AI 资讯

AI models can voluntarily submitted to the government for review 30 days before release under Trump's new executive order.

Microsoft only just announced a new Surface Laptop Ultra at the weekend, and it's now revealing a miniature Surface PC aimed at developers. The new Surface RTX Spark Dev Box is powered by Nvidia's new Arm-based RTX Spark chips, just like the Surface Laptop Ultra, and is optimized for sustained workloads and local AI tasks. […]

After industry objections, President Trump signed a revised AI executive order requiring only voluntary prerelease government reviews of advanced models.

O&amp;#039; Billion dollar companies. It boggles the mind. Probably takes $500K in tokens for AI to figure it out. </rant> submitted by /u/centuryeyes [link] [留言]

Hi! This is an infinite canvas note-taking tool where notes are laid out in a non-Euclidean, hyperbolic geometric space. As you drag and navigate through the view, you’ll experience a unique fluid distortion that naturally leverages your brain's spatial memory. I’ve been obsessed with the concept of space in HCI for years. Many modern UI patterns are essentially workarounds for the lack of screen real estate. While researching zoom-based UIs a while back, I stumbled upon old HCI papers that used

Hey everyone, If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster. Most of the "guides" out there are just static, out-of-date tables or dense walls of text. So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently. What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page. You select the model size (8B, 32B, 70B, etc.). You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ). The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy. It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances. It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive) Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide submitted by /u/Outside-Risk-8912 [link] [留言]

OpenAI is getting serious about courting enterprise users. On Tuesday, the AI lab released a new set of capabilities for Codex, meant to expand the agentic tool’s uses in the workplace. Together with the new tools, the company released an internal report on how Codex is being used for knowledge work, finding its uses go […]

Microsoft’s annual developer conference is kicking off on June 2nd in San Francisco with the keynote presentation streaming live at 12:30PM ET / 9:30AM PT, and we will be following along here with everything as it’s announced. The Verge’s Tom Warren reports that we can expect to hear about new AI models and agentic OpenClaw-like […]

I’ve been experimenting with CRTP and ended up with a variation that enforces a strict interface/implementation boundary without friend declarations. The goal was to eliminate boilerplate I frequently encountered when trying to encapsulate derived class methods. The key idea is using C++23 explicit object parameters this + a small access wrapper type so implementations can only be called through the interface layer. That was about two and a half months ago. Since, I’ve taken the time to better understand it and write an article about it, which you can find below. As explained there, I refer to this approach as Exotic CRTP. Example ```cpp // Reference example of the pattern // See: https://medium.com/@felixolivierdumas/exotic-crtp-rethinking-static-polymorphism-with-c-23-89f9e75e8ffd include <iostream> include <type_traits> include <utility> namespace exotic { template<typename... From> struct crtp_access : From... {}; template<typename T> constexpr decltype(auto) as_crtp(T&& obj) noexcept { using crtp_access_t = crtp_access<std::remove_cvref_t<T>>; return static_cast<crtp_access_t&&>(obj); } } struct Base { void interface(this auto&& self) { exotic::as_crtp(self).implementation(); } }; struct Derived : Base { void implementation(this exotic::crtp_access<Derived> self) { std::cout << "Derived implementation" << std::endl; } }; int main() { Derived d; d.interface(); // perfectly works // d.implementation(); -> doesn't work, Derived only allows .interface() } ``` Not sure yet if this is actually useful in real conditions or just a different way of structuring CRTP, but it seems to be genuinely powerful. Full write-up here: https://medium.com/@felixolivierdumas/exotic-crtp-rethinking-static-polymorphism-with-c-23-89f9e75e8ffd Curious how this compares to traditional CRTP + friend patterns in real codebases :) submitted by /u/Mysticatly [link] [留言]

Season 3 of Silo premieres on July 3.

Board, the startup building what it calls "together tech" designed to bring people into the same room, has closed a Series A led by Union Square Ventures.

This article was originally published on aicoderscope.com Most AI coding tools are generalists—they write code, answer questions, and somewhere in the feature list, review pull requests. CodeRabbit is the opposite: one thing, done obsessively. Every feature, every design decision, every pricing tier revolves around making PR review better. After reviewing the pricing, benchmarks, and comparing it to GitHub Copilot's native code review, here's the honest assessment. What CodeRabbit actually is (and what it isn't) CodeRabbit sits between your developer's git push and the merge button. You connect it to your repository host—GitHub, GitLab, Azure DevOps, or Bitbucket—and it automatically reviews every pull request. No button to click. It reads the diff, checks it against your full codebase for context, runs 40+ static analysis tools, then uses a multi-model AI stack to flag bugs, security issues, and style violations directly in PR comments. What it cannot do: generate application code, scaffold features, or replace a coding assistant. It is review-only. That constraint shapes everything about the product. At $40M ARR as of April 2026 (up 700% year-over-year from $5M ARR in April 2025), with 2 million repositories connected and more than 13 million pull requests reviewed, CodeRabbit has clearly found a market. It currently holds the #1 position among AI apps on GitHub Marketplace. How the review actually works Every CodeRabbit review runs in three stages. Stage 1: Context engine. Before analyzing the diff, CodeRabbit indexes your codebase using a retrieval system similar to what backs its code reviews across millions of repositories. It uses NVIDIA Nemotron for this context-gathering and summarization stage—a lightweight open model optimized for retrieval rather than generation. This is why CodeRabbit catches cross-file issues that pure diff-reviewers miss. Stage 2: Static analysis. A deterministic SAST layer runs linters that don't need AI inference: Biome, ESLint, Ruf

Tenho aproveitado meu tempo sem trabalhar pra estudar, enfim a vida de quem trabalha com tecnologia né? E um dos meus maiores focos tem sido IA, seus usos, como ela entra e pode ser aplicada em áreas diferentes, e todas as novidades que saem todos os dias. Hoje vim compartilhar uma coisa bem legal que aprendi no curso AI-Native Engineering Foundations do Addy Osmani , o problema dos 70%. Existe um padrão claro que tenho observado na prática ao acompanhar dezenas de equipes de engenharia: a Inteligência Artificial resolve com impressionante eficiência 70% de quase qualquer tarefa técnica. Falo daquela camada previsível, repetitiva e baseada em padrões exaustivamente documentados na internet. Coisas como código boilerplate, arquivos de configuração, implementações de CRUDs simples, conversão de sintaxe entre linguagens e a escrita de testes unitários básicos. A IA já "viu" milhões de exemplos disso em repositórios públicos e consegue reproduzir o padrão em segundos. Para essa fatia do trabalho, ela é uma aceleradora fantástica. O grande problema, e o motivo pelo qual muitos projetos com IA começam bem mas falham no meio, é que os outros 30% são justamente os que sustentam o software. É nesses 30% que entram as decisões que inteligência nenhuma consegue tomar sozinha: Contexto de Negócio: A IA não sabe por que aquela feature está sendo construída ou como ela impacta o usuário final. Arquitetura e Manutenibilidade: Escrever código que funciona hoje é fácil; escrever código que outra pessoa consegue alterar daqui a seis meses sem quebrar o sistema é outra história. Casos de Borda e Segurança: A IA tende a gerar o "caminho feliz". Tratar falhas de concorrência, vazamento de memória e vulnerabilidades específicas do seu ecossistema exige malícia técnica. Essas questões não se resolvem apenas digitando linhas de código, elas exigem contexto, experiência, histórico de dores passadas e, acima de tudo, julgamento humano. E é exatamente aqui que a IA ainda não entrega. O Parado

Most teams don't know how many feature flags are in their codebase. They know they have some . They think they cleaned up most . They're not sure about the rest. One command changes that: npx flaglint audit ./src No API key. No credentials. No dashboard to sign up for. Just your source code and an honest answer. The Problem Nobody Talks About According to LaunchDarkly's best practices, most release flags should live for only days to weeks — yet many remain in codebases for months or years. That's not a LaunchDarkly problem. It's a universal one. Flags accumulate because adding one is fast and removing one is work. You ship the feature, move on, and the flag stays. Six months later a new engineer asks "is this safe to delete?" and nobody knows. So it stays another six months. Stale flags make code "more complex and harder to maintain," as developers spend extra time navigating obsolete conditionals. Unused toggles may degrade performance or even inadvertently expose features or data. And here's the part that stings: developers spend 33–42% of their time dealing with technical debt and maintenance. Feature flag debt is a quiet contributor to that number. What "Flag Debt" Actually Looks Like Here's a real checkout service. Nothing exotic — a Node.js backend with LaunchDarkly calls spread across five files. // checkout.ts export async function isCheckoutV2Enabled ( user : User ): Promise < boolean > { const ctx = { targetingKey : user . id , email : user . email , plan : user . plan }; return ldClient . boolVariation ( " checkout-v2 " , ctx , false ); } // discounts.ts const flagKey = `discount- ${ experimentName } ` ; const enabled = await ldClient . boolVariation ( flagKey , ctx , false ); // analytics.ts const state = await ldClient . allFlagsState ( ctx ); Three different patterns. Three very different levels of risk. The first one is fine — static key, known type, safely removable. The second one is a problem — the key is a template literal. You can't statically kn

submitted by /u/YuriPD [link] [留言]

Building a Thriving Package Marketplace: The Complete MarketHub Guide Introduction If you're building a platform where developers can discover, share, and monetize packages, you're tackling one of the most complex problems in the software ecosystem. From managing publisher reputations to handling analytics at scale, marketplace dynamics require careful orchestration across multiple user roles. Enter MarketHub — a comprehensive three-app marketplace system designed to handle exactly this challenge. Whether you're creating a plugin ecosystem, SaaS integrations hub, or package distribution platform, MarketHub provides a battle-tested architecture for managing the complete marketplace lifecycle. The Problem: Why Marketplaces Are Hard Building a marketplace isn't just about creating a catalog. You need to solve several interconnected problems simultaneously: Discovery : How do users find quality packages in a sea of options? Trust : How do you build confidence in unfamiliar publishers? Quality Control : How do you maintain standards without stifling innovation? Incentives : How do you motivate publishers to create excellent packages? Scale : How do you manage analytics, reputation, and community as the ecosystem grows? Most teams try to bolt these features onto a basic catalog — resulting in fragmented systems where reputation tracking doesn't align with analytics, and community features feel disconnected from the review process. MarketHub Architecture: A Three-App Approach MarketHub solves this by separating concerns into three distinct applications, each optimized for its audience: 1. Public Discovery App — The Storefront This is where users find packages. The discovery app features: Intelligent Search & Filtering : Search across package names, descriptions, and tags with category-based filtering Featured Packages : Curated collections to highlight quality and trending packages Smart Ranking Algorithm : Packages rank based on quality signals — not just download counts

Recently, I started building a website for a local car showroom. The budget? 800,000 Indonesian Rupiah (around $50 USD). At first, it sounded impossible. But when working with small businesses in Indonesia, budgets are often very different from what many developers in the US or Europe are used to. Instead of building a complex custom platform, I focused on solving the showroom's real problems. What the client gets Vehicle Management Add and edit car listings Manage prices Vehicle specifications Featured inventory Vehicle Search & Filters Visitors can filter cars by: Brand Model Year Price Condition Built-in CMS The showroom can publish: Car buying guides Automotive news SEO articles Promotions Lead Generation Every car listing includes direct WhatsApp contact buttons to maximize inquiries. Extra Services Included For the same price: Free maintenance for simple issues Free consultation and support 3 free blog articles during the first month AI-powered statistics assistant Why WordPress? Many developers immediately think about Laravel, React, Next.js, microservices, and other modern stacks. For this project, WordPress was the right tool. The client needed: A website they could update themselves Better Google visibility A simple inventory system More WhatsApp leads WordPress delivered all of that quickly. A Lesson I've Learned Small businesses rarely care about technology. They care about outcomes. They don't ask: "Does it use React?" They ask: "Will this help me sell more cars?" And honestly, that's probably the better question. What would you include in a low-budget car showroom website?

Japanese version available on note . Hi, I'm chatii @chatii . I recently attended Laravel Live Japan 2026. Here's what inspired me and what I took home from the conference. Profile Organizer of PHP Conference Kagawa Encountered PHP back in the 4.x era Freelancer English level: "Can read reasonably well," "Can write a little," "Can listen a bit," "Cannot speak at all." 5/23 PHP×Tokyo - Laravel Live Japan PRE-PARTY PHP×Tokyo - Laravel Live Japan PRE-PARTY - connpass (English follows Japanese) PHP×Tokyoは、PHPやLaravelが好きなエンジニアのためのインターナショナルなミートアップです。 日英のライブ翻訳付きなので、英語が得意でなくても大丈夫です！言語の壁を越えて、PHP/Laravelについて語り合いましょう！ 登壇者も募集中です！登壇を希望する方はこちらからご応募ください。 登壇は日本語・英語どちらでも大丈夫です。 #### タイムテーブル * 13:00 - 13:30 受付 & ネットワーキング * 13:30 - 13:40 オープニング * 13:40 - 14:10 "Man... phpxtky.connpass.com I first participated in "PHP×Tokyo March 2026." It was my first time attending a meetup with international participants. I couldn't speak English, but I hoped to be able to communicate somehow. Back in March, David helped me immensely with translation, which made me feel a bit apologetic... At the PRE-PARTY, I took the plunge. During the networking session, I managed to approach Victor Ukam , who gave the talk "Manage AI Prompts as Versioned Files in PHP," and said in English, "I have a question...!" Well... communication after that relied on Google Translate, but I was able to overcome the "first hurdle." You could say I successfully executed <?= "Hello, World" ?> . Also, it was great to see Ivan again, who came from Russia. I first met him in March, and I was so happy he came over to say hello! 5/25 Eve of the Conference, Gyoza Restaurant Zumi organized an unofficial pre-party via the laravel-live-jp channel on the "Laravel Japan" Discord. Participating in these "fringe events" around a conference is always fun. I had booked a hotel from the day before, so I joined in. The real-time translation app that Albert Chen built was incredibly high-performance... 5/26 Day 1 ...Actually, I couldn't sleep at

Using threat modelling to make system dependability observable, testable, and actionable Executive summary Security and Reliability address system degradation. Security addresses degradation from intentional actions, such as denial-of-service attacks, while reliability addresses degradation from failure, load, dependency behaviour, operational change, or complexity. The underlying engineering question is the same: which critical system property can degrade, how would users experience that degradation, how would we detect it, and what controls would prevent, contain, or recover from it? This document proposes a practical way to bridge the two disciplines: anchor analysis on Critical User Journeys, express expected behaviour through SLOs and SLIs, use RAMSS to ensure coverage across dependability dimensions, and adapt PASTA-style threat modelling to reliability scenarios. The goal is not to merge Security and Reliability into one generic practice. The goal is to reuse the strongest habits of each discipline: security's adversarial modelling and reliability's production-oriented measurement, validation, and recovery loops. The most useful outcome is a shared model of degradation scenarios. A degradation scenario links a critical user journey to a concrete reliability or security threat, the system weakness that makes it possible, the signal that would detect it, the objective it would violate, and the mitigation or experiment that would validate the control. This makes risk easier to discuss with engineering teams because it connects abstract concerns to user impact, SLO burn, business loss, and testable remediation. 1. The problem: two disciplines, one degradation model After working in both Reliability and Security, I found that the two domains share much in common: both focus on objectives, weaknesses, control effectiveness, incident response, prioritisation, and residual risk, but often use different rituals, terminology, metrics, and boundaries. This separation ca