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In the early days of AI coding tools, the vibe was simple: point the model at a problem and let it generate code. That worked… sometimes. Mostly on small things. Mostly when you were lucky. But as Viktor Nawrath (who leads Project Weaver at profiq) emphasized: the tooling is moving so fast that “last month’s information is already too old.” The models aren’t always making huge leaps every release, but everything around them is compounding fast: agent harnesses, tool integrations, workflows, and how we actually “drive” agents responsibly. That’s where…

When we first imagined Project Weaver, it was a big, audacious idea: Could we build an AI-powered engineer that truly supports the creation of serious, production-ready backends? Not a toy tool. Not a research prototype. Something that could sit alongside human engineers and help them ship real work with real impact. Over the past several months, we’ve been building in public — documenting our thinking, sharing what we’ve learned, and inviting the community into a conversation about the future of AI-augmented software development. Below is a recap of that journey…

When we started Project Weaver, we weren’t trying to replace engineers or reinvent software development from scratch. We were experimenting with something both simpler and more ambitious: What does it take for an AI system to operate as a genuine engineering partner- not a code autocomplete, but a collaborator that can meaningfully participate in the creation of real backend applications? As soon as we began putting serious workloads through AI agents, a pattern emerged. Whenever the agent succeeded, there was clarity. Whenever it struggled, there was ambiguity. And the difference…

When we started Project Weaver at profiq, we set ourselves a goal that would have sounded unrealistic just a few years ago: Build an AI-powered “engineer” that can help us design and implement serious, production-ready backend applications. Not a toy demo. Not a one-off script. Something that can actually support real teams building real products. Because we’re a software services company first, Weaver also has a second job. It needs to: Make our own engineers faster and more effective. Codify what we’re learning about AI-powered development. Eventually, test if this…

When we started Project Weaver, we weren’t trying to build another AI coding assistant. There are already plenty of tools that can generate snippets, scaffold a project, or even produce a full working prototype. But there is always a ceiling. What’s missing is something that behaves like an actual engineer - an AI that understands structure, follows conventions, and fits into the way real teams build backend systems. Very early on, we realized that achieving that meant making a choice: Weaver shouldn’t try to support every framework or stack under…

At profiq, we’ve always believed that the best engineering work grows from curiosity, collaboration, and honest reflection. As we began shaping Project Weaver - our internal initiative exploring how AI can meaningfully support software development - one thing became clear: this work is too important, too complex, and too full of unknowns to build behind closed doors. So we’re choosing to build in public. Not because it’s trendy. Not because we have all the answers. But because we don’t, and we believe the process of discovering them should be shared.…

When we decided to build our own AI-powered development stack, one question came up immediately: Who should lead it? We needed someone who not only understood the technology, but also felt its impact, someone who had experienced, firsthand, how AI is reshaping what it means to build software. For us, that person was Viktor Nawrath. Seeing the Future, One Prompt at a Time In Viktor’s own words, “During the course of 2025, I felt more and more like AI was giving me superpowers.” It wasn’t marketing hype or wishful thinking.…

Over the past year, AI-powered development has shifted from curiosity to inevitability. Every software organization is experimenting - some quietly, some loudly - with how large language models and AI coding agents can accelerate engineering work without compromising quality, security, or trust. At profiq, we’ve decided not just to experiment, but to build. We’re creating our own AI-powered development environment, internally called Weaver, a focused, opinionated system designed to help our teams (and eventually, our clients) develop production-grade software faster, smarter, and more collaboratively. And we’re building it in public.…

Today, choosing a technology partner is no longer just about speed, cost, and skill. It’s also about trust. The reality is chilling and you may not even be aware of how big a problem this is. U.S. companies have unknowingly funded North Korea’s nuclear weapons program through infiltrated IT contractors. According to U.S. Department of Justice investigations, these operatives stole at least $88 million, compromised hundreds of companies, and infiltrated critical industries from finance to defense.  Operatives use AI-generated LinkedIn profiles, fake interviews with video masks, and stolen Western identities…

Why ClickHouse training supports our goal of engineering excellence  What is the value of ClickHouse Certifications and why does it matter? ClickHouse is a column-based On-Line Analytical Processing (OLAP) database management system (DBMS)  originally developed by Yandex to power their analytics product offering. Those of you old enough to remember Dot-com Boom, might recall the likes of Sybase IQ that would run on big Sun Microsystems servers with tens of CPUs, and easily outperform row-based RDBMS (on the same hardware of course) for ad-hoc SQL queries that provide answers to…