Streamlit | Stefano Blando

RiskSentinel - Agentic Systemic Risk Simulator

Sun, 15 Mar 2026 00:00:00 +0000

RiskSentinel is an agentic systemic risk simulator built around a question I find both practical and conceptually interesting: if a large financial node is hit by a shock, how can we make the propagation of that stress visible, comparable, and explainable in real time?

Built for the Microsoft AI Dev Days Hackathon 2026, the project combines three contagion models, topology-aware analytics, and a bounded multi-agent workflow on top of research-grade market network data. The purpose is not only to simulate cascades, but to make them easier to inspect through natural-language interaction, interactive visualization, and model comparison.

What makes the project work is the combination of several layers that are often separated: a proper network simulation engine, an interface that makes the results explorable, and an agentic layer that helps interpret what is happening without pretending to replace the underlying quantitative machinery.

The result is a practical prototype for systemic risk monitoring, sitting between quantitative finance, complex systems, and AI-assisted decision support.

You can explore the project here:

RiskSentinel for Microsoft AI Dev Days 2026

Sun, 15 Mar 2026 00:00:00 +0000

RiskSentinel is the project I built for the Microsoft AI Dev Days Hackathon 2026.

The core idea was to create a system that could make systemic risk more explorable and more tangible: instead of treating contagion as an abstract output in a paper or a notebook, I wanted an interface where shocks could be launched, propagated, compared, and interpreted in real time.

The project combines network science, contagion modeling, and agentic AI on top of research-grade financial network data covering 210 S&P 500 stocks and 3,081 daily snapshots. Under the hood, it integrates three propagation models, interactive network analytics with Streamlit and Plotly, and an agentic workflow built with Microsoft Agent Framework and Azure OpenAI.

What I like most about this project is that it sits exactly at the boundary between my research interests and practical prototyping: financial networks, complex systems, decision support, and AI agents all in the same tool.

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GitHub:
App:

PokéNexus

Wed, 04 Feb 2026 00:00:00 +0000

PokéNexus was born from something very simple: I have loved Pokémon since I was a child, and at some point I wanted to merge that world with the kind of tools and ideas I now enjoy building with.

The result is a Streamlit app that turns the Pokémon universe into an interactive space where graph visualization, game mechanics, and API-driven data come together. I used NetworkX, PyVis, and Plotly to explore relationships between types and entities, while PokeAPI provides the live data layer for creatures, evolutions, and related information.

Instead of being just a static graph, the project grew into a small playable system: teams can be managed, items stored, badges collected, and different progression loops layered on top of the visualization. That mix of structure, exploration, and play is exactly what made the project fun to build.

It is not a research project, and it is not meant to be. It is a personal experiment where a childhood passion meets the way I now think about networks, interfaces, and interactive Python applications.