22 Apr, 2026 OpenClaw Demo Night (Prague, CZ)

Financial modelling in OpenClaw & safely deploying it

Using OpenClaw to build a Bayesian buy-vs-rent model for Prague real estate, and how to deploy something like that without setting your money on fire.

Two-part talk: the case study is the Prague Housing Analyzer — a Bayesian buy-vs-rent decision model built in OpenClaw, running Monte Carlo simulations in PyMC over district appreciation, mortgage rates (Vasicek mean-reversion), rent inflation, USD/CZK exchange volatility, and US equity returns. The second half is about deploying a model like this safely: how to ship something whose output you’d actually act on without it quietly going wrong.

Event details on luma.com ↗

"RAG is dead" is the take in every other thread in 2026 — and it's wrong: naive retrieval-augmented generation is still a sensible default, beaten only in some cases, and measurement is the only way to know if you're one of them. This talk walks the retrieval pipeline end to end, then turns to the part that matters — telling whether your RAG actually works, with ground truth, retrieval metrics, RAGAS, LLM-as-judge, and error analysis feeding an eval flywheel. view ↗

Choose Your Ground Truth: A Field Guide to Synthetic Data for Evals Evals.cz Meetup #3 (Prague, CZ)

You need an eval set but don't have a hundred real production failures to build it from, so you reach for synthetic data — and most first attempts quietly produce garbage. A field guide to the techniques that actually work, from real-incident seeds to personas to RAG-grounded generation, with one throughline: synthetic data needs its own eval, so choose your technique backwards from the eval you want. view ↗

Cognitive Exhaust Fumes: What Read-Only AI Sees That You Can't ai.engineer/europe (Online)

What happens when AI systems passively observe information without modifying it? Exploring the patterns and insights that read-only AI reveals — the cognitive byproducts humans overlook. view ↗

The Eval Flywheel: From "Works on My Laptop" to Systematic Quality GenAI Meetup (Prague, CZ)

Most teams shipping GenAI products have no evaluation system. They have vibes, a few saved prompts, and hope. This talk starts with observability: if you can't see what's happening in production, nothing else matters. From there, we build the eval flywheel — a practical pattern where production observability feeds error analysis, error analysis generates eval cases, and eval cases prevent recurrence. view ↗

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