thesubhendu

<?php

    public function geoCodeLatLng($lat, $lng)
    {
        $googleGeocodeApiUrl=
            "https://maps.googleapis.com/maps/api/geocode/json?latlng=$lat,$lng&key=".config('googlemapjs.key');

        $response = json_decode(file_get_contents($googleGeocodeApiUrl));
        
        if (!$response) {

thesubhendu

//This worked:-- 

git config core.filemode false

//did not work

git config core.autocrlf input

thesubhendu

<x-guest-layout>
    <div x-data="jobApplication()"  class="max-w-2xl mx-auto p-6">
        <h2 class="text-2xl font-bold mb-6">Developer Job Application</h2>

        <div x-show="Object.keys(errors).length > 0" class="mb-4 p-4 bg-red-100 border border-red-400 text-red-700 rounded">
            <h4 class="font-bold mb-2">Please correct the following errors:</h4>
            <ul class="list-disc list-inside">
                <template x-for="(error, key) in errors" :key="key">
                    <li x-text="error[0]"></li>
                </template>

thesubhendu

Use cases:

Display matching jobs to applicants in Job websites, recommended videos , posts, products etc. One way to do it is using Sql queries which get difficult if there are too many parameters to consider. AI comes to rescue. Simplest way to build recommendation engine is using Vector embeddings .

Here I will use Open AI to generate embeddings, we can use other LLM models as well.

Todos

• Laravel project with two models with data seeded applicants and Jobs

thesubhendu

<?php

namespace Tests\Smoke;

use App\Models\EmailTemplate;
use App\Models\TemporaryPlacement;
use Illuminate\Support\Facades\Route;
use Tests\TestCase;
use App\Models\User;
use App\Models\Customer;