标签：#m

submitted by /u/sayyadirfanali [link] [留言]

The number of cases falls from 1,100 to 437 with increased testing.

Another fusion startup has raised another a massive round to make this type of power a reality.

submitted by /u/yangzhou1993 [link] [留言]

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.

Sprite-based graphics architecture makes first-person 3D a challenge.

Ever looked at std::any and wondered what’s going on behind the scenes? Beneath the intimidating interface is a classic technique called type erasure: concrete types hidden behind a small, uniform wrapper. Starting from familiar tools like virtual functions and templates, we’ll build a minimal std::any . By the end, you’ll have a clear understanding of how type erasure works under the hood. submitted by /u/david-alvarez-rosa [link] [留言]

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.

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

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

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

Ableton already has Max for Live, which allows users to build MIDI effects, synths, and samplers for its digital audio workstation (DAW). But now with its Extensions SDK, features can be added to Ableton Live with common JavaScript. Where Max is largely limited to MIDI and audio processing, Extensions can touch almost any part of […]

T-SQL on Microsoft Fabric - Episode 1: Mastering Data Retrieval with SELECT, WHERE, and ORDER BY Learning Goals In this lesson, you will learn how to: Read data from tables using SELECT Filter rows with WHERE Sort query results with ORDER BY Get familiar with standard T-SQL syntax Practice directly in Microsoft Fabric Warehouse 1. Understanding Database and Schema In Fabric Warehouse, objects are commonly organized like this: Warehouse | |-- sales | |-- Customers | |-- Orders | |-- hr | |-- Employees | |-- finance |-- Transactions Schemas help you: Group related tables Manage permissions Organize large systems more effectively 2. Create a Schema Create a schema for the sales dataset: CREATE SCHEMA sales ; Check existing schemas: SELECT * FROM sys . schemas ; 3. Create Tables Create the Customers table: CREATE TABLE sales . Customers ( CustomerID INT , CustomerName VARCHAR ( 100 ), City VARCHAR ( 50 ), Country VARCHAR ( 50 ) ); Create the Orders table: CREATE TABLE sales . Orders ( OrderID INT , CustomerID INT , OrderDate DATE , Amount DECIMAL ( 10 , 2 ) ); 4. Insert Sample Data Customers INSERT INTO sales . Customers VALUES ( 1 , 'John Smith' , 'New York' , 'USA' ), ( 2 , 'Emma Brown' , 'Chicago' , 'USA' ), ( 3 , 'David Wilson' , 'London' , 'UK' ), ( 4 , 'Sophia Taylor' , 'Manchester' , 'UK' ), ( 5 , 'Michael Lee' , 'Singapore' , 'Singapore' ); Orders INSERT INTO sales . Orders VALUES ( 101 , 1 , '2026-01-10' , 1200 . 00 ), ( 102 , 1 , '2026-01-15' , 800 . 00 ), ( 103 , 2 , '2026-01-20' , 2500 . 00 ), ( 104 , 3 , '2026-02-01' , 500 . 00 ), ( 105 , 5 , '2026-02-05' , 3200 . 00 ); 5. SELECT Get all columns: SELECT * FROM sales . Customers ; Get specific columns: SELECT CustomerName , Country FROM sales . Customers ; 6. Alias Rename columns in the output: SELECT CustomerName AS Customer , Country AS Nation FROM sales . Customers ; 7. WHERE Filter rows using conditions. Customers in the USA: SELECT * FROM sales . Customers WHERE Country = 'USA' ; Orders greater than 100

Your API is fast. Your code is clean. Your architecture looks solid on paper. Then you hit 500,000 records and everything slows down. Queries that ran in 12ms now take 4 seconds. Your dashboards lag. Users start filing support tickets. Your on-call engineer is staring at a query plan at midnight wondering what went wrong. Nine times out of ten, the answer is indexing. Not missing indexes — wrong indexes. Indexes that exist but don't help. Indexes that actively hurt write performance without meaningfully improving reads. This is a breakdown of the most damaging database indexing mistakes in production SaaS systems — and how to fix them before they become incidents. Mistake 1: Indexing Everything "Just in Case" The most common mistake isn't under-indexing. It's over-indexing out of anxiety. New engineers especially fall into this pattern — add an index on every column that appears in a WHERE clause, just to be safe. Seems responsible. It isn't. Every index you add is a write tax. On every INSERT, UPDATE, and DELETE, PostgreSQL (or MySQL) has to update every index on that table. On a table with 8 indexes, every write touches 8 data structures. At low volume, this is invisible. At 10,000 writes per minute, it becomes your bottleneck. The fix: Audit your indexes regularly. In PostgreSQL: SELECT schemaname , tablename , indexname , idx_scan , idx_tup_read , idx_tup_fetch FROM pg_stat_user_indexes ORDER BY idx_scan ASC ; Any index with idx_scan = 0 or near zero hasn't been used since your last stats reset. That's a candidate for removal — not immediately, but after investigation. Mistake 2: Not Understanding Index Selectivity An index on a boolean column ( is_active , is_deleted ) is almost always useless. Here's why: selectivity measures how many distinct values exist relative to total rows. A boolean column has two values. If 95% of your rows have is_active = true , an index on that column tells the query planner almost nothing useful. It will often skip the index entire

submitted by /u/NoPercentage6144 [link] [留言]

The Scenario: The Help Desk Bottleneck From 2019 to 2021, while serving as Lead Backend Software Engineer at a fast-growing company, I occasionally support our Linux System Administration tasks. When the DevOps team encountered a critical bottleneck during an initiative to scale dozens of new server deployments, I stepped in to streamline the infrastructure processes. The DevOps team was being hampered by constant, fragmented requests from the help desk to manually create new Linux accounts for recruits testing the latest application. These interruptions were not only time-consuming but were directly preventing the team from focusing on the high-priority infrastructure deployments that define their core responsibilities. I realized that we weren't just struggling with a task; we were struggling with a scaling bottleneck. To regain the team's focus and ensure we hit our project deadlines, I decided to automate this workflow. The First Step: The Interactive Script My first objective was to develop a robust, automated shell script to efficiently create new Linux user accounts. I started with an interactive Bash script (create-user-interactive.sh) that prompted for input. This was a good educational exercise for learning the fundamentals of Bash—like useradd, passwd, and shell variables. However, I quickly learned that while interactive scripts are great for learning, they are rarely used in professional DevOps environments. Why Manual Scripts Don’t Scale As I transitioned into a more infrastructure-focused role, I realized that manual scripts fail for three key reasons: Lack of Automation: DevOps is about "Infrastructure as Code" (IaC). Asking an engineer to sit at a terminal and type prompts is slow, error-prone, and destroys the ability to automate. Lack of Centralization: In a real team, we aren't creating users on individual local machines. We manage identity across hundreds of servers. Security Risks: Hardcoding passwords or piping them through echo is a major red