開発スタッフがアプリ関連の最新技術やニュースをご紹介します。

In the world of software development, commit messages play a crucial role in tracking changes, understanding the evolution of code, and collaborating effectively within a team. However, writing meaningful commit messages can be a tedious task, often resulting in generic or uninspiring messages. This is where OpenCommit comes into play, aiming to eliminate lame commits with the power of artificial intelligence.

Auto-generate Meaningful Commits in 1 Second

OpenCommit is an innovative tool that leverages AI to automatically generate meaningful commit messages for your code changes. The primary goal is to enhance the quality of commit messages, making them more descriptive and relevant. By doing so, OpenCommit contributes to better code documentation and collaboration.

Getting Started with OpenCommit

Setting up OpenCommit is a breeze, and you can start using it as a command-line interface (CLI) tool. The process involves a few simple steps:

1. Install OpenCommit Globally

   npm install -g opencommit

2. Obtain Your OpenAI API Key

   Get your API key from OpenAI and ensure that you add your payment details to enable API functionality.

3. Set API Key in OpenCommit Config

   oco config set OCO_OPENAI_API_KEY=<your_api_key>

   Your API key is stored locally in the ~/.opencommit config file.

Usage Example

Once OpenCommit is set up, you can use it to generate commit messages for your staged changes. Here’s a quick example:

Stage your changes

git add <files...>

Generate a commit message with OpenCommit

opencommit

Alternatively, you can use the `oco` shortcut:

Stage your changes

git add <files...>

Generate a commit message with OpenCommit using the shortcut

oco

Configuration Options

OpenCommit provides various configuration options to tailor the generated commit messages according to your preferences. These configurations include:

Local per repo configuration

Create a `.env` file in your repository and set OpenCommit config variables such as API key, max response tokens, base path to OpenAI API, description, emoji usage, model selection, language, message template, and prompt module.

OCO_OPENAI_API_KEY=<your OpenAI API token>
OCO_OPENAI_MAX_TOKENS=<max response tokens from OpenAI API>
OCO_OPENAI_BASE_PATH=<may be used to set proxy path to OpenAI api>
OCO_DESCRIPTION=<postface a message with ~3 sentences description of the changes>
OCO_EMOJI=<boolean, add GitMoji>
OCO_MODEL=<either 'gpt-4', 'gpt-3.5-turbo-16k' (default), 'gpt-3.5-turbo-0613' or 'gpt-3.5-turbo'>
OCO_LANGUAGE=<locale, scroll to the bottom to see options>
OCO_MESSAGE_TEMPLATE_PLACEHOLDER=<message template placeholder, default: '$msg'>
OCO_PROMPT_MODULE=<either conventional-commit or @commitlint, default: conventional-commit>

Global Config for All Repos

Set global configurations for OpenCommit, such as the default model and language. But local configurations still take priority, allowing flexibility for specific repository needs.

oco config set OCO_MODEL=gpt-4

Conclusion

OpenCommit is a game-changer in the world of version control. By automating the process of crafting meaningful commit messages, it not only saves time for developers but also contributes to the overall readability and understanding of code repositories. Embrace the power of AI with OpenCommit, and say goodbye to mundane commit messages.

Hope you enjoy that.

Asahi

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Google’s generative AI chatbot, Bard, is set to receive a substantial upgrade with the integration of Gemini, Google’s latest and most advanced AI model. This update is expected to significantly enhance Bard’s capabilities, providing it with improved reasoning, planning, and understanding, among other features. Gemini, available in Ultra, Pro, and Nano sizes, is designed to run seamlessly across a range of devices, from mobile phones to data centers.

The deployment of Gemini to Bard will occur in two phases. Initially, Bard will be upgraded with a tailored version of Gemini Pro, available in English in over 170 countries. Subsequently, Google plans to introduce Bard Advanced, granting users access to Gemini Ultra, the most powerful AI model. These enhancements mark the most significant quality improvement for Bard since its initial launch, promising heightened proficiency in tasks such as understanding and summarizing content, reasoning, brainstorming, writing, and planning.

Gemini Pro underwent rigorous testing against industry-standard benchmarks, showcasing its superiority over GPT-3.5 in six out of eight tests, including MMLU and GSM8K. However, it’s worth noting that GPT-3.5 is over a year old, framing this launch as more of a catch-up move than a revolutionary leap. Despite this, the improvements aim to position Bard as a more capable and versatile AI, offering users an enhanced and refined experience.

Gemini Pro will initially power text-based prompts in Bard, with plans for future expansion to include multimodal support, incorporating both text and images. Looking ahead to 2024, Bard Advanced will make its debut, featuring Gemini Ultra. This version promises a comprehensive AI experience capable of understanding and acting on various types of information, such as text, images, audio, video, and code. Google intends to initiate a trusted tester program for Bard Advanced before a broader release, emphasizing the importance of safety checks in the development process. The update builds upon several recent improvements to Bard, positioning it as a robust and evolving AI chatbot.

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VRデバイスだったMeta Quest 2がカラーパススルーによるAR機能を追加してMeta Quest 3にアップデートされ、また来年度にはApple社より注目されているMRデバイス Apple Vision Proが発売予定とされています。

Image credit: Apple

Meta Quest 3が499ドル（約7.5万円）という比較的安価で発売されているのに対して、Apple Vision Proの販売価格は3,499ドル（約52万円）と高価です。

MRデバイスとして注目されているこれらの2機種ですが、先日、中国のWellsenn XR社より製造原価の推計が発表されました。

(参考記事) Meta Quest 3 & Apple Vision Pro Production Costs Estimated By Supply Chain Analyst Firm
https://www.uploadvr.com/meta-quest-3-apple-vision-pro-production-cost-estimate/

(参考記事) Apple「Vision Pro」の製造原価は「Meta Quest 3」の4倍以上か——中国アナリストが推計
https://www.moguravr.com/manufacturing-costs-for-apple-vision-pro-and-meta-quest-3/

この記事によれば、Meta Quest 3の原価は430ドルに対して、Apple Vision Proの原価は1,726ドルになります。

Meta Quest 3の原価は430ドルで販売価格は499ドルなのでMeta社はMeta Quest 3を販売することで利益はほぼないか、損失している可能性もあるとのことです。Meta社は利益よりもVR/MRの普及とマーケットのシェア獲得に注力しているようです。
Meta Quest 3ではハイエンドのMR/VR用チップセット「Snapdragon XR2 Gen 2」が採用されており、これが最も原価がかかっているパーツとされています。

一方、Apple社は高性能の独自チップセットM2やR1が搭載される予定ですが、Apple Vision Proで最も原価がかかっているパーツはディスプレイで、高価なソニー社マイクロOLEDディスプレイが採用予定で、おそらく画面の綺麗さ、MR合成の自然さはMeta Quest 3以上と推測されます。

現状のMeta Quest 3で高クオリティのMR体験が可能なので、それよりも高品質なApple Vision ProのMRには期待は高まります。

木曜日担当：nishida

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text-generation-webui を使用してローカルでLLMを動かし、
AIとチャットを行ってみました。

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Laravel empowers developers to build efficient and robust APIs effortlessly. Feature testing is an indispensable aspect of ensuring the reliability and functionality of our APIs, especially when it involves user CRUD operations. In this blog post, we’ll take a step-by-step journey through the code to feature test a User CRUD API in Laravel.

Step 1: Setting Up the Test Environment

Create a test class file with the following command.

php artisan make:test UserCrudApiTest

The test class file created can be found at tests/Feature/UserCrudApiTest.php and codes are as follows.

<?php

namespace Tests\Feature;

use Illuminate\Foundation\Testing\RefreshDatabase;
use Illuminate\Foundation\Testing\WithFaker;
use Tests\TestCase;

class UserCrudApiTest extends TestCase
{
    /**
     * A basic feature test example.
     *
     * @return void
     */
    public function test_example()
    {
        $response = $this->get('/');

        $response->assertStatus(200);
    }
}

Step 2: Define and modify the Test Class

Import necessary classes and traits. And remove default test_example method.

<?php

namespace Tests\Feature;
use Illuminate\Foundation\Testing\RefreshDatabase;
use Illuminate\Foundation\Testing\WithFaker;
use Tests\TestCase;
use App\Models\User;

class UserCrudApiTest extends TestCase
{
    use RefreshDatabase;
}

The `UserCrudApiTest` class is declared, extending `TestCase` and using `RefreshDatabase` for database isolation.

`RefreshDatabase` ensures a clean database state for each test, and `TestCase` is the base test case class. `User` is imported to create and interact with user models.

Step 3: Testing User Creation

public function test_user_can_be_created()
{
    $userData = [
        'name' => 'John Doe',
        'email' => 'john@example.com',
        'password' => bcrypt('password123'),
    ];

    $response = $this->json('POST', '/api/users', $userData);

    $response->assertStatus(201)
             ->assertJson([
                 'name' => 'John Doe',
                 'email' => 'john@example.com',
             ]);

    $this->assertDatabaseHas('users', [
        'name' => 'John Doe',
        'email' => 'john@example.com',
    ]);
}

This method tests the creation of a user. The `$this->json(‘POST’, ‘/api/users’, $userData)` line sends a POST request to create a user with the specified data. Assertions ensure a successful response, correct JSON structure, and that the user is stored in the database.

Step 4: Testing User Retrieval

public function test_user_can_be_retrieved()
{
    $user = factory(User::class)->create();

    $response = $this->json('GET', "/api/users/{$user->id}");

    $response->assertStatus(200)
             ->assertJson([
                 'name' => $user->name,
                 'email' => $user->email,
             ]);
}

This method tests the retrieval of a user. A user is created using the factory, and a GET request is made to fetch the user’s details. Assertions verify a successful response and the correctness of the returned data.

Step 5: Testing User Update

public function test_user_can_be_updated()
{
    $user = factory(User::class)->create();

    $updateData = [
        'name' => 'Updated Name',
        'email' => 'updated.email@example.com',
    ];

    $response = $this->json('PUT', "/api/users/{$user->id}", $updateData);

    $response->assertStatus(200)
             ->assertJson($updateData);

    $this->assertDatabaseHas('users', [
        'id' => $user->id,
        'name' => 'Updated Name',
        'email' => 'updated.email@example.com',
    ]);
}

This method tests updating a user’s details. A user is created, and a PUT request updates the user’s data. Assertions check for a successful response, the correctness of the returned JSON, and the updated data in the database.

Step 6: Testing User Deletion

public function test_user_can_be_deleted()
{
    $user = factory(User::class)->create();

    $response = $this->json('DELETE', "/api/users/{$user->id}");

    $response->assertStatus(204);

    $this->assertDatabaseMissing('users', ['id' => $user->id]);
}

This method tests the deletion of a user. A user is created, and a DELETE request is made to delete the user. Assertions ensure a successful response and confirm that the user is no longer present in the database.

After writing the necessary test methods, we can test with the following artisan command.

php artisan test --filter UserCrudApiTest

This command uses the --filter option to specify a particular test class, in this case, UserCrudApiTest. Only the tests within the UserCrudApiTest class will be executed.

The --filter option can also be used to run specific methods within a test class. For example:

php artisan test --filter UserCrudApiTest::test_user_can_be_created

Also php artisan test can be used to run all the tests in the application (all test files under tests dir).

Conclusion

Feature testing User CRUD operations in a Laravel API provides confidence in the functionality and reliability of the endpoints. Each step, from setup to testing each CRUD operation, contributes to a robust testing suite that helps maintain a high standard of code quality in our Laravel applications.

Hope you enjoy that.

Asahi

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