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A hospital network's editors couldn't see their own components inside Drupal's CKEditor, so every page cost extra rounds and sometimes a developer. Here's the cause — and the module we built and contributed to fix it.

Two things are true right now at every shop that's serious about AI-assisted development. The senior engineers have never shipped more, and they've never been more tired. And the junior engineers are worried about never being invited to the party.

In May 2026, Publicis Groupe agreed to acquire LiveRamp for $2.2 billion. On paper, it's an AI infrastructure play: Publicis wants LiveRamp's data collaboration network to power agentic workflows across its agency business. In practice, it just removed the one thing that made LiveRamp valuable to everyone else — neutrality.

Good observability does two things at once: it gives engineers a clear, fast path from a symptom to its root cause, and it scales with your data without forcing hard tradeoffs about what you can afford to watch. When telemetry volumes grow, that second part gets harder, and the architecture underneath your monitoring stack starts to matter as much as the features on top of it.

There's a panic running through software circles right now, and if you've been in the industry long enough, it has a familiar feel. AI agents can write working code, the reasoning goes, so this must be the end of the software engineer.

In my last post, I referenced Michael Bloch’s article on the rapid transformation of what engineers will do in discussing how engineers need to move up the value chain. The engineers that will thrive are those that understand how the business works and translate that into an AI context that agents can build upon. I particularly liked Michael Bloch's assertion that “data is the real interface”. That’s exactly right; the primary challenge in modern product engineering is getting the data exchanged between systems consistent and, more importantly, useful. AI coding will end up making everything except data engineering less and less relevant. In essence, all engineering is increasingly becoming data engineering.

Bias, black boxes, and the decisions that financial institutions must be able to defend.

In today’s data-driven world, speed is an expectation almost everywhere. Whether it’s powering advertising platforms, optimizing revenue streams, or delivering real-time analytics to thousands of users, organizations need infrastructure that can process massive volumes of data.

Insurance companies handle mountains of documents every day. Claim forms, policy updates, medical reports, accident photos, and handwritten statements often create bottlenecks that slow operations. While manual processing may seem like the only way to ensure accuracy, it is both time-consuming and prone to errors.

Lineate welcomes George Chogovadze as Director of Engineering. With 20 years of leadership experience at companies like Microsoft, Cisco, and VMware, George will strengthen engineering practices and accelerate the growth of Lineate’s AI and Generative AI services, helping deliver solutions that create real business value.

Lineate has designed custom Intelligent Document Processing (IDP) solutions for companies across a wide range of industries. But if we had to name the sectors with the greatest need for these innovations, education would certainly be among the leaders. IDP directly addresses some of the most pressing challenges in the education sector and does so in seconds.

Educational institutions handle thousands of documents every month. Admission forms, transcripts, HR files, and exam papers often create bottlenecks that slow down operations and strain resources. Manual processing is time-consuming, error-prone, and takes staff away from their core responsibilities.