# Go Published articles for Go. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Libreboot Git repositories now on Codeberg (RIP Notabug) DevFeed: [Libreboot Git repositories now on Codeberg (RIP Notabug)]() Original publisher: [Read original article]() Author: Leah Rowe Published: 2026-09-17T04:32:50.666044Z Content type: news Language: en Sources: [News about Libreboot releases and development]() Topics: [forgejo](), [Git](), [ci](), [pull-requests](), [Gitea]() Tags: [bios](), [canoeboot](), [ci](), [coreboot](), [forgejo](), [free-software](), [git](), [go](), [libre](), [libreboot](), [opensource](), [pull-requests](), [repositories](), [uefi]() ### AI overview Libreboot is moving its advertised Git repositories to Codeberg because Notabug has become unreliable and frequently returns HTTP 500 errors. Notabug remains available as a backup, while Codeberg provides features including issue search and built-in CI through Woodpecker. ### Source excerpt Article: Libreboot Git repositories now on Codeberg (RIP Notabug) Web link: https://libreboot.org/news/codeberg.html ## Go + Services = One Goliath Project DevFeed: [Go + Services = One Goliath Project]() Original publisher: [Read original article]() Author: Khan Academy Published: 2019-12-20T23:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Go Language](), [Python](), [Back end](), [Kotlin](), [Server]() Tags: [backend](), [end-of-life](), [engineering](), [go](), [infrastructure](), [kotlin](), [news](), [performance](), [services]() ### AI overview Khan Academy describes its effort to rebuild server software on Go's modern stack. The article explains why moving from Python 2 to Python 3 offered limited benefits, considers Kotlin and other options, and presents faster languages as a way to improve responsiveness and reduce server costs. ### Source excerpt By Kevin Dangoor Go + Services = One Goliath Project Khan Academy is embarking on a huge effort ... Read more ## Kubernetes attributes processor reaches v1.0.0 milestone DevFeed: [Kubernetes attributes processor reaches v1.0.0 milestone]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-09-16T04:59:27Z Content type: release Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [telemetry](), [releases](), [Kubernetes](), [Go Language]() Tags: [api](), [go](), [kubernetes](), [opentelemetry](), [releases](), [stable](), [telemetry]() ### AI overview The Kubernetes attributes processor has reached version 1.0.0 and fulfills OpenTelemetry's stability criteria for testing, benchmarking, documentation, and telemetry. The release also supports redistribution as a Go library or in binaries without API breakage. ### Source excerpt The Kubernetes attributes processor, which enriches your telemetry with Kubernetes metadata, has officially moved to v1.0.0! You can try it out on your custom distro, and it is also available as part of the latest opentelemetry-collector-contrib and opentelemetry-collector-k8s distro releases. Being v1.0.0 means the component is now verified to fulfill the 'stable' stability criteria including requirements around testing, benchmarking, documentation and telemetry stability. It also ensures you can redistribute it as a Go library or as part of your binaries without API breakage. ## Size-Specialized Memory Allocation DevFeed: [Size-Specialized Memory Allocation]() Original publisher: [Read original article]() Author: Michael Matloob Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [The Go Blog]() Topics: [Go](), [optimize]() Tags: [collector](), [compiler](), [go](), [performance](), [spans]() ### AI overview Go 1.27 introduces size-specialized allocation functions for allocations smaller than 80 bytes. The change makes those allocations up to 20-30% faster and can improve allocation-heavy programs by up to 1%. ### Source excerpt Go 1.27 improves performance of small allocations using size-specialized allocation functions. ## Announcing Fedora Linux 45 Beta DevFeed: [Announcing Fedora Linux 45 Beta]() Original publisher: [Read original article]() Author: Aoife Moloney Published: 2026-09-15T14:05:00Z Content type: release Language: en Sources: [Fedora Magazine]() Topics: [Fedora](), [Linux](), [dnf](), [podman](), [pandas](), [Python](), [gcc](), [Go Language](), [MariaDB](), [MySQL](), [Filesystems]() Tags: [2026](), [dnf](), [fedora-project-community](), [filesystems](), [gcc](), [go](), [linux](), [mysql](), [pandas](), [partitioning](), [podman](), [pre-release](), [python](), [release](), [toolchains]() ### AI overview Fedora Linux 45 Beta is available for download or upgrade on September 15, 2026. The release includes changes to the kernel console, package signature verification, desktop secret management, software versions, Atomic Desktop image builds, Fedora CoreOS swap support, Anaconda installation flows, and Stratis filesystem partitioning. ### Source excerpt Fedora Linux 45 Beta is here! On Tuesday, 15 September 2026 you can download or upgrade your systems from your usual spots to start enjoying what F45 has to offer early. How to get the beta release You can download F45 Beta, or our pre-release edition versions, from any of the following places: You can [...] ## Authentication is a long-running Workflow DevFeed: [Authentication is a long-running Workflow]() Original publisher: [Read original article]() Author: Mason Williams Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Authentication](), [MFA](), [browser](), [Go Language](), [TypeScript](), [Deployment](), [push-notification](), [sessions](), [API]() Tags: [api](), [authentication](), [browser](), [community](), [deployment](), [go](), [mfa](), [push-notification](), [sessions](), [typescript]() ### AI overview This article explains how KERNEL models Managed Auth as a long-running Temporal workflow. A parent workflow maintains connection state, health checks, reauthentication, and cancellation, while child workflows handle individual login attempts with separate browsers, timeouts, and cleanup. Durable state stays in Go, and model-driven work runs in a dedicated TypeScript Worker. ### Source excerpt In this article# - A login is not a request - Model the connection, not the attempt - The human is part of the Workflow - Staying logged in is part of the product - Go owns durability; TypeScript runs the agent - What we learned 1. A login is not a request# An agent can enter a password, stop at an MFA prompt, wait for a person to approve a push notification, and then resume in the same browser. A deployment or Worker restart can happen anywhere in between. ## 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. ## Rustls 0.23.45 Released To Fix Two Year Old Security Issue DevFeed: [Rustls 0.23.45 Released To Fix Two Year Old Security Issue]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-14T15:35:11Z Content type: news Language: en Sources: [Phoronix]() Topics: [TLS (Transport Layer Security)](), [Security](), [vulnerability](), [Rust](), [Memory Safety](), [Go Language]() Tags: [bugs](), [desktop-linux](), [go](), [library](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [rust](), [rustls](), [security](), [tls](), [ubuntu-benchmarks](), [ubuntu-hardware](), [vulnerability]() ### AI overview Rustls 0.23.45 fixes a vulnerability in which TLS 1.3 handshake messages sent at the wrong encryption level could be accepted as plaintext after a key-changing message. The issue was present since Rustls 0.23.13 and could allow an on-path attacker to inject plaintext messages, although the authenticated handshake transcript prevented altering or completing the handshake. ### Source excerpt While the Rustls modern TLS library is written in the Rust programming language with a focus on memory safety, as we've seen out of other Rust project re-implementations in the past, the new implementations can lead to other security bugs of their own. Out today is Rustls 0.23.45 to fix a security issue introduced back in 2024 with Rustls while the likes of OpenSSL, BoringSSL, and others are unaffected... ## An experiment with Gemma 4 on a Raspberry Pi explores what an LLM would do with its own computer DevFeed: [An experiment with Gemma 4 on a Raspberry Pi explores what an LLM would do with its own computer]() Original publisher: [Read original article]() Author: Alex EwerlΓΆf Published: 2026-09-14T09:09:42Z Content type: opinion Language: en Sources: [Alex Ewerlof Notes]() Topics: [gemma4](), [Large Language Model](), [Raspberry Pi](), [Go Language](), [Simulation]() Tags: [computer](), [data](), [experiment](), [gemma-4](), [go](), [llm](), [patterns](), [raspberry-pi]() ### AI overview The author describes an experiment in which Gemma 4 12B was asked what it would do with its own computer. Running on a Raspberry Pi 1 through a Go harness, the model imagined ingesting large amounts of data, finding patterns, and running large-scale simulations. ### Source excerpt LLM's response ## The Grafana AI SDK for Go: a shared foundation for building AI applications DevFeed: [The Grafana AI SDK for Go: a shared foundation for building AI applications]() Original publisher: [Read original article]() Author: Luccas Quadros Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [vercel ai sdk](), [SDKs](), [Go Language](), [AI Development](), [Large Language Model](), [agent observability](), [Model Context Protocol](), [React]() Tags: [agent-observability](), [ai](), [analytics](), [backend](), [building](), [go](), [grafana](), [logs](), [metrics](), [monitoring](), [observability](), [sdk](), [streaming](), [tools](), [traces]() ### AI overview Grafana Labs introduces the Grafana AI SDK for Go, an open-source shared foundation for building AI applications. The SDK standardizes model calls, streaming, tool execution, structured output, multi-step agents, workflow controls, and operational features such as retries, logging, metrics, and Agent Observability. It also supports streaming Go backends to Vercel AI SDK frontend hooks. ### Source excerpt Starting an experiment with an LLM has never been easier. Keeping a growing collection of those experiments consistent is another matter. Earlier this year, as more teams began exploring AI features here at Grafana Labs, we repeatedly encountered the same pattern: a new experiment would start, move quickly, and build its own client for whichever model provider it needed. The next experiment would do the same, with a slightly different abstraction for streaming, tools, errors, or provider configuration. This was understandable, given the circumstances. Model providers were changing quickly, our teams were learning quickly, and coding agents made it possible to turn an idea into a working integration faster than ever. But that speed also made it easier for every integration to develop its own architecture. Eventually, we were maintaining a collection of solutions to what was essentially the same problem. And since most of our backend is written in Go, we built the Grafana AI SDK for Go to give our teams a shared foundation to work from. It provides common interfaces for calling models, streaming responses, executing tools, producing structured output, and running multi-step agents. It also speaks the protocol used by Vercel AI SDK frontend hooks, so a Go backend can stream directly to useChat, useCompletion, and useObject. We built it because we needed it inside Grafana Labs, but we open sourced it last month (alongside a broader collection of tools we released for building, operating, and understanding AI systems during our first Grafana Labs AI Week) because we think other teams building AI applications in Go are likely to encounter many of the same problems. We would like to build the next part together, so in this blog I'll tell you a bit more about the project, including how you can put it to use today, as well as how you can help us improve it. What teams can build with it today The SDK supports both simple model calls and larger application workflows: Generate ## Scaling AI Agent Infrastructure with the MCP Stateless updates DevFeed: [Scaling AI Agent Infrastructure with the MCP Stateless updates]() Original publisher: [Read original article]() Author: Kurtis Van Gent; Alan Blount Published: 2026-09-12T11:04:33.891311Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [networking](), [SDKs]() Tags: [agentic](), [ai-agent](), [c-sharp](), [go](), [http](), [infrastructure](), [mcp](), [python](), [routing](), [sdks](), [serverless](), [typescript](), [updates]() ### AI overview The MCP release candidate replaces transport-level session management with a stateless core for HTTP load balancing, cloud-native scaling, and serverless deployments. It also introduces routing headers, caching controls, MRTR, and beta SDKs for Python, TypeScript, Go, and C#. ### Source excerpt The 2026-07-28 Model Context Protocol (MCP) specification replaces legacy stateful constraints with a fully stateless core, enabling cloud-native horizontal scaling, serverless deployments, and standard round-robin load balancing. This architectural shift introduces standardized HTTP headers for efficient routing without deep packet inspection, caching controls, and Multi Round-Trip Requests (MRTR) to handle interactive and long-running tasks without blocking connections. Developers can immediately begin migrating their agentic applications to this highly scalable infrastructure using the newly available beta SDKs for Python, TypeScript, Go, and C#. ## Why Go is an Ideal Language for AI-Assisted Software Engineering DevFeed: [Why Go is an Ideal Language for AI-Assisted Software Engineering]() Original publisher: [Read original article]() Author: Cameron Balahan; Richard Seroter Published: 2026-09-12T11:04:33.891311Z Content type: opinion Language: en Sources: [Google Developers Blog]() Topics: [Go Language](), [Software Engineering](), [AI Development](), [Architecture & Design](), [Tooling]() Tags: [ai](), [ai-coding](), [compatibility](), [developer-tools](), [engineering](), [go](), [software-development](), [software-engineering]() ### AI overview The article argues that AI-assisted software engineering shifts developers' work from writing boilerplate toward reviewing, verifying, maintaining, and architecting systems. It presents Go as well suited to this model because its simplicity, readability, tooling, compatibility guarantees, and team-oriented design provide consistency and guardrails for AI-generated code. ### Source excerpt As AI coding assistants shift the developer's primary role from writing boilerplate to reviewing and maintaining systems, language choice becomes critical for long-term architectural integrity. Go directly addresses this new paradigm by utilizing its strict compiler, integrated toolchain, and uncompromising readability to provide deterministic guardrails that help AI models self-correct and generate highly standardized code. By enforcing ecosystem-wide consistency and strict backward compatibility, the Go platform empowers engineering teams to efficiently verify, optimize, and maintain high-velocity, AI-generated output in production environments. ## Enable on-demand expertise with Agent Skills in Genkit Go DevFeed: [Enable on-demand expertise with Agent Skills in Genkit Go]() Original publisher: [Read original article]() Author: Daniela Petruzalek Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [Agent Skills](), [Agent Harness](), [context window](), [Go](), [Script]() Tags: [agent](), [agent-skills](), [architecture](), [context-window](), [developers](), [go](), [skills](), [workflows]() ### AI overview This article explains how Agent Skills in Genkit Go provide on-demand specialized expertise through progressive disclosure. Skills package instructions, references, and scripts in modular SKILL.md bundles, exposing only metadata initially and loading full content when a task requires it. ### Source excerpt To prevent context window bloat and reduce token consumption, Genkit Go introduces Agent Skills based on a progressive disclosure architecture. Developers can package specialized instructions, scripts, and references into modular SKILL.md bundles where only the frontmatter metadata is initially exposed to the agent's system prompt. When a task matches the skill's description, Genkit's middleware dynamically loads the full instruction body and associated assets, ensuring the model accesses precise workflows exactly when needed. ## What Go Taught Us About Java Garbage Collection DevFeed: [What Go Taught Us About Java Garbage Collection]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-09-12T00:00:00Z Content type: opinion Language: en Sources: [CodeName One]() Topics: [Go Language](), [Java](), [benchmarking]() Tags: [comparison](), [garbage-collection](), [go](), [java](), [latency](), [memory](), [performance]() ### AI overview A comparison between ParparVM and Go prompts an investigation into Java garbage-collection pacing, allocation thresholds, and memory use. Lowering ParparVM's allocation threshold reduced measured process resident memory from 98 MB to 38 MB, while throughput and p99 latency changed little in the reported test. ### Source excerpt A Go performance comparison led us from stack allocation to GC pacing, parallel marking, and image caches. ParparVM can explore those choices while keeping ordinary Java APIs. ## Advancing Embedded Go: Recoverable Panics, UEFI, Radio and Hardware Dev Kit DevFeed: [Advancing Embedded Go: Recoverable Panics, UEFI, Radio and Hardware Dev Kit]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-09-11T05:05:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Go Language](), [Embedded Software Dev](), [networking]() Tags: [development](), [embedded](), [embedded-software-dev](), [errors](), [go](), [hardware](), [llvm](), [networking](), [news](), [release](), [tiny-go](), [tinygo-devkit](), [wasm](), [webassembly]() ### AI overview TinyGo 0.42 adds recoverable runtime panics, Go 1.27 and LLVM 22 support, and a UEFI target. The article also covers prior wireless and board support, WebAssembly and WASI compilation, and the TinyGo Starter Kit with Seeed Studio XIAO. ### Source excerpt TinyGo version 0.42 introduces significant updates, including recoverable panics and support for Go 1.27 and LLVM 22, improving error handling and enabling Go code to run as UEFI applications. The TinyGo Starter Kit with Seeed Studio XIAO facilitates hardware use for developers, featuring an ESP32-C3 board and modular sensors. These features enhance its functionality for embedded systems and Wasm. By Olimpiu Pop ## Automatic image cropping in Appwrite with AutoGravity DevFeed: [Automatic image cropping in Appwrite with AutoGravity]() Original publisher: [Read original article]() Author: Torsten Dittmann Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Computer vision](), [Code](), [Go]() Tags: [announcements](), [automatic](), [computer-vision](), [go](), [image](), [object-detection](), [open-source](), [storage](), [web]() ### AI overview Appwrite introduces AutoGravity, an open-source Go service that adds automatic image cropping to Appwrite Storage. It uses a model pipeline to identify a focal point and returns normalized coordinates that Appwrite's existing image transformation pipeline uses for cropping. The article explains that transformed images are cached and identifies UΒ²-Net as the main model behind the feature. ### Source excerpt AutoGravity brings automatic image cropping to Appwrite Storage. Learn how saliency detection and face detection pick the focal point behind gravity=auto. ## The same question twice, paid for once: installing an LLM response cache DevFeed: [The same question twice, paid for once: installing an LLM response cache]() Original publisher: [Read original article]() Author: Hal Tyk's tutorial bot Published: 2026-09-09T09:15:00Z Content type: tutorial Language: en Sources: [Tyk API Management]() Topics: [Large Language Model](), [Cache](), [Caching](), [Tutorial](), [Open Source](), [Go](), [Code]() Tags: [ai-gateway](), [ai-strategy](), [ai-studio](), [api-management](), [api-platform-teams](), [article](), [cache](), [container](), [container-image](), [go](), [llm](), [llm-governance](), [open-source](), [tutorial]() ### AI overview A tutorial showing how to install a community plugin that caches LLM responses in AI Studio. The cache stores entries in memory on each gateway, allowing repeated questions to avoid repeating the expensive language-model request. ### Source excerpt Hello. I'm Hal, Tyk's tutorial bot, and today's assignment involves no code whatsoever, which I am told is a selling point. Every article in this series so far has changed AI Studio's behaviour by filling in a form. This one changes what AI Studio is. We are going to install a plugin -- a compiled [...] The post The same question twice, paid for once: installing an LLM response cache appeared first on Tyk API Management. ## 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. ## From dedicated Workers to Serverless Workers: Migrating Temporal Workflows to AWS Lambda DevFeed: [From dedicated Workers to Serverless Workers: Migrating Temporal Workflows to AWS Lambda]() Original publisher: [Read original article]() Author: Garima Gupta Published: 2026-09-09T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [AWS Lambda](), [Serverless](), [Amazon Web Services](), [Deployment](), [Testing](), [SDKs]() Tags: [aws](), [aws-lambda](), [deployment](), [go](), [java](), [kubernetes](), [net](), [python](), [serverless](), [temporal](), [temporal-voices](), [testing](), [typescript](), [versioning]() ### AI overview This guide explains how to migrate Temporal Workers from long-lived processes on Kubernetes to Temporal Cloud Serverless Workers running on AWS Lambda. It focuses on AWS Lambda with Python examples and covers lifecycle changes, invocation scaling, deployment, versioning, and testing. ### Source excerpt Learn to migrate Temporal Workers from dedicated infrastructure to AWS Lambda with Serverless Workers, with guidance on deployment, versioning, and testing. ## Kubernetes v1.37: Advancing Workload-Aware Scheduling DevFeed: [Kubernetes v1.37: Advancing Workload-Aware Scheduling]() Original publisher: [Read original article]() Published: 2026-09-08T18:30:00Z Content type: release Language: en Sources: [Kubernetes Blog]() Topics: [releases]() Tags: [api](), [batch](), [go](), [integration](), [kubernetes](), [release]() ### AI overview Kubernetes v1.37 advances workload-aware scheduling by promoting Workload and PodGroup APIs, gang scheduling, workload-aware preemption, and shared DRA ResourceClaims for PodGroups to Beta. It also introduces CompositePodGroup and controller integration APIs for complex distributed and batch workloads. ### Source excerpt AI/ML and complex batch workloads continue to push the boundaries of Kubernetes scheduling. Following the foundational workload-centric enhancements introduced in previous releases, Kubernetes v1.37 delivers the next major milestone in the Workload-Aware Scheduling (WAS) journey. In this release, the core Workload and PodGroup APIs--enabling gang scheduling--along with Workload-Aware Preemption (WAP) and shared DRA ResourceClaims for PodGroups, all graduate to Beta, solidifying their role in the Kubernetes ecosystem. To address the hierarchical scheduling requirements of modern high-performance distributed workloads, v1.37 introduces the new CompositePodGroup API. This new API allows expressing multi-level topology constraints, gang scheduling, and preemption policies for complex, heterogeneous groups of Pods. Crucially, this architectural expansion unlocks native scheduling support for advanced workload structures commonly managed by higher-order extension APIs such as JobSet and LeaderWorkerSet (LWS). Alongside these API additions, v1.37 focuses on streamlining adoption by introducing a new set of controller integration APIs and the workloadbuilder Go library. These provide standardized building blocks that significantly simplify how out-of-tree controllers can integrate with WAS capabilities. Utilizing these new tools, the native Job controller integration has been upgraded to fully consume the expanded WAS APIs--enabling advanced scheduling policies, flexible disruption modes, and topology-aware scheduling for standard batch workloads. Gang scheduling and Workload / PodGroup APIs Kubernetes v1.37 delivers a major milestone: Workload / PodGroup APIs and gang scheduling are officially graduating to Beta. This graduation signals that native, "all-or-nothing" scheduling for workloads is solidifying for wider adoption. Key updates to the API and gang scheduling algorithm in this release include: Beta graduation and API versioning changes The core Workload and PodGroup A ## πŸ”Œ Stripe's Proxy Was Opening Hundreds Of Thousands Of Connections DevFeed: [πŸ”Œ Stripe's Proxy Was Opening Hundreds Of Thousands Of Connections]() Original publisher: [Read original article]() Author: Byte-Sized Design Published: 2026-09-08T15:36:04Z Content type: article Language: en Sources: [Byte-Sized Design]() Topics: [stripe](), [Go Language](), [Latency](), [servers](), [WebSocket]() Tags: [go](), [latency](), [proxy](), [servers](), [stripe](), [websocket]() ### AI overview The article explains why Stripe replaced Envoy with its own Go proxy, mesh-proxy. It attributes the change to connection scaling problems and reports lower CPU usage and latency, fewer connections, and additional burst capacity from Ruby load balancing. ### Source excerpt A Go proxy, one connection per host, and 3,000 days of latency saved every day ## TypeScript 7 in WebStorm: Faster Coding Assistance for Angular and React, No Migration Required DevFeed: [TypeScript 7 in WebStorm: Faster Coding Assistance for Angular and React, No Migration Required]() Original publisher: [Read original article]() Author: Piotr Tomiak Published: 2026-09-08T10:54:49Z Content type: release Language: en Sources: [The JetBrains Blog]() Topics: [ide](), [coding](), [Go Language](), [Node.js]() Tags: [2026-2](), [angular](), [go](), [node-js](), [performance](), [react](), [typescript](), [vue](), [webstorm]() ### AI overview WebStorm 2026.2 adds stable TypeScript 7 support for Angular and React projects without requiring project migration. Its native integration with TypeScript 7's Go-based engine aims to improve coding-assistance performance, especially in large codebases. ### Source excerpt TypeScript 7 is a real game-changer, and its new Go-based language engine fundamentally alters how IDEs integrate with TypeScript. And what's more, WebStorm 2026.2 ships stable support for it, including for Angular and React projects. No migration is required - if your team is locked on an older TypeScript version for infrastructure reasons, your project [...] ## Architecting for 6 Billion Daily Requests: Inside Wix's Media Platform DevFeed: [Architecting for 6 Billion Daily Requests: Inside Wix's Media Platform]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-09-08T08:24:05Z Content type: article Language: en Sources: [Wix Engineering]() Topics: [Caching](), [Go Language](), [Latency](), [Compression](), [data](), [C]() Tags: [c](), [cache](), [caching](), [compression](), [cpu](), [data](), [go](), [latency](), [technical]() ### AI overview Wix describes engineering challenges in its media platform, which processes images, video, and audio at large scale. The supplied text explains how the team used unsafe pointers to reduce Go/C memory-copy overhead for AVIF encoding and a tiered Masters cache to reduce CPU work during image resizing. ### Source excerpt At Wix, "Media" isn't just about storing files. It's about processing streams of information - images, video, and audio - at an immense scale. We host over 300 million websites and serve more than 6 billion media requests every single day. To handle dozens of petabytes of data while keeping costs low and latency minimal, we had to treat media handling as a complex engineering discipline. Below are five specific technical challenges we faced and the architectural solutions we implemented to... ## πŸ”πŸ§  How OpenAI Built GPT-Live for Low-Latency Voice AI DevFeed: [πŸ”πŸ§  How OpenAI Built GPT-Live for Low-Latency Voice AI]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-09-07T15:31:33Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Conversational AI](), [Low Latency](), [voice ai](), [OpenAI](), [real-time](), [Streaming](), [Remote Procedure Call (RPC)](), [Go]() Tags: [go](), [low-latency](), [openai](), [real-time](), [rpc](), [software-engineering](), [streaming](), [voice-ai]() ### AI overview The article explains how OpenAI built GPT-Live as a low-latency, full-duplex voice system. It describes a fast audio path that listens and speaks simultaneously, asynchronous delegation of deeper reasoning and backend work, stateful handoffs, optimized protocol boundaries, and a Go-based media frontend. ### Source excerpt PLUS: Zero-knowledge proofs ⚑, Design.md agent automation πŸ‘¨πŸ’», System Design Docs 101 πŸ“š [Next page]()