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AI agents are the new drill in the software engineer’s toolbox. Faster? Absolutely. Revolutionary? Maybe. But a drill doesn’t replace the process of building a house. In this article, I explore why SDLC, architecture, testing, and delivery remain as important as ever.

I run two agentic AI setups in parallel - OpenClaw and Hermes. Both on disposable VMs on a Proxmox host in my office. Here's what the stack actually looks like, what works, and where it still breaks.

I thought the hardest part would be latency. We were building a realtime conversation assistant: a system that listens to speech, understands conversational context, and generates suggestions fast enough for someone to actually use them during a live conversation. On paper, the pipeline sounded straightforward:speech-to-text → context processing → LLM generation → suggestions. In reality, everything broke once humans started talking. It went even worse when tested in the middle of crowded ope

An event that happened this weekend was really great. Overall it was chill, friendly and felt like the main goal of the hosts was to provide a platform. Rules? Yes, bare minimum. Guidelines and flexibility were dominating. And it reduced the atmosphere of competition. And guys were gathered around their project and ideas. Building. And the finale reminded me hard truth: I can do anything, but not everything. In 24 hours we have built a local-first working real-time speech assistant that listen

With AI emerging in a few years it became a hot topic. Lots of new terms that has appeared proves we are facing something new. Language is a perfect mirror, that reflects the reality. New terms means reality requires new abstractions to deal with it. While „vibe coding”, „agentic coding” and other definitions that indicates software development (coding) happens with AI - it does not reflect the entire shift. And it is not yet happening, rather it’s something that stops us. Why we can’t hire A