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> TL;DR — I run four different domains off one Next.js codebase: a marketing site at pagestrike.com , an authenticated app at app.pagestrike.com, a public publishing domain at pagestrike.app, and customer-owned domains. The trick isn't deploying four apps — it's a single proxy.ts that reads the host and rewrites/redirects/passes-through per-request. This post walks through why I chose this shape, the parts I got wrong, and the cookie-domain trick that makes it all stick. Stack: Next.js 16 App Router , Supabase , Vercel , one proxy.ts file (~370 lines). This is the second post in my build-in-public series on PageStrike . Last week I wrote about the 6-CTA architecture — modeling conversion intent as a discriminated union so one launch could be a checkout, a COD form, or a calendar booking. This post is about a different primitive: modeling host as routing context so one codebase can serve four very different audiences. Why four domains, not one Most SaaS apps live at one domain — say myapp.com with /dashboard under it. That works until you grow into edge cases that don't fit: Marketing pages get spammed by your own dashboard headers. Your marketing nav says "Sign in / Pricing / Blog". Your dashboard nav says "Launches / Contacts / Settings". You either A/B them with conditional logic everywhere or you live with the noise. Public user-generated pages share your domain reputation. When a customer publishes a landing page at myapp.com/p/[slug] , every spammy LP from a free-tier user drags down myapp.com 's sender reputation, search trust, and ad-account standing. Google and Meta penalize the host, not the path. Custom domains don't route cleanly. A customer who buys acmewidgets.com and points it at your app expects their LP at acmewidgets.com/ — not myapp.com/p/acme-widgets . You need a rewrite that's transparent to the visitor, doesn't 404 on _next/static/* , and survives RSC prefetches. I split PageStrike — a free AI landing page builder — across four hosts to solve al

I mostly use Claude Code, but lately I've been using Codex CLI and Codex.app (hereafter "Codex") more often too. My environment is macOS. However, after I started using mise in [2026-03-29-1] , I ran into trouble because Codex wouldn't use the mise-managed Ruby, Node.js, and so on. Here's the state I was in: $ where ruby /usr/bin/ruby $ ruby --version ruby 2.6.10p210 (2022-04-12 revision 67958) [universal.arm64e-darwin25] The Solution I solved it by adding the following to ~/.zshenv : # When Codex CLI and Codex.app run commands, .zshrc's mise activate zsh doesn't take effect, # so add mise shims to PATH. if [ -n " $CODEX_SANDBOX " ] ; then PATH = ${ XDG_DATA_HOME } /mise/shims: $PATH fi Here's the state inside Codex after the change: $ where ruby /Users/masutaka/.local/share/mise/shims/ruby /usr/bin/ruby $ ruby --version ruby 4.0.5 (2026-05-20 revision 64336ffd0e) +PRISM [arm64-darwin25] Codex's Command Execution Environment Is a Sandbox I'd vaguely suspected this for a while, but it seems Codex's command execution environment runs inside a sandbox. You can spot the clues from within Codex: $ env | grep CODEX CODEX_CI=1 CODEX_SANDBOX=seatbelt CODEX_THREAD_ID=019e7806-6025-7c13-a3c6-a70d41c13905 "seatbelt" refers to Apple Seatbelt, which appears to be macOS's sandboxing mechanism. 🔗 macOSで手軽にSandbox環境を構築できるApple Seatbeltの実践ガイド しかしながら、Apple Seatbeltは公式にドキュメントを公開されておらず、非推奨とされています。一方で実際には多くのアプリケーションやツールで使用されています。 (English translation) However, Apple Seatbelt has no officially published documentation and is considered deprecated. Yet in practice, it's used by many applications and tools. I see... According to this article, Claude Code also adopts Apple Seatbelt, and I confirmed that it can be enabled with /sandbox (see the official documentation ). Coming from a background of being used to Claude Code, Codex's sandbox is hard to wrap my head around, but the following article covers it in detail. Much appreciated. 🔗 [Codex] sandbox実行の仕組みと設定方法を完全に理解する Codex also seems to r

This article will teach you how to export any list into Excel in C# using the ClosedXML library. Steps to complete Create the data model with dummy data that we'll export into Excel. Create ExportExcel interface methods that accept any type of List (using IEnumerable<T> ) and a Dictionary List and export a byte array. Create extension methods and convert the provided data into rows and columns (using DataTable ). Create a service class that implements the interface methods and export the data table into a Memory Stream (byte array) using ClosedXML . Create one API endpoint that exports data in memory into Excel. Create another endpoint that exports incoming (custom) request data into Excel. Wire up dependencies. Project structure ├── Program.cs ← Project startup & dependency injection │ ├── controllers / │ └── ExportToExcelController.cs ← API entry point ├── services / │ ├── IExportToExcelService.cs ← Export Excel interface │ └── ExportToExcelService.cs ← Export Excel concrete class │ ├── models / │ ├── Car.cs ← Car class definition & dummy data │ ├── ExcelResponse.cs ← Wrapper class for excel file name and data │ └── ExportExcelRequest.cs ← Request class for that accepts any kind of list that will be exported │ └── extensions / └── IEnumerableExtensions.cs ← Extension methods for List<T> and List<Dictionary> 1️⃣ Data model I've created the dummy data model to demonstrate the dynamic implementation. public enum FuelType { Petrol, Diesel, Electric, Hybrid } public class Car { public Guid Id { get; set; } public string Name { get; set; } public string Manufacturer { get; set; } public int YearProduced { get; set; } public string Color { get; set; } public FuelType FuelType { get; set; } public int HorsePower { get; set; } public int NumberOfDoors { get; set; } public bool AutomaticTransmission { get; set; } public double AverageFuelConsumption { get; set; } public int MaxSpeed { get; set; } public decimal Price { get; set; } public static List<Car> GetCars() { ... } }

I'm 15 and Built an AI Energy Dashboard with Next.js 15 + Groq Hey Dev.to! 👋 I'm a 15-year-old student developer from South Korea. I just finished my first real production project — FuelScope AI. What is it? An energy market intelligence dashboard that uses Groq's Llama 3.3 70B to summarize real energy news in real time. 🔗 Live Demo: https://fuelscope-ai.vercel.app What it does ⛽ Regional gas price cards 📈 Energy stock tickers (XOM, CVX, SHEL) 🤖 AI-summarized energy news (Llama 3.3 70B via Groq) 🗺️ Interactive Mapbox station map 📍 GPS nearest station finder 🎨 Apple-inspired clean design Tech Stack Next.js 15 + TypeScript Tailwind CSS Groq API (Llama 3.3 70B) — FREE tier GNews API — FREE tier Mapbox GL JS Vercel deployment What I learned This was my first time building something with: Real API integrations AI summarization pipeline Production deployment on Vercel Apple design system principles Honestly learned more in 4 days building this than months of tutorials. Honest disclosure Gas prices and stock data are mock values — the README includes guides for swapping in real APIs (EIA, Alpha Vantage, etc.). The AI news summaries are 100% live though. Template I'm selling the template for $19 on Gumroad if anyone wants to build on top of it: 👉 https://LZF01.gumroad.com/l/djzoaj Would love any feedback from the community! 🙏 Built with Next.js 15, Groq, GNews, Mapbox

It's the world's worst kept secret that Nvidia is about to announce its own Arm-powered laptop chips at Computex this weekend, and now Microsoft, Nvidia, and Arm are all openly teasing the announcement. The Windows and Nvidia GeForce accounts on X both posted "A new era of PC" earlier today, and now Arm has followed […]

SpaceX already generated one-fifth of its 2025 revenue from government contracts, the company revealed in its IPO filing.

While AI is helping coders produce code faster, it may not be producing better code, researchers warn. And that could cause problems down the road for them.

The Pentagon awarded SpaceX a $4.16 billion contract to build missile-tracking satellites linked with President Donald Trump's planned "Golden Dome" defense system, as reported earlier by Bloomberg. In an announcement on Friday, the US Space Force says the sensor-equipped satellites will allow it to detect and track targets from space. The Elon Musk-owned SpaceX - […]

I'm a maxillofacial surgeon in Ouagadougou, Burkina Faso — and a self-taught builder who's been coding since medical school. Over evenings and weekends, I shipped Readium — a production AI reading app that lets you discuss books with Claude while you read them, in any language. Built AI-paired with Claude, reviewed and deployed by me. Most "I shipped an AI app" write-ups cover the happy path: clone a starter, glue an LLM, deploy to Vercel. The walls I hit weren't there. They were in the spaces between the libraries. Here are five of them — and what I'd tell myself a few weeks ago. Wall 1 — SSE streaming broke at the seam between the LLM and the browser I assumed streaming "just worked" once OpenRouter returned a stream. It does — until your server-side handler, your reverse proxy, or your browser code introduces a buffer somewhere along the path. The chain has at least three places where buffering can silently kill streaming: The LLM API (fine on its own) Your Node server-side handler (fine if you forward chunks instead of accumulating them) The reverse proxy / CDN (often buffers entire responses by default) The failure mode is always the same: the UI looks exactly like the LLM is slow. It isn't — somewhere between OpenRouter and the browser, bytes are being withheld until the connection closes, then dumped in one chunk. What I'd tell past-me: streaming isn't a feature of the LLM, it's a property of your entire request path. If you can't watch tokens land character-by-character in curl -N against your origin, you don't have streaming, you have a slow non-stream pretending. Set Cache-Control: no-transform and X-Accel-Buffering: no headers from your handler, disable response buffering on every layer in front of it, and verify with curl -N before you trust the UI. Wall 2 — fetch hangs forever on certain hosts (and the fix isn't where you think) I had a proxy route that fetched from an external API. Worked locally. Worked in staging. Deployed to production: the route wo

fd vs find vs ripgrep: I Created 10,000 Files to Settle This Debate TL;DR: fd is ~2.5x faster than find for filename searches, rg demolishes grep by ~3x for content searches, and find + grep combined lose on every single benchmark I ran. But there's a catch: both fd and rg skip hidden files by default, which can bite you if you're not paying attention. Here are the receipts. Why I Did This Every time someone posts a shell one-liner using find on Reddit, there's always that guy in the comments: "jUsT uSe fD, iT's fAsTeR." Then someone else chimes in with "actually ripgrep can do that too." I got tired of the anecdotes. I wanted numbers. Real ones. On real files. So I fired up WSL, generated 10,900 files across 1,506 directories (~143 MB of mixed content), and ran actual benchmarks with hyperfine . No synthetic microbenchmarks, no "I feel like X is faster" — just cold, hard terminal output. Methodology The Test Bed I created a directory at /tmp/fd-benchmark containing: Category Count Details Plain text files 2,000 file_*.txt — 20 bytes each, contains "test content line N" Binary files 2,000 data_*.bin — 15 bytes each Log files 1,500 match_*.log — contains unique "match_this_test_N" strings Config files 1,000 nested_file_*.cfg Nested dir files 1,000 level1_*/level2/level3/deep_*.txt + level1_*/shallow_*.txt Hidden root files 1,500 .hidden_* + .config_*.yml Hidden dir files 500 .hidden_dir/subdir/deep_hidden_*.txt Git objects 500 .git/objects/obj_* Multi-ext source files 800 src_*.{py,js,ts,rs,go,java,rb,php,cpp,h,css,html,json,xml,yaml,md} (50 each) Large binary files 100 large_*.dat — 1 MB each (random data) Total 10,900 $ du -sh . 143M . $ find . -type f | wc -l 10900 $ find . -type d | wc -l 1506 Tools Tested Tool Version What It Does find (GNU) 4.9.0 The OG. Ships with every Linux distro. fd 10.2.0 Rust-based find alternative. Smarter defaults, colored output. grep (GNU) 3.11 Content search. Also the OG. rg (ripgrep) 15.1.0 Rust-based grep alternative. Respects .gi

The botnet was reportedly tied to a Russia-based residential proxy network.

Microsoft has delayed its upcoming Fable reboot once again. The game was set to launch in autumn 2026, but Microsoft now says that Fable will come out in February 2027. However, it will show a "new look" at the game at its Xbox Games Showcase on June 7th. "​​This is year is packed with incredible […]

I pondered the other day what's next for Microsoft's Surface PC lineup, and it looks like we're about to find out. Windows and Surface chief Pavan Davuluri has just teased "something new is coming for developers," complete with a mysterious image of what looks like a curved display edge. Davuluri notes that whatever is coming […]

My Experience Using dnd-kit in React and Next.js For a long time, I avoided building drag-and-drop features in frontend projects 😅 Not because I did not need them. Mostly because drag and drop always felt unnecessarily complicated. When you think about implementing it, your brain immediately jumps to: animations sorting logic touch support accessibility performance state synchronization and suddenly a simple UI interaction feels like a massive feature. But recently, in one of my React and Next.js projects, I decided to finally try dnd-kit. Honestly, it changed my perspective completely. I used AI to help me with the initial setup and understanding some concepts like sortable contexts and drag events, but after that, working with the library felt surprisingly smooth. And that's what impressed me most. dnd-kit feels lightweight, modern, and flexible without becoming overwhelming. It gives you the tools you need without forcing a huge architecture or complicated patterns on your app. Things I Personally Liked About dnd-kit Very clean React-first API Lightweight and composable Works really well in React and Next.js projects Building sortable lists feels much simpler than expected Flexible enough for custom UI and interactions Good developer experience overall Something Interesting I Noticed When a library has a clean architecture and predictable patterns, AI becomes much more useful while learning it. The generated examples were easier to understand, easier to debug, and easier to customize compared to many older drag-and-drop solutions. If you are building things like: kanban boards sortable lists draggable cards dashboards reorderable tables I definitely recommend taking a look at dnd-kit. It ended up being much simpler and more enjoyable than I expected. Website https://dndkit.com/

Acer is aiming to take on the MacBook Neo more directly with a new Swift Air 14, a new 14-inch laptop that starts at $699. The laptop will use Intel's lower-cost Core Series 3 "Wildcat Lake" chips - either Core 5 or Core 7 models, both of which are six-core processors. It comes in green, […]

A lot of Linux incident response starts with a login question, not a malware sample. Someone sees a spike of failed SSH attempts. A root login appears in the wrong time window. A service account logs in from an address nobody recognizes. A helpdesk ticket says "the server looks weird" and the only concrete clue is a username or IP address. At that point, the useful question is not "is this host compromised?" It is more boring and more important: Did anyone actually authenticate? Which account was involved? Was it password, key, sudo, su, or a scheduled task? Was the same IP seen in web logs, current sockets, process context, or command history? Did persistence, services, packages, or recent files change near the same time? Can another responder review exactly what evidence was collected? That last point matters. If you let an AI assistant freely run shell commands during the first pass, you can get speed, but you also create a new risk: the model may over-collect, mutate the host, or produce a confident answer that nobody can audit later. For a login anomaly, I prefer a read-only evidence loop. A practical first pass Start with the narrow clue if you have one. If the alert names a user: oi login --user root -s 7d If the alert names an IP address: oi login --ip 203.0.113.44 -s 7d If the alert is vague, start wider: oi login -s 7d oi scan -s 7d The goal of the first pass is not to prove every detail. The goal is to build a timeline that a human responder can challenge. For a suspicious SSH login, I want the initial report to answer five things. 1. Authentication pattern Look for the difference between noise and access. A server can receive thousands of failed SSH attempts from the internet. That is useful background, but it is not the same as a successful session. The first split should be: failed attempts only successful login after many failures accepted key from an unusual source login by an account that normally should not be interactive root login where root SSH

The Cloud Native Computing Foundation has highlighted a new AI-assisted migration approach that enabled engineers to migrate 60 ingress-nginx resources to Higress in roughly 30 minutes, demonstrating how artificial intelligence is increasingly being applied to modernize Kubernetes networking and gateway infrastructure. By Craig Risi

Sex workers appear on the livestreams of famous manosphere influencers to boost their followings—but often end up being degraded.

TL;DR -DevRelCon NYC returns to Industry City in Brooklyn on July 22–23, 2026. It's the flagship...

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built I built and revived Mandi Central , a complete business operations and billing system for mandi-style trading businesses. Live Project: https://mandicentral.in Mandi Central helps manage daily mandi operations like purchases, farmer purchase entries, sales entries, payments, bank and cash ledgers, accounting heads, reports, invoices, outstanding balances, and financial statements from one structured platform. This project was not started as a simple demo. It was a real business-focused software project, but it had many unfinished parts, broken flows, missing reports, pending PDF generation, incomplete mobile API planning, and several UI and accounting issues. For the GitHub Finish-Up-A-Thon Challenge, I brought it back, fixed the pending issues, completed the core business flow, and made the project live on mandicentral.in . Demo Live Website: https://mandicentral.in GitHub Repository: https://github.com/hiijoshi/bill Screenshots and demo cover the completed modules: Business Operations Hub Sales List Bank reconciliation Cash ledger Bank ledger Sales entry Purchase entry Farmer purchase entry Bulk PDF download Accounting reports Party ledger redirection Outstanding reports Mobile API-ready backend The Comeback Story Mandi Central started with a strong idea: create one platform where mandi businesses can manage their complete daily workflow. But before this challenge, the project was stuck with many incomplete and pending items. There were more than 40 pending issues across PDF generation, sales invoices, bank ledger, cash ledger, accounting reports, outstanding reports, purchase calculations, sales calculations, mobile APIs, AWS deployment, domain setup, and UI fixes. Some of the major problems before completion were: PDF generation was not working properly. Bulk sales bill download was missing. Mobile application APIs were pending. Farmer Purchase Entry API was pending. Purchase Entry API w