ShiftMag

Productivity That mismatch is becoming the real cost of AI-assisted development: code ships faster, but the work of understanding it, checking it, and trusting it hasn’t sped up at all. Olena Babenko, Staff Software engineer at Aiven, cut straight to the uncomfortable truth in a recent interview I had with her in London. The oversight model collapses the moment the person approving the output can no longer reliably tell whether it’s good, bad, or just convincingly wrong.

Lovable lets people describe the software they want to build in plain in natural language, and then the platform creates it. It handles security, builds complete solutions, and lets users add artificial intelligence to their applications. Essentially, it helps people build entire apps, and company founders, small businesses, and large corporations all use it. This is how Anton Osika, co-founder of Lovable, described his platform.

While testing a Kotlin microservice backed by MS SQL, I found several places where convenience had quietly become a liability. Fortunately, there was no production incident; most issues were detected during internal testing. It is always better to learn on someone else’s mistakes, this article will help you learn on mine. The service orchestrates WhatsApp campaign delivery through an asynchronous, scheduler-driven pipeline.

In the latest edition of our Developers Answer series, we spoke with developers about their best and worst projects, the reasons behind the challenges, and the ways they’ve made it across the finish line regardless of the hardships. Sure, the term “worst” means different things to different people, but most of the engineers agree: the worst types of projects are the ones that aren’t set up well by humans, not the ones that have big technical requirements.

AI coding assistants have made it look dangerously easy to believe software can now be built by prompt alone. In a recent conversation with a few Infobip engineers, we asked whether that promise holds up in practice – and the answer was clear: AI can generate code fast, but it still cannot understand the problem, define the boundaries, or own the consequences. That part remains the developer’s job.

More work means more tasks, and more tasks requires better organizing. But building a company that lasts in a competitive market is far harder. Amir Sahefinendic did just that with Doist – the company behind the task management tool Todoist and async communication tool Twist. Ahead of the Infobip Shift Conference in Zadar this September (for which you get a special discount as a ShiftMag reader), we spoke with Amir, who will be one of the speakers.

For that reason, the safer pattern, which is minimal permissions and human approval required, is becoming the default. This means agents are increasingly making decisions that require a human response before anything happens next. The question is how that response gets requested. The agent ran. It checked the queue, detected the anomaly, made the call. Then it produced a tidy summary (decision, rationale, confidence score) and delivered it to the person who was watching. That’s the happy path.

We played “Truth or Dare” with developers again, truth only, with no option to dodge the answer by doing push-ups. Developers from different parts of the industry spoke about how they survive tight deadlines, tension inside engineering teams, and the subtle, protective lies they use to get through the week. In engineering teams, pressure, shifting priorities, and the need to move quickly shape communication.

“I work at Google” is a statement that carries prestige. It signals that someone operates in a strong engineering environment at a stable company, with a clear career structure and the opportunity to build products used by millions of people worldwide. But what does that career structure actually look like? To answer that, we explored publicly available discussions on platforms like Reddit and Dev.to, connected common patterns, and compiled a clear overview of the Google engineering ladder.

When companies start tracking engineer token consumption on internal leaderboards, something has gone wrong in the measurement chain. Stephen Poletto, Field CTO at Span, used his CTO Craft Con talk in Toronto to argue that the AI tooling wave has arrived with a familiar problem attached: organizations are reaching for the most legible metric available rather than the most meaningful one.