Matěj Štágl articles - 45 total
The Shift Towards Agentic AI: What It Means for Developers
Agentic AI is moving from experimental prototypes to production systems in 2025, but developers remain cautious due to quality concerns and trust issues.
5 Key Performance Benchmarks for AI Development in 2025
A guide to five critical performance metrics for evaluating AI libraries and frameworks, based on real-world testing of AI-powered workflow automation systems.
Unlocking Developer Productivity with Emerging AI Tools in 2025
Discover how AI-powered development tools are transforming developer productivity in 2025, reducing cognitive load and accelerating feature delivery.
The Evolution of AI Tools: What Developers Need to Know
Explore how AI-powered development tools have transformed since 2021, from GitHub Copilot to modern solutions, with real-world examples and data-driven insights.
Navigating the Ethics of AI: Lessons from 2025
Exploring AI ethics at a critical inflection point where regulatory frameworks meet powerful AI systems. Key lessons learned from real-world AI project development.
The Future of AI: Context Engineering in 2025 and Beyond
A deep dive into context engineering for LLMs, exploring structured approaches and practical techniques discovered through hands-on experimentation with AI systems.
5 Low-Code Platforms to Boost Your C# Development in 2025
Explore how modern low-code platforms have evolved to enhance C# development workflows, making visual development tools more powerful and developer-friendly than ever.
From Zero to AI Agent: My 6-Month Journey with LLMs
A developer's honest account of building their first AI agent, covering mistakes made, essential tools discovered, and key lessons learned over six months of working with LLMs.
Why Your AI Workflow Design Might Be Overcomplicated
Developers are building overly complex AI workflows when simple solutions would suffice. Learn how to recognize when you're overengineering and how to simplify your AI implementations.
Progressive Learning: AI Deployment Strategies from Basic to Advanced
A comprehensive guide to AI deployment in C#, from simple chatbots to production-grade autonomous agent systems using modern frameworks.
Federated Learning in 2025: What You Need to Know
Discover how federated learning solves the challenge of training AI models on sensitive data without compromising privacy, with real-world applications beyond healthcare.
What Really Happens When You Automate Your Development Process
A deep dive into real-world AI automation in development—exploring what works, what doesn't, and the actual impact on productivity and deployment speed.
Why Most AI Coding Tools Fail (And How They Succeed)
A developer's firsthand experience with AI coding assistants reveals why they often fall short and what it takes for them to actually deliver value in real-world development.
Comparing C# AI Libraries: Which One Boosts Dev Productivity Most?
A hands-on comparison of AI libraries for C# developers, testing which tools actually improve workflow and development speed in real-world projects.
MLOps Best Practices: Streamlining AI Deployments in C# for 2025
A practical guide to MLOps practices for C# developers, exploring how to streamline AI model deployments from development to production in 2025.
The AI Data Center Revolution: What It Means for C# Developers in Q4 2025
How modern AI infrastructure is transforming C# development workloads, from machine learning pipelines to real-time inference in today's data centers.
Exploring Google Gemini: New Frontiers for AI Integration in C# Applications
Learn how to integrate Google's Gemini AI models into .NET applications with practical examples covering multimodal inputs, streaming, and real-world implementation patterns.
Legal Research is Changing: How AI Makes Your C# Applications Smarter
Exploring how AI transforms legal research through a C# proof-of-concept application that helps legal professionals analyze case law and documents more efficiently.
Generative AI Tools: Navigating the Landscape for C# Developers in 2025
A practical exploration of generative AI tools and frameworks for C# developers, comparing GitHub Copilot and newer SDKs to help you choose the right solution.
The True Cost of AI Integrations: Comparing Performance and Pricing Models for C# Libraries
A comprehensive guide to understanding the financial and performance implications of AI library choices in C#, helping you save thousands of dollars and avoid costly mistakes.
Understanding Federated Learning: Best Practices for Implementing Privacy-Preserving AI in C# Projects
Learn how federated learning enables AI model training across distributed datasets without sharing sensitive data, with practical implementation strategies for C# developers.
Migration Made Easy: Transitioning from Traditional AI Libraries to Low-Code Solutions in Q4 2025
Learn how teams are transitioning from traditional AI development to low-code solutions, reducing development time while maintaining product quality.
The Rise of Agentic AI: Transforming Workflows in C# Development
Discover how agentic AI is revolutionizing application development in the .NET ecosystem, transforming workflows and enabling smarter C# applications.
The Secret to Efficient AI Workflows in C#: Patterns You Need to Know
Learn essential patterns for building efficient AI-driven applications in C#, focusing on agentic AI and hyperautomation techniques that separate effective implementations from inefficient ones.
Understanding the LlmTornado Codebase: Multi-Provider AI Integration
Comprehensive overview of LlmTornado's architecture including custom provider support, streaming capabilities, and integration with 25+ AI providers like OpenAI and Anthropic.
Five Game-Changing AI Trends in Q4 2025 Every C# Developer Should Know
Discover how AI integration has transformed C# development in 2025, from reducing API complexity to solving real business problems with transformative new capabilities.
How to Tune Your C# AI Model for Maximum Efficiency: A Step-by-Step Guide
Master AI model optimization in C# production environments. Learn the balance between speed, accuracy, and resource consumption with practical tuning techniques.
The Shift to Synthetic Data Markets: How to Prepare Your C# Applications for 2026
Learn how synthetic data markets are transforming AI development and how to prepare your C# applications for this shift by 2026.
Building Autonomous AI Agents in C#: Tips from Real-World Applications
Learn from real-world production deployments: orchestration, memory management, and failure recovery are more critical than LLM selection when building autonomous AI agents in C#.
The Hidden Costs of Common AI SDKs in 2025: What Developers Need to Know
Discover the unexpected costs of AI SDKs beyond API calls—from token overhead to infrastructure complexity—and how to build cost-effective AI applications.