标签：#go

You'll need an M1 or better to run the next release of macOS.

I hope you have a modern Apple Watch or iPad, because otherwise watchOS 27 and iPadOS 27 won't run on your device. Apple often drops support for older devices with its latest software updates, but this year it's culling even more device generations than ever before. Apple is dropping support for at least three generations […]

NotebookLM is getting a big upgrade, but it's only for AI Ultra and enterprise accounts right now.

The Trump administration released the updated version of the list four months ago and then quickly pulled it without explaining why

Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product. A few years ago, choosing a Go mocking framework was mostly a matter of personal preference. Today, things are different. Most Go developers have at least one AI coding assistant generating tests alongside them. Some teams even generate the majority of their unit tests automatically. Yet one area remains surprisingly messy: mocks. Ask an LLM to write a test for the same interface and you'll often get completely different results depending on whether your project uses GoMock, Mockery, MockIO, Minimock, Moq, or hand-written test doubles. The problem isn't that the models are bad. The problem is that mocking libraries represent very different philosophies: Strict vs flexible Generated vs runtime-created DSL-heavy vs idiomatic Go Feature-rich vs minimalist In this article we'll compare the most popular Go mocking libraries in 2026, examine their strengths and weaknesses, and discuss which one may be the best fit for your project. What Makes a Good Mocking Library? Before comparing tools, it's worth defining what matters. A good mocking library should ideally provide: Easy mock generation Clear test failures Minimal boilerplate Strong refactoring support Good IDE experience Readable tests Reliable call verification Different libraries optimize for different parts of this list. That's why there is no universally correct answer. 1. GoMock: The Enterprise Workhorse GoMock remains one of the most widely used mocking frameworks in the Go ecosystem. Originally created by Google and now actively maintained by Uber, it has become the standard choice for many large organizations. Its philosophy is straightforward: define expectations explicitly and verify them rigorously. Example func TestUserService ( t * testing . T ) { ctrl := gomock . NewController ( t ) repo := New

If you’re a user—owner?—of this cryptocurrency, this is important: On May 29, the security researcher Taylor Hornby found a critical vulnerability in Zcash Orchard privacy pool using Claude Opus 4.8. The Zcash team hired Hornby specifically to look for this kind of issue. He found one fast enough to be embarrassing. The Orchard pool is the newest and most advanced shielded transaction system in the cryptocurrency Zcash. Introduced in 2022, it allows users to send and receive ZEC while keeping transaction details private. It uses zero-knowledge proofs to validate transactions without revealing amounts or participants. The bug: a specific check that was supposed to validate transaction inputs wasn’t actually enforcing the rules it appeared to enforce. An attacker could have exploited the flaw to feed false inputs into that check and generate ZEC from nothing, with the zero-knowledge proof system blessing the fraudulent transaction as valid...

Google is making Gemini 3.5 the default model in NotebookLM

Google is rolling out "across the board" updates to NotebookLM. The AI-powered note-taking app now uses Google's upgraded Gemini 3.5 model, which will allow it to respond with "more accurate and reliable information," according to a blog post on Monday. Launched in 2023, NotebookLM allows you to interact with your notes and sources using AI, […]

Google says Gemma 4 12B is "designed to bring agentic, multimodal intelligence directly to your laptop", further noting that the new model can be combined with Google AI Edge to "build and experiment locally, on everyday machines". This integration allows for a wide range of capabilities, from autonomous data processing to generating visual insights and even building webpages or executing tools. By Sergio De Simone

The era of passive data analytics is over. Today, the most forward-thinking data teams aren't just...

There is a class of projects that teaches you more about a language than any tutorial ever could. Building a PDF engine from scratch in Go is one of them. It is not glamorous. It is not trendy. But it forces you to confront memory management, binary serialization, concurrency safety, interface design, and performance profiling all at once, in a domain where correctness is non-negotiable. This article walks through the lessons learned building GoPdfSuit (~500 Github ⭐), a production PDF engine written in Go that generates 1.5 million financial PDFs in roughly 45 minutes on a single node, achieves PDF/A-4 and PDF/UA-2 compliance, and exposes itself as a REST API, a Go library, and Python CGO bindings simultaneously. Note : While I have six years of overall experience including two years working specifically with Go, I rarely encountered these types of challenges in my day-to-day work, as my role focused primarily on implementing new features within an existing architecture. Working on gopdfsuit was an excellent learning experience; it allowed me to dive deep into performance optimization and taught me a great deal. Below are some of the key takeaways. Building GoPdfSuit from a blank editor to a production-grade PDF engine-one that ships PDF 2.0 , PDF/A-4 , PDF/UA-2 , PKCS#7 signing, merge/split, XFDF fill, secure redaction, and a public gopdflib API-forced a shift from “business logic” to “systems engineering.” When you chase ~2,000+ aggregate ops/s on a mixed financial workload (48 workers, PDF/A on) and sub ~10 ms PDF generation, you stop debating frameworks and start fighting the allocator, cache lines, and ISO 32000 semantics. These fifty lessons are drawn from the actual codebase ( internal/pdf , pkg/gopdflib , benchmark harnesses under sampledata/ , and documented optimization passes in guides/cursor/ ). They mix specification pain with Go runtime craft and production reality-not generic blog advice. Part 1: Structural Hurdles & PDF Specification Nightmares Deco

With the release of version 1.7.9, ZenQL’s Collection API, Thor, received substantial performance improvements, largely driven by a series of memory optimization enhancements. These changes reduced unnecessary allocations, improved data handling efficiency, and significantly accelerated query execution, particularly when working with large in-memory datasets. From the early stages of development, we established a baseline benchmark to measure both correctness and performance consistently: filtering a slice of 50 million items and validating the result. The original Collection API completed this benchmark in approximately 9 seconds. Later, we introduced Thor as a replacement for the default Collection API, reducing the benchmark time to around 4 seconds. With the latest round of memory optimizations and internal improvements, Thor now completes the same benchmark in less than one second. benchmark: goos: linux goarch: amd64 pkg: github.com/malikhan-dev/zenql/collections/Thor cpu: 12th Gen Intel(R) Core(TM) i7-12700H BenchmarkQueryEngine BenchmarkQueryEngine-20 88 13636313 ns/op 22727310 B/op 0 allocs/op at Thor_Engine__test . go func BenchmarkQueryEngine ( b * testing . B ) { result := From ( & items ) . Where ( func ( search ComplexObjectToSearch ) bool { return search . Name == "Jane" && search . Flag == false }) . Collect () result2 := From ( & result ) . Any ( func ( search ComplexObjectToSearch ) bool { return ( search . Name != "Jane" ) || ( search . Flag != false ) }) . Assert () if result2 { b . Error ( "result should be false" ) } } Join the Journey We are committed to making ZenQL the fastest and most developer-friendly query engine for Go. As we continue to grow and push the boundaries of performance, we need your support! If you find ZenQL useful, please star our repository on GitHub. Your support helps us reach more developers and keep the project moving forward. Thank you!

Krishnan is reportedly starting a new institution to continue shaping Trump's AI policy.

President Donald Trump said he's discussing deals "where the American people can benefit from the success of AI."

GitHub热门项目 | The Go programming language | Stars: 134,434 | 24 stars today | 语言: Go

The companies announced the deal on Friday, just one week ahead of SpaceX's historic IPO.

Google's AI app Pixel Studio launched less than two years ago, but is now being shut down.

Cybercriminals, part of a gang known as Silent Ransom Group, have sent people pretending to be IT support employees to law firms' offices, where the criminals have stolen data using USB drives or remote access tools.

imagine you are working in a large codebase. you need to fetch different kinds of data and transforming them, grouping them or sorting them. lets take a closer look at Sorting and Heaps in golang Specifically. we already familiar with heaps and its important interface. the Heap.Interface. a very performant and impressive implementation of heaps and its sorting functionality. type Interface interface { sort . Interface Push ( x any ) // add x as element Len() Pop () any // remove and return element Len() - 1. } as a programming language, it couldnt be done better than what it is today. but most of the time we might not need to implement all the interface items. dont get me wrong the functionalities should exist but mostly all that matters for us is that how the sorting will be done. ZenQL's Implementation In the latest version take advantage of sorting and heaps functionality. in a fast and agile way! result := From ( personList ) . Where ( func ( person Person ) bool { return person . Active == true }) . CollectSorted ( func ( person Person , person2 Person ) bool { return person . Identifier < person2 . Identifier }, true ) In the code snippet above we perform a sort on our collections using the thor engine very easily. we just express our desire about how the sorting needs to be done and wether its ascending or descending. and other functionalities are implemented as below: type Sortable [ T any ] struct { Items [] T less func ( a , b T ) bool desc bool } func ( h Sortable [ T ]) Len () int { return len ( h . Items ) } func ( h Sortable [ T ]) Swap ( i , j int ) { h . Items [ i ], h . Items [ j ] = h . Items [ j ], h . Items [ i ] } func ( h * Sortable [ T ]) Push ( x any ) { h . Items = append ( h . Items , x . ( T )) } func ( h * Sortable [ T ]) Pop () any { old := h . Items n := len ( old ) item := old [ n - 1 ] h . Items = old [ : n - 1 ] return item } be faster and more agile with the Golang ZenQL. Click To Visit ZenQLRepository

The Air succeeds as a minimalist, reliable fitness tracker, but Google's AI Health Coach feels unnecessary.