ShiftMag

Recently, when I changed my job position inside the company, everything changed with it. Switching from an internal web app to customer-facing mobile development felt like a downhill skier in full gear competing in the Summer Olympics in the 400-meter hurdles. But the reality hit differently, and I realized I am not only the same developer I used to be, but also a different person. As an aspiring senior developer, I had been working in my domain for almost five years.

Four words show up in almost every eCommerce project we take on here at Neuralab: “Just connect it to our ERP.“ They usually drop near the end of a kickoff call, tossed out as casually as asking for the Wi-Fi password. The word doing all the heavy lifting (and all the damage) there is just. It’s the word that turns a monolithic beast of a legacy system that nobody in the room fully understands into a neat little line item some project manager expects to close in a two-week sprint.

Benoit Schillings has spent more than three decades in Silicon Valley, working in senior engineering roles at Be Inc., Nokia, Yahoo, and Google X, where he served as CTO. Today, at Google DeepMind, he leads research into generative AI for code, deep reasoning algorithms, and the future of pre-training and transformer models. This September, he’ll be a keynote speaker at Infobip Shift Zadar 2026 – and ShiftMag readers can get a special discount.

This post describes a failure mode we addressed while operating Debezium with PostgreSQL 16 and Patroni 3.3.1. After promoting a standby, Debezium needed a logical replication slot from which it could safely continue. Creating a new slot after promotion could start beyond Debezium’s last durable offset and introduce an event gap. The central lesson was to treat PostgreSQL’s replication slot and Debezium’s Kafka Connect offset as parts of one distributed checkpoint.

It’s 2026, and AI agents are taking over more and more of our busywork. I personally rely on them for a lot of boring, but increasingly complex tasks. We do hear about security, staying safe, and the term prompt injection gets thrown around. But that can sound technical; devs are supposed to solve that, with code and stuff, right?

We spoke with 3 engineers to ask the blunt question: is AI making junior developers better or just faster at shipping code? If AI can already draft code, fix bugs, and untangle unfamiliar systems, what’s left for junior engineers to learn the hard way?

Model Context Protocol (MCP) extends what your AI agent can do beyond its built-in tools like database access, internal APIs, Confluence, Jira, Bitbucket, and more. You connect your agent to an MCP server, and it gains new capabilities. But do you know how much your MCP is actually costing you? Or your users? Wait, MCP’s cost money?!​ Every time you send a request, your MCP tools and their parameters are loaded into the context window, before your prompt even gets processed.

Everyone is busy asking how big the model is, but the real question is what you fed it first - because if your PDFs are mangled and your OCR is sloppy, even the smartest LLM starts from the wrong answer. At the WeAreDevelopers World Congress 2026 in Berlin, I spoke with Luca Mancinelli (Solutions Engineer, Apryse), about a part of the AI stack that tends to receive far less attention than models themselves:the document pipeline.

This year, I attended the RAISE Summit in Paris. Among the conversations I followed, one panel in particular caught my attention: “The ROI on Intelligence: Turning AI Investment into Enterprise Transformation.” What made it especially interesting to me was that it moved beyond the familiar question of how much AI can speed up an individual developer.

AI agents are already being trusted with real work, but handing them raw credentials is a dangerous shortcut. That's why the next security challenge is controlling what an agent can do once it gets access. At the Raise Summit in Paris, I caught up with Richard Kirby, Office of the CTO at 1Password.