Today's Name Changes Reflect a Strategic Shift in Infrastructure Security DevFeed: [Teleport's Evolution: Today's Name Changes Reflect a Strategic Shift in Infrastructure Security]() Original publisher: [Read original article]() Author: diana.jovin@goteleport.com (Diana Jovin) Published: 2025-02-25T00:00:00Z Content type: release Language: en Sources: [Teleport]() Topics: [Security](), [Zero Trust](), [zero trust networking](), [zero trust security](), [trust](), [Architecture & Design]() Tags: [change](), [evolution](), [platform](), [product](), [rebranding](), [security](), [zero-trust](), [zero-trust-networking](), [zero-trust-security]() ### AI overview Teleport announces new names for its product suite, including Teleport Infrastructure Identity Platform, Teleport Zero Trust Access, Teleport Identity Governance, and Teleport Identity Security. The article says the changes emphasize infrastructure identity, zero trust, governance, and security operations. ### Source excerpt Teleport is evolving to better align with our mission and help customers understand the full potential of our platform with new names for our product suite. ## Gradle's Evolution - A Conversation with Adam Murdoch DevFeed: [Gradle's Evolution - A Conversation with Adam Murdoch]() Original publisher: [Read original article]() Author: Laura Kassovic Published: 2024-06-12T04:00:00Z Content type: article Language: en Sources: [The Gradle Blog]() Topics: [Gradle](), [Automation](), [Dependency management](), [Deployment](), [Android](), [Kotlin]() Tags: [android](), [automation](), [build-tool](), [build-tools](), [dependency-management](), [evolution](), [gradle](), [kotlin]() ### AI overview An interview with Gradle CTO Adam Murdoch traces Gradle's origins, growth, and evolution as a build tool. It discusses Gradle's expanding role across dependency management, deployment, production, Kotlin, and Android, along with major product milestones from 2012 through 2018. ### Source excerpt We had a conversation with Adam Murdoch, the CTO of Gradle, about his journey from the very origins of Gradle. Adam offers insights into the latest features and discusses the future of build tools. The Genesis of Gradle Adam began his journey with Gradle around 2008 while seeking a robust alternative to the Ant-based build systems that he used at the time. He recalls, "I was looking for a replacement for our Ant-based build system, came across this new tool called Gradle, started using it, [and] started sending in patches for improvements." Hans Dockter, the founder of Gradle, recognized Adam's contributions and invited him to join as a maintainer. This collaboration marked the beginning of a transformative journey in build automation. "Here we are 15 years later," Adam remarks, reflecting on the vast changes since those early days. Recognizing a growing ecosystem Reflecting on the evolution of build tools over the years, Adam notes the dramatic expansion of the ecosystem. "When we started Gradle, there were no microservices [...] building your software meant compiling some classes, making adjustments, and then you were done." Today, the landscape is vastly different. Build tools now need to handle various tasks from deployment to production, which were not considered when Gradle first began. The introduction of languages like Kotlin and platforms like Android has further expanded Gradle's role in the developer community. 2012: Gradle Build Tool 1.0 introduces a faster, more accurate dependency resolution engine and expands plugin support. 2014: Gradle Build Tool 2.0 improves performance and memory efficiency, introduces new dependency management features, and enhances incremental build support. 2016: Gradle Build Tool 3.0 enables the Gradle Daemon by default and enhances IDE support with auto-completion and navigation with Kotlin DSL. 2016: Gradle, Inc. releases Gradle Enterprise as a commercial product to build software better and faster with Build Scan® and Build C ## The latest in CSS and web UI: I/O 2024 recap DevFeed: [The latest in CSS and web UI: I/O 2024 recap]() Original publisher: [Read original article]() Published: 2024-06-11T00:00:00Z Content type: article Language: en Sources: [Una Kravets]() Topics: [CSS](), [ui](), [Web platform](), [Web]() Tags: [css](), [evolution](), [features](), [innovation](), [latest](), [platform](), [recap](), [ui](), [web](), [web-platform]() ### AI overview A recap of I/O 2024 covering the latest CSS and web UI features, presented in the context of ongoing web platform innovation. ### Source excerpt The web platform is alive with innovation, with CSS and web UI features at the forefront of this exciting evolution. ## The History and Evolution of WebAssembly in Kubernetes DevFeed: [The History and Evolution of WebAssembly in Kubernetes]() Original publisher: [Read original article]() Author: Matt Butcher Published: 2024-05-16T12:00:00Z Content type: article Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [WebAssembly](), [Kubernetes](), [Cloud]() Tags: [cloud](), [evolution](), [history](), [kubernetes](), [modern](), [webassembly]() ### AI overview This article examines how WebAssembly and Kubernetes evolved together and describes their role in modern cloud environments. ### Source excerpt Discover how WebAssembly and Kubernetes evolved together to create ultra-efficient, high-performing modern cloud environments. ## The Humane AI Pin and the Future of Computer Interfaces DevFeed: [The Humane AI Pin and the Future of Computer Interfaces]() Original publisher: [Read original article]() Published: 2024-04-22T00:00:00Z Content type: opinion Language: en Sources: [Radek Pazdera]() Topics: [interfaces](), [Computing]() Tags: [computer](), [evolution](), [interfaces](), [smartphones](), [tech-industry]() ### AI overview This commentary examines the Humane AI Pin and broader attempts to develop computer interfaces beyond conventional smartphones. It compares foldable phones, spatial computing, voice assistants, screenless devices, touchscreens, speech interfaces, vision, and LLMs, while questioning whether newer interfaces can replace smartphones. ### Source excerpt Are smartphones indisruptable? ## Evolution of translations management for Android at Revolut DevFeed: [Evolution of translations management for Android at Revolut]() Original publisher: [Read original article]() Author: Yaroslav T Published: 2023-12-05T17:28:51Z Content type: article Language: en Sources: [Revolut Engineering]() Topics: [Android](), [Development](), [migration](), [Script](), [Gradle](), [Groovy]() Tags: [android](), [development](), [evolution](), [gradle](), [gradle-plugin](), [groovy](), [kotlin](), [management](), [migration](), [translation]() ### AI overview An engineering retrospective on how Revolut evolved Android translation management across multiple apps, modules, and languages. It describes using Lokalise, moving translation downloads from app startup to scripts, and migrating the script from Python to Groovy through a Gradle task. ### Source excerpt Here's everything you need to know about how translations have evolved at Revolut. We currently have 6 apps, with 330 modules containing strings.xml files. And don't forget, we need to support 33 languages. To make the translation process smooth, we use the API of Lokalise. When one of our engineers adds a new string on the platform, it's translated into all languages within 1 hour. After that, we need to download and place the translations into the correct module. Now, from an engineering perspective, here comes the most interesting part! Initial implementation In 2017, we began our expansion, and the first supported languages were French and Polish. We quickly reached a stage where fast translation became an integral part of the feature release process. Using our partner, Lokalise.com, we started implementing translations for a single app (only Revolut retail app existed at the time). We added Lokalise library to our project and used keys from it via a Singleton pattern: https://medium.com/media/b55574683f8ebc328b4049d46f1114bf/href Library downloads translations from the server once the user opens the app. This approach was fast to implement, but had 2 limitations: API has user limit, which is critical for the fast-growing application Download translations on app launch degrades the performance First script To answer both of these problems, we created a simple local script that downloads strings and puts them into the resources' folder. Before releasing a new version of the app, one of the engineers has to call this script and merge translated files into the release branch. https://medium.com/media/78c6e72e4148de66b8d461b467b0df55/hrefSimplifying developers' life Later on, one of our engineers introduced an additional script to upload keys to Lokalise. The goal was to let engineers create new keys in strings.xml during the development process, automatically upload them to the provider, translate them, and then download them back. However, we soon discovered that ## A new visual language for Slack DevFeed: [A new visual language for Slack]() Original publisher: [Read original article]() Author: lcarmen2 Published: 2023-10-27T20:58:07Z Content type: opinion Language: en Sources: [Slack Design]() Topics: [Information Architecture](), [Slack](), [ui]() Tags: [architecture](), [communication](), [design](), [evolution](), [information-architecture](), [information-density](), [interface](), [layout](), [productivity](), [slack](), [what-we-do]() ### AI overview Slack designers describe a two-week sprint exploring a new visual language alongside a major evolution of Slack's information architecture. The proposed direction preserves information density and productivity while using gradients, transparent surfaces, rounded controls, and depth to create a more approachable interface. ### Source excerpt As we embarked on the largest evolution to Slack's information architecture, us designers believed it was the perfect opportunity to evolve Slack's iconic look and feel. While the undertaking would be no small feat, we were inspired to make Slack even more pleasant, more personal, and easier to understand. Make it more Slack-y. We decided [...] The post A new visual language for Slack appeared first on Slack Design. ## Swift Evolution proposals in Alfred DevFeed: [Swift Evolution proposals in Alfred]() Original publisher: [Read original article]() Author: Ole Begemann Published: 2023-03-09T22:33:14Z Content type: release Language: en Sources: [Ole Begemann]() Topics: [Swift](), [Tool](), [Script]() Tags: [download](), [evolution](), [github](), [improvements](), [markdown](), [script](), [swift](), [tool](), [update](), [workflow]() ### AI overview The article announces version 2.1.0 of Karoy Lorentey's swift-evolution workflow for Alfred. The update fixes a break caused by Swift Evolution data format changes and adds improved proposal-title display, copy actions, and metadata variables for custom actions. ### Source excerpt I rarely participate actively in the Swift Evolution process, but I frequently refer to evolution proposals for my work, often multiple times per week. The proposals aren't always easy to read, but they're the most comprehensive (and sometimes only) documentation we have for many Swift features. For years, my tool of choice for searching Swift Evolution proposals has been Karoy Lorentey's swift-evolution workflow for Alfred. The workflow broke recently due to data format changes. Karoy was kind enough to add me as a maintainer so I could fix it. The new version 2.1.0 is now available on GitHub. Download the .alfredworkflow file and double-click to install. Besides the fix, the update has a few other improvements: The proposal title is now displayed more prominently. New actions to copy the proposal title (hold down Command) or copy it as a Markdown link (hold down Shift + Command). The script forwards the main metadata of the selected proposal (id, title, status, URL) to Alfred. If you want to extend the workflow with your own actions, you can refer to these variables. ## The Evolution of Kotlin Multiplatform Mobile - 2022 Edition (OLD) - Russell Wolf DevFeed: [The Evolution of Kotlin Multiplatform Mobile - 2022 Edition (OLD) - Russell Wolf]() Original publisher: [Read original article]() Published: 2021-11-01T15:31:26Z Content type: opinion Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [Kotlin Multiplatform](), [kotlin multiplatform mobile](), [Kotlin](), [multiplatform](), [Library](), [Maven Central](), [jetbrains]() Tags: [evolution](), [jetbrains](), [kotlin](), [kotlin-multiplatform](), [kotlin-multiplatform-mobile](), [kotlin-native](), [libraries](), [maven-central](), [mobile](), [multiplatform]() ### AI overview Russell Wolf reviews the evolution of Kotlin Multiplatform Mobile from its early experimental period to broader production use. The article describes improvements in Kotlin/Native memory management, Xcode integration, dependency distribution, Swift interoperability, and the growth of the KMM library ecosystem. ### Source excerpt Touchlab's Russell Wolf looks at how the evolution of KMM has been playing out since the first introduction of Kotlin Multiplatform to today ## Trino on ice II: In-place table evolution and cloud compatibility with Iceberg DevFeed: [Trino on ice II: In-place table evolution and cloud compatibility with Iceberg]() Original publisher: [Read original article]() Author: Brian Olsen Published: 2021-07-12T00:00:00Z Content type: article Language: en Sources: [Trino Blog]() Topics: [Apache Iceberg](), [schema-evolution](), [Cloud](), [migration](), [Filesystems]() Tags: [cloud-storage](), [compatibility](), [data-lake](), [evolution](), [migration](), [schema-evolution]() ### AI overview This Trino on Ice article explains how Apache Iceberg supports in-place partition and schema evolution while addressing compatibility with cloud and object storage. It contrasts Iceberg's live partition-spec updates with Hive's table migration requirements. ### Source excerpt Welcome to the Trino on ice series, covering the details around how the Iceberg table format works with the Trino query engine. The examples build on each previous post, so it's recommended to read the posts sequentially and reference them as needed later. Here are links to the posts in this series: Trino on ice I: A gentle introduction to Iceberg Trino on ice II: In-place table evolution and cloud compatibility with Iceberg Trino on ice III: Iceberg concurrency model, snapshots, and the Iceberg spec Trino on ice IV: Deep dive into Iceberg internals The first post covered how Iceberg is a table format and not a file format It demonstrated the benefits of hidden partitioning in Iceberg in contrast to exposed partitioning in Hive. There really is no such thing as "exposed partitioning." I just thought that sounded better than not-hidden partitioning. If any of that wasn't clear, I recommend either that you stop reading now, or go back to the first post before starting this one. This post discusses evolution. No, the post isn't covering Darwinian nor Pokémon evolution, but in-place table evolution! ## Programming Language Evolution DevFeed: [Programming Language Evolution]() Original publisher: [Read original article]() Author: Roman Elizarov Published: 2020-11-23T16:18:08Z Content type: opinion Language: en Sources: [Stories by Roman Elizarov on Medium]() Topics: [Programming](), [Programming language](), [Code](), [Object-oriented programming (OOP)](), [C](), [Java](), [Python]() Tags: [code](), [evolution](), [history](), [improvements](), [language](), [loops](), [oop](), [patterns](), [pointers](), [programming](), [programming-language](), [programming-languages](), [software]() ### AI overview The article examines how programming languages evolve through gradual improvements that follow changes in programming practice. It uses the transition from GOTOs to structured loops and the adoption of object-oriented patterns, classes, methods, and implicit object references as examples. ### Source excerpt Photo by Anne Nygård on Unsplash The history of programming languages is ripe with evolution. Existing languages constantly evolve and new languages are created to address the emerging needs. Sometimes there are radical, revolutionary breakthroughs, with a complete paradigm shift, but often there are just gradual improvements and refinements. The latter is the topic of this story. The practice of programming at any given era usually goes ahead of capabilities that programming languages provide, while programming language designers recognize it and catch up to fulfill the demand. Let us see some examples to the point. From GOTOs to the structured code In early languages, you had to write a lot of repetitive code just to do a simple loop. The loop was such a common programming pattern, that it was adopted even by the primitive higher-level languages in the era predating structured programming. So, there was a time when you still had GOTOs in your programming language but a significant fraction of the code had structured loops: 10 LET N=10 20 FOR I=1 TO N 30 PRINT "Hello, World!" 40 NEXT I As we know, the subsequent generation of languages not only added structured IF statements but also made the structure explicit in the source and ended up abolishing GOTOs completely. This kind of evolution can be seen in other areas, too. Objects and pointers Let's take a brief look at OOP. The object-oriented style of programming does not need an object-oriented language. Even nowadays you can find software written in C where methods are just a convention of writing functions whose first parameter is a pointer to the receiver: void Point_move(Point* self, int dx, int dy) { ... } Virtual methods are routinely implemented in pure C, too, explicitly keeping a virtual methods table with references to methods somewhere in the object's structure. However, the rising popularity of object-oriented programming back in the day cemented the growth of languages that incorporated these patterns ## Kotlin 1.4 Release - Kevin Galligan DevFeed: [Kotlin 1.4 Release - Kevin Galligan]() Original publisher: [Read original article]() Published: 2020-08-28T18:18:23Z Content type: opinion Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [Kotlin](), [multiplatform](), [Jetpack Compose](), [Ktor]() Tags: [evolution](), [kmm](), [kotlin](), [kotlin-multiplatform](), [multiplatform](), [release]() ### AI overview This article discusses Kotlin 1.4 as a platform milestone, focusing on compiler and platform improvements, Jetpack Compose's Alpha release, and implications for Kotlin Multiplatform and Kotlin Multiplatform Mobile. It also describes updates to Touchlab libraries and related tooling. ### Source excerpt The Kotlin 1.4 release is a major milestone in the evolution of Kotlin as a platform. Touchlab shares thoughts on how 1.4 impacts the KMM platform. ## ViewModel. How it works. DevFeed: [ViewModel. How it works.]() Original publisher: [Read original article]() Author: Vladislav Puryev Published: 2020-01-18T15:40:09Z Content type: tutorial Language: en Sources: [Stories by Vladislav Puryev on Medium]() Topics: [Android](), [Code](), [Framework](), [configuration](), [Google](), [Java]() Tags: [abstract-class](), [android](), [android-framework](), [code](), [configuration](), [constructor](), [evolution](), [java](), [screen](), [state](), [technical](), [viewmodel]() ### AI overview This technical tutorial examines how Android ViewModel works internally and how it helps preserve screen state across configuration changes. It explains that ViewModel itself is a simple abstract class and that state retention results from processes within the Android framework, with correct initialization and factories required for dependencies. ### Source excerpt Android ViewModel. How it works. Hello everybody. This post will be about popular solution from Google for Android developers -- ViewModel (if you are not familiar with it yet -- get started from official documentation). The main goal of article -- show you how viewModel works, how this tool helps us to save state of our screens. First of all it is better to note that viewModel -- is just java code inside Android framework and there is no any magic... almost. This mechanism was improved something about three times from its origin (2017) till the present day. Let's take a look at "Evolution of anatomy". WARNING! This article contains a lot of technical content and internals of Android framework. And please pay attention -- all demonstrated code you can find in your Android Framework using middle-click or ctrl + left-click. There is no any of self-made code -- all is inside Android framework. Attention again -- most of code will be provided in shortened form. Only necessary details. Full versions you can find in Android Framework source codes straight inside your IDE. ViewModel 2017 Honestly, this part (I mean exactly ViewModel's version of 2017) can be unnecessary, because Medium has several articles, that describes viewModel saving state mechanism in first version. I like this one. But I suggest you more code than there. First of all let's take a look at ViewModel class itself. According to official documentation: The ViewModel class allows data to survive configuration changes such as screen rotations. It can lead to research this class. https://medium.com/media/3966f6a014283670bd0d0430100902cd/href It is just simple abstract class. Without any secrets. With one empty method. Can documentation be wrong (or obsolete) so long time? This question is not so obvious. From one side, if you will create your child viewModel and initialize it using constructor, in this case saving states mechanism will not work. But if you are doing everything in the right way (using special initial ## Features composability DevFeed: [Features composability]() Original publisher: [Read original article]() Published: 2019-03-19T14:27:48Z Content type: opinion Language: en Sources: [Blog by Michael Dubakov]() Topics: [implementation](), [Software](), [App]() Tags: [component](), [evolution](), [implementation](), [reuse](), [software](), [structure]() ### AI overview The article argues that software features should avoid depending on other features' internal details. Treating features as independent components with public interfaces and extension points improves composability, reuse, and software evolution. ### Source excerpt When we add features to a product, we rarely think about composability. We assume that it is OK for any feature to rely on internal... ## The Evolution of the Developer Retreat DevFeed: [The Evolution of the Developer Retreat]() Original publisher: [Read original article]() Author: Bruce Eckel Published: 2018-09-06T00:00:00Z Content type: opinion Language: en Sources: [Bruce Eckel - Computing Thoughts]() Topics: [Kotlin](), [Elixir](), [Flutter](), [Erlang](), [Android](), [iOS]() Tags: [android](), [conferences](), [developer](), [elixir](), [erlang](), [events](), [evolution](), [flutter](), [ios](), [kotlin](), [self-organizing]() ### AI overview The author reflects on a developer retreat that used no central theme or fixed structure. Participants worked independently, shared discussions, and explored Kotlin, Elixir, Erlang, and Flutter, among other topics. ### Source excerpt I've been experimenting with different forms of events for many years now. Mostly this has been in the form of self-organizing conferences, but in recent years I've tried going further, to even less structure. I've held a number of "developer retreats" where we had a theme that everyone focused on, and those have been quite interesting and valuable. To be honest, what I really wanted to try with the developer retreats is zero structure. ## Threat Model Thursday: Legible Architecture DevFeed: [Threat Model Thursday: Legible Architecture]() Original publisher: [Read original article]() Author: Adam Published: 2018-08-23T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [systems](), [Cloud](), [consistency](), [Data visualization]() Tags: [abstraction](), [architecture](), [cloud](), [complexity](), [consistency](), [evolution](), [systems]() ### AI overview The article argues that system models should be legible: organized and comprehensible enough to help people find threats. It presents legibility as a tool that supports design, evolution, interaction, and architectural consistency, while noting that cloud architectures can temporarily simplify accumulated complexity. ### Source excerpt [no description provided] [Next page]()