2026 Java trends: TIOBE and Stack Overflow rankings, enterprise adoption by industry, Spring Boot and framework data, Java version distribution, AI/ML integration, and runtime performance benchmarks.
Vibe Coding Trends 2026: Adoption, Productivity, and Code Quality Data
2026 vibe coding trends: 92% daily U.S. developer adoption, $4.7B market, 63% non-developer users, and a deepening quality crisis. Data for engineering leaders.
How We Used LLMs to Understand and Modernize a Legacy Delphi Application
Many legacy modernization projects start with a simple question: what does this thing actually do?
In this project, we were modernizing a decades-old Delphi application with limited documentation, no meaningful test coverage, engineers long since moved on, and significant unknowns about the environment in which it operated.
Modernizing legacy systems is challenging, particularly when documentation is limited and system knowledge has been lost over time. When LLMs and AI are applied thoughtfully, they can help teams understand legacy systems faster and reduce modernization risk.
This article focuses on how we used LLMs to understand, document, and de-risk an unfamiliar legacy system before modernization began. Once the application was understood and the architecture was defined, the team leveraged AI-assisted development workflows to accelerate the Delphi-to-.NET rewrite itself. Evan Sanning shares that side of the project in his companion article, How We Used LLMs to Rewrite a Legacy Delphi Application in C#.
Legacy Modernization Trends: 2026 Market Size, Growth Drivers, and Enterprise Adoption Data
2026 legacy modernization trends: market size data, growth rates, regional breakdowns, and industry adoption stats from 8+ analyst sources.
How We Used LLMs to Rewrite a Legacy Delphi Application in C#
We rewrote a legacy Delphi (Object Pascal) application into a .NET C# service worker in three months, beating a five-month deadline for a client in the healthcare software space.
Here’s what actually made that possible and the pitfalls we hit using LLMs along the way.





