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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]

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

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

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

When most people think about Java, they immediately picture enterprise applications, banking systems, massive backend services, or decades-old corporate software. While Java has earned its reputation in the enterprise world, that is only part of the story. Today, Java can run on devices as small as a Raspberry Pi, opening the door to hardware projects, edge computing, home automation, education, and hands-on learning experiences. Combining Java with Raspberry Pi creates a powerful platform for experimentation, learning, and building real-world solutions that go far beyond traditional enterprise development. Raspberry Pi teaches us about hardware. Java allows us to apply professional software engineering practices to that hardware. Together, they create a powerful platform for learning, prototyping, and building real-world IoT and edge computing solutions. Java Is More Than Enterprise Software Java's enterprise success has sometimes created the misconception that it only belongs in large organizations. In reality, modern Java offers: Excellent support for Linux and ARM architectures. High performance and low resource consumption. Modern frameworks such as Spring Boot, Quarkus, and Micronaut. Strong support for IoT and edge computing. Access to hardware through mature libraries. One of the largest developer ecosystems in the world. The Raspberry Pi highlights a different side of Java—one focused on creativity, experimentation, and direct interaction with the physical world. Instead of building another web application, you can build systems that sense, react, and interact with their environment. Java at the Edge One of the most exciting technology trends today is Edge Computing. Traditionally, devices send data to cloud services where processing and decision-making occur. Edge computing shifts part of that processing closer to where the data is generated. A Raspberry Pi running Java can: Process sensor data locally. Apply business rules before sending information to th

The password manager giant said hackers were able to 'brute-force' its two-factor system, allowing them to access customer accounts and download their password vaults.

This article was originally published on aifoss.dev --- title: 'Chroma vs Qdrant vs Weaviate 2026: RAG Database Compared' description: 'Compare Chroma, Qdrant, and Weaviate for local RAG in 2026: version snapshots, filtering tradeoffs, hybrid search, quantization, and a clear pick by use case.' pubDate: 'May 27 2026' tags: ["vectordb", "ai", "rag", "python", "opensource"] The three most commonly recommended open-source vector databases for RAG — Chroma, Qdrant, and Weaviate — are not interchangeable. Chroma is a prototyping tool that grew into a real product. Qdrant is a production workhorse written in Rust with the best filtering performance of the three. Weaviate is an enterprise-grade platform with hybrid search and the most built-in integrations. Using Weaviate when you need Chroma adds unnecessary ops overhead. Using Chroma when you need Qdrant means migrating under pressure when your collection outgrows it. Versions covered: ChromaDB v1.5.9 (May 2026), Qdrant v1.17.1 (March 2026), Weaviate v1.37 (May 2026). The quick answer Situation Best choice Local prototyping, notebooks, under 100K vectors Chroma Embedded in a Python process — no separate service Chroma Production RAG with filtering-heavy queries Qdrant Multi-user deployment, concurrent queries Qdrant Memory-constrained deployment at millions of vectors Qdrant Hybrid search (BM25 + vector in one query) Weaviate Multi-modal retrieval (text + images + audio) Weaviate Built-in re-ranking or generative AI modules Weaviate Kubernetes, team-operated, agentic MCP workflows Weaviate Getting from zero to working RAG in 10 minutes Chroma What each tool actually is ChromaDB (Apache 2.0, chroma-core/chroma ) started as a pure-Python embedded database and was rebuilt in Rust for the v1.0 release. The Rust core eliminates Python's GIL bottlenecks and delivers roughly 4× faster writes and queries compared to the pre-1.0 implementation — write throughput went from ~10K to ~40K+ vectors/second in server mode. Chroma's des

Opal, the company famous for making a fancy webcam, has pivoted to making other consumer electronics. Fueled by big investments from OpenAI and Samsung, it’s working on an audio gadget first.

Intel bet big on its new Series 3 chips, made on its new 18A process. But can it get enough of those chips out of the door to laptop manufacturers?

Voice agent for MacOS Discussion | Link

"The propellant farm, oxygen, liquid hydrogen, and LNG tanks are all in good shape."

The global AI race is often framed as a battle between the United States and China. But at VivaTech, Europe is expected to make the case for an entirely different model.

The global AI race is often framed as a battle between the United States and China. But at VivaTech, Europe is expected to make the case for an entirely different model.

Meta told TechCrunch that it's considering ways to monetize the new feature, but didn't share specifics on what that could look like.