# fragmentation Published articles for fragmentation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Modernizing the Trade Lifecycle With Governed Data and AI DevFeed: [Modernizing the Trade Lifecycle With Governed Data and AI]() Original publisher: [Read original article]() Author: Kim Hatton; Andrea DeSosa Published: 2026-09-17T14:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [data](), [Artificial Intelligence](), [databricks](), [decision-making](), [execution](), [Risk](), [systems]() Tags: [ai](), [data](), [databricks](), [decision-making](), [execution](), [financial-services](), [fragmentation](), [industries](), [modernization](), [real-time](), [risk](), [systems]() ### AI overview Capital-markets firms are modernizing trade lifecycle workflows as data volumes grow, AI initiatives move toward production, settlement cycles shorten, and regulatory scrutiny increases. The article argues that governed, discoverable, reliable data across research, trading, risk, operations, and compliance is more durable than isolated models. ### Source excerpt Capital-markets firms are modernizing the trade lifecycle under pressure from every direction: growing data volumes... ## IP Addressing, Part III: Inside the IP Packet DevFeed: [IP Addressing, Part III: Inside the IP Packet]() Original publisher: [Read original article]() Author: Franco Fernando Published: 2026-08-07T09:49:52Z Content type: tutorial Language: en Sources: [The Polymathic Engineer]() Topics: [Network](), [Protocol (disambiguation)]() Tags: [fragmentation](), [internet](), [ipv4](), [ipv6](), [network](), [payload](), [protocol]() ### AI overview This tutorial examines the IPv4 datagram and header fields, explains fragmentation and MTU constraints, discusses why TCP and IP use checksums, and describes changes introduced by IPv6. ### Source excerpt What actually travels on the wire: the IPv4 header, fragmentation, and what IPv6 changed. ## Discovered Stacks: One Place for All Your Infrastructure DevFeed: [Discovered Stacks: One Place for All Your Infrastructure]() Original publisher: [Read original article]() Author: Alejandro Cotroneo Published: 2026-07-30T00:00:00Z Content type: release Language: en Sources: [Pulumi]() Topics: [Infrastructure as code](), [migration](), [AWS CloudFormation](), [Azure](), [Terraform](), [Cloud]() Tags: [aws-cloudformation](), [azure](), [cloud](), [deployment](), [features](), [fragmentation](), [governance](), [infrastructure](), [insights](), [migration](), [product](), [pulumi-cloud](), [terraform]() ### AI overview Pulumi launches Discovered Stacks, a Pulumi Cloud capability that models AWS CloudFormation stacks and Azure Resource Manager deployments alongside Pulumi stacks. It tracks resource migration status from live state and supports migration into Pulumi management with verification before changes are made. ### Source excerpt Today we're launching Discovered Stacks: Pulumi Cloud now models your AWS CloudFormation stacks and Azure Resource Manager deployments as stacks, right alongside your Pulumi IaC stacks. And when you're ready to bring them under Pulumi management, migration is built in, with every resource tracked until the code provably matches the cloud. Why: your infrastructure doesn't live in one tool Almost nobody's cloud estate is a single technology. There's the CloudFormation that came with the AWS account, the ARM templates from the Azure team, the Terraform from an acquisition, and the Pulumi you're standardizing on. Each tool has its own console, its own grouping, its own idea of state, and no single place shows you everything you run. That fragmentation is also why migrations stall. Moving a stack to Pulumi has never been the hard part; knowing where you stand is. The tracking lives in a spreadsheet, the spreadsheet goes stale the day it's written, and six months later nobody can say which of the 800 resources made it across and which were quietly forgotten. Nothing gets lost Discovered Stacks gives you confidence that your migration plan or governance efforts will include all resources. This catches a common failure mode where resources are missed by your existing migration scripts or automations. When Pulumi Insights scans your accounts, every CloudFormation stack and ARM deployment becomes a discovered stack, and every resource in it appears as a row with an explicit migration status: ready to migrate, requiring review before migration, or already migrated. Every status is computed from live state on both ends -- what Pulumi manages and what the source tool reports -- so it's never a stale annotation someone forgot to update. Each resource shows its origin type (AWS::S3::Bucket) next to its Pulumi type (aws:s3/bucket:Bucket), with the origin properties side by side with Pulumi's view, so you can verify that Pulumi sees exactly what your source tool sees before you change ## Introducing the J2sw MTU and Encapsulation Calculator DevFeed: [Introducing the J2sw MTU and Encapsulation Calculator]() Original publisher: [Read original article]() Author: j2sw Published: 2026-07-28T16:37:53Z Content type: tutorial Language: en Sources: [Justin Wilson (j2sw)]() Topics: [Network](), [Tool](), [Ethernet](), [VXLAN]() Tags: [calculator](), [cisco](), [ethernet](), [fragmentation](), [gre](), [headers](), [ipsec](), [ipv4](), [ipv6](), [juniper](), [mikrotik](), [mpls](), [mtu](), [network-engineering-resources](), [network-tools](), [network-troubleshooting](), [overhead](), [payload](), [pppoe](), [vlan](), [vxlan]() ### AI overview The article introduces the J2SW MTU and Encapsulation Calculator, which estimates inner IP MTU and required underlay MTU for combinations of PPPoE, GRE, VLAN, MPLS, VXLAN, and other encapsulations. It explains how Layer 2 tags affect Ethernet frame size separately from IP MTU and describes overhead calculations, including cases where fragmentation can be avoided. ### Source excerpt Ever wondered what the effects of different combinations of PPPoE, GRE, and others have on the MTU of a packet? I am excited to announce the first of many J2 Network tools: the J2SW MTU and Encapsulation Calculator. You select the service MTU and the encapsulation used on the path. The calculator then works out ... Read more The post Introducing the J2sw MTU and Encapsulation Calculator appeared first on Justin Wilson (j2sw). ## DevOps Technologies in 2026: What Actually Matters DevFeed: [DevOps Technologies in 2026: What Actually Matters]() Original publisher: [Read original article]() Author: Eric Minick Published: 2026-07-22T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [DevOps](), [Software](), [Artificial Intelligence](), [ai-coding](), [GitHub Actions]() Tags: [ai-coding](), [ai-tools](), [automation](), [devops](), [fragmentation](), [governance](), [observability](), [software-delivery]() ### AI overview This article surveys DevOps technologies in 2026, describing a shift from specialized point tools toward unified platforms spanning software delivery, infrastructure, security, and observability. It argues that automation, observability, and governance are central priorities, and discusses how AI coding tools are changing delivery risks and practices. ### Source excerpt Explore the DevOps technologies transforming software delivery in 2026. Learn which tools matter, how AI is changing the game, and what to focus on next. | Blog ## Open rails for agentic commerce at Open Source Summit North America 2026 DevFeed: [Open rails for agentic commerce at Open Source Summit North America 2026]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-06-16T23:16:03Z Content type: opinion Language: en Sources: [Google Open Source Blog]() Topics: [Artificial Intelligence](), [Open Source](), [systems](), [Protocol (disambiguation)]() Tags: [2026](), [agentic](), [agentic-commerce](), [ai](), [ai-agents](), [business](), [capabilities](), [checkout](), [consumer](), [e-commerce](), [features](), [fragmentation](), [integration](), [interoperability](), [open-source](), [open-standards](), [payment](), [platforms](), [shopping](), [standard](), [ucp](), [universal-commerce-protocol](), [workflows]() ### AI overview An article from Open Source Summit North America 2026 explains why agentic commerce may require shared, open rules and integrations. It presents Universal Commerce Protocol (UCP) as an open standard intended to let agents, businesses, consumer surfaces, and payment providers work together across shopping, checkout, fulfillment, and post-purchase flows. ### Source excerpt by Anurag Sinha, Universal Commerce Protocol (UCP) At Open Source Summit North America 2026, I shared why agentic commerce needs open rails. As AI agents become more capable, the shopping journey is shifting from "show me" to "help me." Instead of browsing, comparing, clicking, and checking out step by step, people can increasingly ask an agent to help them decide what to buy and, in some cases, complete the purchase. Industry forecasts suggest agentic shopping could account for roughly 10% to 25% of U.S. e-commerce by 2030 (Bain), which points to a meaningful shift in how digital commerce will work. Watch the full keynote here. Why shared rules matter That shift also exposes a challenge. Commerce is still highly fragmented. Different businesses, payment providers, and platforms operate with their own rules, workflows, and business logic. Every new surface adds more integration work. Every bespoke connection creates more complexity. And that fragmentation makes it harder for AI systems to understand and perform commerce actions consistently across businesses. A shared language lowers that barrier for everyone. A common language for agentic commerce That is the problem Universal Commerce Protocol (UCP) is designed to solve. We launched the Universal Commerce Protocol, or UCP, with industry leaders to establish an open standard for agentic commerce, built to work across the shopping journey. UCP creates a common language for agents and systems to operate together across consumer surfaces, businesses, and payment providers, so the ecosystem does not need a different bespoke integration for every new agent or platform. Just as importantly, UCP is designed for the real world. Every business has its own way of selling. Checkout, fulfillment, loyalty, policy logic, shipping, and post-purchase flows can vary widely between a local shop, a marketplace, and a large retailer. UCP is built to support that reality. A layered architecture for a shared commerce language UCP uses a ## OSTEP Chapter 15: Address Translation DevFeed: [OSTEP Chapter 15: Address Translation]() Original publisher: [Read original article]() Author: Murat (noreply@blogger.com) Published: 2026-05-18T02:03:48Z Content type: article Language: en Sources: [Metadata]() Topics: [virtualization](), [Hardware](), [cpu](), [Processes](), [Kernel]() Tags: [book-chapters](), [cpu](), [fragmentation](), [hardware](), [memory](), [os](), [ostep](), [process](), [series](), [virtualization]() ### AI overview This chapter explains address translation through dynamic relocation, where hardware converts virtual addresses to physical addresses while the operating system maintains control. It covers base-and-bounds registers, exception handling, process context switching, and internal fragmentation. ### Source excerpt This is part of our series going through OSTEP book chapters. The OSTEP textbook is freely available at Remzi's website if you like to follow along. This chapter extends the CPU virtualization playbook to memory. It's the same recipe: let the program run directly on the hardware, but interpose at carefully chosen points so the OS retains control. For memory, this happens at every memory access. Every load, store, and instruction fetch gets translated by hardware from a virtual to a physical address. The mechanism here is called dynamic relocation, dating to the late 1950s. The base register holds the physical address where the process's address space starts, the bounds register holds its size. On every memory reference the hardware adds base and checks against bounds. If the address is out of range, the CPU raises an exception, and the OS kills the offender. This takes collaboration between hardware and the OS. Hardware provides privileged mode, the base/bounds registers, translation circuitry, exception generation, and privileged instructions to update the registers. The OS provides memory allocation (a free list, in the simplest case), base/bounds management across context switches, and the exception handlers themselves. Because there is only one base/bounds pair per CPU, the OS must save and restore them in the process control block(PCB). This means that while a process is descheduled, the OS can freely move its address space and then update the saved base. The process wakes up oblivious to this, hence the name dynamic relocation. The chapter is transparent about what base-and-bounds gets wrong. The relocated process gets a fixed-size slot, but its stack and heap occupy only a small fraction of it, which means that the space in between causes internal fragmentation. With every process getting the same fat slot regardless of actual footprint, the physical memory fills up quickly. The segmentation discussion, coming next chapter, aims to fix this. ## The Glue Problem in Modern AI Development DevFeed: [The Glue Problem in Modern AI Development]() Original publisher: [Read original article]() Author: James Skelton Published: 2026-04-02T21:30:07Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Artificial Intelligence](), [AI Platform](), [Development](), [Cloud](), [Deployment](), [Orchestration](), [Inference]() Tags: [ai](), [ai-development](), [ai-ml](), [cloud](), [deployment](), [developers](), [efficiency](), [engineering](), [fragmentation](), [infrastructure](), [orchestration](), [overhead]() ### AI overview The article examines how fragmented AI infrastructure creates a glue-code problem for developers. It argues that integrated cloud platforms can reduce integration work, operational overhead, and total cost of ownership when deploying and scaling AI applications. ### Source excerpt AI is now central to modern software development. Teams across industries are turning to AI to solve product and workflow problems in software. But building production systems is still complex. The hardest part of deploying AI isn't the model, it's everything around it. That complexity becomes a glue-code problem when storage, compute, orchestration, networking, authentication, and inference live in separate systems with different operating models. The more seams a workflow crosses, the more developer effort shifts from building product logic to wiring services together. A more integrated platform model reduces that burden. This article examines what it takes to deploy and operate AI applications in today's cloud landscape. Using two examples, we will compare the process in two landscapes: a neocloud combined with a hyperscaler versus a vertically integrated cloud stack. While surface-level costs may look similar, the integrated model offers clear advantages in efficiency by reducing the time developers spend writing glue code and managing the problems that emerge as AI products scale. Key Takeaways The biggest cost in AI systems isn't infrastructure: it's integration. Fragmented, multi-provider stacks force developers to spend time writing and maintaining glue code instead of building product features, turning engineering effort into the real cost center. Raw infrastructure pricing is no longer the differentiator; total cost of ownership is. Even when platform costs are nearly identical, the added complexity of cross-cloud orchestration increases operational overhead, failure points, and staffing requirements at scale. The future of AI platforms is vertical integration, not more tools. Platforms that unify compute, storage, and inference reduce friction, accelerate development, and allow smaller teams to build and scale AI applications more efficiently. The Real Problem Is Fragmentation Consider the modern landscape for AI deployment. AI applications rely on far mo ## How UUIDv4 Primary Keys Affect Database Insert Performance DevFeed: [How UUIDv4 Primary Keys Affect Database Insert Performance]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Database](), [MySQL](), [ordering]() Tags: [b-tree](), [database](), [database-performance](), [fragmentation](), [innodb](), [latency](), [mysql](), [performance](), [system-design](), [uuid]() ### AI overview This article explains how random UUIDv4 primary keys can reduce insert performance in clustered indexes. Random inserts land in the middle of B+ tree pages, causing page splits, fragmentation, additional writes, and buffer-pool churn. It recommends UUIDv7 or ULID, or an auto-increment clustered key with UUIDs in a unique secondary index. ### Source excerpt The problem with UUIDv4 primary keys is not uniqueness, it is that random keys fight the storage engine's physical layout. In a clustered index the primary key is the table, so random inserts land mid-page, trigger page splits, leave pages half-full, and can inflate a 50 GB dataset to roughly 100 GB while wasting buffer pool on empty space. Use UUIDv7 or ULID, or keep an auto-increment clustered key with the UUID in a unique secondary index. ## How Project Structure Helps AI Agents Write Consistent TypeScript Backends DevFeed: [How Project Structure Helps AI Agents Write Consistent TypeScript Backends]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-02-24T00:00:00Z Content type: article Language: en Sources: [Encore Updates]() Topics: [Artificial Intelligence](), [TypeScript](), [backends](), [Framework]() Tags: [ai](), [ai-agents](), [backend](), [fragmentation](), [framework](), [typescript]() ### AI overview The article argues that AI agents often create inconsistent TypeScript backends because projects offer many valid choices for frameworks, validation, databases, and infrastructure. It presents Encore.ts as a framework that uses typed APIs, declared infrastructure, and consistent service structure to guide agents toward existing project patterns and business logic. ### Source excerpt And what happens when you actually let them. ## 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 ## Thread Network Credentials Sharing DevFeed: [Thread Network Credentials Sharing]() Original publisher: [Read original article]() Author: John Lee Published: 2026-01-06T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Protocol (disambiguation)](), [interoperability](), [Matter](), [migration](), [User Experience](), [m5stack]() Tags: [blog](), [border-router](), [connectivity](), [demo](), [fragmentation](), [interoperability](), [m5stack](), [matter](), [migration](), [network](), [onboarding](), [thread](), [user-experience]() ### AI overview This article introduces Thread Credentials Sharing, a Thread 1.4 feature for securely sharing administrative access and network credentials with new Thread Border Routers and devices. It explains Operational Dataset credentials, deployment benefits, usage scenarios, and a demonstration using an M5Stack Thread Border Router and the SmartThings app. ### Source excerpt This article introduces the Thread Credentials Sharing feature, explains why it is important, how it works, and provides a demo using M5Stack Thread Border Router and SmartThings app. ## Project Mainline: How Google Reshaped Android Updates DevFeed: [Project Mainline: How Google Reshaped Android Updates]() Original publisher: [Read original article]() Author: Kirill Rozov Published: 2025-08-18T13:47:15Z Content type: article Language: en Sources: [Stories by Kirill Rozov on Medium]() Topics: [Android](), [Google](), [Operating system](), [samsung]() Tags: [android](), [android-app-development](), [androiddev](), [architecture](), [devices](), [fragmentation](), [google](), [os](), [samsung](), [update]() ### AI overview The article examines Android update delays caused by vendor customization and fragmentation, then introduces Project Mainline as part of Google's multi-year effort to modularize Android, reduce dependence on manufacturers, and deliver some features without waiting for a full OS release. ### Source excerpt Android is the most popular operating system in the world, powering devices from hundreds of manufacturers. Every year, Google releases a new version of Android, but only its own Pixel devices receive it immediately. Third-party vendors, such as Samsung, typically roll out the update to their flagship devices after about three months. A decade ago, this waiting period used to be as long as 9 to 12 months -- and many mid-range or budget devices never received updates at all. This naturally raises a question: will Android ever reach the same point as Apple, where new versions are released simultaneously for all supported devices? In this article, we'll explore how Google has gradually re-architected Android to shorten update delays, reduce dependency on device manufacturers, and eventually deliver new features directly -- without waiting for a new OS version. This multi-year journey led to one of the most important initiatives in Android's history: Project Mainline. The Fragmentation Problem When Android was first introduced, Google positioned itself as the developer of the OS, licensing its services to manufacturers rather than building its own hardware. Each vendor would take the base Android build from Google and heavily customize it: Replacing default apps with their own, Changing the visual design, Adding proprietary features, And often even modifying core Android mechanisms. While this strategy allowed Android to spread quickly, it created fragmentation. The market became flooded with devices running different OS versions, custom skins, and preinstalled apps. Watch my video with more information about solving the Fragmentation problem on YouTubeWhy Updates Took So Long For years, vendors controlled when and how their devices received new Android versions. The update process took anywhere from 6 to 18 months. In fact, a new Android version could already be released while a vendor was still working on adapting the previous one. And that was the best-case scenario -- ## How PayFit used Nx to revive its monorepo and improve development velocity DevFeed: [How PayFit used Nx to revive its monorepo and improve development velocity]() Original publisher: [Read original article]() Author: Juri Strumpflohner Published: 2025-07-24T00:00:00Z Content type: article Language: en Sources: [Juri Strumpflohner]() Topics: [monorepo](), [CI/CD](), [Development](), [Tooling](), [Deployment](), [Software as a service]() Tags: [ci-cd](), [company](), [customer-story](), [deployment](), [development](), [efficiency](), [europe](), [fragmentation](), [monorepo](), [nx](), [speed](), [startup](), [tooling](), [velocity]() ### AI overview This case study describes how PayFit's four-person platform team addressed monorepo abandonment, fragmented repositories, deployment complexity, and CI/CD inefficiencies by improving monorepo health with Nx and focusing on execution and support. ### Source excerpt Discover how a 4-person platform team at Payfit turned monorepo abandonment into a competitive advantage using strategic Nx implementation. ## How Blinkit's Droid Dex Adapts Android App Performance to Device Capabilities DevFeed: [How Blinkit's Droid Dex Adapts Android App Performance to Device Capabilities]() Original publisher: [Read original article]() Author: Karan Gourisaria Published: 2025-06-26T07:03:41Z Content type: article Language: en Sources: [Grofers]() Topics: [Android](), [Optimization](), [Development](), [real-time](), [Caching](), [User experience (UX)]() Tags: [adaptive](), [android](), [android-development](), [anr](), [app-performance](), [caching](), [engineering](), [fragmentation](), [kotlin](), [mobile-development](), [performance](), [real-time]() ### AI overview This article describes Blinkit's Droid Dex, a system that classifies device performance and adapts Android app behavior accordingly. It presents device fragmentation as a source of out-of-memory errors, slower screen rendering, and ANRs, and describes adaptations such as concurrency limits, caching levels, power-saving mode, and simplified animations. ### Source excerpt How Blinkit Cracked Android's Performance Puzzle with Droid DexAdaptive real-time performance tuning -- fewer ANRs, smoother UX, and smarter device-specific optimization Picture this: Your app runs buttery-smooth on Pixel 7 Pro while throwing ANRs on a Redmi Note 4. Users on a Fold 6 have to experience the same janky transitions as those on a INR 6,000 device. Sounds familiar? Welcome to Android development in 2025, where device fragmentation is one of the biggest challenges. This is the story of how Blinkit solved Android's most notorious problem: intelligent, real-time performance adaptation. 📱 The Problem: One Codebase, Infinite Devices Device Fragmentation isn't just a developer headache -- it's a business liability. At Blinkit, we serve millions of users across India's most diverse Android ecosystem, from ultra-budget to flagship devices. Consider these jaw-dropping stats from our production data: 57% of total OOMs occur on devices with less than 4GB of RAM The average time to render key screens is 2.5 times slower on budget phones compared to flagships 20% of users drop off after experiencing a single ANR Traditional solutions? They're all broken: 🔴 The Conservative Trap: Design for the weakest device. Result? Premium users get a subpar experience. 🔴 The Aggressive Fallacy: Optimize for flagships. Result? 60% of users face OOMs and ANRs. We needed something better -- something smarter, that could make apps think about performance in real-time. 🔥 Introducing: Droid Dex Imagine your app could sense the device it's running on and instantly adapt: "This phone can handle 4 concurrent videos, aggressive caching, and premium transitions" or "This device needs power-saving mode, minimal caching, and simplified animations"? That's exactly what Droid Dex does. It's not just another performance library -- it's an intelligent performance classification system that lets your app adapt to its environment. // Make your app performance-aware with a single call DroidDex.getPerformanc ## Juniper MX Jumbo Frames Can Cause Fragmentation on the PFE-RE Ethernet Link DevFeed: [Juniper MX Jumbo Frames Can Cause Fragmentation on the PFE-RE Ethernet Link]() Original publisher: [Read original article]() Published: 2025-06-10T05:41:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [Ethernet](), [networking](), [Routing (disambiguation)]() Tags: [blog](), [blog-post](), [ethernet](), [fragmentation](), [fragments](), [queue](), [routing](), [worth-reading]() ### AI overview The article explains that the Ethernet link between the Packet Forwarding Engine and Routing Engine in Juniper MX has a fixed 1500-byte MTU. Jumbo frames can therefore be fragmented, and dropped fragments may remain in the reassembly queue when control-plane policing intervenes. ### Source excerpt Did you know that there's an Ethernet link between the Packet Forwarding Engine (PFE - data plane) and Routing Engine (RE - control plane) in every Juniper MX? That's why you have to run two VMs to emulate it (sometimes conveniently packed into one larger VM, proving RFC 1925 rule 6a). That Ethernet link happens to have the MTU fixed at 1500 bytes. Guess what happens in the world where everyone uses jumbo frames? Did you say fragmentation? Bingo! And what do you think happens when one of those fragments gets dropped due to control-plane policing, and the rest of them are stuck in the reassembly queue? You'll find the gory details in a lengthy blog post by Nitzan Tzelniker. ## Hybrid Frameworks Can Serve Static and Server-Rendered Pages DevFeed: [Hybrid Frameworks Can Serve Static and Server-Rendered Pages]() Original publisher: [Read original article]() Published: 2025-05-08T00:00:00Z Content type: opinion Language: en Sources: [Dan Abramov]() Topics: [Framework](), [React](), [Astro](), [Cloudflare](), [Hugo](), [Jekyll]() Tags: [cdn](), [cloudflare](), [flexibility](), [fragmentation](), [hosting](), [hugo](), [jekyll](), [static]() ### AI overview The article argues that hybrid frameworks, which support both server and static output, can reduce tooling fragmentation and give developers more flexibility. It explains that static pages can be generated by running a server framework during the build process and storing the resulting responses. ### Source excerpt You wouldn't download a site. ## Thread Count Scaling Part 3. Zstandard DevFeed: [Thread Count Scaling Part 3. Zstandard]() Original publisher: [Read original article]() Author: Denis Bakhvalov Published: 2024-05-10T04:00:00Z Content type: article Language: en Sources: [Denis Bakhvalov]() Topics: [Compression](), [Concurrent Programming](), [data]() Tags: [benchmarks](), [book-chapters](), [case-study](), [compression](), [fragmentation](), [memory](), [memory-pool](), [performance](), [performance-analysis](), [thread](), [threads]() ### AI overview This article examines why Zstandard compression does not scale linearly with thread count. It discusses compression-level trade-offs, worker-thread interactions, waiting periods, input-buffer shortages, and memory-pool fragmentation, based on an instrumented timeline. ### Source excerpt Subscribe to my newsletter, support me on Patreon, Github, or by PayPal donation. This blog is an excerpt from the book. More details in the introduction. Next on our list is the Zstandard compression algorithm, or Zstd for short. When compressing data, Zstd divides the input into blocks, and each block can be compressed independently. This means that multiple threads can work on compressing different blocks simultaneously. ## Stressing the JVM GC on Android DevFeed: [Stressing the JVM GC on Android]() Original publisher: [Read original article]() Author: Amanjeet Singh Gurtatta Published: 2024-03-10T08:49:55Z Content type: tutorial Language: en Sources: [Amanjeet Singh]() Topics: [Android](), [Java](), [Code](), [now-in-android]() Tags: [adb](), [android](), [android-vitals](), [anr](), [fragmentation](), [gc](), [java](), [leak](), [memory](), [memory-leak](), [mobile](), [mobile-performance](), [now-in-android](), [outofmemory](), [performance](), [stress](), [stresstest]() ### AI overview This tutorial describes GCStress, a Java application that deliberately creates garbage-collection pressure through repeated random byte-array allocations and cache removals. It explains how to integrate it into Google's Now in Android app, trigger it with an Android broadcast and adb, and observe effects such as skipped UI frames, possible ANRs, and potential out-of-memory errors. ### Source excerpt This post talks about a way to stress Java garbage collector. ## Remove Application Logic from Kubernetes Config DevFeed: [Remove Application Logic from Kubernetes Config]() Original publisher: [Read original article]() Author: Dylan Anthony Published: 2024-03-05T09:09:00Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [Microservice](), [GraphQL](), [Kubernetes](), [YAML](), [API](), [Architecture & Design]() Tags: [apollo-federation](), [backend](), [bugs](), [fragmentation](), [gateway](), [graphql](), [ingress](), [kubernetes](), [yaml]() ### AI overview This article explains how duplicating microservice application logic in Kubernetes YAML can cause fragmented information, inconsistencies, and bugs. It presents GraphQL and Apollo Federation as a way to reduce configuration, prevent conflicting routes, keep routing aligned with application code, and make service dependencies declarative. ### Source excerpt The traditional approach to microservices in Kubernetes requires duplicating application logic into YAML config files. This leads to information fragmentation, inconsistencies, and ultimately bugs. With GraphQL and Apollo Federation, you can reduce the amount of required config, prevent duplication, and have a better experience maintaining your API. In particular, Apollo Federation can: - Prevent conflicting API routes, and give better visibility into the entire API. ## 10 Years of Advocacy as a Google Developer Expert DevFeed: [10 Years of Advocacy as a Google Developer Expert]() Original publisher: [Read original article]() Author: Mike Wolfson Published: 2024-01-04T11:18:00Z Content type: opinion Language: en Sources: [My Big Appetite - Mike Wolfson]() Topics: [Android](), [android-development](), [coding-community](), [Development](), [Google](), [Jetpack](), [Kotlin](), [dev-tools](), [Product Management](), [User interface design](), [Learning](), [hackathons]() Tags: [android](), [android-development](), [careers](), [conferences](), [developer](), [developer-community](), [developers](), [development](), [engineering](), [fragmentation](), [gdg](), [google](), [jetpack](), [kotlin](), [mentor](), [product](), [technical]() ### AI overview A Google Developer Expert reflects on a decade of Android advocacy, describing changes in Android development and work supporting developer communities through educational content, conferences, mentoring, and collaboration with Google engineering and product teams. ### Source excerpt I was just renewed for my 10th year as a Google Developer Expert (GDE) for Android, and I want to reflect on my experience in the program. Being a GDE is incredibly impactful to my life and I am super grateful to be involved. Excited for my 10th year in the gde program During my decade in the program, the landscape of Android development has changed dramatically. From the early days of fragmentation to the rise of Kotlin and Jetpack, each new chapter brought new challenges and opportunities for advocacy. From the start, I knew that being a GDE wasn't just about staying up to date on the latest technologies; it was about building bridges, fostering communities, championing the voices of developers and inspiring others to grow their own skills and careers. ME IN THE BLUE HOODIE WITH A LOT OF VERY YOUNG ANDROID GDE'S IN 2014 Helping the Developer Community Thrive As a successful GDE, I set out to break down complex concepts into something that is accessible to everyone. Over that time, my inspiration led me to write blog posts/tutorials, create videos, speak at conferences and even write a book. I continue to embrace every opportunity to build bridges between the technical intricacies of technology and the minds of the developer community. The Android community is incredibly strong. I still actively participate in online forums, organize local developer groups, and mentor aspiring developers. I continue to collaborate directly with the engineering and product teams at Google, and use these opportunities to champion the voice of the common developer. Seeing the community grow, learn from each other, and build amazing things together has truly been the most rewarding aspect of my GDE journey. Speaking SPEAKING at GDE Worldwide Summit (google conference center\sunnyvale, ca) Google has never limited my topics for presentations or questioned my content, allowing me full freedom to share what I know. I feel so fortunate that many organizers are interested in the subjects I ## Android Fragmentation: How Google Addressed It and Why Version Updates Matter Less DevFeed: [Android Fragmentation: How Google Addressed It and Why Version Updates Matter Less]() Original publisher: [Read original article]() Author: Kirill Rozov Published: 2023-10-23T01:40:30Z Content type: opinion Language: en Sources: [Stories by Kirill Rozov on Medium]() Topics: [Android](), [Google](), [Mobile](), [Operating system](), [Open Source]() Tags: [android](), [android-13](), [applications](), [data](), [development](), [devices](), [fragmentation](), [google](), [google-play](), [mobile](), [open-source](), [os](), [programming](), [treble]() ### AI overview An opinion article examines Android fragmentation, its historical causes, Google's efforts to address it, and why independent updates have made device Android-version updates less important. It uses Android's Photo Picker as an example of functionality extended to older devices through Google Play Services. ### Source excerpt Android Fragmentation state. Did Google fix it?Image generated by Kandinsky 2.2 In Android 13 a new feature called Photo Picker appeared. It allows applications to access individual user photos and videos without giving them full access to the photo/video or the entire memory of the device. This is a good feature for limiting an application's access to a user's personal data. It was immediately announced that this feature would also work on Android 11 and newer (with support for Mainline updates), and later its support was added to all devices with Android 4.4 and above via Google Play Services. I immediately asked myself, why hasn't it been done before? And the answer is simple -- Android didn't allow to implement this, because Google was struggling with fragmentation and working on the possibility of an independent update of Android, it became a reality. In this article, I share my opinion about Android fragmentation. You will learn what it is, the reasons for its appearance, and why it is no longer so relevant. I'll also tell you about the ways Google did with this problem in the first 10 years of Android's existence and why device updates to newer versions of Android will not be as important as before. The article is my own opinion and it can be wrong, but I open to discuss it in commentsBrief History of Android OS To understand the problems that OS developers faced, we need to go back in time. The history of Android begins in 2004. Andy Rubin decided to develop an operating system for digital cameras, which were very popular at the time. However, due to funding problems and the pretty small market for digital cameras, the system was refocused on smartphones as a competitor to Symbian and Windows Mobile. In 2005, the company was acquired by Google. The Good Corporation decided to enter the mobile phone market by selling its OS to device manufacturers. In 2007, the Open Handset Alliance was created, and at the same time, Android was announced as an open-source mob ## Delivering personalized travel experiences with GraphQL DevFeed: [Delivering personalized travel experiences with GraphQL]() Original publisher: [Read original article]() Author: Tushar Bhushan Published: 2023-07-14T12:47:41Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [GraphOS](), [Microservice](), [data]() Tags: [apis](), [apollo-federation](), [architectures](), [dependencies](), [fragmentation](), [graphos](), [graphql](), [recommendations](), [rest-apis](), [services](), [state]() ### AI overview This article explains how Apollo GraphOS and Apollo Federation can provide a unified view of a traveler's data while preserving microservice architectures. It contrasts this federated approach with REST-based aggregation, where clients make multiple requests and manage data dependencies and business logic. ### Source excerpt This post is a part of our "How to build connected travel apps with Apollo GraphOS" series. Also in this series: - Ship products faster with SDUI - Mitigate scraping and bot attacks with GraphOS - Seamlessly integrate with partners to enhance the travel experience In the ever-evolving travel industry, companies are recognizing the value of delivering personalized experiences to their customers. ## Are Kubernetes Validating Admission Policies the end of admission controllers? DevFeed: [Are Kubernetes Validating Admission Policies the end of admission controllers?]() Original publisher: [Read original article]() Published: 2023-03-31T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Kubernetes](), [supply-chain-security](), [API](), [Structured-data]() Tags: [admission-controller](), [api](), [api-server](), [blog-post](), [chainguard-enforce](), [common-expression-language](), [complexity](), [deployment](), [fragmentation](), [integration](), [kubernetes](), [kubernetes-admission-controller](), [performance](), [policy](), [security-policies](), [software-supply-chain](), [supply-chain](), [validation]() ### AI overview The article explains how Kubernetes Validating Admission Policies, introduced in alpha in Kubernetes 1.26, let users evaluate many admission checks natively in the API server using Google's Common Expression Language (CEL). It argues that these policies improve performance, reliability, and integration by reducing reliance on webhooks, while noting that admission controllers remain necessary for policies beyond CEL's deliberately restricted capabilities. ### Source excerpt Validating Admission Policies are here in Kubernetes 1.26. Read on to learn how they work and what they mean for admission controllers. [Next page]()