# Architectures Published articles for Architectures. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Our Transition to React Native DevFeed: [Our Transition to React Native]() Original publisher: [Read original article]() Author: Khan Academy Published: 2020-07-09T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [React Native](), [Android](), [iOS](), [Mobile](), [React]() Tags: [android](), [android-apps](), [architectures](), [backend](), [bridge](), [business-logic](), [change](), [code](), [engineering](), [ios](), [mobile](), [news](), [react-native]() ### AI overview Khan Academy describes completing its transition to React Native for its iOS and Android apps. The article explains why the team moved away from separate platform codebases and outlines the transition's exploration, straddling, and extinction phases. ### Source excerpt In 2017, Khan Academy started using React Native in our iOS and Android apps. As of this year, we've reached a big milestone: our transition to React Native is complete! Every screen in the app is rendered in React Native. ## Aiven, Confluent, Redpanda, StreamNative and Ververica Form Streamhouse Working Group DevFeed: [Aiven, Confluent, Redpanda, StreamNative and Ververica Form Streamhouse Working Group]() Original publisher: [Read original article]() Author: Streamhouse Working Group Published: 2026-09-15T07:00:00Z Content type: release Language: en Sources: [Confluent: Data in motion]() Topics: [data-architecture](), [real-time](), [data](), [Streaming](), [Artificial Intelligence]() Tags: [agents](), [ai](), [architectures](), [confluent](), [data](), [distributed](), [news](), [real-time](), [streaming]() ### AI overview Aiven, Confluent, Redpanda, StreamNative, and Ververica announced the Streamhouse Working Group and published a vendor-neutral definition of Streamhouse. The proposed data architecture is designed to keep business context continuously available to production applications, analytics, and AI agents, with real-time, production-native, and decentralized attributes. ### Source excerpt New industry initiative establishes an open category for data architectures that power real-time applications and AI agents ## How Solaris' Turnstile Influenced the Modern System Designs of Web Browsers and Language Runtimes DevFeed: [How Solaris' Turnstile Influenced the Modern System Designs of Web Browsers and Language Runtimes]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-09-15T06:06:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [systems](), [Programming](), [Kernel](), [observability](), [browsers](), [web browsers]() Tags: [architecture](), [architecture-design](), [architectures](), [blocking](), [concurrency](), [development](), [efficiency](), [kernel](), [locking](), [memory](), [news](), [observability](), [operating-systems](), [solaris](), [storage](), [systems](), [turnstile-system-design]() ### AI overview The article examines how Solaris innovations influenced modern systems engineering. It discusses the Slab Allocator, OpenZFS, DTrace, Zones, and turnstiles, focusing on how turnstiles reduce per-lock memory overhead while supporting priority inheritance for contended locks. ### Source excerpt Sun's Solaris influenced modern systems engineering, with key innovations like the Slab Allocator for efficient memory management, OpenZFS for advanced storage, and DTrace for observability. Its turnstile mechanism addressed issues with mutexes, promoting lean locking designs that are reflected in contemporary programming languages and web browsers, enhancing performance and memory efficiency. By Olimpiu Pop ## Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills DevFeed: [Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills]() Original publisher: [Read original article]() Author: Alex Porcelli Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Agent Skills](), [Artificial Intelligence](), [Large Language Model](), [NeMo]() Tags: [agent](), [agent-skills](), [agentic](), [agentic-ai-architecture](), [agents](), [ai](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [architectures](), [artificial-intelligence](), [business](), [decision-models-agentic-ai](), [development](), [enterprise](), [enterprise-architecture](), [governance](), [infoq](), [large-language-models](), [llms](), [ml-data-engineering](), [models](), [nemo](), [presentation](), [production](), [qcon-ai-boston-2026](), [qcon-software-development-conference](), [skills](), [transcripts]() ### AI overview Alex Porcelli explains how DMN decision models can be integrated with LLMs, agent skills, and NeMo guardrails to create auditable and deterministic agentic architectures for high-stakes enterprise decisions. ### Source excerpt Alex Porcelli discusses the critical gap in enterprise AI: non-deterministic output and lack of accountability in high-stakes decisions. He shares how integrating DMN decision models with LLMs, agent skills, and NeMo guardrails creates auditable, deterministic agentic architectures - allowing business leaders to own decision logic while engineers maintain robust architectural governance. By Alex Porcelli ## Data Engineering Weekly #287 DevFeed: [Data Engineering Weekly #287]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-09-14T02:52:23Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [Kafka](), [data observability](), [Embeddings](), [Multi-tenancy](), [Event-Streaming](), [OpenAI](), [Library](), [Code]() Tags: [architecture](), [architectures](), [data-engineering](), [data-quality](), [embedding](), [engineering](), [kafka](), [multi-tenancy](), [object-storage](), [observability](), [openai](), [streaming](), [vector]() ### AI overview Data Engineering Weekly #287 covers building data platforms from scratch, including composable architectures, data quality, and observability. It also previews talks on governed machine-executable ontologies for marketing activation and fair, order-preserving Kafka consumption for many tenants. The issue links to OpenAI's storage platform scaling for ChatGPT and Pinterest's embedding retrieval platform. ### Source excerpt The Weekly Data Engineering Newsletter ## BeOS-Inspired Haiku Now Supports Changing Audio Outputs Live, Other Improvements DevFeed: [BeOS-Inspired Haiku Now Supports Changing Audio Outputs Live, Other Improvements]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-11T00:55:29Z Content type: news Language: en Sources: [Phoronix]() Topics: [Operating system](), [USB](), [cpu]() Tags: [2026](), [architectures](), [audio](), [build-system](), [cpu](), [desktop-linux](), [development](), [kernel](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [os](), [phoronix](), [phoronix-test-suite](), [power-management](), [release](), [report](), [ubuntu-benchmarks](), [ubuntu-hardware](), [update](), [usb]() ### AI overview Haiku's August 2026 development report describes live audio-output switching, automatic stopping of inactive outputs, continued Bluetooth and USB audio work, power-management improvements, CPU feature-detection updates, kernel fixes, build-system updates, and progress on PowerPC and ARM64 support. ### Source excerpt In addition to August bringing the long-awaited Haiku R1 Beta 6 release, there was also a lot of development progress on this BeOS-inspired operating system too during the course of the past month... ## Get to VMware Cloud Foundation Faster with VMware Pro Cloud Services DevFeed: [Get to VMware Cloud Foundation Faster with VMware Pro Cloud Services]() Original publisher: [Read original article]() Author: paula swanson Published: 2026-09-03T20:21:10Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [Cloud]() Tags: [architectures](), [best-practices](), [cloud](), [cloud-infrastructure](), [cloud-services](), [infrastructure](), [modernization](), [private-cloud](), [vcf-professional-services](), [vmware](), [vmware-cloud-foundation]() ### AI overview The article presents VMware Pro Cloud Services as a way to deploy VMware Cloud Foundation faster and deliver a public-cloud experience on premises. It describes pre-packaged services for designing, deploying, configuring, integrating, and operationalizing a secure private cloud, using VMware reference architectures and practices developed through customer engagements. ### Source excerpt Public clouds have upped the ante when it comes to what we expect from private cloud solutions. Our smartphones and fast access to public cloud resources have conditioned us to expect instant access to what we need when we need it. Anything less feels antiquated and cumbersome, leaving your business users frustrated and disenchanted. To ... Continued The post Get to VMware Cloud Foundation Faster with VMware Pro Cloud Services appeared first on VMware Blogs. ## Equinix Inference Exchange Brings NVIDIA Compute and 200+ Open Models Closer to Enterprise Data DevFeed: [Equinix Inference Exchange Brings NVIDIA Compute and 200+ Open Models Closer to Enterprise Data]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-03T16:22:15Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AI Inference](), [Inference Performance](), [AI Infrastructure](), [model-serving](), [Open Source Models & Datasets](), [data centers](), [networking]() Tags: [ai](), [ai-inference](), [ai-infrastructure](), [architectures](), [data-centers](), [enterprise](), [hardware](), [inference](), [latency](), [networking](), [open-source](), [production]() ### AI overview Equinix Inference Exchange is a distributed AI inference platform that places NVIDIA compute and Together AI's open-model serving closer to enterprise data, users, and applications. It combines Equinix's interconnection infrastructure, NVIDIA hardware, and support for more than 200 open-source models to address latency, data sovereignty, networking complexity, and inference costs. ### Source excerpt Equinix has expanded its partnership with NVIDIA and entered a new collaboration with Together AI to launch Equinix Inference Exchange. Designed as a distributed AI inference architecture for enterprise deployments, the platform aims to shift compute workloads closer to core data repositories, end users, and operational applications. Announced alongside Equinix Fabric One at the Equinix The post Equinix Inference Exchange Brings NVIDIA Compute and 200+ Open Models Closer to Enterprise Data appeared first on StorageReview.com. ## How we built a benchmarking framework to horizontally accelerate transaction model research DevFeed: [How we built a benchmarking framework to horizontally accelerate transaction model research]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-03T13:53:30Z Content type: article Language: en Sources: [Nubank]() Topics: [benchmarking](), [Framework](), [Transformer](), [data]() Tags: [architectures](), [automated](), [benchmarking](), [data](), [data-science-machine-learning](), [experimentation](), [framework](), [model](), [performance](), [pipelines](), [training]() ### AI overview Nubank built an automated benchmarking framework for horizontally evaluating transformer-based transaction representation models across multiple downstream tasks and trials. The framework made experimentation reproducible and statistically rigorous, helping the team identify improvements that generalize across applications. It increased the team's capacity to run experiments by roughly five times per month while reducing operational overhead. ### Source excerpt The framework that transformed weeks of manual experimentation into automated pipelines for horizontal transaction model research The post How we built a benchmarking framework to horizontally accelerate transaction model research appeared first on Building Nubank. ## LTO Tape Shipments Up 57% in Q1 2026 as AI and Archive Demand Accelerate DevFeed: [LTO Tape Shipments Up 57% in Q1 2026 as AI and Archive Demand Accelerate]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-02T15:22:26Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [data](), [Disk image](), [Resilience](), [data centers](), [Artificial Intelligence](), [ibm]() Tags: [2026](), [ai](), [architectures](), [data](), [data-center](), [datasets](), [enterprise](), [enterprise-storage](), [hardware](), [ibm](), [report](), [resilience](), [retention](), [storage](), [training](), [upgrade]() ### AI overview LTO tape capacity shipments rose 57% year over year in Q1 2026, driven by continued LTO-9 adoption, the rollout of LTO-10, and growing enterprise data and archival needs. The article explains how tape supports economical cold storage, long-term retention, AI training datasets, and data center power constraints. ### Source excerpt The Linear Tape-Open (LTO) Program Technology Provider Companies, comprising Hewlett Packard Enterprise, IBM Corporation, and Quantum Corporation, have released their annual tape media shipment report. Following sustained enterprise data expansion, the report highlights a strong start to 2026, driven by continued LTO-9 adoption and the rollout and initial capacity ramp-up of LTO-10 media. According to The post LTO Tape Shipments Up 57% in Q1 2026 as AI and Archive Demand Accelerate appeared first on StorageReview.com. ## Hyperloop B: the coroutine engine behind Appwrite 2.0 DevFeed: [Hyperloop B: the coroutine engine behind Appwrite 2.0]() Original publisher: [Read original article]() Author: Luke B. Silver Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Coroutines](), [Concurrency](), [PHP](), [Benchmark]() Tags: [architectures](), [backend](), [benchmark](), [concurrency](), [coroutine](), [coroutines](), [cpu](), [memory](), [php](), [platform](), [process](), [redis]() ### AI overview The article explains Hyperloop B, a coroutine-based engine behind Appwrite 2.0. It describes how yielding during I/O lets one process serve many concurrent requests, reporting 7x higher requests per second and 6x lower memory use on an I/O-bound benchmark. ### Source excerpt Hyperloop B is the Swoole coroutine engine behind Appwrite 2.0. Here is what it is, the concurrency problem it solves, and the 7x I/O throughput it buys. ## Hot Chips 2026: Interviewing IBM's Christian Zoellin & Christian Jacobi DevFeed: [Hot Chips 2026: Interviewing IBM's Christian Zoellin & Christian Jacobi]() Original publisher: [Read original article]() Author: George Cozma Published: 2026-08-30T17:31:05Z Content type: article Language: en Sources: [Chips and Cheese]() Topics: [Arm](), [ibm](), [systems]() Tags: [2026](), [architecture](), [architectures](), [arm](), [core](), [decoding](), [ibm](), [interviewing](), [systems]() ### AI overview An interview with IBM engineers at Hot Chips 2026 discusses the company's next-generation z/Architecture processor, which supports both the z/Architecture and Arm instruction sets. The conversation covers its shared decode pipeline, separate instruction decoders, and handling of differing endianness in the load-store unit. ### Source excerpt Hello you fine Internet folks, ## The CI/CD moment for data analytics DevFeed: [The CI/CD moment for data analytics]() Original publisher: [Read original article]() Author: Gaurav Nanda Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [data analytics](), [DataOps](), [real-time](), [data-engineering](), [CI/CD](), [Kafka](), [apache-flink]() Tags: [ai](), [analytics](), [apache-flink](), [architectures](), [bridging](), [cost](), [data-analytics](), [data-engineering](), [engineering](), [event](), [fraud](), [google](), [insights](), [kafka](), [latency](), [real-time](), [scale](), [streaming](), [systems](), [tools]() ### AI overview The article argues that data analytics is approaching a CI/CD-like transition toward continuous analytics. It describes how multi-step pipelines introduce latency, operational risk, and maintenance burden, and explains why real-time insight is becoming a baseline platform capability for applications such as personalization, fraud detection, reliability, and AI-driven features. ### Source excerpt How converging OLTP and OLAP architectures are driving 'Continuous Analytics', the CI/CD moment for data to deliver real-time, unified insights and operational simplicity. ## 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... ## Applying OpenTelemetry Security Practices in Legacy Environments DevFeed: [Applying OpenTelemetry Security Practices in Legacy Environments]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-05-19T13:38:41Z Content type: article Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [Security](), [observability](), [telemetry](), [systems](), [Architecture & Design](), [Networks](), [Instrumentation]() Tags: [architectures](), [legacy](), [observability](), [opentelemetry](), [security](), [systems](), [telemetry]() ### AI overview This article explains how securing OpenTelemetry in legacy and industrial environments differs from securing cloud-native systems. It focuses on applying controls at appropriate points in the telemetry pipeline when source systems cannot be modified, instrumented, patched regularly, or protected with modern authentication and TLS. ### Source excerpt OpenTelemetry is gaining traction in manufacturing and other legacy environments as organizations explore modern observability approaches. However, applying these practices in traditional systems introduces a different set of security challenges. The constraints of legacy infrastructure fundamentally change where and how security controls must be applied. Legacy and industrial environments often include: Systems that cannot be modified or instrumented Long equipment life cycles and limited patching windows Flat or weakly segmented networks Sensitive operational data that is not typical PII This article focuses on what is different about securing OpenTelemetry in these environments, and how to adapt your approach accordingly. ## Embedded Finance Solutions: How SaaS Platforms Add Banking Features Without a License DevFeed: [Embedded Finance Solutions: How SaaS Platforms Add Banking Features Without a License]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-30T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Transactions]() Tags: [architectures](), [compliance](), [embedded-finance](), [financial-services](), [models](), [payments](), [providers](), [saas]() ### AI overview This guide explains how SaaS platforms can embed payments, payouts, lending, cards, and treasury features by partnering with regulated providers instead of becoming regulated financial institutions. It covers available features, technical architectures, legal responsibilities, partners, and revenue-sharing models. ### Source excerpt How SaaS platforms add embedded finance features (payments, payouts, lending, cards) without becoming a regulated bank. Architectures, partners, and revenue models. ## Embedded Payments for SaaS Platforms: A 2026 Implementation Guide DevFeed: [Embedded Payments for SaaS Platforms: A 2026 Implementation Guide]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-29T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [API](), [tokenization](), [ui](), [Mobile]() Tags: [2026](), [api](), [app](), [architectures](), [checkout](), [compliance](), [conversion](), [guide](), [integration](), [mobile](), [patterns](), [payment](), [payments](), [pci-dss](), [platform](), [regulatory](), [revenue](), [saas](), [subscription]() ### AI overview A 2026 guide to implementing embedded payments in SaaS platforms. It compares inline checkout, modal overlays, and raw card APIs, with attention to PCI compliance, regulatory obligations, fraud, settlement risk, support, and implementation patterns. ### Source excerpt How modern SaaS platforms add embedded payments without becoming a payments company. Architectures, compliance, revenue share, and implementation patterns. ## Razorpay's Linked Payments Architecture for Combining Payment Methods DevFeed: [Razorpay's Linked Payments Architecture for Combining Payment Methods]() Original publisher: [Read original article]() Author: Vatsal Mehta Published: 2026-04-08T09:56:17Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [Architecture & Design](), [Microservices](), [Transactions](), [Database]() Tags: [architectures](), [authorization](), [fintech](), [microservices](), [monolithic-architecture](), [payment](), [payment-gateway](), [payment-processing](), [payments](), [razorpay](), [recovery](), [technical](), [transactions]() ### AI overview Razorpay describes Linked Payments, an architecture that lets customers combine a gift card with card, UPI, or another payment method for one order. The article explains the resulting challenges around authorization sequencing, failure recovery, and split settlement across payment-method microservices. ### Source excerpt You have a $50 gift card. Your cart is $75. The checkout says "pick one payment method." This is a solvable problem. If you've ever tried to use a gift card for an online purchase, you know this frustration intimately. The gift card covers most of it, but not quite all. The checkout forces you to choose: use the gift card and abandon some items, or ignore the gift card and pay the full amount another way. Either choice feels wrong. This isn't a technical limitation of payment processing. It's an architectural one. Most payment systems treat each method as an isolated, complete transaction. You pay with a card OR UPI OR a gift card. The concept of composing multiple methods to fulfill a single order simply doesn't exist in traditional payment gateway architectures. At Razorpay, this limitation was costing merchants real money. Gift card redemption rates suffered because customers abandoned partial-value cards. Average order values stayed lower because customers couldn't combine store credit with additional payment. The business case for solving this was clear. That's why we built Linked Payments, a system that treats payment methods as composable building blocks. Customers can now use a gift card for $50, then cover the remaining $25 via card, UPI, or any other method. The system handles authorization sequencing, failure recovery, and settlement splitting automatically. The Complexity Hidden in "Just Combine Them" The challenge sounds simple until you consider what payment systems actually do. Traditional payment flows are beautifully simple. Customer initiates payment. System authorizes the full amount from one method. If authorization succeeds, capture the funds. Settle to the merchant. Either the payment worked or it didn't. One authorization, one capture, one settlement. Linked payments shatter this simplicity. Now you have multiple authorizations for a single order. Sequential dependencies where the second payment only happens if the first succeeds. Partial fail ## ESP-IDF v6.0: Default libc Switches from Newlib to PicolibC DevFeed: [ESP-IDF v6.0: Default libc Switches from Newlib to PicolibC]() Original publisher: [Read original article]() Author: John Lee Published: 2026-04-02T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [ESP-IDF](), [C](), [Embedded Systems](), [migration](), [Streams](), [Library](), [POSIX]() Tags: [apis](), [architectures](), [blog](), [c](), [compatibility](), [embedded](), [embedded-systems](), [esp-idf](), [firmware](), [libc](), [libraries](), [memory](), [migration](), [newlib](), [performance](), [picolibc](), [posix](), [streams](), [systems](), [toolchains]() ### AI overview ESP-IDF v6.0 changes its default C library from Newlib to PicolibC. The article compares the libraries' memory usage, standard I/O behavior, compatibility, and migration considerations, including measurements on ESP32-C3. ### Source excerpt ESP-IDF v6.0 switches the default C library from Newlib to PicolibC. This article compares both libraries in terms of memory usage, stdio behavior, compatibility, and migration tradeoffs, and explains when keeping Newlib still makes sense. ## CockroachDB Plans Native X.509 SAN Support for SPIFFE and SPIRE Integration DevFeed: [CockroachDB Plans Native X.509 SAN Support for SPIFFE and SPIRE Integration]() Original publisher: [Read original article]() Author: Sanchit Khanna,Biplav Saraf Published: 2026-03-13T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [Databases](), [SPIFFE](), [SPIRE](), [certificates](), [Zero Trust](), [Security](), [Microservice]() Tags: [architectures](), [auditability](), [aws-iam](), [certificates](), [cockroachdb](), [compliance](), [cryptographic](), [database](), [distributed](), [processor](), [regex](), [security](), [soc](), [soc-2](), [spiffe](), [spire](), [standards](), [teams](), [verification](), [zero-trust]() ### AI overview The article previews planned CockroachDB authentication support for Subject Alternative Name fields in X.509 certificates. It explains how SAN support is intended to enable integration with SPIFFE and SPIRE and support regex-based identity mapping for cloud-native workloads. ### Source excerpt In the evolution of cloud-native security, identity has become the new perimeter. ## Call relocation types DevFeed: [Call relocation types]() Original publisher: [Read original article]() Published: 2026-02-16T08:00:00Z Content type: article Language: en Sources: [MaskRay]() Topics: [x86](), [Assembly](), [Code]() Tags: [architectures](), [binutils](), [function](), [linker](), [static-linking](), [symbols](), [x86](), [x86-64]() ### AI overview This technical post explains why some architectures use separate ELF relocation types for direct function calls and tail calls. It contrasts static linking, where a PC-relative relocation can often be reused, with dynamic linking, where calls may use PLT indirection and therefore require relocation types that encode call semantics. ### Source excerpt Most architectures encode direct branch/call instructions with a PC-relative displacement. This post discusses a specific category of branch relocations: those used for direct function calls and tail calls. Some architectures use two ELF relocation types for a call instruction: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 # i386, x86-64 call foo # R_386_PC32, R_X86_64_PC32 call foo@plt # R_386_PLT32, R_X86_64_PLT32 # m68k bsr.l foo # R_68K_PC32 bsr.l foo@plt # R_68K_PLT32 # s390/s390x brasl %r14, foo # R_390_PC32DBL brasl %r14, foo@plt # R_390_PLT32DBL # sparc call foo, 0 # not PIC: R_SPARC_WDISP30 call foo, 0 # gas -KPIC: R_SPARC_WPLT30 ## Node.js vs Python vs Java: Choosing the Right Backend to Learn in 2026 DevFeed: [Node.js vs Python vs Java: Choosing the Right Backend to Learn in 2026]() Original publisher: [Read original article]() Author: Prachi Kothiyal Published: 2026-01-27T09:37:46Z Content type: comparison Language: en Sources: [Backend Archives | Talent500 blog]() Topics: [backend-development](), [Java](), [Node.js](), [Python](), [Development](), [Cloud](), [Microservice]() Tags: [architectures](), [backend](), [cloud-native](), [core-philosophy-how-each-backend-thinks](), [how-to-choose-the-right-backend-to-learn-in-2026](), [java](), [jobs-salaries-and-career-directions-in-2026](), [learning](), [learning-curve-and-developer-experience](), [microservices](), [newsletters](), [node-js](), [performance-scalability-and-ecosystem-strength](), [python]() ### AI overview This comparison examines Node.js, Python, and Java as backend technologies to learn in 2026. It contrasts their architectures, learning curves, performance, ecosystems, and career applications, describing Node.js as event-driven and non-blocking, Python as readable and approachable, and Java as focused on enterprise reliability, performance, security, and maintainability. ### Source excerpt Backend development has changed dramatically in the last decade. Modern companies now expect developers to understand cloud-native architectures, microservices, performance [...] The post Node.js vs Python vs Java: Choosing the Right Backend to Learn in 2026 appeared first on Talent500 blog. ## Long branches in compilers, assemblers, and linkers DevFeed: [Long branches in compilers, assemblers, and linkers]() Original publisher: [Read original article]() Published: 2026-01-25T08:00:00Z Content type: article Language: en Sources: [MaskRay]() Topics: [toolchain](), [Compiler](), [Assembly]() Tags: [architectures](), [article](), [assembler](), [binutils](), [compilers](), [implementation](), [linker](), [llvm](), [toolchain]() ### AI overview This article explains how compilers, assemblers, and linkers handle branch instructions whose PC-relative targets exceed the supported range. It describes the division of responsibility across toolchain stages and compares branch-range limitations across architectures, including AArch32, AArch64, and LoongArch. ### Source excerpt Branch instructions on most architectures use PC-relative addressing with a limited range. When the target is too far away, the branch becomes "out of range" and requires special handling. Consider a large binary where main() at address 0x10000 calls foo() at address 0x8010000-over 128MiB away. On AArch64, the bl instruction can only reach ±128MiB, so this call cannot be encoded directly. Without proper handling, the linker would fail with an error like "relocation out of range." The toolchain must handle this transparently to produce correct executables. This article explores how compilers, assemblers, and linkers work together to solve the long branch problem. Compiler (IR to assembly): Handles branches within a function that exceed the range of conditional branch instructions Assembler (assembly to relocatable file): Handles branches within a section where the distance is known at assembly time Linker: Handles cross-section and cross-object branches discovered during final layout ## Murat and Aleksey Read Papers: "Cloudspecs: Cloud Hardware Evolution Through the Looking Glass" DevFeed: [Murat and Aleksey Read Papers: "Cloudspecs: Cloud Hardware Evolution Through the Looking Glass"]() Original publisher: [Read original article]() Author: Aleksey Charapko Published: 2026-01-14T15:41:13Z Content type: opinion Language: en Sources: [Aleksey Charapko]() Topics: [Cloud](), [Amazon Web Services](), [Hardware](), [Benchmark](), [dataset]() Tags: [analysis](), [architectures](), [aws](), [benchmark](), [cloud](), [compute](), [cost](), [cpu](), [dataset](), [efficiency](), [graviton](), [hardware](), [network](), [other-thoughts](), [paper](), [papers](), [reading](), [summary]() ### AI overview This article reviews the CIDR paper "Cloudspecs: Cloud Hardware Evolution Through the Looking Glass," which examines AWS virtual hardware capabilities over ten years from a cost-efficiency perspective. The paper finds that cloud CPU cost efficiency improved about twofold, while core counts improved tenfold for non-Graviton offerings; network bandwidth cost efficiency improved substantially more. The article also notes limitations in the paper's analysis of memory bandwidth and specialized hardware features. ### Source excerpt The "Cloudspecs: Cloud Hardware Evolution Through the Looking Glass" CIDR paper by Till Steinert, Maximilian Kuschewski, and Viktor Leis was the first paper I and Murat read this year. It was a short, but interesting read. Below is our reading video and my one-paragraph summary. The paper discusses the evolution of AWS cloud (virtual) hardware [...] [Next page]()