# toolchain A collection of software development tools used together to build programs, typically including a compiler, assembler, linker, and runtime libraries. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Babel in Khan Academy's i18n Toolchain DevFeed: [Babel in Khan Academy's i18n Toolchain]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-06-22T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Babel](), [Internationalization (i18n)](), [toolchain](), [es6](), [JavaScript](), [Parser](), [Universal Syntax Tree.](), [Open Source]() Tags: [api](), [architecture](), [babel](), [build](), [build-system](), [code](), [engineering](), [es6](), [eslint](), [frontend](), [javascript](), [news](), [plugin](), [source](), [structure](), [syntax](), [tests](), [toolchain](), [types](), [web-frontend](), [workflow]() ### AI overview Khan Academy describes switching its ES6 and JSX frontend compilation from jstransform to Babel. The article explains a custom Babel plugin for preserving the project's internationalization behavior and discusses matching generated output, line numbers, whitespace, and the existing build chain. ### Source excerpt By Kevin Barabash We've been using ES6 (along with JSX) for sometime at Khan Academy. Right now, we're ... Read more ## ESPHome 2026.9.0: Faster builds and encrypted updates DevFeed: [ESPHome 2026.9.0: Faster builds and encrypted updates]() Original publisher: [Read original article]() Author: Jesse Hills Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [ESPHome - Smart Home Made Simple - Blog]() Topics: [esphome](), [PlatformIO](), [ESP8266](), [Encryption](), [build performance](), [ChaCha](), [toolchain](), [Home Assistant]() Tags: [2026](), [benchmarks](), [builds](), [ci](), [encryption](), [esp32](), [esp8266](), [esphome](), [ota](), [parallel](), [releases](), [toolchain](), [updates]() ### AI overview ESPHome 2026.9.0 improves build performance by parallelizing PlatformIO setup, lays groundwork for a native ESP8266 toolchain, adds Noise-encrypted OTA updates, and reduces ESP8266 RAM usage. The release also includes component additions, platform updates, and fixes. ### Source excerpt ESPHome 2026.9.0 parallelizes PlatformIO installs, lays the groundwork for a native ESP8266 toolchain, adds Noise-encrypted OTA updates, and frees ESP8266 RAM. ## Module Tracking in Swift Debug Info DevFeed: [Module Tracking in Swift Debug Info]() Original publisher: [Read original article]() Author: Adrian Prantl Published: 2026-09-11T15:30:00Z Content type: tutorial Language: en Sources: [Swift.org]() Topics: [debugging](), [debug](), [toolchain](), [C++]() Tags: [cmake](), [debug](), [debugger](), [debugging](), [jit](), [swift]() ### AI overview Swift 6.3 and the upcoming Swift 6.4 improve how explicitly built Swift modules are referenced in debug information. The article explains how this helps LLDB locate modules for expression evaluation, improving debugging reliability and reducing build products, while noting that custom build systems may need adjustments. ### Source excerpt When your Swift program hits a breakpoint and stops so you can inspect it, the debugger's expression evaluator has to find the exact Swift module your code was built from. Until now, that lookup wasn't always precise. The upcoming Swift 6.4 release will include changes, begun in Swift 6.3, that address this by updating how the Swift compiler references explicitly-built Swift modules in debug info. The majority of developers will automatically benefit from faster, more reliable debugging and smaller build products, without any modifications to their SwiftPM or Xcode projects. For developers who maintain their own build systems using, for example, Bazel, Buck, or CMake, some adjustments may be necessary to take advantage of these changes. This article explains how the debugger uses Swift modules. Next, it describes how Swift 6.3 changes the way modules are tracked in debug info to solve several problems with the previous representation. Finally, it shows how to adjust build systems to take advantage of the new representation and eliminate some build steps that are no longer necessary. Swift modules and expression evaluation LLDB's standout feature is its powerful expression evaluator. Because LLDB embeds the Clang and Swift compilers, it can JIT-compile any valid source code and run it in the context of your application while stopped at a breakpoint. This includes not just calling code in your application, but also defining new data types, functions, and closures. Debugging features that are usually reserved for interpreted or JIT-compiled languages like JavaScript become available to ahead-of-time-compiled languages like C++ and, of course, Swift! In order to JIT-compile user expressions that make use of data types defined in the debugged program, LLDB's embedded Swift compiler needs to import the Swift modules defining those types. In a world before explicitly-built modules, LLDB would find the base name of the main module at the current breakpoint in the debug info ## Mold High Speed Linker Being Rewritten In Rust, Hopes To Be The Default Linker On Linux DevFeed: [Mold High Speed Linker Being Rewritten In Rust, Hopes To Be The Default Linker On Linux]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-11T13:21:42Z Content type: news Language: en Sources: [Phoronix]() Topics: [Rust](), [Linux](), [Programming](), [toolchain]() Tags: [compatibility](), [desktop-linux](), [firmware](), [kernels](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [rust](), [speed](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview The Mold high-speed linker is being rewritten from C++ in Rust for Mold 3.0. Its goals include preserving Mold 2.x performance, improving safety, supporting missing linker-script features, and potentially adding incremental linking. ### Source excerpt The Mold high performance linker that typically outperforms the likes of LLVM lld, GNU LD, Wild, and others is being rewritten in the Rust programming language and also has an ambitious goal of being adopted as the default linker on Linux systems... ## Chainguard's Factory-Scale Automation for Toolchain Upgrades and Testing DevFeed: [Chainguard's Factory-Scale Automation for Toolchain Upgrades and Testing]() Original publisher: [Read original article]() Published: 2026-09-09T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard](), [toolchain](), [Automation](), [Testing](), [Open Source](), [Go Language]() Tags: [automation](), [chainguard](), [go](), [open-source](), [security](), [testing](), [toolchain](), [upgrades]() ### AI overview Chainguard describes how its Factory uses automation to manage toolchain version selection, upgrades, builds, and testing across open source packages. The approach uses version streams and rebuilds packages with newer toolchain versions when builds and tests succeed, helping deliver updates quickly while maintaining reliability and security. ### Source excerpt See how Chainguard automates toolchain upgrades and testing to keep thousands of open source packages current, secure, and reliable at scale. ## Introducing CUDA Rust: Two Tracks for Writing GPU Kernels DevFeed: [Introducing CUDA Rust: Two Tracks for Writing GPU Kernels]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-08T12:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [Code generation](), [toolchain](), [C++]() Tags: [c-plus-plus](), [cuda](), [cuda-tile](), [data-science](), [developer-tools-techniques](), [dynamo](), [gpu](), [kernels](), [nemo-retriever](), [open-source](), [programming-languages-compilers](), [rust](), [toolchains]() ### AI overview NVIDIA introduces CUDA Rust for writing GPU kernels natively in Rust and compiling them to PTX. The article explains the SIMT and Tile programming tracks, recommends Tile as the default, and describes cuda-oxide's Rust-based code-generation path. ### Source excerpt In September 2026, NVIDIA announced it is leaning into native GPU programming in Rust. CUDA C++ and CUDA Python are mature, enterprise-grade toolchains, and... ## Kotlin Toolchain 0.12: Multiplatform Library Publishing, Wasm Apps, and More DevFeed: [Kotlin Toolchain 0.12: Multiplatform Library Publishing, Wasm Apps, and More]() Original publisher: [Read original article]() Author: Joffrey Bion Published: 2026-09-03T10:40:05Z Content type: release Language: en Sources: [Kotlin : A concise multiplatform language developed by JetBrains | The JetBrains Blog]() Topics: [toolchain](), [multiplatform](), [compose-hot-reload](), [web applications](), [Spring Boot 4]() Tags: [compose-hot-reload](), [gradle](), [intellij-idea](), [kotlin](), [multiplatform](), [news](), [publication](), [releases](), [toolchain](), [toolchains](), [wasm]() ### AI overview Kotlin Toolchain 0.12.0 adds multiplatform library publishing and previews support for building Wasm modules as web applications. It also includes command-line Compose Hot Reload and changes intended to reduce Maven Central publication files. ### Source excerpt Kotlin Toolchain 0.12.0 is out. This release brings some long-awaited features: multiplatform libraries publication, a preview of Wasm application support, Compose Hot Reload from the command line, and more. Read on for the details, and check the release notes for the full list of changes and bug fixes. Additionally, klibs.io now uses the Kotlin Toolchain [...] ## The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough DevFeed: [The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-02T17:15:57Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA](), [Optimization](), [GPU](), [toolchain](), [debug](), [Instrumentation](), [Algorithms]() Tags: [algorithms](), [benchmark](), [cuda](), [debug](), [developer-tools-techniques](), [gpu](), [how-to](), [image-processing](), [instrumentation](), [memory](), [optimization]() ### AI overview A step-by-step CUDA optimization walkthrough that improves an image-processing pipeline with debugging, benchmarking, optimized algorithms, GPU memory management, pinned transfers, streams, and asynchronous transfers. ### Source excerpt NVIDIA CUDA remains the foundation of GPU-accelerated computing, powering everything from scientific simulations to large-scale AI training. But writing... ## Introducing the Gradle Security Subscription DevFeed: [Introducing the Gradle Security Subscription]() Original publisher: [Read original article]() Author: Gradle Build Tool Published: 2026-08-31T04:00:00Z Content type: release Language: en Sources: [The Gradle Blog]() Topics: [Gradle](), [Security](), [toolchain](), [Vulnerabilities]() Tags: [gradle](), [open-source](), [releases](), [security](), [toolchain](), [vulnerabilities]() ### AI overview Gradle announces the Gradle Security Subscription, which provides security fixes under a service-level agreement, maintenance for Gradle versions beyond the open source end-of-life policy, and advance notice of newly discovered vulnerabilities. The article explains build-tool security risks and argues that the subscription does not change the open source community offering. ### Source excerpt The Gradle Security Subscription is available today. It provides security fixes under a service-level agreement, security maintenance for Gradle versions beyond the open source end-of-life policy, and advance notice of newly discovered vulnerabilities ahead of public disclosure. Coverage details are on our subscription page. The rest of this post explains why the subscription is important and what changes for the open source community (in short, nothing). The toolchain as an attack surface In 1984, while accepting the Turing Award, Ken Thompson described a compiler he had modified to insert a backdoor into the programs it compiled. Reviewing the source of the affected program revealed nothing. And reviewing the source of the compiler revealed nothing either. His conclusion was that you cannot fully trust code you did not create entirely yourself. Four decades later, we find ourselves facing the same dilemma. Software composition analysis scans the dependencies a project declares. Static analysis scans the source a team writes. But neither examines the build tool that turns that source and those dependencies into the artifact that goes to production. This gap has been exploited repeatedly. In 2015, XcodeGhost distributed a modified version of Apple's IDE that inserted malicious code into every application compiled with it, reaching thousands of App Store applications whose developers had written nothing malicious. In 2020, SolarWinds released a compromised version of their IT software, but the attackers modified no source code. They gained access to the build environment and installed malware that monitored the compilation process, injecting a backdoor into the resulting binary as it was produced. Approximately 18,000 organizations installed the signed update, including the U.S. Departments of Treasury, State, Defense, and Homeland Security. There are two more recent incidents. The Codecov Bash Uploader compromise ran undetected for roughly two months in 2021, exfilt ## CUDA Python 1.0: Stable APIs, One Foundation, Full Platform Access DevFeed: [CUDA Python 1.0: Stable APIs, One Foundation, Full Platform Access]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-25T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA](), [Python](), [GPU](), [C++](), [toolchain](), [PyTorch](), [RAPIDS](), [releases]() Tags: [c-plus-plus](), [cuda](), [data-science](), [developer-tools-techniques](), [gpu](), [numba](), [python](), [pytorch](), [rapids](), [release](), [toolchain]() ### AI overview CUDA Python 1.0 makes the CUDA platform accessible from Python through stable, versioned components, including runtime access, low-level CUDA C API bindings, parallel algorithms, component discovery, and NVIDIA math libraries. The release establishes a shared foundation for Python GPU libraries and introduces semantic-versioning commitments for public APIs. ### Source excerpt For years, a Python developer who needed a GPU had two realistic choices: Learn NVIDIA CUDA C++ well enough to write an extension, set up a build toolchain, and... ## Enabling the next-generation trait solver on nightly DevFeed: [Enabling the next-generation trait solver on nightly]() Original publisher: [Read original article]() Author: lcnr Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Rust Blog]() Topics: [toolchain]() Tags: [bugs](), [diagnostics](), [inference](), [performance](), [rust](), [types]() ### AI overview Rust is enabling its next-generation trait solver by default on nightly ahead of planned stabilization. The compiler-internal replacement changes trait and associated-type handling, fixes many known issues, and needs testing for breakage, performance regressions, and diagnostics. ### Source excerpt After nearly 4 years of active development, the next-generation trait solver is close to stabilization. We are enabling it by default on nightly to surface any remaining issues and plan to stabilize it in the next months. This is the largest single change to the Rust compiler since its initial release. It completely replaces how we prove where-clauses, normalize associated types, and much more. Please try out the latest nightly and open an issue if you encounter any bugs or regressions. This is an internal component of the compiler. The main benefits of this rework will come in the future. The removal of the old implementation will unblock features such as Type Alias Impl Trait and Return Type Notation, allow us to add new implicit default trait bounds (e.g., Move and Forget), and enable us to fix the remaining type system unsoundnesses. Even so, this already fixes a huge number of issues. As an underapproximation, we currently know of more than 200 issues on GitHub fixed by this change. This also has a significant impact on compile times; more on that later. When developing on nightly, you may accidentally rely on behavior only supported by the new trait solver. This is an incredibly big change which results in a non-trivial amount of breakage. Most of these changes are intended improvements to type inference or the removal of undesirable behavior. We are tracking the known issues and breakage in a pinned GitHub issue. What can I do? Please update to the latest nightly version by using rustup update nightly and use it to test your existing projects and libraries. Please tell us if you encounter any breakage, compile-time performance regression, or bad diagnostics. We have not yet spent too much time on error messages for the next-generation trait solver, so we would also appreciate you using this nightly for development to find poor diagnostics and other bugs in our error handling. If you encounter any issue, take a quick look at the pinned GitHub issue to see if t ## 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. ## How Google is Making Private AI Practical with Homomorphic Encryption DevFeed: [How Google is Making Private AI Practical with Homomorphic Encryption]() Original publisher: [Read original article]() Author: Jeremy Kun Published: 2026-08-14T14:00:00Z Content type: article Language: en Sources: [Security]() Topics: [Encryption](), [Artificial Intelligence](), [Google](), [AI Inference](), [Compiler](), [Cryptography](), [toolchain](), [Open Source](), [Security](), [Cloud]() Tags: [ai](), [ai-inference](), [cloud](), [cryptographic](), [data](), [encryption](), [google](), [none](), [open-source](), [security](), [toolchain]() ### AI overview Google introduces HEIR, an open-source compiler toolchain designed to make private AI inference practical with homomorphic encryption. The approach lets servers compute on encrypted data and return encrypted results without exposing the underlying information, while addressing the usability challenges of adopting homomorphic encryption. ### Source excerpt heir logo ## MSVC Build Tools Preview updates - August 2026 DevFeed: [MSVC Build Tools Preview updates - August 2026]() Original publisher: [Read original article]() Author: Eric Brumer Published: 2026-08-13T15:01:10Z Content type: article Language: en Sources: [C++ Team Blog]() Topics: [MSVC](), [C++](), [Compiler](), [toolchain](), [Code generation](), [Optimization](), [Visual Studio]() Tags: [c-plus-plus](), [code-generation](), [diagnostics](), [features](), [ide](), [msvc](), [optimization](), [release](), [tools](), [updates](), [visual-studio](), [visual-studio-2026]() ### AI overview The article summarizes August 2026 updates to the MSVC Build Tools Preview, targeting the v14.52 release. It covers compiler conformance, diagnostics, C++ modules, code generation, optimization, and ARM64 improvements. ### Source excerpt Try the latest updates to MSVC Build Tools Preview The post MSVC Build Tools Preview updates - August 2026 appeared first on C++ Team Blog. ## C++ libraries linking DevFeed: [C++ libraries linking]() Original publisher: [Read original article]() Author: Nenad Rakocevic (noreply@blogger.com) Published: 2026-07-29T19:07:38Z Content type: release Language: en Sources: [Red]() Topics: [Red](), [C++](), [toolchain]() Tags: [build](), [build-tools](), [c-plus-plus](), [clang](), [compilation](), [directx](), [gcc](), [libraries](), [linux](), [macos](), [msvc](), [static-linking](), [toolchain](), [windows](), [x86]() ### AI overview The Red toolchain now supports statically linking C++ libraries, including their runtime, on Windows, Linux, and macOS. It accepts libraries built with MSVC, GCC, or clang through the existing import system and compilation switch, with platform-specific prerequisites and constraints. ### Source excerpt When we introduced static linking of C libraries, the promise was simple: name a .lib or .a archive in your #import, compile with -s, and ship one self-contained executable, no DLLs riding along, nothing to install on the target machine. There was one big frontier left, and everyone saw it coming: the libraries people want most (vision, GUI, audio, machine learning) are written in C++. A C++ library is a very different animal to link: it brings global constructors, exceptions, RTTI, templates, thread-local storage, and an entire language runtime that expects to be wired up just so. Until now, that was the line where you switched back to DLLs. That line is gone. The Red toolchain now statically links C++ libraries too (their runtime included) on every platform Red targets: Windows, Linux (x86 and ARM), and macOS. The best part: nothing changes. It is the same import system and same compilation switch: red -r -s myapp.red Libraries built with MSVC, GCC or clang are all accepted, in their native object formats. What you need preinstalled ➤ Windows: for C++ libraries (or C code built against Microsoft's static runtime), install the free Visual Studio Build Tools with the "Desktop development with C++" workload. Just one installer, and Red locates everything by itself: no vcvarsall, no PATH, no environment variables. Plain C libraries still need nothing at all and that now extends to C libraries touching COM, DirectX or MediaFoundation: the GUID constants such code references ship inside the toolchain, so a fresh Windows 11 with only red-toolchain executable on it links them! ➤ Linux: the GNU runtime archives from your distribution's gcc packages (libstdc++.a, libgcc.a and friends) placed next to your library. If one is missing, the linker names exactly what it needs. ➤ macOS: nothing beyond the toolchain; the system C++ runtime binds automatically. Some constraints 32-bit libraries for now, until the 64-bit toolchain is ready. The imported surface must be C (extern "C", ## DevOps Toolchain: Build a Stack That Actually Scales DevFeed: [DevOps Toolchain: Build a Stack That Actually Scales]() Original publisher: [Read original article]() Author: Eric Minick Published: 2026-07-22T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [DevOps](), [toolchain](), [CI/CD](), [ai-coding]() Tags: [ai-coding](), [ci-cd](), [devops](), [productivity](), [software-delivery](), [toolchain]() ### AI overview This article explains that a scalable DevOps toolchain should be a small, unified stack with shared governance and golden paths rather than a large collection of specialized tools. It argues that tool sprawl increases integration work, governance gaps, context switching, and rollback complexity, especially as AI increases code volume. ### Source excerpt Discover the essential DevOps toolchain for modern software delivery. Learn why consolidation, not sprawl, is how teams scale safely with AI acceleration. | Blog ## Running unmodified Doom in the SQLite bytecode language DevFeed: [Running unmodified Doom in the SQLite bytecode language]() Original publisher: [Read original article]() Author: Glauber Costa Published: 2026-07-17T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Database](), [toolchain](), [.NET]() Tags: [browser](), [c](), [doom](), [engineering](), [llvm](), [open-source](), [rust](), [sqlite](), [turso]() ### AI overview Turso ports unmodified Doom to SQLite's VDBE bytecode VM and runs it in the browser. The article explains Turso's database-bytecode ambitions and its C-to-VDBE compiler. ### Source excerpt We made Turso into the LLVM of databases: it now runs Doom ## Optimized CDN caching and deploying of immutable static assets DevFeed: [Optimized CDN caching and deploying of immutable static assets]() Original publisher: [Read original article]() Author: Niklas Mischkulnig Published: 2026-07-17T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Caching](), [Cache](), [Optimization](), [Next.js](), [toolchain]() Tags: [cache](), [caching](), [cdn](), [next-js](), [optimization](), [toolchain](), [vercel]() ### AI overview Vercel now reuses immutable, content-addressed static assets across deployments for supported frameworks. The zero-configuration optimization reduces CDN requests and transferred bytes, speeds deployments, and can significantly lower global time to first byte. Next.js 16.3 preview and later support it by default, with additional framework support planned. ### Source excerpt Vercel now reuses static files across deployments for frameworks that output content-addressed assets. It's zero-config: Vercel uses Framework-defined infrastructure to manage these immutable files alongside your code changes, handling the hard parts like hash collisions, file lifecycles, and efficient routing. This brings several benefits: Lower costs and improved cache hit rates -- For frameworks using query-parameter-based Skew Protection (such as Next.js), we saw 17% fewer CDN requests and 24% fewer bytes transferred for static content. Faster Deployments -- Deployments complete up to 30% faster on average. Faster Time to First Byte -- Immutable assets can remain cached across deployments, giving frequently deployed projects up to a 60% reduction in global TTFB. Vercel's supported frameworks will automatically take advantage of this optimization, even for projects without Skew Protection enabled. Next.js 16.3 preview and later already have it enabled out of the box, and support for additional frameworks (including Nitro) is coming soon. No configuration is required: upgrading to a supported framework version is all you need to do. If you're a framework author and want to implement this in your own toolchain, see the immutable static files section of the Build Output API docs. Read more ## Verifying Rust cryptography in SymCrypt, from standards to code DevFeed: [Verifying Rust cryptography in SymCrypt, from standards to code]() Original publisher: [Read original article]() Author: Son Ho, Cédric Fournet, Antoine Delignat-Lavaud, Samuel Lee, Jason Fisher, Jessica Krynitsky Published: 2026-07-13T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [Rust](), [Rust formal verification](), [Cryptography](), [Formal verification](), [Post-quantum cryptography](), [Lean](), [toolchain](), [Microsoft]() Tags: [cryptography](), [formal-verification](), [memory-safety](), [microsoft](), [post-quantum](), [research](), [research-blog](), [rust](), [security]() ### AI overview Microsoft Research describes how SymCrypt uses safe Rust, Lean, and the Aeneas toolchain to formally verify production cryptographic implementations. The approach combines Rust's memory-safety guarantees with machine-checked proofs of functional correctness, with initial verified code for SHA-3 and ML-KEM and support from independently verifiable proof-writing agents. ### Source excerpt Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research. ## Vercel Sandbox now support Custom Images DevFeed: [Vercel Sandbox now support Custom Images]() Original publisher: [Read original article]() Author: Tom Lienard Published: 2026-06-30T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [toolchain](), [Docker](), [boot]() Tags: [boot](), [compute](), [container-registry](), [dependencies](), [docker](), [documentation](), [filesystem](), [images](), [os](), [performance](), [registry](), [sandbox](), [sandboxes](), [snapshots](), [toolchain](), [vercel]() ### AI overview Vercel Sandboxes now support custom images in public beta. Users can provide a custom root filesystem with their own operating system, toolchain, and dependencies. Images are pulled from Vercel Container Registry, and the feature is designed to preserve cold start performance through precompiled snapshots. ### Source excerpt Vercel Sandboxes now supports custom images. Launching in public beta today, images allow Sandboxes to start with your own custom root filesystem. Images are pulled from Vercel Container Registry, so anything you docker push is immediately available. Bring your own OS, toolchain and dependencies into the Sandbox without needing to spin up compute and create Snapshots. Images in the background for Fluid Compute and boot from a precompiled snapshot in the same format as our Sandbox Snapshots - so you get the convenience of a custom filesystem without sacrificing cold start performance. Read more about running images with Sandbox in the documentation. Read more ## Static linking support DevFeed: [Static linking support]() Original publisher: [Read original article]() Author: Nenad Rakocevic (noreply@blogger.com) Published: 2026-06-29T18:39:33Z Content type: release Language: en Sources: [Red]() Topics: [Red](), [toolchain](), [C](), [parquet](), [MSVC](), [Claude Code](), [codex]() Tags: [agents](), [c](), [claude-code](), [codex](), [compression](), [mod-player](), [msvc](), [soundtracker](), [static-linking](), [toolchain]() ### AI overview The Red toolchain now supports statically linking C libraries, allowing Red/System programs to be distributed as single self-contained executables. The article explains the linker's handling of object formats, symbols, sections, and relocations, and demonstrates the process with the miniz compression library. ### Source excerpt We must free ourselves of the hope that the sea will ever rest. We must learn to sail in high winds. -Aristotle Onassis The coding agents revolution is taking the world by storm and we are right in the middle of it. Like most of you, we have experimented with the agent's amazing (and frustrating) capabilities, pondering the role of Red and our vision in that new world. The conclusion is (un)surprisingly clear, Red is still very relevant and will be even more so as we improve it to better work with agents. In the meantime, here are some treats, starting with expanding our toolchain to support static linking of libraries written in C, allowing you to distribute single executables with all dependencies packed inside. This work has been done with the heavy assistance of frontier models and local harnesses (Claude Code and Codex). You might expect that to be a small addition, but a static linker has to read each platform's object format, pull in just the pieces it needs, fold duplicated sections, resolve system symbols and patch relocations by hand, so there was quite a bit of machinery to put together. The reward is the result everyone wants: a single, self-contained binary, with nothing to install beside it. A simple example Let's compress some data without shipping a compression library next to our program. For something concrete we will use miniz, a small, MIT-licensed library that implements the well-known zlib and deflate APIs. It is distributed as a single `miniz.c` / `miniz.h` pair, which makes it especially convenient to compile and link. The first step is to compile miniz into a static library. There are only two things to keep in mind. Red/System currently produces 32-bit code, so the object has to be 32-bit too; and it helps to switch off a couple of compiler extras (C++ exception tables and stack canaries) that would otherwise make the object reference runtime helpers we do not need. On Windows, with MSVC: cl /c /MT /GS- /EHs-c- /GR- miniz.c lib /out:miniz.l ## WASI 0.3 Launched DevFeed: [WASI 0.3 Launched]() Original publisher: [Read original article]() Author: \["Bailey Hayes", "Yosh Wuyts"\] Published: 2026-06-11T00:00:00Z Content type: release Language: en Sources: [Bytecode Alliance]() Topics: [WebAssembly](), [toolchain]() Tags: [async](), [component](), [components](), [native](), [support](), [toolchain](), [wasi](), [webassembly]() ### AI overview WASI 0.3.0 has been ratified as a stable specification. It rebases WASI on the WebAssembly Component Model's native async primitives, making most changes from WASI 0.2 mechanical and simplifying interface signatures. Runtime and toolchain support is beginning to land. ### Source excerpt WASI 0.3 is official, and async is now native to WebAssembly Components. The WASI Subgroup voted to ratify WASI 0.3.0, rebasing WASI onto the WebAssembly Component Model's async primitives. The 0.3.0 specification is now stable, and runtime and toolchain support is landing now. ## How much do amd64 microarchitecture levels help in Go? DevFeed: [How much do amd64 microarchitecture levels help in Go?]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-06-06T20:25:19Z Content type: article Language: en Sources: [Daniel Lemire]() Topics: [Go Language](), [Benchmark](), [cpu](), [Data structures](), [toolchain](), [intel]() Tags: [avx](), [benchmark](), [cpu](), [data-structure](), [go](), [intel](), [performance](), [toolchain]() ### AI overview This article examines how amd64 microarchitecture levels affect Go program performance. It explains the instruction-set levels and reports a benchmark methodology using the Roaring Bitmaps library on an Intel Xeon Gold 6548N with Go 1.26.2 and Roaring v2.18.2. The supplied text does not include the benchmark results. ### Source excerpt Our 64-bit Intel and AMD processors have evolved over decades. When you compile a Go program for a 64-bit Intel or AMD processor, the compiler targets, by default, a nearly 20-year-old instruction set. The binary that comes out runs on essentially any x64 chip, but it also leaves on the table every instruction that was ... Continue reading How much do amd64 microarchitecture levels help in Go? ## How Gradle Is Javamaxxing DevFeed: [How Gradle Is Javamaxxing]() Original publisher: [Read original article]() Author: Laura Kassovic Published: 2026-05-28T04:00:00Z Content type: article Language: en Sources: [The Gradle Blog]() Topics: [Gradle](), [Java](), [toolchains](), [toolchain]() Tags: [bytecode](), [compiler](), [garbage-collectors](), [gradle](), [java](), [java-8](), [jdk](), [jvm](), [openjdk](), [optimization](), [performance-optimization](), [release]() ### AI overview Gradle aggressively adopts new JDK releases because newer JVMs can improve build speed, memory use, startup behavior, and maintainability. JVM toolchains allow Gradle to run on JDK 26 while compiling and testing projects for older Java versions, including producing Java 8-compatible bytecode. ### Source excerpt Some people optimize one part of their lives to the absolute limit, then past it. They call it "-maxxing." Houseplantmaxxing. Sleepmaxxing. Tokenmaxxing. The Gradle Build Tool is Javamaxxing. TL;DR Breathe easy knowing you can run modern Gradle on JDK 26 while still shipping Java 8-compatible artifacts: Gradle aggressively adopts new JDKs because newer JVMs make builds faster, leaner, and easier to maintain. Upgrading the JDK that runs Gradle is often the easiest performance win. JVM toolchains fully separate the JVM running Gradle, the JVM compiling your code, and the JVM running your tests. NOT TL;DR We aggressively adopt new JDK releases as soon as we responsibly can. Not because it's trendy. Because newer JDKs make Gradle faster, leaner, and easier to maintain. Every OpenJDK release ships improvements to the JVM; garbage collectors, compiler infrastructure, startup behavior, memory layout, and runtime APIs. As a high-performance JVM build tool, Gradle benefits directly from all of it. The question people immediately ask is: Does this mean my project also has to run on the newest JDK? No. That coupling effectively disappeared once Gradle introduced JVM toolchains. With toolchains, the JVM that runs Gradle is completely separate from the JVM that compiles, tests, and executes your code. You can run Gradle on JDK 26 today and still produce Java 8 bytecode. What Is the Current State of Gradle and Java? We add daemon support for every new JDK as soon as the ecosystem permits: Java 24 in Gradle 8.14.0, Java 25 in 9.1.0 (released two days after JDK 25 GA), Java 26 in 9.4.0. The full mapping is in the Compatibility Matrix. We then bump the minimum JVM required to execute Gradle as users move. Gradle 9.0.0, released on July 31, 2025, raised the minimum JVM required to run the Gradle daemon to Java 17. That was the first daemon JVM floor increase since Gradle 5.0 made Java 8 the minimum back in 2018. The next major transition is already underway, Gradle 10.0.0 will likely [Next page]()