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🤖3 years ago I've been starting this blog and made a promise avoiding AI content here. Things are changing, yet I don't want to give up on the promises. I'm introducing "ai-augmented" tag that will be attached to all upcoming posts that was processed heavily by AI. You don't have to guess - decide what matters to you. AI agents introduce more chaos. AI augmented closed-circuit loops bring changes. Here is how I made a useful AI agent for content management. AI does not replace people. I proc

🤖3 years ago I’ve been starting this blog and made a promise avoding AI content here. Things are changing, yet I don’t want to give up on the promises. I’m introducing “ai-augmented” tag that will be attached to all upcoming posts that was processed heavily by AI. You don’t have to guess - decide what matters to you. My Hermes agent hit the memory cap. So I built a second layer. Most agents stop at one. Mine has two. Using the Google’s OKF specification. The first layer is a 2,200-character f

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