Taming the Wild World of Open Source
Explore open source benefits, risks, and responsible usage. Learn to evaluate projects, assess licensing and security concerns, and understand project health for informed decision-making in development.
Open Source Java Frameworks: Modern Observability Practices
Explore modern observability practices for open-source Java web applications. Learn to use Open Telemetry and JVM-level insights for performance monitoring and transparency in production environments.
Accessibility Reimagined: Moving Beyond Minimum Standards for Inclusive Digital Experiences
Reimagine digital accessibility beyond minimum standards. Learn to integrate open source principles, utilize design tokens, and unify design systems for truly inclusive user experiences.
An AI-Powered Journey to Learning Python
Explore how AI tools like GitHub Copilot and ChatGPT can enhance the process of learning Python, with insights and stories to apply to your own tech learning journey.
Spinning Your Drones with Cadence Workflows, Apache Kafka and TensorFlow
Explore building a real-time drone delivery app using Cadence and Apache Kafka, then apply TensorFlow for machine learning on streaming data to predict busy shops.
Red Hat OpenShift as a Sandbox
Explore an open-source mechanism for multi-tenant sandboxes on OpenShift clusters, enabling free community access and resource management within namespaces.
Exploring the Possibilities of GitHub Actions
Explore GitHub Actions' versatility beyond software workflows. Learn to automate tasks, increase productivity, and streamline processes for efficient and enjoyable development experiences.
Introduction to GitHub Copilot
Explore GitHub Copilot's AI-powered coding assistance features, including code generation and autocompletion across various programming languages. Learn how it can enhance your development workflow.
DynaTask: A New Open Source Approach for AI Benchmarking
Explore DynaTask, a novel open-source AI benchmarking paradigm using dynamic adversarial data collection to evaluate and assess AI model vulnerabilities to human deception.
Managing Machine Learning Experiments with MLflow
Learn to manage ML experiments, reproduce pipelines, and deploy models using MLflow, an open-source tool for streamlining the machine learning lifecycle.
How to Automate Machine Learning with GitHub Actions
Automate machine learning workflows using GitHub Actions to speed up development cycles and streamline model deployment.
Fighting Pipeline Debt With Great Expectations
Learn to combat pipeline debt using Great Expectations, an open-source library for data validation, documentation, and profiling in complex data systems.
How F.A.T. is Your ML Model Quality in the Era of Software
Explore F.A.T. (Fairness, Accountability, Transparency) properties for assessing ML model quality. Learn evaluation approaches, governance benchmarks, and bias mitigation techniques through case studies.
The Do's and Don'ts of Delivering AI Projects: A Practitioner's Guide
Discover strategies for successful AI project delivery, including evaluation tools and techniques to overcome common challenges in this rapidly evolving field.
Prompt Engineering: 7 Tricks to Solve Problems with AI - Part 4
Master prompt engineering techniques to enhance AI problem-solving. Learn 7 key tricks and explore practical applications using LangChain for building AI-powered solutions.
How to Evaluate LLM Performance for Domain-Specific Use Cases
Explore advanced LLM evaluation techniques for enterprise AI, including data-centric approaches, quality modeling, and fine-grained benchmarking to overcome common challenges and improve performance.
Task Me Anything: Revolutionizing Multimodal Model Benchmarking
Explore Task Me Anything platform for efficient multimodal model evaluation. Learn programmatic task generation and active learning techniques to create custom benchmarks for data science projects.
JEP Explained - Vector API in Java 23
Explore Java 23's Vector API with expert Zoran Sevarac. Learn about scalar and vector computations, API basics, HotSpot's auto-vectorization, and practical applications for developers.
Migrating Kotlin Plugins to K2 Mode
Learn to update Kotlin plugins for K2 mode compatibility, ensuring smooth functionality and user experience. Explore migration steps, code adaptation, and troubleshooting techniques.
Streamlining Testing in a Large Python Codebase
Optimize Python testing in large codebases using parallel execution, efficient startup, and targeted coverage reporting. Boost development speed and code quality with practical strategies and open-source tools.
Adventures in Not Writing Tests - Using Hypothesis for Code Validation
Explore Hypothesis library for creating robust test inputs, focusing on edge cases. Learn to use Ghostwriting to generate tests from type hints, saving time and validating code.