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Javacodegeeks

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Spring AI Testing: AI Evaluators Example

Spring AI provides tools for evaluating Large Language Models (LLMs) like Ollama and TestContainers, ensuring accuracy and reliability in AI applications.
Ollama is a platform for running LLMs locally, offering speed, offline capabilities, and simplified model deployment without cloud dependencies.
TestContainers is an open-source library for creating disposable instances of services like databases in Docker containers, enhancing test reliability and setup.
Combining Ollama with TestContainers enables developers to create controlled environments for evaluating AI models efficiently.
The code example demonstrates setting up a Spring Boot application integrating Ollama in a TestContainer and evaluating LLM responses using Spring AI evaluators.
Spring dependencies like spring-boot-starter-web and testcontainers are utilized to create and run Ollama instances seamlessly.
The LlmService communicates with the AI model through OpenAiClient, while evaluators like RelevanceEvaluator and FactCheckingEvaluator assess response quality.
The LlmController generates LLM responses, evaluates their relevance and accuracy, and returns structured JSON results, enhancing AI response quality.
Proper application setup is crucial, including defining properties like server port and AI model configurations in application.properties.
Unit testing using LlmEvaluatorTest class helps validate the functionality of the LlmController, ensuring AI-generated responses meet expected criteria.

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Medium

Other Neural Network Models

Autoencoders are neural network models that learn to compress and decompress images.
They can be used as generative image models to generate random images or images from text.
Autoencoders can also be utilized in semi-supervised learning to train a language model to produce vector representations of images from captions.
Adversarial networks, like generative adversarial networks (GANs), are trained to predict certain outputs and not predict others.

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Medium

254

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“Operators in Programming: The Hidden Power Behind Every Line of Code”

Perform Arithmetic and Mathematical Operations: Operators like +, -, *, and / help perform calculations, making it possible to handle numerical data efficiently.
Enable Decision-Making with Comparisons: Comparison operators (==, !=, >, <) help in making logical decisions by comparing values.
Implement Logical Conditions: Logical operators (and, or, not) help combine multiple conditions in control structures like loops and if statements.
Work with Data Efficiently: Operators like in, not in (membership operators) and is, is not (identity operators) are useful for working with lists, sets, and other collections.

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Medium

138

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Returning Values vs. Declaring Variables in Go: Understanding the Key Differences

When writing Go programs, you often need to store and use data.
Declaring a variable means reserving space in memory and giving it a name.
Returning values without declaring variables makes sense when you don't need to reuse the result later.
By making smart choices between declaring variables and returning values, your Go programs will be faster, cleaner, and easier to maintain.

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Dev

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The Rise of AI-Powered Code Assistants in 2025: Revolutionizing Development

AI-powered code assistants are revolutionizing the development landscape in 2025.
The demand for faster development and increased productivity has led to the rising popularity of AI coding tools.
AI assistants offer context-aware suggestions, bug detection, and natural language coding, transforming developers' workflows.
While AI coding tools provide numerous benefits such as speed and learning opportunities, challenges include over-reliance and ethical concerns.

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3 Likes

Medium

290

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Level Up Your Flutter Workflow: Must-Have Tools for Developers

Flutter Version Management Pro (FVM Pro) simplifies managing multiple Flutter SDK versions. Effortlessly switch between projects and environments, boosting the efficiency of Flutter project tasks.
Rapidly design and prototype beautiful Flutter apps without writing extensive code. FlutterFlow generates clean and maintainable Dart code, integrates effortlessly with Firebase for quick authentication, database, and storage setup, and allows custom code injection for complex logic.
Bring your apps to life with interactive animations. Rive simplifies complex animations, making your app stand out without a steep learning curve.
Simpler and cleaner than Provider, Riverpod provides a straightforward way to manage your app’s state, improving readability and maintainability.

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Dev

169

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DevLog #1: Why I’m Building 14 Projects (and What Comes After)

The author challenges themselves to build 14 small Python projects in 14 days.
Initially focusing on basic CLI apps, the author decided to raise the bar and incorporate simple GUIs.
The author has completed several projects, including a file renamer, a calculator, a journaling app, a time tracker, and a password generator.
The author aims to share their work, embrace imperfections, and continue learning by finishing projects in public.

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10 Likes

Dev

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Scheduling Tasks with Cronjobs in Laravel

Laravel provides an elegant way to schedule recurring tasks using Task Scheduling, eliminating the need to manually configure multiple cronjobs.
Task scheduling in Laravel is managed through the app/Console/Kernel.php file.
To ensure the scheduler works correctly, we need to set up a single cronjob on the server.
Laravel's Task Scheduling feature simplifies recurring task execution and eliminates the complexity of traditional cronjobs.

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CSS-Tricks

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Automated Visual Regression Testing With Playwright

Automated Visual Regression Testing with Playwright offers a solution to guard against visual regressions while refactoring stylesheets and introducing new features.
Playwright's built-in visual comparison feature helps in comparing visual artifacts efficiently without relying on external packages.
The setup involves creating a dedicated directory for visual testing, setting up dependencies, defining test environments, and configuring browsers.
Snapshot testing with Playwright involves taking screenshots to establish baselines for comparison and identifying any visual changes.
Automated generation of tests for every page using a web crawler and dynamically generating tests based on identified URLs simplifies the testing process.
Exceptions in visual testing, like flakiness or randomness, can be handled by injecting custom CSS using Playwright.
Adjusting viewport size for capturing full page screenshots and waiting for network idle state improves test accuracy but may increase resource usage.
Despite the initial simplicity expected, implementing visual regression testing with Playwright requires careful setup and considerations like layout shifts and timeouts.
The article discusses the author's journey to setting up automated visual regression testing with Playwright and reflects on the challenges and solutions discovered.
The process involved leveraging Playwright's capabilities, configuring test environments, generating tests dynamically, handling exceptions, and optimizing page capture for accurate testing results.

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Dev

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🧪 Types of Software Testing and What They Actually Accomplish

Unit Testing: Tests individual functions or methods in isolation. Goal: To verify that each "unit" of your code (like a function or class) works as expected. Accomplishes: Catches bugs early, Makes refactoring easier, Improves code reliability
Integration Testing: Tests multiple components or modules working together. Goal: To ensure integrations between services, modules, or layers are functioning. Accomplishes: Catches issues with data flow, Verifies contracts between components, Prevents regression when APIs change
End-to-End (E2E) Testing: Tests the entire application from the user’s perspective, from frontend to backend. Goal: To simulate real user scenarios and verify the app works as expected. Accomplishes: Confirms the system works as a whole, Detects real-world failures, Increases confidence before release
Regression Testing: Re-running old test cases to ensure existing functionality still works after updates. Goal: To prevent bugs from creeping back into previously working code. Accomplishes: Ensures stability with each release, Helps maintain trust in legacy systems, Reduces risk during updates/refactors

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Dev

218

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I Made a TUI in C... and regretted it

Working on a TUI in C, the writer found it challenging due to the low-level nature of the language, requiring meticulous memory management and manual control of program features.
Ncurses, a popular C library for text-based user interfaces, provided essential tools but demanded intricate handling for even simple tasks, making UI updates a manual process.
Another library, cJSON, despite being a single file with about 5000 lines, played a significant role, highlighting the writer's heavy reliance on it for their TUI project.
Libcurl, used for network operations, required detailed manual setup for basic tasks like creating headers, showcasing the hands-on approach needed in C development.
Documentation for these libraries lacked modern user-friendly features, relying more on traditional text-based manuals, posing a contrast to more contemporary language frameworks.
The writer's motivation for choosing C and creating a TUI stemmed from a desire to challenge themselves and move away from tools that automate many aspects of development.
The article serves as a cautionary tale for beginners in C programming, as the writer acknowledges potential shortcomings and areas for improvement in the code.
The project involved handling HTTP requests, rendering menus from JSON data, managing tasks, and creating a functional text-based interface with navigation and task management features.
Despite the challenges and complexities faced, the writer successfully implemented a working TUI in C, emphasizing the manual effort required for tasks that might be more streamlined in higher-level languages.
The writer's decision to build the TUI in C was framed as a personal challenge to delve into the intricacies of low-level programming and to gain a deeper understanding of the technical nuances involved.
Overall, the article highlights the demanding nature of developing a text-based interface in C, showcasing the labor-intensive processes involved in handling UI elements and network operations within a TUI project.

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13 Likes

Medium

187

CheckADevice.com fast, no-fuss online device testing tool

CheckADevice.com is a free online tool for testing hardware components of computers or mobile devices.
It allows real-time testing of webcams, microphones, keyboards, mice, and speakers for functionality.
The tool operates within the browser, eliminating the need for downloaded software and providing fast and seamless testing.
Future features will include internet speed tests, battery health checks, and advanced diagnostics.

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Hackernoon

142

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Why I Stopped Overthinking and Shipped My CSS Cheat Sheet

Perfection kills momentum.
The author stopped overthinking and finally shipped an Advanced CSS Selectors & Techniques Cheat Sheet.
The cheat sheet received 84 organic views and was found helpful by real people.
The author encourages others to stop waiting and just hit publish to learn and build confidence.

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8 Likes

Hackernoon

357

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Oracle’s New AI Platform Lets You Build Chatbots That Tell Medieval Tales

The article introduces beginners to Oracle Cloud Infrastructure (OCI) Generative AI service, covering creating an account, setting up the environment, and running the first AI task in Python.
OCI offers AI services like GPU infrastructure and GenAI-powered apps for tasks like text generation, summarization, and embeddings.
Generative AI in OCI has Large Language Models (LLMs) for various tasks, and users can fine-tune models with custom data sets and deploy them on dedicated AI clusters.
Models like Meta Llama and Cohere's Command R and R+ are accessible through OCI SDK in Python, Java, and Node.js.
Initial steps include creating an account, navigating the OCI Console, generating an API key, and creating a configuration file for authentication.
Setting up the environment involves installing necessary libraries in a virtual environment using tools like venv or conda.
Running the first AI task involves creating a Python script, setting up authentication with OCI Generative AI client, and interacting with model endpoints.
Examples are provided for using OCI models like Meta Llama and Cohere's Command R by composing chat requests and displaying the generated responses.
The article concludes by highlighting the steps for getting started with OCI Generative AI and encourages users to refer to the documentation for more information.

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Dev

384

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Alpha vs Beta Testing: Key Differences & Best Practices

Alpha and Beta Testing are crucial phases in software development that help ensure stable, high-quality releases by catching bugs and improving usability.
Alpha Testing is the initial testing phase conducted internally by developers and QA teams in a controlled environment, while Beta Testing involves real users trying the software in a real-world setting.
The goals of Alpha Testing include catching major functional bugs early, identifying usability issues, and ensuring core functionality works as expected.
In contrast, Beta Testing focuses on validating real-world performance, gathering user feedback for improvements, and ensuring scalability before the final release.
Alpha Testing is more about bug fixes and feature validation, while Beta Testing focuses on product refinements and usability improvements.
Key differences between Alpha and Beta Testing include the testers involved, the testing environment, goals, bug severity, test focus, and outcomes.
Tools such as bug tracking, testing automation, performance profiling, and API testing are essential for successful Alpha Testing, while user feedback, crash reporting, and feature flagging tools are crucial for Beta Testing.
Skipping Alpha Testing can increase risks and harm a product's reputation, emphasizing the importance of both testing phases in the software development lifecycle.
Beta Testing challenges include ensuring active user participation, effective feedback management, and analyzing diverse real-world conditions.
Choosing diverse Beta Testers representing the target audience is crucial for collecting comprehensive feedback and ensuring the product meets varying user needs.

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