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Waterworks, airports, the energy grid or telephone traffic: all interesting targets in the Netherlands for cyber or hybrid attacks from countries like Russia and China, say intelligence experts. A highly digitalized world asks for a movement from DevOps to DevSecOps, this means us as developers should specialize in security now too! More than ever, a hack on your software can have serious impact on the daily lives of people: from leaking their personal information to shutting down the energy supply to Dutch homes. Think the leak of private data of police in 2024, and the one of the Bevolkingsonderzoek Nederland in 2025. In other words your software might not be responsible for trains to be able to ride, or payments being done through banks, but most companies do process at least some personal data. Just like accessibility, security often is an afterthought, and that will just not do anymore. Security needs to be considered from day one in every software project. Tonight we will give the stage to three developers to talk about their security approach.

Program 17.45 walk in / dinner (vegetarian) 18.30 Intro Girl Code by Ineke Scheffers 18.35 Intro CGI by Janine Tjassens, CGI BU Leader Randstad and North Netherlands

18.40 Secure Software Development - Erica Welling, Software Security Specialist at CGI Erica will discuss several core principles that help make IT structurally more secure. With these insights into our daily work, we can build systems that are not only fast and beautiful, but also resilient and future-proof. 19.00 Supply Chain Attacks: Here to Stay - Jasmijn van Genesen, Application Security Engineer at KVK Supply chain attacks like Shai Hulud show how hackers exploit package managers and CI/CD pipelines to steal secrets. In her talk Jasmijn will plead for protecting the whole chain—not just writing secure code—by making mitigation of risks a standard in DevSecOps. 19.20 Business Logic Flaws: How to Prevent Them in Your APIs - Alexandra Charikova, The Elephant in AppSec podcast host & community manager at Escape Attackers exploit valid features in unintended ways to bypass rules, abuse workflows, or manipulate sensitive operations. In this talk, Alexandra explores real-world API examples, how attackers chain valid actions for harmful outcomes, and how defenders can catch issues early through ie. threat modeling and security integration in CI/CD. 19.40 Q&A with all speakers 20.00 - 21.00 drinks, snacks, mingle

"At CGI, we value and actively work together to foster an environment where every voice is heard and respected and where every member has equal opportunity to share their ideas, lead and grow. Estimates are that less than 30% of technology jobs are held by women. And according to the numbers above that seems a little too generous even. To increase this it is important to inspire and empower women, which is why initiatives such as Girl Code are so important. A little while ago we hosted Girl Code at CGI, in short, it was a great success! Looking at the current (political) climate the topic of diversity is more important than ever. With that in mind we couldn't be more excited to host another Girl Code event!" - Sara Larsson\, Software Architect & Engineer at CGI

Team Ineke Scheffers - organizer and founder Girl Code Sara Larsson - host CGI Jeroen de Bekker - host CGI Frédérique Doek - host CGI Kimberly Bisschops - host CGI

Attendance policy We take attendance. If you RSVP'd but didn't show up, it will have consequences. Also be sure to cancel on time, 'cause cancelling 1 day before will be too late and count as a no-show. (Illness is of course an exception to this rule).

Public transport It's a 5 min walk from station Rotterdam Alexander. At the station take the exit which ISN’T connected to the metro. Find the entrance by following the George Hintzenweg. Call reception at the visitors door, take the stairs up, it's the 1st building on the left.

Car The employee garage is open for us between 17.30 and 18.30. Before or after: use the intercom. The parking garage can be reached along George Hintzenweg. Take the stairs in the middle, then you will find the CGI office on the left (road as orientation).

Accessibility If you need disability parking or use a wheelchair contact us at [email protected], so we can make the right arrangements.

​LLMs are powerful, but they still hallucinate facts, especially when asked about entities, relationships, or claims that require up-to-date or structured knowledge.

​In this hands-on workshop, we'll explore how to use Wikidata as a grounding and fact-checking layer for LLMs to reduce hallucinations and make AI systems more reliable.

​We'll start with a short introduction to Wikidata and then set up the Wikidata MCP so an LLM can retrieve and verify facts rather than relying solely on its internal memory. This already provides a practical way to ground LLM outputs in verifiable data.

​From there, we’ll go beyond LLM-only approaches and build a small experimental fact-checking pipeline. The system combines semantic retrieval, LLM-based reranking, and natural language inference (NLI) to validate claims against evidence in a more controlled and interpretable way.

​This workshop focuses on evidence-driven verification pipelines that make LLM's reasoning steps explicit and easier to inspect, debug, and improve.

​What we'll cover:

• ​Wikidata as a structured source for factual verification
• ​Setting up and querying Wikidata using MCP
• ​Verifying claims with MCP + an LLM
• ​Moving beyond pure GenAI to evidence-based fact-checking
• ​Finding relevant Wikidata statements with semantic search
• ​Ranking candidate evidence with an LLM
• ​Verifying claims using an NLI model

​​What you'll leave with By the end of the workshop, you'll be able to:

• Ground LLM outputs in structured data to reduce hallucinations
• ​Understand when LLM-only fact-checking is not enough
• ​Build a small, transparent fact-checking pipeline you can adapt to real projects

​About the speaker:

​Philippe Saadé is the AI/ML project manager at Wikimedia Deutschland. His current work focuses on making Wikidata accessible to AI application with projects like the Wikidata vector database and the Wikidata Model Context Protocol.

Join our Slack: https://datatalks.club/slack.html ​

​This event is sponsored by Wikimedia

This two-part discussion series will explore how to make humanitarian spreadsheets more “AI-ready,” bringing together UN OCHA’s new guidance project with real-world lessons from recent AI spreadsheet extraction experiments.

UN OCHA is developing a short, practical guide to help humanitarian teams publish “AI-ready” public datasets that work better with tools like ChatGPT, Copilot, Gemini and open source models like Kimi K2 and GPT OSS running on providers like Groq when users simply upload a CSV or Excel file and start asking questions. The focus is on non-technical users who will not configure agents, write code, or reverse-engineer cryptic column names, but instead expect the AI to correctly interpret the file structure and labels out of the box. By recommending clear naming, consistent tabular layouts, and lightweight documentation, the guidance aims to reduce misinterpretation, hallucinations, and broken analyses when consumer AI tools encounter real-world humanitarian data.

Jan Zheng, a Developer Relations Engineer at Groq who helps people design and build AI prototypes, is exploring exactly these challenges from the model and tooling side. His recent experiments with spreadsheet extraction show that messy, multi-table spreadsheets routinely confuse even advanced models and agent frameworks, leading to unreliable extraction, off‑by‑one errors, looping agents, and high costs. These problems are amplified when complex datasets or vast amounts data are processed by non-technical users of commercial AI tools and open models. Lessons learned through research and usage can inform UN OCHA guidance by clarifying which spreadsheet patterns break current AI tools, which structures make extraction more robust, and how to balance “ideal” AI-ready formats with the messy realities of operational humanitarian spreadsheets.

Over two separate meetup discussions, staff from UN OCHA will introduce the AI‑ready data project in more detail, walk through the specific use case they are targeting, and answer questions from participants about scope, constraints, and potential applications in humanitarian settings. These sessions are designed to surface real-world experiences from practitioners who publish, manage, or use open humanitarian data, and to gather concrete examples of what works and what breaks when datasets are run through consumer AI tools and open source tools running through providers like Groq.

On a following date, Jan will join a dedicated session to react to the project, share his experimental findings on spreadsheet extraction, and discuss how infrastructure choices such as model selection, speed, and prompting strategies interact with the way humanitarian data is structured and published. His perspective will help bridge the gap between guidance aimed at data publishers and the realities of building and tuning AI systems that can reliably interpret messy, real-world spreadsheets used across the humanitarian sector.

This two-part discussion series will explore how to make humanitarian spreadsheets more “AI-ready,” bringing together UN OCHA’s new guidance project with real-world lessons from recent AI spreadsheet extraction experiments.

UN OCHA is developing a short, practical guide to help humanitarian teams publish “AI-ready” public datasets that work better with tools like ChatGPT, Copilot, Gemini and open source models like Kimi K2 and GPT OSS running on providers like Groq when users simply upload a CSV or Excel file and start asking questions. The focus is on non-technical users who will not configure agents, write code, or reverse-engineer cryptic column names, but instead expect the AI to correctly interpret the file structure and labels out of the box. By recommending clear naming, consistent tabular layouts, and lightweight documentation, the guidance aims to reduce misinterpretation, hallucinations, and broken analyses when consumer AI tools encounter real-world humanitarian data.

Jan Zheng, a Developer Relations Engineer at Groq who helps people design and build AI prototypes, is exploring exactly these challenges from the model and tooling side. His recent experiments with spreadsheet extraction show that messy, multi-table spreadsheets routinely confuse even advanced models and agent frameworks, leading to unreliable extraction, off‑by‑one errors, looping agents, and high costs. These problems are amplified when complex datasets or vast amounts data are processed by non-technical users of commercial AI tools and open models. Lessons learned through research and usage can inform UN OCHA guidance by clarifying which spreadsheet patterns break current AI tools, which structures make extraction more robust, and how to balance “ideal” AI-ready formats with the messy realities of operational humanitarian spreadsheets.

Over two separate meetup discussions, staff from UN OCHA will introduce the AI‑ready data project in more detail, walk through the specific use case they are targeting, and answer questions from participants about scope, constraints, and potential applications in humanitarian settings. These sessions are designed to surface real-world experiences from practitioners who publish, manage, or use open humanitarian data, and to gather concrete examples of what works and what breaks when datasets are run through consumer AI tools and open source tools running through providers like Groq.

On a following date, Jan will join a dedicated session to react to the project, share his experimental findings on spreadsheet extraction, and discuss how infrastructure choices such as model selection, speed, and prompting strategies interact with the way humanitarian data is structured and published. His perspective will help bridge the gap between guidance aimed at data publishers and the realities of building and tuning AI systems that can reliably interpret messy, real-world spreadsheets used across the humanitarian sector.

This two-part discussion series will explore how to make humanitarian spreadsheets more “AI-ready,” bringing together UN OCHA’s new guidance project with real-world lessons from recent AI spreadsheet extraction experiments.

UN OCHA is developing a short, practical guide to help humanitarian teams publish “AI-ready” public datasets that work better with tools like ChatGPT, Copilot, Gemini and open source models like Kimi K2 and GPT OSS running on providers like Groq when users simply upload a CSV or Excel file and start asking questions. The focus is on non-technical users who will not configure agents, write code, or reverse-engineer cryptic column names, but instead expect the AI to correctly interpret the file structure and labels out of the box. By recommending clear naming, consistent tabular layouts, and lightweight documentation, the guidance aims to reduce misinterpretation, hallucinations, and broken analyses when consumer AI tools encounter real-world humanitarian data.

Jan Zheng, a Developer Relations Engineer at Groq who helps people design and build AI prototypes, is exploring exactly these challenges from the model and tooling side. His recent experiments with spreadsheet extraction show that messy, multi-table spreadsheets routinely confuse even advanced models and agent frameworks, leading to unreliable extraction, off‑by‑one errors, looping agents, and high costs. These problems are amplified when complex datasets or vast amounts data are processed by non-technical users of commercial AI tools and open models. Lessons learned through research and usage can inform UN OCHA guidance by clarifying which spreadsheet patterns break current AI tools, which structures make extraction more robust, and how to balance “ideal” AI-ready formats with the messy realities of operational humanitarian spreadsheets.

Over two separate meetup discussions, staff from UN OCHA will introduce the AI‑ready data project in more detail, walk through the specific use case they are targeting, and answer questions from participants about scope, constraints, and potential applications in humanitarian settings. These sessions are designed to surface real-world experiences from practitioners who publish, manage, or use open humanitarian data, and to gather concrete examples of what works and what breaks when datasets are run through consumer AI tools and open source tools running through providers like Groq.

On a following date, Jan will join a dedicated session to react to the project, share his experimental findings on spreadsheet extraction, and discuss how infrastructure choices such as model selection, speed, and prompting strategies interact with the way humanitarian data is structured and published. His perspective will help bridge the gap between guidance aimed at data publishers and the realities of building and tuning AI systems that can reliably interpret messy, real-world spreadsheets used across the humanitarian sector.

This two-part discussion series will explore how to make humanitarian spreadsheets more “AI-ready,” bringing together UN OCHA’s new guidance project with real-world lessons from recent AI spreadsheet extraction experiments.

UN OCHA is developing a short, practical guide to help humanitarian teams publish “AI-ready” public datasets that work better with tools like ChatGPT, Copilot, Gemini and open source models like Kimi K2 and GPT OSS running on providers like Groq when users simply upload a CSV or Excel file and start asking questions. The focus is on non-technical users who will not configure agents, write code, or reverse-engineer cryptic column names, but instead expect the AI to correctly interpret the file structure and labels out of the box. By recommending clear naming, consistent tabular layouts, and lightweight documentation, the guidance aims to reduce misinterpretation, hallucinations, and broken analyses when consumer AI tools encounter real-world humanitarian data.

Jan Zheng, a Developer Relations Engineer at Groq who helps people design and build AI prototypes, is exploring exactly these challenges from the model and tooling side. His recent experiments with spreadsheet extraction show that messy, multi-table spreadsheets routinely confuse even advanced models and agent frameworks, leading to unreliable extraction, off‑by‑one errors, looping agents, and high costs. These problems are amplified when complex datasets or vast amounts data are processed by non-technical users of commercial AI tools and open models. Lessons learned through research and usage can inform UN OCHA guidance by clarifying which spreadsheet patterns break current AI tools, which structures make extraction more robust, and how to balance “ideal” AI-ready formats with the messy realities of operational humanitarian spreadsheets.

Over two separate meetup discussions, staff from UN OCHA will introduce the AI‑ready data project in more detail, walk through the specific use case they are targeting, and answer questions from participants about scope, constraints, and potential applications in humanitarian settings. These sessions are designed to surface real-world experiences from practitioners who publish, manage, or use open humanitarian data, and to gather concrete examples of what works and what breaks when datasets are run through consumer AI tools and open source tools running through providers like Groq.

On a following date, Jan will join a dedicated session to react to the project, share his experimental findings on spreadsheet extraction, and discuss how infrastructure choices such as model selection, speed, and prompting strategies interact with the way humanitarian data is structured and published. His perspective will help bridge the gap between guidance aimed at data publishers and the realities of building and tuning AI systems that can reliably interpret messy, real-world spreadsheets used across the humanitarian sector.

En este episodio te contamos con cuáles consejos financieros NO estamos de acuerdo… y por qué. 💛💸

Aquí hablamos de por qué anotar cada gasto puede volverse agotador, por qué un fondo de emergencia de “US$1,000 para todo el mundo” no tiene sentido, y muchos más👀 Dale ME GUSTA 👍 a este video y COMENTA 💬 ¿Cuál de estos consejos sientes que ya no aplica para ti?

🔔 Suscríbete al canal y compártelo con tus amigas, tu pareja o tu familia… para que todos entren al 2025 con las finanzas en orden. 💛

“On Air at Microsoft Ignite” will deep dive into key announcements with expert interviews, demonstrations, and real-world applications for all the latest news. ​​

Hour 2 will feature:​ Azure Infrastructure: Jeremy Winter ​ AI Skills Navigator: Kavitha Radhakrishnan​ Varonis: Shawn Hays​ NVIDIA: Andrew Hester​ GitHub Copilot: Martin Woodward​ AMD: Arjun Oberoi & Daniel Kim

Artisans. Let's meet again, in-person event at a new wonderful event location (Wikimedia Deutschland).

## Talks

• Please suggest talks!

Send us a text In this episode of Data Topics, Ben speaks with Kim Smets, VP Data & AI at Telenet, about his journey from early machine learning work to leading enterprise-wide AI transformation at Telenet. Kim shares how he built a central data & AI team, shifted from fragmented reporting to product thinking, and embedded governance that actually works. They discuss the importance of simplicity, storytelling, and sustainable practices in making AI easy, relevant, and famous across the business. From GenAI exploration to real-world deployment, this episode is packed with practical insights on scaling AI with purpose.

A talk about managing anxiety, burnout, and career decisions in tech. Kim Scott discusses emotional agility, thought models, and coaching that helped her align with her values, stay calm, and make bold career moves. The session includes a personal journey through data science and related roles, with practical tools to stay focused and authentic.