HACK_002 in Vienna: Pflegegeld-Prüfer wins Track A1
HACK_002 recap from Vienna: the Pflegegeld-Prüfer, a care allowance checker, wins Track A1 but misses my advance target of 12 of 14 court cases. It gets 10.
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HACK_002 recap from Vienna: the Pflegegeld-Prüfer, a care allowance checker, wins Track A1 but misses my advance target of 12 of 14 court cases. It gets 10.
Two hackathons in Vienna, one week apart, and the building only starts at the event. What I still get ready beforehand, and why my code stays home until then.
About 20 people paid for my product, not one of them twice. What the numbers actually say and why the gap sits in activation, not in acquisition.
I wanted a 35-second hero video. I built an 11-second loop instead: it loads faster, never shifts the layout, and lands the whole message in eleven seconds.
Three Vienna appearances in two weeks: a career panel, a live session-orchestrator walkthrough, and a software architecture panel.
Three agents run around the clock: Sven scouts AI ideas, BuilderBob runs agenticbuilders.at with 19 cron jobs; session-orchestrator drives a gated coding loop.
An open-source multi-agent tool matures in 9 days across 18 sessions: from the /plan to /evolve lifecycle across four runtimes. What daily self-use made of it.
Established companies with approval processes lag on AI not from inertia, but due to a growing gap between what tools can do and what their structures allow.
AI-assisted development has shifted its bottleneck from writing code to judging it. Why verification now matters, from daily practice, not theory.
Two weeks after my first hackathon: 1st place at BitGN PAC 2026 at AI Factory Austria, Vienna. Autonomous AI agent, 104 tasks, 5 security layers, blind scoring.
At OpenClaw Hackathon Vienna, 150+ met at House of Innovation. Four people built an AI-powered B2B sales agent in one night, then made a video without sound.
A week of GitLab migration, n8n automations, an Instagram content pipeline, and the current limits of AI agents.
Three practical conclusions from the 2026 Gartner and HBR work-trend reports, viewed through my own work with AI agents.
A practical starting point for small and medium enterprises: choose one process, inspect the data, define a test, and keep a human decision point.
What sits between an AI prototype and daily use: data, failure handling, ownership, and deciding when an experiment has answered its question.