# Compiler A compiler translates source code through intermediate representations into target-specific code. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Mojo 1.1 Released, Now Accepting Community Contributions To The Compiler DevFeed: [Mojo 1.1 Released, Now Accepting Community Contributions To The Compiler]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-17T16:26:41Z Content type: release Language: en Sources: [Phoronix]() Topics: [releases](), [Compiler](), [Code](), [Development](), [Nvidia](), [Qualcomm](), [GitHub]() Tags: [ai](), [amd](), [code](), [compiler](), [contributions](), [desktop-linux](), [development](), [github](), [language](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [nvidia](), [open](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [qualcomm](), [releases](), [standard-library](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview Mojo 1.1 and MAX 26.6 have been released. Mojo 1.1 adds inferred member references, improved compile times, better generated code performance, and more stable standard-library APIs, while the development process now accepts community contributions to the compiler. MAX 26.6 adds audio generation support and kernel speedups across AMD and NVIDIA GPUs. ### Source excerpt Last month Modular's Mojo language was open-sourced following the acquisition of Modular by Qualcomm. Modular was the AI startup founded by Chris Lattner of LLVM and Swift fame. Released today was Mojo 1.1 and also marks the project recently now beginning code contributions from the community to its compiler... ## Intel Compute Stack Enables LEO For Nova Lake, Introduces Intel Portable ISA "PISA" DevFeed: [Intel Compute Stack Enables LEO For Nova Lake, Introduces Intel Portable ISA "PISA"]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-17T13:05:07Z Content type: release Language: en Sources: [Phoronix]() Topics: [intel](), [Nova](), [OpenCL](), [GPU](), [Linux](), [LLVM](), [Compiler](), [parallel]() Tags: [desktop-linux](), [gpu](), [intel](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [llvm](), [nova](), [open-source-graphics](), [opencl](), [phoronix](), [phoronix-test-suite](), [release](), [runtime](), [ubuntu-benchmarks](), [ubuntu-hardware](), [update]() ### AI overview Intel Compute Runtime 26.35.39758.10 adds several GPU compute features and enables the OpenCL LEO driver by default for upcoming Nova Lake S processors on Linux. The release also introduces initial support for Intel's Portable ISA (PISA) format. ### Source excerpt Released today was the Intel Compute Runtime 26.35.39758.10 update for Windows and Linux systems. With this updated OpenCL and Level Zero GPU compute driver stack comes some exciting updates for this monthly feature update... ## Adafruit's New CircuitPython 'Turbo' Brings Native Code To Tiny Boards DevFeed: [Adafruit's New CircuitPython 'Turbo' Brings Native Code To Tiny Boards]() Original publisher: [Read original article]() Author: EditorDavid Published: 2026-09-17T02:34:00Z Content type: news Language: en Sources: [Slashdot]() Topics: [CircuitPython](), [Microcontroller](), [MicroPython](), [Python](), [Compiler](), [Hardware](), [Open Source]() Tags: [benchmarks](), [circuitpython](), [code](), [compiler](), [examples](), [hardware](), [micropython](), [open-hardware](), [opensource](), [optimized](), [programming](), [programming-language](), [python]() ### AI overview Adafruit has published CircuitPython Turbo, a workflow that compiles selected Python functions into native machine code for compatible microcontroller boards while leaving the rest of an application in Python. In a documented Mandelbrot benchmark, the compiled computation ran 19.71 times faster than bytecode, though the result applies to the measured computation rather than the whole application. ### Source excerpt Targeting students and beginners, Adafruit released "CircuitPython" in 2017 (as a derivative of the MicroPython microcontroller-optimized programming language). Now Adafruit managing director Phillip Torrone (also long-time Slashdot reader ptorrone) brings this update: Adafruit has published CircuitPython Turbo, a workflow that compiles selected Python functions into native machine code on a computer, then loads them onto compatible microcontroller boards. It builds on MicroPython's Native and Viper emitters. In a documented Metro RP2040 fixed-point Mandelbrot test, Viper cut computation time from 8.335 seconds to 0.423 seconds, a 19.71x speedup over bytecode. The rest of the application stays in Python. The guide includes benchmarks, source code and hardware demos. The speedup is for the measured computation, not the whole application. "Turbo support is now included in the latest official CircuitPython builds for RP2040 and RP2350 boards..." explains Torrone's announcement at Adafruit.com. "The new Turbo in CircuitPython helps when the board spends time calculating: making neopixel effects, drawing fractals, processing audio, filtering sensor readings, or preparing lots of pixels. Those projects can get smoother animation, quicker responses, or room to do more things at once." With Turbo, it's easier, better, and now even faster to make LED light up costumes that also reacts to sound at the same time, a sensor dashboard with animated graphics, or a tiny game doing physics while drawing the screen. Turbo speeds up the busy Python parts. It won't make a slow sensor or display connection faster... Your computer turns selected functions into instructions the chip can run directly. Python still handles the rest. We have measured speedups, real display captures, and examples you can pull apart to see what happened. None of this arrived alone. CircuitPython, MicroPython, PyMCU, compiler tools, open hardware, and people sharing their work gave us pieces to connect. The Bao ## How to Write a Linux Kernel Module That Actually Builds DevFeed: [How to Write a Linux Kernel Module That Actually Builds]() Original publisher: [Read original article]() Author: Chris Roy Published: 2026-09-15T21:10:57Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [Kernel](), [Linux](), [modules](), [Compiler](), [Secure Boot]() Tags: [compiler](), [kernel](), [linux](), [linux-kernel](), [module](), [secure-boot](), [tutorial]() ### AI overview This tutorial explains how to build and load a Linux kernel module, including the generated object files, metadata, symbols, debug information, required kernel headers and compiler setup, and common Secure Boot and kernel lockdown restrictions. ### Source excerpt A Linux kernel module is a small piece of code that can be loaded into the running kernel without rebuilding the entire kernel. That sounds simple enough, but even a minimal module produces a surprisi ## Generating Java bytecode: Write yourself a compiler, Part V DevFeed: [Generating Java bytecode: Write yourself a compiler, Part V]() Original publisher: [Read original article]() Published: 2026-09-14T22:00:00Z Content type: tutorial Language: en Sources: [Tomasz Nurkiewicz around Java and concurrency]() Topics: [Java](), [Compiler](), [Programming]() Tags: [bytecode](), [compiler](), [file](), [go](), [interpreter](), [java](), [jvm](), [virtual-machine](), [writing-compiler]() ### AI overview This tutorial explains how to extend a toy-language compiler to emit valid JVM bytecode in a .class file that can run on the Java Virtual Machine. It introduces JVM instruction families for pushing integer values and explains how the constant pool stores constants and symbolic references for reuse. ### Source excerpt Last time we created a virtual machine for our toy language. I think you can agree that designing a language which barely recognizes expressions like 2 + 3w and building a brand-new virtual machine for it seems a bit tedious. So what about keeping our microscopic language for now, but running it on a real, production-ready, battle-proven virtual machine? Like the Java Virtual Machine? Our task for today is to emit JVM bytecode in the form of a valid .class file. That file can then be fed directly to the JVM to run our program. ## Linux 7.4 Could End Up Seeing Kernel Builds ~36% Faster, Incremental Builds ~70% Faster DevFeed: [Linux 7.4 Could End Up Seeing Kernel Builds ~36% Faster, Incremental Builds ~70% Faster]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-14T10:31:16Z Content type: news Language: en Sources: [Phoronix]() Topics: [Linux](), [build performance](), [build times](), [Arm](), [Compiler](), [Git](), [Artificial Intelligence]() Tags: [ai](), [arm](), [build-times](), [compiler](), [desktop-linux](), [git](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview A second revision of Linux kernel patches could make full allmodconfig builds about 36% faster and incremental builds about 70% faster. The work, led by Lorenzo Stoakes of Arm, addresses single-threaded bottlenecks and includes fixes, an upstream rebase, and an optional parallel Rust compiler front end. The patches are being considered for inclusion in Linux 7.4. ### Source excerpt Earlier this month I wrote about a patch series posted to the Linux kernel mailing list that addressed a lot of "hideous code" to make Linux kernel builds faster. A number of single-threaded bottlenecks were tracked down and fixed within the Linux kernel thanks to the assistance of AI. A second revision of those patches hit the mailing list this morning and there is hope they could be upstreamed for Linux 7.4... ## Kotlin 2.4.20 Collection Checks and Experimental Context-Sensitive Resolution DevFeed: [Kotlin 2.4.20 Collection Checks and Experimental Context-Sensitive Resolution]() Original publisher: [Read original article]() Author: Andrii Dubovyk Published: 2026-09-14T06:32:51Z Content type: tutorial Language: en Sources: [ProAndroidDev - Medium]() Topics: [Kotlin](), [Compiler](), [Code]() Tags: [android](), [android-development](), [collections](), [compiler](), [experimental](), [features](), [kotlin](), [list](), [mobile-app-development](), [syntax](), [technology]() ### AI overview The article explains two Kotlin features: allDistinctBy and allEqualBy in Kotlin 2.4.20 for checking collection values, and experimental context-sensitive resolution introduced in Kotlin 2.2 to reduce repetition when the expected type is known. ### Source excerpt Kotlin 2.4.20 gives your collections opinions, and 2.2's compiler finally learns to read the room Some Kotlin features get a lot of attention when they arrive. Others are much less noticeable, but still make everyday code a little easier to write. Two examples of the latter are allDistinctBy/ allEqualBy in Kotlin 2.4.20 and context-sensitive resolution, which was introduced experimentally in Kotlin 2.2. allDistinctBy/ allEqualBy: A Simpler Way to Check Collections For years, checking whether all values in a collection were unique often looked something like this: val users = listOf( User(name = "A", age = 20), User(name = "B", age = 25), User(name = "C", age = 30) ) users.map { it.age }.distinct().size == users.size // true It works, but it creates another list, removes duplicates, counts the result, and then compares the sizes just to get a Boolean answer. Kotlin 2.4.20 adds a more direct way to express the same check: users.allDistinctBy { it.age } // true users.allEqualBy { it.age } // false allDistinctBy returns false as soon as it finds a duplicate, so it doesn't need to process the entire collection or create a separate Set. allEqualBy does the opposite: it checks whether all elements produce the same value when passed through the selector. Both functions work with Iterable, Sequence, and arrays. They use structural equality. For floating-point values, they follow Double.equals semantics, so NaN is considered equal to NaN, while -0.0 is different from 0.0. They're still @ExperimentalStdlibApi, so you'll need to opt in before using them in production code. The underlying allDistinct() and allEqual() requests have also been around in YouTrack for quite a while, which makes their appearance in the standard library feel long overdue. Context-Sensitive Resolution: Less Repetition in when The second feature is more about syntax. When the compiler already knows the type you're working with, you can sometimes leave out the type qualifier. Before: enum class Problem { ## Faster Starts, Less JavaScript Overhead DevFeed: [Faster Starts, Less JavaScript Overhead]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [JavaScript](), [Compiler](), [Instrumentation]() Tags: [benchmarks](), [compiler](), [instrumentation](), [javascript](), [profiling]() ### AI overview The article describes startup-performance work in Codename One. It identifies launch delays caused by repeated screen-scale queries, theme scans, unnecessary synchronous dispatches, premature GC-park handshakes, and JavaScript suspension preparation. The fixes publish screen state atomically, avoid waiting when operations can proceed immediately, index theme keys, and improve instrumentation so profiling reveals hidden stalls. ### Source excerpt Codename One removes native startup waits, repeated style scans, and unnecessary JavaScript suspension. Profiles and compiler benchmarks expose costs that bundle size and frame rates miss. ## HeyGen x Google Cloud: Bringing Avatar IV to TPUs DevFeed: [HeyGen x Google Cloud: Bringing Avatar IV to TPUs]() Original publisher: [Read original article]() Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Google](), [Cloud](), [Artificial Intelligence](), [Optimization](), [AI Infrastructure](), [Transformer](), [PyTorch](), [Compiler](), [Streaming](), [real-time](), [Hardware](), [API]() Tags: [ai](), [ai-infrastructure](), [api](), [cloud](), [code](), [compiler](), [engineering](), [generation](), [google](), [hardware](), [model](), [optimization](), [performance](), [pytorch](), [real-time](), [streaming](), [time](), [tpu](), [transformers](), [video]() ### AI overview HeyGen and Google Cloud describe porting the 18B+ parameter Avatar IV talking-head video generation pipeline to an eight-chip Trillium TPU host. Using torchax, JAX, XLA, FSDP sharding, Ulysses sequence parallelism, and custom Pallas kernels, the team improved performance by 1.86x for real-time chunked streaming while preserving output quality through strict quality gates. ### Source excerpt HeyGen ported their 18B+ parameter Avatar IV video generation model to Google Cloud's Trillium (v6e) TPUs via torchax and XLA, utilizing FSDP and Ulysses sequence parallelism across an eight-chip mesh. To achieve a 1.86x speedup for real-time streaming, the engineering team pipelined exposed all-to-all collectives, aligned sparse attention block sizes to eliminate mask padding, and bypassed softmax serial dependencies using a precomputed Cauchy-Schwarz upper bound. These custom Pallas kernel and compiler optimizations were deployed only after passing rigorous two-tier quality gates to guarantee byte-identical or mathematically equivalent pixel outputs. ## tsgolint Reaches Stable v7, Bringing Go-Powered Type-Aware Linting to Oxlint DevFeed: [tsgolint Reaches Stable v7, Bringing Go-Powered Type-Aware Linting to Oxlint]() Original publisher: [Read original article]() Author: Daniel Curtis Published: 2026-09-11T12:02:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [ESLint](), [Compiler]() Tags: [benchmarks](), [development](), [eslint](), [go-language](), [javascript](), [news](), [performance](), [release](), [speed](), [tsgolint-oxlint-typescript](), [typescript](), [web-development]() ### AI overview tsgolint v7 is now stable, adding Go-powered, type-aware TypeScript linting to Oxlint. It supports 59 of 61 typescript-eslint type-aware rules and is reported as 12 to 18 times faster than ESLint in the cited benchmarks. ### Source excerpt tsgolint has released a stable v7, enhancing TypeScript linting with native Go speed. It offers type-aware linting, leveraging TypeScript's semantic analysis through the typescript-go compiler. Oxlint manages configurations and file discovery. The release, compatible with TypeScript 7.0.2, handles 59 of 61 type-aware rules and shows significant performance improvements over ESLint. By Daniel Curtis ## GCC 13.5 Released With 265+ Bug Fixes DevFeed: [GCC 13.5 Released With 265+ Bug Fixes]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-11T09:47:06Z Content type: news Language: en Sources: [Phoronix]() Topics: [gcc](), [Compiler](), [bug](), [cpu](), [intel]() Tags: [amd](), [bug-fixes](), [bugs](), [compiler](), [cpu](), [desktop-linux](), [gcc](), [intel](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [production](), [release](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview GCC 13.5 has been released with more than 265 bug fixes. It is the final planned point release for the GCC 13 branch, which is now closed to further development. ### Source excerpt For those continuing to rely on the GCC 13 compiler in production that debuted more than three years ago, GCC 13.5 released today with more than 265 bug fixes. GCC 13.5 also caps off the GCC 13 series as the last planned point release... ## Why is the x86 undefined instruction called ud2? Why 2? DevFeed: [Why is the x86 undefined instruction called ud2? Why 2?]() Original publisher: [Read original article]() Author: Raymond Chen Published: 2026-09-10T14:00:00Z Content type: article Language: en Sources: [Raymond Chen]() Topics: [x86](), [Assembly](), [Compiler](), [Exception](), [intel]() Tags: [compiler](), [exception](), [intel](), [old-new-thing](), [other](), [processor](), [x86]() ### AI overview The article explains why the x86 undefined instruction is called ud2. It describes how software used byte sequences that reliably raised an invalid opcode exception, how Intel later provided a standardized instruction, and how compiler output can use it to mark unreachable code and cause a deliberate crash. ### Source excerpt It came after ud0 and ud1. The post Why is the x86 undefined instruction called ud2? Why 2? appeared first on The Old New Thing. ## Koin Compiler 1.2: Compile-Time Safety for ALL Koin DSL DevFeed: [Koin Compiler 1.2: Compile-Time Safety for ALL Koin DSL]() Original publisher: [Read original article]() Author: Arnaud Giuliani Published: 2026-09-10T12:01:01Z Content type: release Language: en Sources: [Koin developers - Medium]() Topics: [Compiler](), [Kotlin](), [Gradle]() Tags: [compiler](), [dependencies](), [dependency-injection](), [gradle](), [implementation](), [koin](), [kotlin](), [kotlin-multiplatform]() ### AI overview Koin Compiler Plugin 1.2, with 1.2.1 as the latest patch, extends compile-time validation to Koin's reified, constructor, and classic lambda DSLs. It also adds visibility across Gradle modules, supports additional entry points, and provides one artifact for Kotlin 2.3.20 through 2.4.20 without requiring a DSL migration. ### Source excerpt Hello, dear Koin community 👋 I'm happy to share that Koin Compiler Plugin 1.2 is out, with 1.2.1 already available as the latest patch. Developer mascott unboxing new Koin Features!TL;DR Compile-time validation now works on the Koin application you already have, where before 1.2 it only covered code written specifically for the compiler plugin. Koin DSL -- All now covered! All Koin DSL: the plugin's reified DSL, Koin's constructor DSL (singleOf(::T)), and the classic lambda DSL (single { Foo(get()) }). Across Gradle modules: definitions are now visible two or more implementation hops away. More entry points: Ktor's install(Koin) and KoinApplication.withConfiguration(). One artifact for Kotlin 2.3.20 through 2.4.20. You get there with no migration to a new DSL, and nothing to rewrite. Let's go through what shipped. Covering now all Koin DSL Until now, compile-time safety only covered the plugin's own reified DSL, which means the two DSL styles you have most likely already written were invisible to it. val appModule = module { single() // checked singleOf(::UserRepository) // not checked before 1.2 single { AnalyticsHelper(get()) } // not checked before 1.2 } Three ways to declare a definition, and only the first was validated. If your UserRepository needed a Database that no module provided, your build passed and Koin threw at runtime. In 1.2, all three are validated. The two DSL styles get there differently. For the constructor DSL, the referenced constructor's parameters become real requirements, exactly like single(), and any named() qualifier you put on the registration is matched against the consumers that ask for it. The classic lambda DSL works differently. The plugin doesn't try to work out dependencies from free-form lambda code. What it validates instead are the get() calls you wrote inside the lambda, treated as ordinary resolution call sites. Before 1.2 those calls were skipped on purpose, because a hand-written get() is difficult to dis ## \[$\] Stabilizing Rust's never type DevFeed: [\[$\] Stabilizing Rust's never type]() Original publisher: [Read original article]() Author: daroc Published: 2026-09-08T13:34:38Z Content type: article Language: en Sources: [LWN.net]() Topics: [Rust](), [Compiler](), [Code](), [function]() Tags: [change](), [code](), [compiler](), [error](), [function](), [maintainers](), [optimize](), [rust]() ### AI overview The article explains Rust's never type, denoted by "!", and reports that it was stabilized after more than two years of work. It discusses the feature's practical benefits for optimizing generic code and its role in type inference, while noting a small breaking change to previous Rust editions. ### Source excerpt A function's return type is supposed to indicate the kind of data that it produces. Rust's "never" type, which is denoted by an exclamation mark ("!"), is the type the language uses to mark a function that never returns and other places where a value can never occur. For a long time, the never type was used internally by the compiler, but was considered an unstable feature. On August 24, after more than two years of work, Rust-compiler-contributor "waffle" finally managed to stabilize the type. It took so long, in part, because it involved a small breaking change to previous Rust editions, which the compiler maintainers needed to ensure did not impact much real code. ## Updates on HEIR, the homomorphic encryption compiler project DevFeed: [Updates on HEIR, the homomorphic encryption compiler project]() Original publisher: [Read original article]() Published: 2026-09-04T18:53:40Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [homomorphic encryption](), [Compiler](), [Encryption](), [Cryptography](), [Machine learning](), [Inference](), [GitHub](), [bazel](), [Kaggle]() Tags: [bazel](), [ckks](), [compiler](), [cryptography](), [encryption](), [github](), [homomorphic-encryption](), [inference](), [kaggle](), [mathematics](), [ml](), [programming]() ### AI overview This companion article explains HEIR, a homomorphic encryption compiler that converts programs to operate directly on encrypted data. It discusses compiling pre-trained machine-learning models for private inference, describes the repository and setup, and reports an example involving encrypted credit-card fraud detection. ### Source excerpt On 2026-08-14 I published an article on the Google Security blog with an update on HEIR, our homomorphic encryption (HE) compiler. This is a companion article, in which I have no limits on word count or jargon, and I can feel free to be honest. So strap in. Assuming you won't read the linked corporate blog post, HEIR is a compiler that converts an input program to a program that operates directly on encrypted data. ## TinyGo 0.42 adds support for 87 Puya PY32 MCU variants and two EmbedFire boards DevFeed: [TinyGo 0.42 adds support for 87 Puya PY32 MCU variants and two EmbedFire boards]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-04T04:33:53Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Go Language](), [Embedded Systems](), [Microcontroller](), [Compiler](), [Arm](), [Internet of things](), [LLVM]() Tags: [arm](), [article](), [code](), [compiler](), [development](), [development-board](), [devices](), [embedded-systems](), [firmware](), [go](), [gpio](), [guest-post](), [hardware](), [iot](), [llvm](), [mcu](), [microcontrollers](), [news](), [programming](), [puya](), [software-management](), [tooling]() ### AI overview TinyGo 0.42 adds support for the Puya PY32 family of 32-bit Arm microcontrollers, covering 87 MCU configurations and two EmbedFire development boards. The port includes startup code, linker scripts, GPIO, UART, and generated device-register definitions. ### Source excerpt CNXSoft: This is a guest post by Pavel Burgr, who recently added Puya PY32 MCU support to TinyGo 0.42, the latest version of the Go compiler for microcontrollers. We first covered TinyGo in 2019, and the project is still going strong with 17.7k stars, 1.1k forks, and 243 contributors. I just made a few edits and added illustrations and a few links to the article. TinyGo 0.42 adds support for the Puya PY32 family of 32-bit Arm microcontrollers. The new port includes startup code, generated device-register definitions, linker scripts, GPIO and UART support, and targets for 87 individual MCU configurations. It also includes board definitions for two inexpensive EmbedFire development boards available from AliExpress. The port was contributed by Pavel Burgr and merged through TinyGo pull request #5106. One motivation was to establish a solid, easy-to-use firmware platform for small IoT devices built around the Registry service and the BleRiot [...] The post TinyGo 0.42 adds support for 87 Puya PY32 MCU variants and two EmbedFire boards appeared first on CNX Software - Embedded Systems News. ## Build Hints That Fail Before the Build Server DevFeed: [Build Hints That Fail Before the Build Server]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [Compiler](), [Java](), [configuration]() Tags: [autocomplete](), [build](), [compilation](), [compiler](), [configuration](), [java]() ### AI overview Codename One moves 87 common build hints into Java annotations so the compiler can catch misspelled names, incorrect value types, and unsupported options before builds reach the server. The change preserves the existing key-and-string format used by older projects and the build service. ### Source excerpt Codename One now exposes 87 common build hints as Java annotations, giving the compiler enough information to reject misspelled names, wrong types, and unsupported values before a build reaches the server. ## Your First Virtual Machine: Write yourself a compiler, Part IV DevFeed: [Your First Virtual Machine: Write yourself a compiler, Part IV]() Original publisher: [Read original article]() Published: 2026-08-24T22:00:00Z Content type: tutorial Language: en Sources: [Tomasz Nurkiewicz around Java and concurrency]() Topics: [Code](), [Programming](), [Programming language](), [Compiler]() Tags: [arithmetic](), [bytecode](), [clojure](), [code](), [compiler](), [cpu](), [feature](), [go](), [interpreter](), [reverse-polish-notation](), [stack](), [virtual-machine](), [vm](), [writing-compiler]() ### AI overview This tutorial explains how to build a virtual machine that reads and executes a binary intermediate representation. It covers the instruction loop, operand stack, arithmetic operations, postfix notation, and how the resulting executable compares with JVM files and .NET assemblies. ### Source excerpt In the previous article, we emitted an intermediate representation (IR) for our programming language that is easier to process than source code. However, we did not build a program that could read and execute that IR. Such a program is called a virtual machine. Technically, it's still an interpreter. But instead of interpreting source code, it interprets IR. Our IR is binary, compact, structured, and generally faster to interpret than the original source. Moreover, as you'll see later, the VM's instruction set can express programs that our source language cannot produce yet! ## Value Classes Still Need Compiler Sympathy DevFeed: [Value Classes Still Need Compiler Sympathy]() Original publisher: [Read original article]() Author: Johan Sjölén Published: 2026-08-24T00:00:00Z Content type: article Language: en Sources: [Inside Java]() Topics: [JDK 28](), [Optimization](), [Compiler]() Tags: [compiler](), [immutability](), [jdk-28](), [jep-401](), [jvm](), [openjdk](), [optimization](), [payload](), [profiling](), [project-valhalla](), [valhalla]() ### AI overview This article examines value classes as a preview feature in JDK 28 and explains how giving up object identity can enable JVM optimizations such as flattening and scalarization. It also describes limitations caused by representation changes, abstraction boundaries, mutability, and generic virtual calls. ### Source excerpt Declaring a value class is first and foremost a semantic decision. It tells our fellow programmers that its instances are defined entirely by their state and do not need identity. That clearer model is valuable in itself! The JVM's additional freedom to optimize how those values are represented is a welcome bonus. C2 can do amazing things with that freedom, but it cannot always recover information hidden behind abstraction boundaries. Profiling and inspecting the generated code remain the best ways to understand what is happening. To get the best results, we may still need to have a little sympathy for the compiler. ## How dbt works, and why orchestrators shouldn't split it into tasks DevFeed: [How dbt works, and why orchestrators shouldn't split it into tasks]() Original publisher: [Read original article]() Author: Ruben Fiszel Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Windmill Blog]() Topics: [Compiler](), [Job](), [airflow](), [data](), [Command-line interface]() Tags: [airflow](), [bigquery](), [cli](), [dagster](), [data](), [data-pipelines](), [dbt](), [dbt-data-pipelines-airflow-dagster-orchestration](), [job](), [orchestration](), [scheduler](), [series]() ### AI overview This primer explains dbt's architecture and examines five ways teams orchestrate it in production. It argues that running a project as one dbt command, while reading dbt's per-model execution state, is generally more efficient and less fragile than creating one orchestrator task per model. It discusses Dagster and astronomer-cosmos's convergence on this approach, including a reported cost comparison. ### Source excerpt How is dbt actually orchestrated, and why does running it as one job beat one task per model? A primer on dbt as a compiler with a scheduler attached, the five ways teams wrap it, and why both Dagster and astronomer-cosmos converged on a single dbt invocation with per-model state projected out of it. ## Rust Function Overloading - Call for Experimentation DevFeed: [Rust Function Overloading - Call for Experimentation]() Original publisher: [Read original article]() Author: teor Published: 2026-08-19T00:00:00Z Content type: release Language: en Sources: [Inside Rust Blog]() Topics: [Rust](), [C++](), [Compiler](), [interoperability]() Tags: [c-plus-plus](), [compiler](), [interoperability](), [languages](), [rust](), [syntax]() ### AI overview The Rust Project is experimenting with function and method overloading for FFI bindings through the incomplete nightly "splat" compiler feature. The experiment aims to assess implementation complexity and improve interoperability with C++, while its design may change or be removed. ### Source excerpt In partnership with the Rust Foundation's Rust-C++ Interop Initiative, the Rust Project has been experimenting with function overloading for FFI bindings. This experiment is now at a stage where compiler and interop tool developers can start exploring function overloading. Stable Rust already supports a form of overloading using tuples and traits, but calling these overloaded functions looks strange, because the overloaded arguments have to be passed as a single tuple argument, like this: hypot((2.0, 3.0, 6.0)). Stable Rust also allows overloading of built-in operators with user-defined types, via traits like Add (the + operator) and Neg (the - value negation operator). We are running an unstable nightly Rust language experiment to answer questions like: How much overloading can we do with Rust's existing trait system? Could this help us call C++ from Rust ergonomically? In the tradition of yeet (to avoid bikeshedding), we are using basic syntax in the first stage of the experiment: the #[rustc_splat] attribute. Alternative syntaxes can be considered later, if the experiment generates useful outcomes. Experimental Function Overloading Rust nightly builds from 2026-07-31 onwards have experimental support for more ergonomic function and method overloading, using the incomplete "splat" compiler feature; if you're a compiler or interop tool developer, we encourage you to experiment with it! This experiment lets overloaded functions be called with separate arguments, like this: hypot(2.0, 3.0, 6.0). No double parentheses required! But type inference and type checking still happen as they would in a stable Rust overload. We are experimenting with splat to get a feel for the complexity of the implementation, and to see if it solves some language interoperability use cases. Like most Rust language experiments, this nightly feature has no RFC, and can change or be removed at any time. Experiment Design We expect the feature to change significantly in future, or to be replace ## Add the dependency graph to a Kotlin K2 migration checklist DevFeed: [Add the dependency graph to a Kotlin K2 migration checklist]() Original publisher: [Read original article]() Published: 2026-08-18T00:00:00Z Content type: tutorial Language: en Sources: [Koin - Cloud-Inject.io -Kotzilla]() Topics: [Kotlin](), [migration](), [Dependency injection](), [Graphs](), [Compiler](), [toolchain](), [Android Gradle Plugin](), [Compose]() Tags: [android-gradle-plugin](), [compiler](), [compose](), [dependency-injection](), [graph](), [kotlin](), [migration](), [rollback](), [toolchain](), [verification]() ### AI overview A practical checklist for Kotlin K2 migrations in projects that generate or validate dependency wiring. It recommends mapping the compiler and build toolchain, upgrading from a known compatibility matrix, cleaning and inspecting generated output, verifying dependency graphs across flavors and targets, testing runtime slices, and keeping rollback changes coherent. ### Source excerpt A K2 migration is usually planned around source compatibility and compiler diagnostics. Projects that generate or validate dependency wiring need another checklist: compiler plugin versions, generated sources, metadata compatibility, and graph verification on every target. Map the toolchain first Record the Kotlin plugin, Compose compiler plugin, KSP or annotation tooling, dependency-injection compiler plugin, Android Gradle plugin, and target libraries. Upgrade from a known matrix instead of selecting each latest version independently. ## Experiment in reducing target directory size on nightly DevFeed: [Experiment in reducing target directory size on nightly]() Original publisher: [Read original article]() Author: Jakub Beránek Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Inside Rust Blog]() Topics: [Rust](), [Compiler](), [data]() Tags: [compilation](), [compiler](), [disk-space](), [information](), [rust](), [space](), [state]() ### AI overview The Cargo Team is testing -Zembed-metadata=no by enabling it by default on the nightly channel. The experiment aims to reduce Rust target directory size by avoiding duplicated crate metadata in build artifacts, while gathering feedback about viability and user impact. ### Source excerpt TL;DR: The Cargo Team will be rolling out an experiment to identify user impact for a proposed change. Cargo will enable the -Zembed-metadata=no feature on the nightly channel by default, which can help reduce the size of the target directory somewhat. This is an experiment designed to gather feedback about viability of this feature. Users are not expected to migrate to support this feature, but to report any issues and opt-out if needed in the meantime. What is this about? High disk usage of Rust compilation artifacts is frequently cited as one of the biggest annoyances of Rust users. In our 2025 State of Rust survey, it was actually the second most commonly reported problem, right after compilation speed. There are various reasons why the target directory can become quite large, such as: Cargo compiles the whole crate graph from scratch by default, which produces a lot of build artifacts. Debug information takes a lot of disk space. Incremental compilation artifacts take a lot of disk space. While you can disable debug information or incremental compilation to reduce the target directory size, that of course comes with severe trade-offs in compilation speed and debuggability of your program. However, there is one source of data in the target directory that currently takes too much size even though it doesn't really have to. It is the "crate metadata", which can be duplicated across multiple files. We will focus on that in this blog post. What causes duplicated (meta)data For years, Cargo has been using pipelined compilation to speed up building of crate graphs. When compiling a library crate, it tells the compiler to produce an .rmeta file (which contains all the crate metadata required to use this library) as soon as possible, even before having the final executable code available. This enables dependent crates to start compiling sooner. However, once the library does finish compiling, the final produced .rlib file will contain both the executable code and the Ru ## Anders Hejlsberg on Rewriting the TypeScript Compiler in Go DevFeed: [Anders Hejlsberg on Rewriting the TypeScript Compiler in Go]() Original publisher: [Read original article]() Author: Ryan Peterman Published: 2026-08-17T13:03:31Z Content type: opinion Language: en Sources: [The Developing Dev]() Topics: [TypeScript](), [Compiler](), [Go Language](), [Artificial Intelligence](), [Software Engineering]() Tags: [ai](), [compiler](), [go](), [podcasts](), [software-engineering](), [typescript]() ### AI overview A podcast interview with Anders Hejlsberg discusses the decision to rewrite the TypeScript compiler in Go, the limited use of LLMs during the rewrite, and his views on AI's impact on software engineering. ### Source excerpt It was interesting talking to Anders Hejlsberg, the creator of TypeScript and C#, about all the technical details behind rewriting the TypeScript compiler in Go. 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