Software testing has always been the part of the development cycle that everyone agrees is important and almost everyone underinvests in. The reasons are structural. Writing good tests is time-consuming. Maintaining test suites as code changes is even more so. QA engineers are perpetually under-resourced relative to the volume of work they are expected to validate. And under deadline pressure, testing is the discipline that gets compressed first — with consequences that typic

Most engineering teams that are serious about AI have already adopted AI coding tools. Some have restructured their code review process. A smaller number have started experimenting with autonomous AI agents for bounded tasks. Very few have answered the harder question: what does the whole team actually look like when AI is a first-class member of the delivery process — not an add-on tool, but a structural part of how work gets planned, built, tested, and shipped? That is the

The developer who uses AI to write faster is an asset. The team that uses AI without discipline is a liability. "Move fast and break things" is a fine philosophy for a startup MVP. It is a catastrophic one for production software that handles real users, real data, and real money. AI can accelerate your coding speed by 10x — but it cannot replace the engineering discipline that keeps those 10x more lines of code from becoming 10x more problems. Vibe coding is real, it is exci

AI coding agents — from GitHub Copilot to Devin — have crossed a threshold. This is the practical guide to which tools do what, how to restructure your team around them, and where the real pitfalls are.

"The question is no longer whether AI can write code. The question is whether AI can own the entire process of building software — and what that means for the humans alongside it."

Object counting with computer vision sounds straightforward. In practice, it is one of the most technically nuanced CV deployments you can attempt. This is a complete map — from first principles to live production — including how Cluedo Tech solved it.

The modern warehouse is being transformed by AI that can see — identifying items, tracking movements, detecting errors, and optimising space in real time. Here's how it works and what the numbers look like.

Agentic AI marks a fundamental shift in how businesses use artificial intelligence — from systems that respond to prompts to systems that plan, act, and complete multi-step tasks autonomously. In 2026, it is no longer experimental. Here is what it means and how enterprises are deploying it.

Introduction One of the first questions every organisation faces after deciding to deploy an AI system is: how do we make this model know our business? A general-purpose LLM knows a great deal about the world as of its training cutoff. It does not know your product catalogue, your internal policies, your customer history, your proprietary processes, or anything else that makes your organisation specific. For most enterprise use cases, this gap between general knowledge and bu

Introduction The statistics are uncomfortable. Depending on which research you read, somewhere between 70% and 85% of enterprise AI projects fail to move from pilot to sustained production. This is not because the technology doesn't work. The demos work. The pilots work. The proof-of-concepts impress the boardroom. The failure happens in the journey from controlled experiment to live, scaled, operationally embedded system. And the failures are not random — they follow a patte

Computer vision is no longer a technology reserved for factories and warehouses. It is quietly transforming the shop floor — tracking products on shelves, understanding how customers move through a store, and flagging potential theft before a human security guard would even notice. For retailers under pressure from e-commerce competitors, AI-powered cameras are becoming a genuine competitive differentiator. The shift is being driven by the convergence of affordable hardware,

For the past two years, the easiest path to AI capability has been an API call. Organisations of every size have connected their applications to OpenAI, Google, Anthropic, or one of dozens of other AI providers, passing their data across the internet to a model running on someone else's infrastructure. It is fast, it is capable, and it requires almost no upfront investment. But a growing number of enterprises are pausing and asking a question their legal, compliance, and secu