SpaceX shares soar 30% midday, vaulting it to top six most valuable U.S. companies
The company made its heavily anticipated debut on Friday, trading higher than its initial $135 IPO price.
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The company made its heavily anticipated debut on Friday, trading higher than its initial $135 IPO price.
SpaceX’s stock market debut has thrust the richest man in the universe into an unexplored frontier of wealth.
The trading platform says some customers experienced intermittent disruptions, but those issues have resolved.
Elon Musk is now officially the world's first trillionaire. That is a colossal amount of wealth (and by proxy, power) for one individual to have. Its scale - a thousand times more than a billion - is difficult to fathom for those of us who aren't among the 3,363 billionaires that currently exist in our […]
The SpaceX IPO has boosted Musk's paper wealth to more than $1,000,000,000,000 at a time when he is more hated -- and powerful -- than ever.
The company made its heavily-anticipated debut on Friday, trading higher than its initial $135 IPO price.
Part 1 of the ERTH Architecture Series: Launching local backends on Port 0, dynamic port negotiation, and establishing the dual-core desktop backbone. If you are building a modern desktop application, you are probably tired of the same old options. On one hand, you have Electron . It’s the industry standard, but it forces you to bundle a full Chromium browser and a Node.js runtime with every app. Even a simple "Hello World" takes up 200MB+ of disk space and eats hundreds of megabytes of RAM. For background utility utilities or AI assistants that need to be nimble, this is a massive tax. On the other hand, you have Tauri . It solves the bundle size issue by binding to OS-native WebViews and using Rust for the backend. But unless you are already a Rust expert, you will find yourself fighting the compiler's borrow checker and async lifecycles, slowing down your development velocity. But what if you want to use Python for its rich AI ecosystem (Ollama, SQLModel, PyTorch), but still keep the UI lightweight, fast-loading, and responsive? Welcome to the ERTH Stack ( E lectroBun + R obyn + T urso + H TMX). In this first post of our 5-part series, we will break down how to launch a high-performance Python sidecar backend directly from a Bun-based desktop shell, bypassing the bloated Electron environment entirely. The Concept: Heterogeneous Dual-Core In the ERTH architecture, the desktop app is split into two physical processes: The Main Process (Bun) : Responsible for native OS window management, IPC (Inter-Process Communication), and hosting the HTML/CSS view. We use ElectroBun —a next-generation, ultra-lightweight wrapper that binds directly to the OS-native WebKit engine (no Chromium bloat!). The Sidecar Process (Python/Robyn) : Responsible for heavy computations, database access, and local LLM orchestration. We use Robyn , an incredibly fast, Rust-based async Python web framework. Here is how the lifecycle and process boundaries interact: Step 1: Spawning the Robyn Sidec
How the ERTH Architecture (ElectroBun + Robyn + Turso + HTMX) breaks the obesity of modern desktop app development. If you’ve tried to build a cross-platform desktop application recently, you’ve likely faced the classic developer’s dilemma: Electron makes developer velocity fast, but at the cost of dragging a bloated Chromium kernel and Node.js runtime into every build. A simple "Hello World" easily eats up 200MB+ of disk space and hundreds of megabytes of RAM. Tauri solves the footprint issue by using OS-native WebViews and Rust, but forces you onto Rust’s steep learning curve, sacrificing the agility of rapid prototyping. The Python Distribution Hell : With the rise of local LLMs and Edge AI, Python is the de facto language for AI orchestration. Yet, packaging Python, its heavy dependencies, and databases into a double-click-to-run package for non-technical users remains a nightmare. Faced with these shackles, I decided to take a step back and rewrite the physical laws of desktop development. Today, I’m introducing the ERTH Stack ( E lectroBun + R obyn + T urso + H TMX)—a heterogeneous, local-first, zero-JS desktop application architecture designed for independent full-stack creators. And yes, the entire bundle—including a browser shell, a high-performance Python sidecar, a local database, and local AI agent execution—packages into a single 128MB standalone binary. Our open-source implementation is live on GitHub: 👉 GitHub Repository: bnpysse/erth_assistant The Core Pillars of the ERTH Architecture To achieve a minimalist footprint without sacrificing developer velocity, we structured the architecture into four core layers: ERTH ARCHITECTURE [E]lectroBun (UI Shell) ───[HTMX]───► [H]TMX (Zero-JS Frontend) │ ▲ (IPC) (HTTP) ▼ │ [R]obyn (Python Sidecar) ───────────► [T]urso / libSQL (Local-First DB) 1. [E]lectroBun: The Lightweight Shell Instead of Electron’s heavy Chromium, ElectroBun binds directly to the OS-native WebKit engine (Cocoa WebKit on macOS, WebView2 on W
Model Context Protocol (MCP): Giao Thức Kết Nối Thế Giới Cho Trí Tuệ Nhân Tạo Trong thế giới AI đang phát triển với tốc độ chóng mặt, việc xây dựng các ứng dụng thông minh, có khả năng tương tác linh hoạt với dữ liệu và công cụ bên ngoài là một thách thức lớn. Các mô hình ngôn ngữ lớn (LLM) như GPT, Claude, hay Gemini dù mạnh mẽ nhưng thường hoạt động trong "vùng cô lập", thiếu khả năng truy cập trực tiếp vào các hệ thống bên ngoài theo thời gian thực. Đây chính là lúc Model Context Protocol (MCP) xuất hiện như một giải pháp cách mạng. MCP là một giao thức mở, được thiết kế để tiêu chuẩn hóa cách thức các ứng dụng cung cấp ngữ cảnh (context) cho LLM, giúp phá vỡ rào cản giữa trí tuệ nhân tạo và thế giới thực. Bài viết này sẽ đi sâu vào phân tích Model Context Protocol , từ định nghĩa, kiến trúc, đến các lợi ích và ứng dụng thực tế, giúp bạn hiểu tại sao nó được coi là "ngôn ngữ chung" của tương lai AI. Model Context Protocol (MCP) Là Gì? Model Context Protocol (MCP) là một giao thức mở, được phát triển để tạo ra một chuẩn giao tiếp thống nhất giữa các LLM và các nguồn dữ liệu, công cụ bên ngoài. Hãy tưởng tượng MCP như một "cổng USB" dành cho AI. Thay vì mỗi ứng dụng AI phải viết mã tích hợp riêng lẻ với từng loại cơ sở dữ liệu, API, hay hệ thống tệp tin (mỗi loại một kiểu "phích cắm" khác nhau), MCP cung cấp một giao diện chuẩn. Bất kỳ ứng dụng nào hỗ trợ MCP đều có thể kết nối với bất kỳ nguồn tài nguyên nào cũng hỗ trợ MCP một cách liền mạch. Mục Đích Cốt Lõi Của MCP Mục tiêu chính của Model Context Protocol là giải quyết vấn đề "fragmentation" (phân mảnh) trong hệ sinh thái AI. Trước MCP, việc tích hợp thường diễn ra rời rạc: Mỗi nhà phát triển ứng dụng phải tự xây dựng các "kết nối" tùy chỉnh. Mỗi lần cập nhật mô hình hoặc công cụ có thể làm hỏng các tích hợp cũ. Khó khăn trong việc chia sẻ và tái sử dụng các công cụ AI giữa các dự án. MCP giải quyết những vấn đề này bằng cách cung cấp một lớp trừu tượng chuẩn hóa. Kiến Trúc Và Cách Thức Hoạt Động Của MCP Kiến
One of those nuances to keep in your back pocket when writing for screen readers. There’s no need to include ‘navigation’ in your navigation labels originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.
After Elon Musk and Tommy Robinson stoked anger over a horrific knife attack in Belfast, a youth group linked to a global neo-Nazi movement quietly orchestrated anti-immigrant riots.
If you still manage APT repositories as long one-line deb ... entries, you are working with a format APT now explicitly marks as deprecated. It still works, but it is harder to read, harder to automate safely, and easier to get wrong when you add options like arch= or signed-by= . The better option is deb822 style .sources files. This post shows how to: read the structure of a .sources file migrate a legacy .list entry safely use Signed-By without falling back to apt-key disable a repository cleanly without deleting it verify that APT accepts the new configuration I am focusing on practical host administration, not packaging theory. Why move to deb822 now? The sources.list(5) man page now says the traditional one-line .list format is deprecated and may eventually be removed, though not before 2029. More importantly, deb822 solves real operational annoyances: fields are explicit instead of positional one stanza can describe multiple suites or types Enabled: no is cleaner than commenting lines in and out machine parsing is much easier Signed-By is clearer and safer in structured form On a current Debian host, you may already be using it without noticing: find /etc/apt/sources.list.d -maxdepth 1 -type f -name '*.sources' On my test system, the default Debian repository is already stored as /etc/apt/sources.list.d/debian.sources . The old format vs the new format A traditional one-line entry looks like this: deb [arch=amd64 signed-by=/etc/apt/keyrings/example.gpg] https://packages.example.com/apt stable main The same source in deb822 format becomes: Types: deb URIs: https://packages.example.com/apt Suites: stable Components: main Architectures: amd64 Signed-By: /etc/apt/keyrings/example.gpg That is the core win. Instead of cramming everything into one line and hoping spacing stays correct, each field says exactly what it means. Example 1, a clean Debian .sources file Here is a practical example for Debian using separate stanzas for the main archive and the security arch
I Cut My Next.js + Supabase App Load Time by 73% - Here Are the 5 Techniques That Actually Worked Last month, our SaaS dashboard was embarrassingly slow . 4.2 seconds to load the main page. Users were complaining. Conversion rates were tanking. Today? 1.1 seconds . 73% faster. Here's exactly what worked (and what didn't). The Problem: Death by a Thousand Database Calls Our dashboard showed user projects, team members, recent activity, and notifications. Sounds simple, right? Wrong. Each component was making its own database calls. The projects list fetched projects, then made separate calls for each project's stats. The activity feed loaded events, then fetched user details for each event. Classic N+1 query problem, but worse. Technique #1: Strategic Data Fetching Consolidation Before: 47 database calls to load the dashboard After: 3 database calls The fix wasn't fancy. We consolidated related data into single queries using Supabase's nested select syntax: // ❌ Before: Multiple separate calls const projects = await supabase . from ( ' projects ' ). select ( ' * ' ) const stats = await Promise . all ( projects . map ( p => supabase . from ( ' project_stats ' ). select ( ' * ' ). eq ( ' project_id ' , p . id )) ) // ✅ After: Single consolidated call const projects = await supabase . from ( ' projects ' ) . select ( ` *, project_stats(*), team_members(count), recent_activity:activities(*, user:users(name, avatar_url)) ` ) . limit ( 10 ) Result: Dashboard load time dropped from 4.2s to 2.8s (33% improvement) Technique #2: Aggressive Caching with Smart Invalidation Most dashboard data doesn't change every second. We implemented a three-tier caching strategy: // Static data: Cache indefinitely const categories = await supabase . from ( ' categories ' ) . select ( ' * ' ) . cache ({ revalidate : false }) // Semi-static data: Cache with revalidation const userProjects = await supabase . from ( ' projects ' ) . select ( ' * ' ) . eq ( ' user_id ' , userId ) . cache ({ revali
Next.js + Supabase Performance Optimization: From Slow to Lightning Fast Last month, I optimized a Next.js + Supabase application that was frustratingly slow. Initial page load took 4.2 seconds, Lighthouse performance score was 62, and users were complaining. After applying these optimization techniques, we achieved: 70% faster load times (4.2s → 1.3s) Lighthouse score of 96 (up from 62) LCP improved by 65% (3.8s → 1.3s) 50% reduction in database queries Here's exactly how we did it. The Starting Point: Measuring Performance Before optimizing anything, we measured current performance using: Lighthouse (Chrome DevTools): Performance: 62 First Contentful Paint (FCP): 2.1s Largest Contentful Paint (LCP): 3.8s Total Blocking Time (TBT): 420ms Cumulative Layout Shift (CLS): 0.18 Real User Monitoring: Average page load: 4.2s Time to Interactive: 5.1s Database query time: 850ms average The Problems: Unoptimized database queries No caching strategy Large JavaScript bundles Unoptimized images Blocking render paths Too many client-side fetches Let's fix each one. 1. Database Query Optimization Problem: N+1 Queries The biggest performance killer was N+1 queries. We were fetching posts, then fetching the author for each post individually. // ❌ Bad: N+1 queries (1 + N database calls) async function getPosts () { const { data : posts } = await supabase . from ( ' posts ' ) . select ( ' id, title, author_id ' ) // Fetching author for each post = N queries const postsWithAuthors = await Promise . all ( posts . map ( async ( post ) => { const { data : author } = await supabase . from ( ' users ' ) . select ( ' name, avatar ' ) . eq ( ' id ' , post . author_id ) . single () return { ... post , author } }) ) return postsWithAuthors } Impact: 50 posts = 51 database queries (850ms total) Solution: Use Joins // ✅ Good: Single query with join (1 database call) async function getPosts () { const { data : posts } = await supabase . from ( ' posts ' ) . select ( ` id, title, author:users(nam
Even in blue states, nonreligious tech entrepreneurs and CEOs are increasingly asking for “traditional” and “conservative” women, matchmakers tell WIRED.
Pada series sebelumnya, kita sudah melakukan instalasi nix di VirtualBox dan setup SSH agar dapat diakses diluar VirtualBox. Sebelum kita lanjut untuk melakukan konfigurasi system lagi, kita butuh mengetahui bagaimana syntax dalam menulis program Nix dan di artikel ini kita akan mempelajari dasar syntax-nya. Nix Language Nix adalah purely functional language yang lazy-evaluated , digunakan untuk mengkonfigurasi Nix package manager dan NixOS. Karakteristik utama: Purely functional : sebuah function hanya bisa mengembalikan nilai berdasarkan inputnya, tidak bisa mengubah variabel di luar scope-nya (no side effects), dan tidak ada variabel yang bisa diubah setelah didefinisikan (no mutation). Kalau kamu familiar dengan const di beberapa bahasa pemrograman, semua variabel di Nix berperilaku seperti itu. Lazy evaluation : Nix tidak menghitung nilai suatu ekspresi sampai nilai itu benar-benar dibutuhkan. Ini artinya kamu bisa mendefinisikan ribuan package di nixpkgs tanpa semuanya dievaluasi sekaligus. Hanya yang kamu gunakan saja yang akan diproses. Semua adalah expression : tidak ada statement di Nix, setiap baris kode selalu menghasilkan sebuah nilai. if/else bukan statement seperti di bahasa pemrograman pada umumnya, melainkan expression yang harus mengembalikan nilai dari kedua cabangnya. Tidak ada loops : karena variabel tidak bisa diubah, loop seperti for atau while tidak ada artinya di Nix. Sebagai gantinya, kamu menggunakan fungsi seperti map dan filter , atau rekursi untuk mengolah kumpulan data. 1. Basic Data Type Konsep JavaScript Nix String "hello" "hello" Number 42 , 3.14 42 , 3.14 Boolean true , false true , false Null null null List [1, 2, 3] [ 1 2 3 ] Object { a: 1 } { a = 1; } ⚠️ Perbedaan Penting List di Nix menggunakan spasi sebagai pemisah, bukan koma Attribute set menggunakan = bukan : , dan setiap entry diakhiri dengan ; Nix let name = "Alice" ; age = 30 ; scores = [ 10 20 30 ]; person = { name = "Bob" ; age = 25 ; }; in person Javascript const name
The company has set aside an unusually high number of shares for retail investors. Still, experts say, you’re just getting the crumbs.
The rocket maker debuts on Nasdaq today under a wave of criticism about Musk’s near-absolute control. It’s how the company has worked from the start.
In this podcast, Shane Hastie, Lead Editor for Culture & Methods spoke to Craig McLuckie, co-creator of Kubernetes and CEO of Stacklok, about the impact of AI coding tools on open source communities and engineering teams, designing deliberate organisational culture, and navigating evolving career paths for engineers in the age of AI. By Craig McLuckie
MIT Technology Review Explains: Let our writers untangle the complex, messy world of science and technology to help you understand what’s coming next. You can read more from the series here. Your brain lives in the dark space of your skull. Yet it knows when the wind lifts the hairs on your skin, when your heart is…