Eccoci giunti al momento dell'anno: il Linux Day, giornata dedicata alla sponsorizzazione e diffusione di informazioni riguardanti il software FOSS.

Per maggiori informazioni rimandiamo alla pagina dedicata sul nostro sito!

Vi aspettiamo!

Linux Day 2026 – Sovranità Digitale e Cybersecurity

🚨 Pensi ancora di possedere la tua tecnologia... o hai accettato di esserne solo un utente in affitto?

Se la dipendenza dai soliti giganti tech ti va stretta e vuoi smettere di subire passivamente regole decise da altri, il Linux Day 2026 è l'evento da non perdere. Organizzato da Free Circle 🐧, il panel "Sovranità Digitale e Cybersecurity" non è la solita passerella teorica: è un'iniezione di consapevolezza per chi vuole riprendersi le chiavi della propria vita digitale.

🔥 DI COSA PARLEREMO (SENZA FILTRI E SENZA CENSURE):

- AI Agentica e Open Source — Gli agenti autonomi stanno prendendo decisioni al posto nostro: l'Open Source è l'unico modo per evitare una scatola nera incontrollabile?

- Digital Forensics, Mobile Forensic & OSINT — Un tuffo negli strumenti d’indagine open source e nelle distribuzioni specializzate come Tsurugi Linux.

🎙 CHI SALE SUL PALCO: Ricercatori, addetti ai lavori ed esperti di sicurezza pronti a smontare falsi miti e a darti soluzioni concrete, visioni strategiche e tool pronti all'uso.

👉 Riprenditi il tuo spazio digitale. Il Software Libero non è un'alternativa per pochi: è l'unica vera strada per la libertà. Ti aspettiamo!

📍 Dove: Palermo – Cantieri Culturali alla Zisa

👥 Chi: studenti, professionisti, sviluppatori, appassionati e chiunque voglia riscoprire la libertà digitale

👉 Partecipa anche tu al Linux Day 2026: la libertà digitale comincia da te!

Per i primi 100 partecipanti registrati che si presenteranno, un gadget speciale da montare!

Clicca qui → https://linuxday.thefreecircle.org per info e contatti

DevRomagna

Abbiamo tutti visto demo accattivanti. Ma quando si prova a costruire qualcosa del genere in una vera azienda? Benvenuti nel mondo della proprietà dei dati frammentata, delle convenzioni di denominazione incoerenti e dei controlli di accesso complessi. La maggior parte delle demo ignora questa realtà disordinata. I sistemi in produzione non possono permettersi questo lusso.

Vedremo cosa serve realmente per costruire un agente per i dati su Databricks che funzioni nel rispetto dei vincoli del mondo reale. La governance non è qualcosa che si aggiunge alla fine: deve essere parte integrante dell'architettura fin dal primo giorno. Il controllo degli accessi e l'applicazione delle policy dovrebbero influenzare il modo in cui l'agente pensa e ragiona, non soltanto ciò che viene filtrato a posteriori.

Vedrete l'architettura alla base di un sistema in produzione: come ancorare gli agenti ai metadati, generare SQL, eseguire query nel rispetto delle policy e con consapevolezza delle stesse, e valutare la correttezza e la sicurezza, non soltanto quanto le risposte sembrino convincenti.

Questo intervento vi aiuterà a considerare tutto questo come un vero e proprio sistema di gestione dei dati e a evitare le trappole che trasformano i prototipi in incubi.

Hacklab Cormano

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Oct 29, 2026

Online. Register for the Zoom!

We’ll walk through how modern agentic systems move beyond simple prompts—leveraging structured tools like dataset operations, embeddings, evaluation pipelines, and model execution to take real action. You’ll see how these agents can tag data, run inference, evaluate performance, and surface insights automatically, all within a unified workflow.

By combining natural language interfaces with programmable building blocks, teams can dramatically reduce manual effort, accelerate experimentation, and unlock faster decision-making across the ML lifecycle.

Whether you're building data-centric AI systems, managing large-scale vision datasets, or exploring agentic workflows for the first time, this session will give you a practical blueprint for getting started.

GRUSP

GRUSP

Hacklab Cormano

Rust Language Milan

🙏 A Huge Thank You to Our Host & Co-Organizer!

🎙️ The Talk: Bare-Metal Embedded Rust

Speaker: Cristiano Chieppa

Digital model trains rely on the DCC and RailCom industry standards for digital signal generation and two-way communication. Combining his passion for railway modeling with Rust, Cristiano built a complete DCC command station running on bare-metal ESP32 hardware to generate DCC wave signals and control model locomotives!

We are super excited to have Cristiano Chieppa—a backend and embedded developer with over 30 years of software engineering experience—leading this session. After decades of managing memory leaks and unsafe code in C++, Cristiano discovered Rust three years ago and never looked back.

Cristiano will walk us through how he integrated the Z21 specification (Roco's UDP communication standard) and the complex RailCom protocol for bidirectional signal feedback.

What you’ll learn:

• Driving ESP32 hardware in bare-metal Rust with zero-cost abstractions.
• Handling real-time UDP network signals via the Z21 protocol.
• Managing complex hardware constraints and memory safety in low-level systems.

🎛️ Live Demo: An on-site demonstration featuring the digital command station and actual model trains in action!

📍 Logistics & Agenda

• 18:30 – Doors Open & Welcome
• 18:45 – Talk & Live Demo: Digital Model Trains and Rust (30–45 mins)
• 19:30 – Q&A & Community Discussion
• 20:00 – Networking, Pizza & Drinks 🍕🍻
• Level: Intermediate
• Language: English
• Venue: Kalpa
• Co-Organizers: Rust Milano & Italian Embedded
• Open Source Repo: github.com/cc90202/DCC-esp32
• Seats are limited due to room capacity, so grab your spot fast! RSVP now to lock in your seat! 🦀⚡

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Nov 05, 2026

Online. Register for the Zoom!

Talks will include:

U-Net Framework for Micro-Scale Surface Damage Segmentation in High-Resolution Soybean Seed Imagery

Accurate detection of surface-level seed damage is critical for soybean seed quality assurance, yet automated micro-scale damage detection in high-resolution imagery remains an open challenge due to extreme spatial variability in damage scale, severe class imbalance across damage types, and the computational demands of processing ultra-high resolution agricultural imagery at scale. This research addresses the semantic segmentation of fine-grained soybean seed surface defects, such as wrinkles, dark spots, and general surface damage, in 6048 × 4024 pixel images where target damages can be as small as 18 × 18 pixels, using an augmented dataset of 77,000 individual seed images.

To overcome the difficulties of micro-scale detection, spatial sparsity, and class confusion, we propose a two-stage training and dual model inference framework built on an optimized U-Net architecture with a ResNet34 encoder. In the first stage, a damage specialist model is trained using weighted loss functions and a class-balanced approach that prioritizes damage classes.

In the second stage, transfer learning is applied to initialize a healthy seed specialist model from Stage 1 weights, with rebalanced class weights and tile probabilities that identify healthy seeds. During inference, a confidence-gated damage filter suppresses low-confidence predictions, and healthy seed labels are assigned only when the specialist model's confidence exceeds that of the damage model.

The two specialist models achieve validation accuracies of 94% and 98.53%, respectively, and the combined inference system successfully detects and localizes all three damage categories across unseen test images under conditions of extreme spatial variability and class imbalance. Qualitative evaluation confirms close alignment of predicted boundaries with ground truth annotations across all damage types, including dark spots.

These results demonstrate that confidence-gated dual model inference can reduce class confusion in imbalanced micro-scale segmentation, advancing the feasibility of fine-grained automated seed quality inspection at the scale.

About the Speaker

Saurav Upadhyaya is an AI/ML researcher with a Master's degree in Computer Science, whose work spans agricultural AI, conversational systems, and public health, consistently translating advanced technology into tangible real-world impact.

From camera to drone: wildfire detection and integration at the edge

Wildfires are getting larger and more expensive every year. But caught early, a fire is a small job for a small team; caught late, nothing stops it. The cameras to catch them are already installed, so the work is not necessary more sensors but making the ones we have act.

This talk walks the whole chain: a small vision model running on the camera that flags smoke, an integration layer that scores the alert against terrain, weather and history to decide whether it matters, and a drone dispatched to confirm before anyone commits a crew. We will cover what has to run at the edge and why, how small the model can get before it stops seeing smoke, and what it takes to trust a detection nobody has looked at yet.

About the Speaker

Maxime Carriere is co-founder of Kernwerk in Berlin, where he works on compressing AI models to run on cheap embedded hardware.

5,000 Flights to Answer One Question: Can a Drone Sample as Well as a Human?

Environmental testing still begins with a person walking onto a site with a shovel. We built a system that turns a plain-language brief into a sampling campaign, flies it, and returns soil, water and vegetation samples with GPS, timestamps and chain of custody intact: 5,000 flights, roughly 9,000 miles and 570 flight hours over a 185-acre site in Roatan, much of it under canopy where GPS degrades and clearances are tight.

I will walk through the vision and planning stack, including multispectral and thermal canopy sensing, terrain and land-cover mapping, and the constraint solver that clears a route against airspace and battery budget before anything leaves the ground. Then the harder problem: evidentiary comparability.

A drone-collected result means nothing to a regulator unless it agrees with a hand-collected split under the same method, so I will show the dual-collection protocol we designed to test exactly that. I will close on why calibrated uncertainty and knowing when a model should refuse to answer matter more than raw accuracy once the output lands in a regulatory filing.

About the Speaker

Rohan Talwadia is co-founder and CTO of LabGPT, which automates environmental field sampling using drones and edge analysis, with results returned through ISO/IEC 17025-accredited partner laboratories.

Un sabato pomeriggio tutto speciale dedicato a migliorare la mappa di Angera in OpenStreetMap.

L'incontro è previsto dopo pranzo presso la biblioteca di Angera per una breve introduzione ad OSM e ai suoi concetti base, per una descrizione degli obiettivi della giornata, per configurare i terminali dei mappatori e poi dividerci in squadre per mappare! Al termine di una sessione di circa due / tre ore torneremo al punto di partenza e valuteremo la bontà del nostro lavoro con qualche numero molto concreto a proposito delle modifiche alla mappa.

Ai mappatori verrà richiesto di usare il proprio smartphone (possiamo fornire assistenza su molti dispositivi Android, un poco su quelli della mela) per trovare e correggere errori, aggiungere informazioni mancanti e aggiungere nuovi oggetti alla mappa. Gli verrà inoltre richiesto di crearsi un account, eventualmente con la nostra assistenza, sul sito di OSM in quanto premessa essenziale per poter contribuire.

Per maggiori informazioni riferitevi alla pagina dedicata del nostro sito.

OpenStreetMap è un solido progetto per la mappatura aperta e contributiva della Terra che può competere con ben più noti prodotti commerciali. Si basa, come per Wikipedia, sul lavoro di milioni di volontari che mappano individualmente oppure importano massivamente dati ottenuti dalle amministrazioni pubbliche. I dati così immessi sono liberamente accessibili, previo rispetto della licenza con cui vengono distribuiti.

Dedicheremo questo pomeriggio a contribuire ad un progetto con una comunità di utenti che è mondiale!

Hacklab Cormano

Rome AI, Machine Learning and Computer Vision Meetup

Time, Date and Location

Nov 11, 2026

Online. Register for the Zoom!

Agentic RAG: Beyond Retrieve-and-Generate

Retrieval-Augmented Generation (RAG) has become the default pattern for grounding large language models in external knowledge, but most implementations still follow a rigid retrieve-once, generate-once pipeline — one that struggles with multi-hop questions, ambiguous queries, and knowing when its own retrieved context is insufficient. This talk introduces Agentic RAG, where retrieval is treated as an action within an agent's reasoning loop rather than a fixed upstream step.

We'll examine query decomposition and routing for breaking complex questions into targeted sub-queries, self-reflective and corrective retrieval loops that let an agent judge and re-query its own results, and tool-orchestration patterns (via MCP) that let retrieval sit alongside other agent actions like database lookups and API calls. Using a live architecture — evolving a standard RAG chatbot into an agentic, MCP-connected system — we'll walk through what changes in design, and where these systems introduce new failure modes: grounding drift, latency and cost from repeated retrieval loops, and cases where a simpler RAG pipeline still wins.

Attendees will leave with a practical framework for deciding when the added complexity of agentic RAG is worth it, and a set of design patterns for building it correctly.

About the Speaker

Balaji Venkatasubramaniyar is a Technical Lead at Wisdom Infotech, leading a 15+ person engineering team delivering enterprise solutions. With 13+ years of experience in enterprise software and insurance technology, he specializes in agentic AI systems, RAG architectures, and vector databases.

GeoAI for the Physical World: Earth Observation, Foundation Models, and Urban Digital Twins

Earth observation provides a unique form of computer vision for understanding the physical world at city to continental scales. In this talk, I will show how satellite imagery, geospatial data, machine learning, and foundation-model representations can be combined to characterize urban environments and environmental conditions.

I will present UrbanScope Open, an open GeoAI digital-twin prototype integrating Earth observation with 3D buildings, vegetation, land-surface temperature, air quality, noise, population, and other urban data. I will also share lessons from my research using geospatial foundation-model embeddings for environmental prediction across Europe.

The talk will discuss how these approaches can contribute to increasingly multimodal AI systems capable of reasoning about real-world environments.

About the Speaker

Cesar Alvarez is a researcher at the University of Augsburg working at the intersection of GeoAI, Earth observation, remote sensing, and environmental intelligence. His research applies machine learning, computer vision, and geospatial foundation models to problems including urban environments, climate risk, air quality, and agriculture.

Can agents get curious?

Most AI agents are good at answering a question once we tell them exactly what to look for. The harder problem is building agents that can explore a complex dataset autonomously: generating hypotheses, deciding which analyses are worth running, allocating additional compute when evidence is ambiguous, and knowing when they have enough evidence to stop.

In this talk, I’ll show an architecture for autonomous research agents that combines structured knowledge, iterative tool use, and explicit evidence tracking to turn open-ended questions into a sequence of testable investigations. I’ll discuss practical lessons from building and evaluating these systems, including why more test-time compute does not automatically produce better research and how provenance and evaluation can make long-running agents more reliable.

I’ll close with a live example of an agent exploring a dataset, revising its hypotheses, and choosing what to investigate next.

About the Speaker

Srivatsa P is a member of technical staff at Sigma Computing, where he works on AI agents that reason over complex enterprise data. Previously, he worked on machine learning at Apple and conducted research at the MIT Media Lab; outside of traditional ML, he has also worked on mapping coral reefs through underwater imaging, which sparked an enduring interest in how intelligent systems make sense of messy real-world data.

Does Cross-Validation Work in Deep Learning?

Cross-validation is a standard in hyperparameter optimization in classical ML, but is generally considered computationally prohibitive and unnecessary in deep learning. We investigated whether CV produces 1. better DL models 2. better estimates of future performance of said models. We focused on medical image classification as an important deep learning application domain.

Our results show that cross-validation consistently and materially reduces the gap between development and independent testing error for relatively small training datasets (up to about 1000 images). The test performance improvement was generally modest. We discuss the implications for deep-learning practitioners, including whether the increased computational cost of cross-validation is justified.

About the Speaker

Ljubomir Buturovic is an AI scientist and biotech executive with extensive experience in clinical diagnostics, including work at Inflammatix and Pathwork Diagnostics. His current research focuses on deep-learning image classification, infectious disease adjudication using LLMs, and development of AI models for helping critically ill patients.

The South Tyrol Free Software Conference, SFSCON, is one of Europe’s most established annual conferences on Free Software. SFSCON promotes the use of Free Software in digital infrastructures as a tool to achieve greater innovation and competitiveness. Here decision-makers and developers meet, learn and get inspired.

LUGMan

Dobbiamo procedere alla pulizia e clonare i PC con l'installazione di Zorin Education + i programmi specifici per l'apprendimento.

Tutti i dettagli nella Wiki:

Hacklab Cormano

GRUSP

PyVenice

Ci troviamo in #presenza giovedì 19 novembre alle ore 19:00.

La scaletta prevede uno speech seguito da discussione. 🗣 Mattia Carolo ci racconterà come fare girare Qweb su vLLM e infrastruttura Google GKE, attraverso le cose che si sono rotte davvero e nessuno tutorial ti ha mai detto.

E dopo\, per chi vuole\, resteremo lì per \( 🍸 \| 🍺 \) e \( 🍕 \| 🍔 \)

📽 E se avete piacere di condividere idee, progetti, argomenti, o proporvi come prossimi speaker, lascio un form Google e la pagina Sessionize !

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Nov 19, 2026

Online. Register for the Zoom!

Robot learning data doesn't look like the data that trains most vision-language models. It's temporal, low-dimensional in the parts that matter most, embodiment-specific, and expensive to collect, and increasingly, it's shipped in Hugging Face's LeRobot format. But packaging robot data consistently isn't the same as being able to see it: episodes hide broken metadata, silently dropped sensor modalities, and mismatched schemas between robots that look identical on paper.

Starting from the core differences that make robot data hard (temporal structure, proprioception, action grounding, embodiment heterogeneity, data scarcity), we'll move into live debugging: a metadata bug that broke multiple dataset imports, sensor streams that vanish without an error, and two robots running an identical benchmark whose data is structurally incompatible. By the end, you'll know what to check before you trust a LeRobot dataset, and how to actually look inside one.

GRUSP

PNLUG

GRUSP

PNLUG

PNLUG

Hacklab Cormano

Un pomeriggio a condividere conoscenze sulla Intelligenza Artificiale, insieme a esperti del settore.

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Dec 10, 2026

Online. Register for the Zoom!

Your mAP is fine. Your slices are not. Finding the images that actually matter

A single mAP can look acceptable while one slice is unusable, small objects, occlusion, or a confused class. I will run a pretrained detector on a few hundred public images (COCO-style, not a production crawl), report the headline metric, then break the same run by slice and walk through the worst samples.

The claim is modest: you do not need a million images or a trained-from-scratch model to see what the mean is hiding, and you cannot relabel everything, so the next labeling budget should follow those slices. No employer data and no confidential dashboards.

About the Speaker

Amandeep Jiddewar is a Machine Learning Engineer at Pinterest, with prior work in U.S. manufacturing analytics (CertainTeed / Saint-Gobain) and marketplace allocation (QuinStreet). Emory Goizueta MSBA (2019), Kaggle Expert. This talk is in his personal capacity and does not represent his current or prior employers.

Production AI Agents: Tool Use, Evaluation, Guardrails and Observability

Building an AI agent is easy. Knowing whether it is behaving reliably in production is much harder. This session explores how to evaluate agents that use tools and external systems, including tool-call validation, tracing, observability, failure analysis, guardrails, permissions, and human review.

Attendees will learn practical patterns for understanding not just what an agent answered, but why it acted and whether the action was safe.

About the Speaker

Akshay Talathi is a Vice President at Goldman Sachs with over 10 years of experience building large-scale, cloud-native, and distributed systems across financial services and technology. His current focus is GenAI and agentic AI, including production AI agents, MCP, enterprise integrations, guardrails, observability, and reliable AI-driven workflows.

From Virtual Worlds to Physical AI: Building Simulation Pipelines That Survive the Real World

Robots are moving beyond perception systems that only detect and classify objects toward systems that must understand a scene, learn behaviors, and act in the physical world. In this talk, I’ll show a practical Physical AI workflow using OpenUSD, BowerBot, NVIDIA Isaac Sim and Isaac Lab to build a robot’s virtual world, including its body, cameras, sensors, and environment.

We’ll explore how demonstrations and reinforcement learning can teach behaviors in simulation, how vision and sensor data become observations for a robot policy, and what needs to match when transferring that behavior to real hardware. The demo follows a small robot from a simulated environment toward a real-world task, exposing both the power and the limitations of sim-to-real.

The goal is to make the path from pixels to actions concrete for computer vision and machine learning practitioners.

About the Speaker

Arturo Morales Rangel is the founder of Binary Core LLC, a Physical AI engineering company working across simulation, perception, robot learning, and deployment.

Prompting Video-Language Models in Production: Lessons from Enterprise Video Tagging and Evaluation

Video-language models can now describe, tag and answer questions about long-form video, but production results depend heavily on prompt design and evaluation. This talk shares field lessons from deploying such models for media, news, streaming and sports organizations, including how to structure prompts for consistent, schema-bound outputs across hours of footage.

On one sports video-tagging project, consolidating the prompt definitions cut estimated processing cost about 3x, and about 90% of the originally reported issues were later resolved through prompting alone. I'll also walk through building evaluation frameworks for video outputs, including for hard domains like egocentric video, and what changes when migrating between model versions.

Attendees will leave with reusable prompt patterns and an evaluation checklist they can apply to their own video pipelines.

About the Speaker

Yash Gugale is a Senior Forward Deployed Engineer at TwelveLabs in New York, integrating video foundation models (Marengo for embeddings and search, Pegasus for video-language understanding) for media, news, streaming and sports customers. He previously spent about six years in video streaming engineering at Peloton and Samsung TV Plus, and holds an MS in Computer Graphics Technology from Purdue.

GRUSP

PyData Venice

We’ll meet #InPresence on Thursday, December 17 at 7:00 PM.

👥 We’ll have two interesting sessions, in Italian 🇮🇹 or in English 🇬🇧 or one and one:

And afterwards\, for those who want\, we’ll stay there for \( 🍸 \| 🍺 \) & \( 🍕 \| 🍔 \) and #networking until late ‼️

📽 And if you’d like to share ideas, projects, topics, or propose yourself as the next speaker, here’s the usual Google form or Sessionize !