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Before accurate nautical charts existed, sailors did not navigate badly. They navigated blind. The problem was not seamanship, not courage, not the quality of the vessels. It was the absence of an honest picture of the territory. Coastlines were approximated. Depths were guessed. The distance between what the map showed and what was actually there was filled in with assumption, with rumour, with the testimony of people who had passed that way before and survived. The moment a

Why every code analysis tool stops short of the only conversation that matters Every serious advisory discipline speaks two languages. The first is technical — the precise description of what is wrong, why it is wrong, and how significant the problem is. The second is financial — what it costs to fix, what it costs to ignore, and what the gap between those two numbers means for the organisation. The first language informs. The second language decides. Software has always stop

In 1931 a structural engineer named Herbert Semple signed off on a building in downtown Chicago. The building stood for forty years before anyone noticed that three of the load-bearing calculations were wrong. Not catastrophically wrong. Just quietly, incrementally wrong in a way that nobody would spot until the conditions were right. When they were, the repair bill was eleven times the original construction cost. Nobody had lied. Nobody had been negligent in any obvious way.

There is a well-documented phenomenon in cognitive psychology called the curse of knowledge. Once you know something, it becomes almost impossible to remember what it felt like not to know it. Experts consistently overestimate how much others understand, because their own fluency makes the knowledge feel obvious. It stops feeling like something that was learned and starts feeling like something that simply is. Software teams have a version of this that nobody talks about open

When it comes to understanding and improving complex software, having the right tools can make all the difference. I’ve found that Decoder offers a powerful solution for code analysis, especially when dealing with legacy systems or AI-generated applications. What excites me most is their free code analysis trial . It’s a fantastic opportunity to explore how Decoder can help you deeply understand your code, identify issues, and enhance security without any upfront commitment.

When working with complex software systems, understanding the codebase is crucial. Whether you are dealing with legacy systems or AI-generated applications, having the right tools can make all the difference. That’s where Decoder software steps in. It offers a powerful way to analyse, improve, and secure your software code. In this post, I’ll walk you through the top features and benefits of Decoder software, explaining why it’s becoming an essential tool for developers, appl

If you've ever tried to make sense of a sprawling microservices architecture spread across a dozen repositories, you'll know the pain. You've got duplicated logic hiding in plain sight, API endpoints that mirror each other across services, and code quality issues that no single-repo tool ever surfaces — because no single-repo tool can see the full picture. That's exactly the problem we built Systems to solve. What Is a System in DeCoder? A System is a new high-level construct

DeCoder's new Change Advisor lets you describe a code change in plain English and receive a full impact assessment — risks, timelines, affected files, and a step-by-step implementation plan — in seconds. "How long will this take?" and "What could go wrong?" are the two questions that haunt every engineering team before a significant code change. DeCoder now answers both — before you write a single line. Software development is full of unknowns. You have a clear idea of what n

Technical debt is a challenge that many development teams face. It creeps into projects as shortcuts, rushed fixes, or outdated code. Over time, it slows down progress, increases bugs, and makes maintenance harder. But how can we effectively reduce technical debt without halting development? In this post, I’ll share practical strategies that help manage and reduce technical debt efficiently. How to Reduce Technical Debt Without Disrupting Development Reducing technical debt m

Three trending Go-heavy codebases. Strong quality scores. Familiar operational risk patterns. This week we used DeCoder to analyse three popular public GitHub repositories (anonymised), with a specific focus on Go-based projects . Go has earned a strong reputation for building clean, performant, production-grade systems. It’s widely used for CLI tools, platform services, cloud-native utilities, and infrastructure automation — and many engineering teams see Go codebases as i

At a high level, all three codebases scored strongly: Overall Quality Score: 80/100 across all three Complexity: low Maintainability: high Performance risks: none detected Even with this high quality score across all three codebases, DeCoder still surfaced multiple operational and governance concerns. Most teams wouldn’t detect these issues until late in a migration, scaling event, or security review. Codebase Comparison Summary Metric Codebase #1 Codebase #2 Codebase #3

Understanding complex software code can be a daunting task. Whether you’re dealing with sprawling legacy systems or newly generated AI applications, the challenge remains the same: how do you deeply understand, improve, and secure your code efficiently? This is where AI-powered tools like Decoder come into play. Today, I want to walk you through a practical demonstration of Decoder’s capabilities and show how it can transform the way you approach code analysis. Why AI for Cod

When it comes to maintaining high-quality software, code analysis tools are indispensable. They help identify bugs, security vulnerabilities, and code smells before these issues escalate into costly problems. Today, I want to dive deep into two prominent players in this space: Decoder and SonarQube . Both tools offer powerful features, but they cater to slightly different needs and use cases. By the end of this post, you’ll have a clear understanding of how these tools stack

Migrating an application to a modern hosting platform can unlock new performance, security, and scalability benefits. Yet, many migrations fail or run into costly delays because the application’s codebase is not fully understood before the move. Using a tool like Decoder to analyse your application code is a crucial first step that reveals hidden risks, dependencies, and modernisation opportunities. This post explains why analysing code with Decoder matters and how it sets th

Legacy applications often form the backbone of many enterprises, yet they come with hidden risks and limitations. These older systems may contain outdated code, security vulnerabilities, and inefficiencies that slow down business growth. Analyzing the source code of legacy apps uncovers these issues and sets the stage for effective remediation. With the rise of generative AI tools like Claude Code, OpenAI Codex, and Gemini, enterprises now have powerful options to rewrite and

Vibe coded applications have gained popularity for their dynamic features and user-friendly interfaces. Yet, many developers and organisations face persistent challenges when it comes to maintaining the quality and security of these apps. Code issues in vibe coded apps can lead to performance bottlenecks, security vulnerabilities, and increased maintenance costs. This post explores common problems found in vibe coded applications and explains how using a tool like Decoder can