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Laravel 11 with Servbay – Quick Start Guide

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Laravel 11 with Servbay – Quick Start Guide

Introduction Laravel 11 has been released, bringing many improvements. It introduces a streamlined application structure, per-second rate limiting, health routing, and more. In addition, Laravel Reverb, a first-party, scalable WebSocket server has been introduced to provide robust real-time capabilities to your applications. In today's guide, I will setup an example laravel application, and install ServBay for local development on Mac. NOTE: ServBay is your one-stop-shop for local PHP development on Mac. It bundles together everything you need like web servers, databases, programming languages, mail servers and queue services. Create Example Application Let's get started generating an example applicaiton using composer. composer create-project laravel/laravel example-app This will create an example-app directory, cd to it. cd example-app Now you can start the server: php artisan serve Go to http://127.0.0.1:8000 and you'll see the default Laravel welcome page. Install ServBay We'll use ServBay to manage our local PHP development environment on Mac. ServBay includes multiple versions of PHP/Node/MariaDB/PostgreSQL, and it can install Redis/Memcached for you. These are the steps to install ServBay: 1. Go to Servbay's home page: https://www.servbay.com 2. Download the latest version 3. Open the installer and drag ServBay.app into the Applications folder 4. Open ServBay.app and follow the initialization steps Add a New Host After installing ServBay, you can add a new host for your example laravel application. Go to the Hosts tab, click the plus button, and you'll see the Add Host form. 1. Set Name to "Laravel 11 Demo" 2. Set Domain to "lara.host", 3. Set Root Directory to the public folder of example-app [图片] Hit the "Add" button and you're ready to go. Now visit lara.host and you will see the default welcome page from the example-app. From now on, you can use ServBay for Laravel local development, happy coding!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, using · Missing: agents, macos, agent
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, host · Missing: platform, intuitive, reviews
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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