# transformation Published articles for transformation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Why AI Transformations May Repeat the Failures of Earlier Digital and Agile Transformations DevFeed: [Why AI Transformations May Repeat the Failures of Earlier Digital and Agile Transformations]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-08-27T14:03:51Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Artificial Intelligence](), [Agile](), [digital]() Tags: [agile](), [ai](), [alignment](), [collaboration](), [culture](), [strategy](), [teams](), [transformation]() ### AI overview This opinion article argues that many AI transformation efforts may repeat the failures of earlier digital and Agile transformations. It attributes these failures to organizations neglecting foundational issues such as culture, strategy, alignment, collaboration, and clarity, and argues that AI can amplify existing confusion and organizational problems. ### Source excerpt Different approach, same outcomes. ## Why GitHub feels less reliable lately DevFeed: [Why GitHub feels less reliable lately]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-08-23T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [GitHub](), [incident](), [migration](), [systems](), [Azure](), [GitHub Actions](), [pull-requests]() Tags: [autoscaling](), [capacity](), [concurrency](), [data-centers](), [deployment](), [github](), [incident](), [incident-management](), [istio](), [reliability](), [request](), [resilience](), [sre](), [transformation]() ### AI overview The article argues that GitHub's recent reliability problems reflect the difficult middle of a major infrastructure transformation. It connects incidents to migration complexity, unsafe automation, configuration mistakes, capacity and concurrency weaknesses, database migration errors, and autoscaling problems. ### Source excerpt GitHub is not having one outage problem. Its recent incident reports show the difficult middle of a platform transformation. ## Shifting readiness left: What AI can (and can't) do for organisational readiness DevFeed: [Shifting readiness left: What AI can (and can't) do for organisational readiness]() Original publisher: [Read original article]() Author: Nel Mathams Published: 2026-07-27T14:48:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [data]() Tags: [ai](), [artificial-intelligence](), [building-shared-understanding](), [delivery](), [discovery](), [ethics](), [funding](), [governance](), [institute-for-government](), [objectives](), [organisational-goals](), [organisational-readiness](), [shift-left](), [transformation]() ### AI overview This opinion article argues that organisational readiness depends on shared understanding rather than only governance, funding, or delivery structures. It examines how AI can help align goals, surface hidden assumptions, and accelerate discovery while keeping human judgement in control, including ethical considerations around data. ### Source excerpt Organisational readiness is often treated as a matter of governance, funding and delivery structures. In this post, I argue that true readiness is about building shared understanding, and explore how AI can help organisations align on goals, surface hidden assumptions and accelerate discovery work, while keeping human judgement firmly in control. ## Lambda-powered functions land in OTTL DevFeed: [Lambda-powered functions land in OTTL]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-07-22T20:04:49Z Content type: release Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [telemetry](), [sensitive data](), [data]() Tags: [collection-operations](), [experimental](), [functions](), [lambda](), [opentelemetry](), [pipelines](), [release](), [sensitive-data](), [telemetry](), [transformation]() ### AI overview OpenTelemetry Collector Contrib v0.157.0 introduces lambda expressions to OTTL, enabling reusable inline logic in generic higher-order functions. The release adds eight experimental functions for collection transformations, including filtering, mapping, finding, reducing, and conditional operations. ### Source excerpt As telemetry pipelines become more sophisticated, so do the transformations they need to perform: sanitizing sensitive data, normalizing inconsistent schemas, and enforcing attribute contracts. While OTTL provides a rich set of transformation functions, expressing collection operations has required dedicated functions with hardcoded behavior for each new use case. OpenTelemetry Collector Contrib v0.157.0 changes that by introducing lambda expressions to OTTL. Lambdas let users pass inline logic directly to generic higher-order functions, making complex collection transformations both reusable and concise. The release includes eight new functions that leverage this capability: Filter, MapEach, MapKeys, Any, All, Find, Reduce, and When. ## Technology Waves and Security - Is This Time Really Different? DevFeed: [Technology Waves and Security - Is This Time Really Different?]() Original publisher: [Read original article]() Author: phil7672 Published: 2026-07-11T15:52:48Z Content type: opinion Language: en Sources: [Risk and Cyber]() Topics: [Security](), [Vulnerabilities](), [Architecture & Design]() Tags: [architectures](), [cybersecurity](), [leadership](), [security](), [technology](), [transformation](), [vulnerabilities]() ### AI overview This commentary examines whether current technology waves are fundamentally different for security. It argues that benchmarking should focus on control effectiveness and outcomes rather than budgets, while discussing recurring waves of vulnerabilities and possible responses including increased speed. ### Source excerpt Most people have been through at least one wave of technology transformation. Some of us have been through a few and all carry the wisdom and scars from these. When you've experienced these changes you learn to appreciate, as the adage goes, that history might not repeat but it certainly does rhyme. In my working lifetime I caught the tail end of the mainframe to PC transition, the proliferation of client/server and distributed system architectures, wide-spread Internet adoption, mobile... ## Tambara Equipment DevFeed: [Tambara Equipment]() Original publisher: [Read original article]() Author: Bartosz Milewski Published: 2026-07-11T08:13:34Z Content type: article Language: en Sources: [Bartosz Milewski's Programming Cafe]() Topics: [modules](), [Haskell](), [Code]() Tags: [category-theory](), [code](), [double-categories](), [double-category](), [haskell](), [modules](), [optics](), [proarrow-equipment](), [profunctors](), [structure](), [tambara-modules](), [tannakian-reconstruction](), [theory](), [transformation]() ### AI overview This article explains Tambara modules through category theory and illustrates the concepts with Haskell code. It discusses their relationship to profunctors, monoidal actions, double categories, proarrow equipment, and Tannakian reconstruction. ### Source excerpt I was originally attracted to category theory when trying to understand Haskell optics. I was puzzled by the van Laarhoven's functor representations and Kmett's use of Tambara modules. By playing Tetris with the Yoneda lemma I was able to make some progress, attacking more and more esoteric topics. With a group of researcher and students [...] ## Why JSON and other languages should allow more flexible separators DevFeed: [Why JSON and other languages should allow more flexible separators]() Original publisher: [Read original article]() Author: Hillel Wayne Published: 2026-06-10T12:22:04Z Content type: opinion Language: en Sources: [Newsletter feed for Hillel Wayne's Newsletter]() Topics: [JSON](), [Haskell](), [Parsing]() Tags: [json](), [language](), [standard](), [transformation]() ### AI overview The article argues that requiring separators in particular positions makes common code transformations harder. Using JSON as the main example, it compares trailing, leading, and both-sided separator styles across languages including Haskell, TLA+, Prolog, Python, Go, and Alloy. ### Source excerpt This is valid JSON: { "a": 1, "b": 2, "c": 3 } This is invalid JSON: { "a": 1, "b": 2, "c": 3, } The difference is the last comma. The JSON grammar specifies that a comma can separate two members of an object but not postcede ("trail") a member. I think this was a design mistake. Say we want to add two new keys to the struct, one before the "a" member and one after the "c" member. Here's what it would look like if trailing commas were permitted: { + "x": 0, "a": 1, "b": 2, "c": 3, + "y": 4, } It's the exact same text transformation regardless of where we add the key. In the current model, we instead have this: { + "x": 0, "a": 1, "b": 2, - "c": 3 + "c": 3, + "y": 4 } Those are different transformations! Similarly if you want to remove an element, you can't just delete the corresponding line1, you have to delete the line and then check that the last line doesn't have a trailing comma. Don't even get me started on all the special cases involved in swapping two lines. JSON isn't the only language with this problem. Haskell writes record types like this: -- from https://play.haskell.org/ data Drone = Drone { xPos :: Int , yPos :: Int , zPos :: Int } This "partial bullet point" style of putting separators at the beginning of rows makes it easier to change the last row but harder to change the first one. TLA+ has this problem too: \* both valid VARIABLES a, b, c vars == <> \* both invalid VARIABLES a, b, c, vars == <> This one's annoying because 1) you're constantly adding new top-level variables while working on a spec and 2) the PlusCal DSL does not have this problem: \* Totally fine! (*--algorithm foo { variables a; b; c; The worst offenders, IMO, are logic languages like Prolog. Not only don't you have trailing separators, you have a special terminating symbol: foo(A, B, C) :- A = 1, % comma B = 2, % comma C = 3. % period! I guess you can sort of think of it as funny-lookin' braces: foo(A, B, C) :- A = 1, B = 2, C = 3 . But this is not standard synt ## Seasons time-lapse - alignment DevFeed: [Seasons time-lapse - alignment]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-05-24T00:00:00Z Content type: tutorial Language: en Sources: [Nicolas Fränkel]() Topics: [Computer vision](), [OpenCV](), [image distortion](), [Algorithms]() Tags: [algorithm](), [algorithms](), [art](), [camera](), [computer-vision](), [development](), [iphone](), [open-source](), [python](), [time-lapse](), [transformation]() ### AI overview This article explains how the Seasons time-lapse project aligns photographs taken from slightly different viewpoints and with different phone cameras. It describes using OpenCV feature matching with ORB, followed by RANSAC and a homography to estimate the geometric transformation. ### Source excerpt In the previous post, I described the Seasons project: a time-lapse of hundreds of pictures taken from nearly the same viewpoint over the years. The hardest challenge wasn't taking the pictures or assembling them, but aligning them. You might have noticed the nearly part about viewpoint in the above paragraph. Indeed, it's an approximation. I'm a human being, not a tripod. The position changes ever so slightly, and so does the exact angle. ## How AI leaders are borrowing from the design playbook DevFeed: [How AI leaders are borrowing from the design playbook]() Original publisher: [Read original article]() Author: Andrew Hogan Published: 2026-04-21T21:30:00Z Content type: opinion Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [Development]() Tags: [agents](), [ai](), [design](), [executive](), [transformation](), [workflows]() ### AI overview The article argues that leaders guiding AI transformation should apply design principles to create better ways of working. It emphasizes hands-on use of AI tools, attention to day-to-day trade-offs, and involving non-designers in design decisions. ### Source excerpt A new class of leaders is leaning on design principles to guide their companies through AI transformation. ## A Collaborative Tech Innovation Day: Where Big Ideas Begin DevFeed: [A Collaborative Tech Innovation Day: Where Big Ideas Begin]() Original publisher: [Read original article]() Author: Global Technology Published: 2026-04-14T17:16:21Z Content type: opinion Language: en Sources: [McDonald's Technical Blog - Medium]() Topics: [Artificial Intelligence](), [Automation](), [Cloud](), [systems]() Tags: [ai](), [automation](), [cloud](), [collaboration](), [innovation](), [systems](), [technology](), [transformation]() ### AI overview McDonald's Enterprise Products and Platforms team hosted a Tech Innovation Day that brought business and technology teams together to identify more than 35 actionable ideas. Sessions examined how AI, automation, and cloud solutions could address real enterprise challenges. ### Source excerpt Behind-the-scenes of a day built for bold thinking, big questions, and breakthrough ideas -- and what it revealed about innovating at scale through partnership. by: Henry Lyles, Director, Operations Excellence & Kris Heller, Manager, Operations Excellence Quick Bytes: McDonald's Enterprise Products and Platforms (EPP) hosted a Tech Innovation Day that brought together Business and Tech teams to surface 35+ actionable ideas Teams found opportunities for AI and automation to make a big impact across enterprise systems When given the space, intentional collaboration models can shape the future of innovation When McDonald's signed a strategic partnership to accelerate the transformation of our global enterprise systems through AI and cloud solutions, the goal wasn't just to deploy new technology. It was to rethink how we solve problems together. For the McDonald's Domain Automation team, that partnership represented a meaningful inflection point in a journey more than seven years in the making: using automation to simplify complexity, improve processes, and rethink how work gets done across the enterprise. That partnership came to life this year when the Enterprise Products and Platforms (EPP) team hosted a Tech Innovation Day, a collaborative event designed to spark creativity, empower our employees, challenge assumptions, and explore how new technology can add value to the systems we rely on every day. Thinking beyond the status quo The day was built around a simple but powerful set of questions: What if? Imagine the possibilities. How might we do this differently together? Across focused sessions within key EPP Domains -- Supply Chain, Finance, People, and Corporate Systems -- participants explored how AI, automation, and intelligent solutions could be applied to real challenges they face today. These sessions were structured like a job fair or convention floor, with booths dedicated to major business categories such as People, Finance, Corporate, Supply Chain, Operatio ## Making Sense of Digital Transformation Initiatives: Seeing Past the Fuzziness DevFeed: [Making Sense of Digital Transformation Initiatives: Seeing Past the Fuzziness]() Original publisher: [Read original article]() Author: Josh Korr Published: 2026-02-13T23:41:00Z Content type: article Language: en Sources: [8th Light Insights]() Topics: [digital](), [product mindset](), [Optimization]() Tags: [digital-transformation](), [optimization](), [organizations](), [product](), [product-strategy-innovation](), [project](), [strategy](), [structured](), [transformation]() ### AI overview This article explains why digital transformation initiatives often become unclear in practice and argues that short, structured strategy and shaping phases can create shared clarity. It discusses how these initiatives may involve business-model shifts, cultural change, product thinking, and process optimization, illustrated by client examples. ### Source excerpt Digital transformation always sounds clear in an RFP or slide deck, but in practice things can get fuzzy fast. What exactly are we transforming? Is this about customers, or about ourselves? Are we reshaping workflows, or the entire business model? Without answers to these questions, projects stall, swirl, or never quite get started in the first place. As a Certified Sense-MakerTM and fuzziness connoisseur, I love helping clients make sense of their digital transformation initiatives.* This includes helping them understand the bigger picture, what their plans will mean for business operations, and what tools or solutions can help. By implementing short, structured strategy and shaping phases, organizations can gain shared clarity and overcome transformational fuzziness. * Just kidding: there isn't a Sense-Maker(tm) certification. (Sigh.) Fuzziness Factor #1: The Big Picture Digital transformation initiatives are typically triggered by something bigger: business model shifts or expansions, operational maturation, rebrands or relaunches. And these initiatives go far beyond technology upgrades. There is a very real shift in culture, a move towards a product mindset, and an optimization of processes that meet the demands of the market. When done well, these initiatives have a lasting impact on the organization and the benefits show up on the bottom line. But teams that are at the "ground level" of this transformative work often have a fuzzy understanding of the macro-strategy and how the immediate near-term effort fits in. Take a Fortune 500 media company I worked with. They had debated for a year whether to introduce self-serve sales alongside their high-touch direct sales model. Nothing with the project had moved because no one had named the real issue: This wasn't just a sales project -- it was a business model transformation. Misalignment across stakeholders was causing them to delay decisions. Once reframed as a fundamental cultural and business model shift, two thin ## 2025 Year in Review - Top 10 DevFeed: [2025 Year in Review - Top 10]() Original publisher: [Read original article]() Author: Phil Venables Published: 2026-01-10T14:36:22Z Content type: opinion Language: en Sources: [Risk and Cyber]() Topics: [Cybersecurity](), [Architecture & Design](), [Detection engineering](), [Vulnerabilities]() Tags: [2025](), [benchmarking](), [cybersecurity](), [execution](), [leadership](), [risk](), [technology](), [transformation](), [vulnerabilities](), [year-in-review]() ### AI overview A 2025 year-in-review highlighting posts about cybersecurity as a business leadership, strategic design, and sustainable execution function. It emphasizes moving from reactive security practices toward scalable, proactive systems and cautions against benchmarking based only on inputs such as budgets rather than control effectiveness. ### Source excerpt The most read posts in 2025 coalesced around the concept that successful cybersecurity is fundamentally a function of business leadership, strategic design, and sustainable execution . The unifying themes across the top posts emphasize shifting security from an artisanal, reactive craft to an industrial-scale, proactive capability focused on building scalable, self-reinforcing systems (flywheels). Transformation requires leaders to manage stakeholder expectations carefully, particularly by... ## OSS Japan 2025: A Breakthrough Year for Open Automotive Innovation DevFeed: [OSS Japan 2025: A Breakthrough Year for Open Automotive Innovation]() Original publisher: [Read original article]() Author: Cody Zuschlag Published: 2025-12-16T11:35:48Z Content type: article Language: en Sources: [Blog - Xen Project]() Topics: [Open Source](), [virtualization](), [systems](), [Arm]() Tags: [2025](), [amd](), [arm](), [automotive](), [cloud](), [collaboration](), [data-centers](), [event](), [future](), [infrastructure](), [open-source](), [oss](), [summit](), [talks](), [transformation](), [virtualization]() ### AI overview The article reports on the Xen Project's participation in Open Source Summit Japan and Automotive Linux Summit 2025. It presents open source as a foundation for software-defined vehicles, highlighting Xen's role in mixed-criticality workloads, cockpit virtualization, and vehicle software stacks. The article also emphasizes collaboration among silicon vendors, automotive manufacturers, and cloud and server infrastructure companies. ### Source excerpt The Xen Project is back from Open Source Summit Japan and Automotive Linux Summit 2025. This year's event felt like a true watershed moment for the automotive industry and for open source. Across talks, demos, and hallway conversations, one thing was clear: open source is now a foundational ## Accenture and OpenAI accelerate enterprise AI success DevFeed: [Accenture and OpenAI accelerate enterprise AI success]() Original publisher: [Read original article]() Published: 2025-12-01T05:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [ChatGPT](), [Deployment](), [Security]() Tags: [agentic-ai](), [ai](), [certifications](), [chatgpt](), [collaboration](), [company](), [consulting](), [deployment](), [enterprise](), [growth](), [implementation](), [openai](), [security](), [transformation](), [workflows]() ### AI overview Accenture and OpenAI are expanding their collaboration to help enterprises adopt agentic AI. Accenture will provide ChatGPT Enterprise to tens of thousands of professionals, while OpenAI becomes a primary AI partner for Accenture's AI-powered services. The companies are also launching a flagship client program combining OpenAI's enterprise products with Accenture's consulting expertise, implementation playbooks, use cases, security guidance, and deployment knowledge. ### Source excerpt Accenture and OpenAI are collaborating to help enterprises bring agentic AI capabilities into the core of their business and unlock new levels of growth. ## How AWS Is Helping Enterprises Build, Scale, and Innovate With AI DevFeed: [How AWS Is Helping Enterprises Build, Scale, and Innovate With AI]() Original publisher: [Read original article]() Author: TJ URGLAVITCH, CLOUD SERVICES PRACTICE LEAD @ TOPTAL Published: 2025-11-30T23:00:00Z Content type: article Language: en Sources: [Toptal Blog]() Topics: [Amazon Web Services](), [Artificial Intelligence](), [cloud-infrastructure](), [Security & compliance, Cloud security](), [AI Infrastructure]() Tags: [ai](), [ai-adoption](), [ai-infrastructure](), [aws](), [cloud](), [cloud-infrastructure](), [cloud-services](), [collaboration](), [governance](), [podcast](), [scale](), [security](), [transformation]() ### AI overview This Executive Guidance podcast discusses how AWS helps enterprises adopt AI, scale innovation, and modernize operations through cloud infrastructure, partner collaboration, responsible design, security, and governance. ### Source excerpt Discover how AWS's Head of Partner Solutions Architecture, Divyesh Shah, is helping enterprises scale responsibly and innovate faster through the power of cloud and AI. In this episode of the Executive Guidance podcast, Shah joins Toptal's Cloud Services Practice Lead, TJ Urglavitch, to discuss how AWS builds trust, empowers collaboration, and drives transformation across industries. ## AI at trivago: from experimentation to everyday impact DevFeed: [AI at trivago: from experimentation to everyday impact]() Original publisher: [Read original article]() Author: Carolina Muradas Strategy; Operations Lead; AI Ambassador group founder Published: 2025-10-30T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Artificial Intelligence](), [AI Strategy]() Tags: [2025](), [ai](), [ai-adoption](), [ai-at-work](), [artificial-intelligence](), [company](), [copilot](), [knowledge-sharing](), [productivity](), [survey](), [transformation]() ### AI overview Trivago describes how its use of artificial intelligence developed from isolated experimentation into company-wide integration between 2023 and 2025. The article reports widespread employee use, positive sentiment, time savings, AI Ambassadors, and adoption of the internal trivago Copilot assistant. ### Source excerpt Between 2023 and 2025, our approach to artificial intelligence shifted from isolated experimentation to company-wide integration. This transformation has delivered measurable productivity gains,... ## Personalization at Scale: How AI Will Rewrite the Marketing Playbook DevFeed: [Personalization at Scale: How AI Will Rewrite the Marketing Playbook]() Original publisher: [Read original article]() Author: JEFF GANGEMI, GROWTH MARKETING PRACTICE LEAD @ TOPTAL Published: 2025-10-07T22:00:00Z Content type: article Language: en Sources: [Toptal Blog]() Topics: [Artificial Intelligence](), [personalization](), [Automation](), [AI Strategy]() Tags: [ai](), [automation](), [b2b](), [growth](), [marketing](), [personalization](), [podcast](), [transformation]() ### AI overview This podcast episode examines how artificial intelligence could replace outdated B2B marketing automation practices with personalization at scale. Jon Miller discusses buyer experience, customer value, the balance between brand and demand, and strategies for building and scaling marketing technology companies. ### Source excerpt Jon Miller has spent his career building category-defining marketing platforms. Now, as co-founder and CEO of a stealth AI startup, he shares why the old marketing automation playbook is broken--and how AI will drive personalization at scale. ## Bean Machine Retrospective, part 8 DevFeed: [Bean Machine Retrospective, part 8]() Original publisher: [Read original article]() Author: ericlippert Published: 2023-02-23T20:54:17Z Content type: article Language: en Sources: [Eric Lippert]() Topics: [Code](), [Functional programming](), [Compiler](), [Code review]() Tags: [algorithm](), [code](), [compiler](), [functional](), [source](), [test](), [transformation](), [uncategorized]() ### AI overview This retrospective explains Beanstalk's combinator-inspired source code transformation system. It uses functional programming for AST transformations, with patterns, partial transformation rules, and combinators. The approach aims to make rewriting logic concise, testable, and easy to adapt to different traversal strategies. ### Source excerpt Before getting into the details of how my combinator-inspired source code transformation system works, I should say first, what is a general overview of the system? and second, why did I build it at all? In my experience, a typical ... Continue reading -> ## Wearing Many Hats DevFeed: [Wearing Many Hats]() Original publisher: [Read original article]() Author: Adam Published: 2022-01-24T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [Security](), [networking](), [Software]() Tags: [cryptographic](), [export](), [government](), [history](), [networking](), [report](), [security](), [software](), [surveillance](), [transformation]() ### AI overview The article reviews Wearing Many Hats: The Rise of the Professional Security Hacker, a history of how 1990s hackers established legitimacy as employable and trustworthy security experts. It praises the report while noting that early cypherpunks also addressed surveillance, activist targeting, and cryptographic export controls, with overlap between hacker and cypherpunk communities. ### Source excerpt Fascinating history of a transformation in how hackers were seen. ## Hands on Jetpack Compose VisualTransformation to create a phone number formatter DevFeed: [Hands on Jetpack Compose VisualTransformation to create a phone number formatter]() Original publisher: [Read original article]() Author: Julien Salvi Published: 2021-12-21T15:05:42Z Content type: tutorial Language: en Sources: [Stories by Julien Salvi on Medium]() Topics: [Jetpack Compose](), [Compose](), [Design system](), [ui](), [Google]() Tags: [aircall](), [android](), [android-app-development](), [androiddev](), [component](), [compose](), [constructor](), [design-system](), [filter](), [function](), [google](), [jetpack-compose](), [mapping](), [parameter](), [transformation](), [ui]() ### AI overview This tutorial explains how to use Jetpack Compose's VisualTransformation to format phone-number input for international display. It covers the filter() function, TransformedText, and OffsetMapping for mapping positions between original and transformed text. ### Source excerpt At Aircall, we are currently rewriting our design system with Jetpack Compose, the new UI framework made by Google. One of our UI component is a text input that formats a phone number to its international format according to its origin country. With the View UI toolkit, we can use a PhoneNumberFormattingTextWatcher, which is available since API 1, to format the input text. But no equivalent has been implemented with Jetpack Compose so far. In this article we are going to cover how we managed to create a reliable alternative for Compose with VisualTransformation. VisualTransformation explained First, let's have a look at the VisualTransformation interface to understand how it works under the hood and how you can use it to format the input text of a TextField as you wish. https://medium.com/media/bb42609ef6212c4b0f63f06f83c0534c/href In order to filter the text, you will need to implement the filter() function which takes the original text to transform as a parameter and returns a TransformedText where all the magic happens! If we take a look at the constructor it takes a text (which will be transformed) and an OffsetMapping which provides a bidirectional mapping between the original text and the new one. Which means that we'll need to implement the transformation from and to the original. Let's have a look at the password transformation that the framework provides to see the OffsetMapping in action: https://medium.com/media/5e50cd5af8fd0d3acd9300d03665735b/href Here, the implementation is pretty straightforward as we just need to transform the input text where every character will be replaced by a mask, so the text will have the same length. The transformed text will contain the mapped text and the offsetMapping will handle the transformation. Here, as we don't have any extra character to be displayed in the returned text, the two functions will return the original offset. The two transformations can become a bit more complex if we need to display additional characte ## Alive2 Part 3: Things You Can and Can't Do with Undef in LLVM DevFeed: [Alive2 Part 3: Things You Can and Can't Do with Undef in LLVM]() Original publisher: [Read original article]() Author: regehr Published: 2020-07-31T20:33:05Z Content type: tutorial Language: en Sources: [Embedded in Academia]() Topics: [LLVM](), [Compiler](), [C](), [C++](), [functions]() Tags: [c](), [c-plus-plus](), [compiler](), [compilers](), [computer-science](), [functions](), [llvm](), [software-correctness](), [transformation](), [undef]() ### AI overview This article explains how LLVM handles undef and poison values and why they complicate compiler transformations. It uses equivalent C and C++ functions to show that LLVM can replace addition with a shift under refinement, but cannot generally reverse that transformation. ### Source excerpt [Also see Part 1 and Part 2 in this series.] Let's talk about these functions: unsigned add(unsigned x) { return x + x; } unsigned shift(unsigned x) { return x << 1; } From the point of view of the C and C++ abstract machines, their behavior is equivalent: in a program you're writing, you [...] ## Loading Zip Codes with SmarterCSV Gem DevFeed: [Loading Zip Codes with SmarterCSV Gem]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-03-18T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Rails](), [CSV](), [Code](), [import](), [data]() Tags: [code](), [covidnearme-org](), [csv](), [data](), [import](), [rails](), [rake](), [trace](), [transformation](), [zip]() ### AI overview A Rails example shows how to load U.S. ZIP code data with the SmarterCSV gem while preserving leading zeros. It uses a rake task and excludes the ZIP field from numeric conversion because the documented transformation did not work as expected. ### Source excerpt Zip code data is always problematic due to the leading zeros. The smarter_csv gem in Rails defaults to automatically handling numbers as numerics, not strings, which means that you lose 00408 to just 408. Here's an example of a work around using a rake task to load data: # bundle exec rake data:import_geography --trace task :import_geography => :environment do path = File.join(Rails.root, 'lib/tasks/data/', 'Geocodes_USA_with_Counties.csv') geographies = SmarterCSV.process(path, { hash_transformations: [ :convert_values_to_numeric_unless_leading_zeros], convert_values_to_numeric: { except: [:zip] }} ) geographies.each do |g| g_obj = Geography.find_or_create(g) end end Note: This wasn't the accepted work around but a hack; sigh. The :convert_values_to_numeric_unless_leading_zeros transformation doesn't seem work. Issue filed; I hope to fix this myself down the road. ## The Phoenix Project DevFeed: [The Phoenix Project]() Original publisher: [Read original article]() Author: Tom Published: 2018-08-18T00:00:00Z Content type: article Language: en Sources: [Tomasz Gągor]() Topics: [DevOps](), [Learning](), [Software](), [Deployment]() Tags: [business](), [culture](), [deployment](), [devops](), [history](), [leadership](), [learning](), [management](), [novel](), [software](), [technology](), [transformation]() ### AI overview A review of The Phoenix Project, a narrative book about an IT manager who tries to save a failing software project through DevOps practices and organizational change. It explains the book's Three Ways, discusses its historical value, and notes that some referenced technologies and deployment methods are outdated. ### Source excerpt Projekt Feniks Powieść o IT, modelu DevOps i o tym, jak pomóc firmie w odniesieniu sukcesu Authors: Gene Kim, Kevin Behr, George Spafford This is the seminal work that introduced DevOps principles to a broader audience through narrative storytelling. The book follows an IT manager tasked with saving a failing software project by implementing DevOps practices and organizational change. Note ## Friday Q&A 2018-06-29: Debugging with C-Reduce DevFeed: [Friday Q&A 2018-06-29: Debugging with C-Reduce]() Original publisher: [Read original article]() Author: Mike Ash Published: 2018-06-29T13:35:00Z Content type: tutorial Language: en Sources: [Mike Ash]() Topics: [debugging](), [C](), [test](), [Shell](), [Code]() Tags: [c](), [code](), [debugging](), [dependencies](), [dockerignore-usage](), [how-to](), [shell-script](), [test](), [transformation]() ### AI overview This tutorial explains how C-Reduce automatically shrinks programs into reduced test cases. It covers reduction passes, scriptable interestingness tests, installation, and a simple C example for isolating a compiler warning or bug. ### Source excerpt Debugging a complex problem is tough, and it can be especially difficult when it's not obvious which chunk of code is responsible. It's common to attempt to produce a reduced test case in order to narrow it down. It's tedious to do this manually, but it's also the sort of thing computers are really good at. C-Reduce is a program which automatically takes programs and pares them down to produce a reduced test case. Let's take a look at how to use it. (Read More) [Next page]()