For our first meetup of 2026, we're bringing you two deeply technical stories from the front lines of applied AI, together with AI Native Netherlands. We'll hear how the ANWB navigates the challenges of imperfect data in a legacy organization, and then dive into a practical guide for building production-grade AI agentic workflows with Elastic.

We’ll cover:

• ANWB's journey from manual operations to real-world AI predictions.
• Balancing the human and technical challenges of AI innovation.
• A practical guide to building production-grade agentic workflows.
• Integrating LLMs, vector search, and security into a simplified stack.

Speakers 1: Yke Rusticus & David Brummer (ANWB) Yke is a data engineer at ANWB with a background in astronomy and artificial intelligence. In the industry, he learned that AI models and algorithms often do not get past the experimentation phase, leading him to specialise in MLOps to bridge the gap between experimentation and production. As a professional in this field, Yke has developed ML platforms and use cases across different cloud providers, and is passionate about sharing his knowledge through tutorials and trainings.

David is a self-acclaimed “not your typical Data Scientist” who loves analogue photography, vegan food, dogs, and holds an unofficial PhD in thrifting and sourcing second-hand pearls. With a background in growth hacking and experience in the digital marketing trenches of a startup, a scale-up, and a digital agency, he now brings together lean startup thinking, marketing know-how, and sales pitches, blending it all with a passion for creativity and tech at the ANWB. As a bridge between business and data, David focuses on building AI solutions that don’t just work, but actually get used.

Talk: How AI is helping you back on the road We learn at school what AI can do when the data is perfect. We learn at conferences what AI can do when the environment is perfect. In this talk, you'll learn what AI can do when neither is perfect. This story is about the process of overcoming these challenges in an organisation that has been around since the invention of the bike. We'll balance the technical aspect of these solutions with the human aspect throughout the talk. Because in the end, it's not actually AI helping you back on the road, it's people.

Speaker 2: Hans Heerooms (Elastic) Hans Heerooms is a Senior Solutions Architect at Elastic. He has worked in various roles, but always with one objective: helping organisations to get the most out of their data with the least amount of effort. His current role at Elastic is all about supporting Elastic’s customers to help them evolve from data driven decisions to AI guided workflows.

Talk: Building Production-Grade AI Agentic Workflows with Elastic This talk tells and shows how Elastic Agent Builder can help to build and implement agentic workflows. It addresses the complexity of traditional development by integrating all necessary components—LLM orchestration, vector database, tracing, and security—directly into the Elasticsearch Search AI Platform. This talk will show you how to build custom agents, declare and assign tools, and start conversations with your data.

Agenda: 17:45 — Arrival, food & drinks 18:30 — Talk #1 \| Yke & David (ANWB) 19:15 — Short break 19:30 — Talk #2 \| Hans Heerooms (Elastic) 20:15 — Open conversation, networking & more drinks 21:00 — Wrapping up

Please note that the main door will close at 18.00. You will still be able to enter our office, but we might ask you to wait a little bit while we come down to open the door for you.

What to bring: Just curiosity and questions. If you're working on MLOps, applied AI, or building agentic workflows, we’d love to hear your thoughts.

Who this is for: Data scientists, AI/ML engineers, data engineers, MLOps specialists, SREs, architects, and engineering leaders focused on building and using real-world AI solutions.

Where to find us: Elastic's office in Amsterdam Keizersgracht 281, 1016 ED Amsterdam

Hi everyone, Many of you asked for more practical, real-world AI use-cases, and we listened!

For our first meetup of 2026, we're bringing you two deeply technical stories from the front lines of applied AI. We'll hear how the ANWB navigates the challenges of imperfect data in a legacy organization, and then dive into a practical guide for building production-grade AI agentic workflows with Elastic.

A huge thank you to our friends at Elastic for hosting us at their Amsterdam office. Food and drinks will be provided!

We’ll cover:

• ANWB's journey from manual operations to real-world AI predictions.
• Balancing the human and technical challenges of AI innovation.
• A practical guide to building production-grade agentic workflows.
• Integrating LLMs, vector search, and security into a simplified stack.

Speakers 1: Yke Rusticus & David Brummer (ANWB) Yke is a data engineer at ANWB with a background in astronomy and artificial intelligence. In the industry, he learned that AI models and algorithms often do not get past the experimentation phase, leading him to specialise in MLOps to bridge the gap between experimentation and production. As a professional in this field, Yke has developed ML platforms and use cases across different cloud providers, and is passionate about sharing his knowledge through tutorials and trainings.

David is a self-acclaimed “not your typical Data Scientist” who loves analogue photography, vegan food, dogs, and holds an unofficial PhD in thrifting and sourcing second-hand pearls. With a background in growth hacking and experience in the digital marketing trenches of a startup, a scale-up, and a digital agency, he now brings together lean startup thinking, marketing know-how, and sales pitches, blending it all with a passion for creativity and tech at the ANWB. As a bridge between business and data, David focuses on building AI solutions that don’t just work, but actually get used.

Talk: How AI is helping you back on the road We learn at school what AI can do when the data is perfect. We learn at conferences what AI can do when the environment is perfect. In this talk, you'll learn what AI can do when neither is perfect. This story is about the process of overcoming these challenges in an organisation that has been around since the invention of the bike. We'll balance the technical aspect of these solutions with the human aspect throughout the talk. Because in the end, it's not actually AI helping you back on the road, it's people.

Speaker 2: Hans Heerooms (Elastic) Hans Heerooms is a Senior Solutions Architect at Elastic. He has worked in various roles, but always with one objective: helping organisations to get the most out of their data with the least amount of effort. His current role at Elastic is all about supporting Elastic’s customers to help them evolve from data driven decisions to AI guided workflows.

Talk: Building Production-Grade AI Agentic Workflows with Elastic This talk tells and shows how Elastic Agent Builder can help to build and implement agentic workflows. It addresses the complexity of traditional development by integrating all necessary components—LLM orchestration, vector database, tracing, and security—directly into the Elasticsearch Search AI Platform. This talk will show you how to build custom agents, declare and assign tools, and start conversations with your data.

Agenda: 17:45 — Arrival, food & drinks 18:30 — Talk #1 \| Yke & David (ANWB) 19:15 — Short break 19:30 — Talk #2 \| Hans Heerooms (Elastic) 20:15 — Open conversation, networking & more drinks 21:00 — Wrapping up

Please note that the main door will close at 18.00. You will still be able to enter our office, but we might ask you to wait a little bit while we come down to open the door for you.

What to bring: Just curiosity and questions. If you're working on MLOps, applied AI, or building agentic workflows, we’d love to hear your thoughts.

Who this is for: Data scientists, AI/ML engineers, data engineers, MLOps specialists, SREs, architects, and engineering leaders focused on building and using real-world AI solutions.

Where to find us: Elastic Amsterdam Keizersgracht 281, 1016 ED Amsterdam

Unlock the power of Oracle Database 23AI and Autonomous Database Serverless (ADB-S) with this comprehensive guide to the latest innovations in performance, security, automation, and AI-driven optimization. As enterprises embrace intelligent and autonomous data platforms, understanding these capabilities is essential for data architects, developers, and DBAs. Explore cutting-edge features such as vector data types and AI-powered vector search, revolutionizing data retrieval in modern AI applications. Learn how schema privileges and the DB_DEVELOPER_ROLE simplify access control in multi-tenant environments. Dive into advanced auditing, SQL Firewall, and data integrity constraints to strengthen security and compliance. Discover AI-driven advancements like machine learning-based query execution, customer retention prediction, and AI-powered query tuning. Additional chapters cover innovations in JSON, XML, JSON-Relational Duality Views, new indexing techniques, SQL property graphs, materialized views, partitioning, lock-free transactions, JavaScript stored procedures, blockchain tables, and automated bigfile tablespace shrinking. What sets this book apart is its practical focus—each chapter includes real-world case studies and executable scripts, enabling professionals to implement these features effectively in enterprise environments. Whether you're optimizing performance or aligning IT with business goals, this guide is your key to building scalable, secure, and AI-powered solutions with Oracle 23AI and ADB-S. What You Will Learn Explore Oracle 23AI's latest features through real-world use cases Implement AI/ML-driven optimizations for smarter, autonomous database performance Gain hands-on experience with executable scripts and practical coding examples Strengthen security and compliance using advanced auditing, SQL Firewall, and blockchain tables Master high-performance techniques for query tuning, in-memory processing, and scalability Revolutionize data access with AI-powered vector search in modern AI workloads Simplify user access in multi-tenant environments using schema privileges and DB_DEVELOPER_ROLE Model and query complex data using JSON-Relational Duality Views and SQL property graphs Who this Book is For Database architects, data engineers, Oracle developers, and IT professionals seeking to leverage Oracle 23AI’s latest features for real-world applications

These are the notes of the previous "How to Build a Portfolio That Reflects Your Real Skills" event:

Properties of an ideal portfolio repository:

• Built to prove employable skills and readiness for real work
• Fewer projects, carefully chosen to match job requirements
• Clean, readable, refactored code, and follows best practices
• Detailed READMEs (setup, features, tech stack, decisions, how to deploy, testing strategy, etc)
• Logical, meaningful commits that show development process <- you can follow the git history for important commits/features
• Clear architecture (layers, packages, separation of concerns) <- use best practices
• Unit and integration tests included and explained <-- also talk about them in the README
• Proper validation, exceptions, and edge case handling
• Polished, complete, production-like projects only
• “Can this person work on our codebase?” <-- reviewers will ask this
• Written for recruiters, hiring managers, and senior engineers
• Uses industry-relevant and job-listed technologies <- tech stak should match the CV
• Well-scoped, realistic features similar to real products
• Consistent style, structure, and conventions across projects
• Environment variables, clear setup steps, sample configs
• Minimal, justified dependencies with clear versioning
• Proper logging, and meaningful log messages
• No secrets committed, basic security best practices applied
• Shows awareness of scaling, performance, and future growth <- at least have a "possible improvements" section in the README
• a list of ADRs explains design choices and trade-offs <- should be a part of the documentation

📌 Backend & Frontend Portfolio Project Ideas

These projects are intentionally reusable across tech stacks. Following tutorials and reusing patterns is expected — what matters is:

• understanding the architecture
• explaining trade-offs
• documenting decisions clearly

☕ Junior Java Backend Developer (Spring Boot)

1. Shop Manager Application

A monolithic Spring Boot app designed with microservice-style boundaries. Features

• Secure user registration & login
• Role-based access control using JWT
• REST APIs for:
• Users
• Products
• Inventory
• Orders
• Automatic inventory updates when orders are placed
• CSV upload for bulk product & inventory import
• Clear service boundaries (UserService, OrderService, InventoryService, etc.)

Engineering Focus

• Clean architecture (controllers, services, repositories)
• Global exception handling
• Database migrations (Flyway/Liquibase)
• Unit & integration testing
• Clear README explaining architecture decisions

2. Parallel Data Processing Engine

Backend service for processing large datasets efficiently. Features

• Upload large CSV/log files
• Split data into chunks
• Process chunks in parallel using:
• ExecutorService
• CompletableFuture
• Aggregate and return results

Demonstrates

• Java concurrency
• Thread pools & async execution
• Performance optimization

3. Distributed Task Queue System

Simple async job processing system. Features

• One service submits tasks
• Another service processes them asynchronously
• Uses Kafka or RabbitMQ
• Tasks: report generation, data transformation

Demonstrates

• Message-driven architecture
• Async workflows
• Eventual consistency

4. Rate Limiting & Load Control Service

Standalone service that protects APIs from abuse. Features

• Token bucket or sliding window algorithms
• Redis-backed counters
• Per-user or per-IP limits

Demonstrates

• Algorithmic thinking
• Distributed state
• API protection patterns

5. Search & Indexing Backend

Document or record search service. Features

• In-memory inverted index
• Text search, filters, ranking
• Optional Elasticsearch integration

Demonstrates

• Data structures
• Read-optimized design
• Trade-offs between custom vs external tools

6. Distributed Configuration & Feature Flag Service

Centralized config service for other apps. Features

• Key-value configuration store
• Feature flags
• Caching & refresh mechanisms

Demonstrates

• Caching strategies
• Consistency vs availability trade-offs
• System design for shared services

🐹 Mid-Level Go Backend Developer (Non-Kubernetes)

1. High-Throughput Event Processing Pipeline

Multi-stage concurrent pipeline. Features

• HTTP/gRPC ingestion
• Validation & transformation stages
• Goroutines & channels
• Worker pools, batching, backpressure
• Graceful shutdown

2. Distributed Job Scheduler & Worker System

Async job execution platform. Features

• Job scheduling & delayed execution
• Retries & idempotency
• Job states (pending, running, failed, completed)
• Message queue or gRPC-based workers

3. In-Memory Caching Service

Redis-like cache written from scratch. Features

• TTL support
• Eviction strategies (LRU/LFU)
• Concurrent-safe access
• Optional disk persistence

4. Rate Limiting & Traffic Shaping Gateway

Reverse-proxy-style rate limiter. Features

• Token bucket / leaky bucket
• Circuit breakers
• Redis-backed distributed limits

5. Log Aggregation & Query Engine

Incrementally built system: Step-by-step

1. REST API + Postgres (store logs, query logs)
2. Optimize for massive concurrency
3. Replace DB with in-memory data structures
4. Add streaming endpoints using channels & batching

🐍 Mid-Level Python Backend Developer

1. Asynchronous Task Processing System

Async job execution platform. Features

• Async API submission
• Worker pool (asyncio or Celery-like)
• Retries & failure handling
• Job status tracking
• Idempotency

2. Event-Driven Data Pipeline

Streaming data processing service. Features

• Event ingestion
• Validation & transformation
• Batching & backpressure handling
• Output to storage or downstream services

3. Distributed Rate Limiting Service

API protection service. Steps

• Step 1: Use an existing rate-limiting library
• Step 2: Implement token bucket / sliding window yourself

4. Search & Indexing Backend

Search system for logs or documents. Features

• Custom indexing or Elasticsearch
• Filtering & time-based queries
• Read-heavy optimization

5. Configuration & Feature Flag Service

Shared configuration backend. Steps

• Step 1: Use a caching library
• Step 2: Implement your own cache (explain in README)

🟦 Mid-Level TypeScript Backend Developer

1. Asynchronous Job Processing System

Queue-based task execution. Features

• BullMQ / RabbitMQ / Redis
• Retries & scheduling
• Status tracking

2. Real-Time Chat / Notification Service

WebSocket-based system. Features

• Presence tracking
• Message persistence
• Real-time updates

3. Rate Limiting & API Gateway

API gateway with protections. Features

• Token bucket / sliding window
• Response caching
• Request logging

4. Search & Filtering Engine

Search backend for products, logs, or articles. Features

• In-memory index or Elasticsearch
• Pagination & sorting

5. Feature Flag & Configuration Service

Centralized config management. Features

• Versioning
• Rollout strategies
• Caching

🟨 Mid-Level Node.js Backend Developer

1. Async Task Queue System

Background job processor. Features

• Bull / Redis / RabbitMQ
• Retries & scheduling
• Status APIs

2. Real-Time Chat / Notification Service

Socket-based system. Features

• Rooms
• Presence tracking
• Message persistence

3. Rate Limiting & API Gateway

Traffic control service. Features

• Per-user/API-key limits
• Logging
• Optional caching

4. Search & Indexing Backend

Indexing & querying service.

5. Feature Flag / Configuration Service

Shared backend for app configs.

⚛️ Mid-Level Frontend Developer (React / Next.js)

1. Dynamic Analytics Dashboard

Interactive data visualization app. Features

• Charts & tables
• Filters & live updates
• React Query / Redux / Zustand
• Responsive layouts

2. E-Commerce Store

Full shopping experience. Features

• Product listings
• Search, filters, sorting
• Cart & checkout
• SSR/SSG with Next.js

3. Real-Time Chat / Collaboration App

Live multi-user UI. Features

• WebSockets or Firebase
• Presence indicators
• Real-time updates

4. CMS / Blogging Platform

SEO-focused content app. Features

• SSR for SEO
• Markdown or API-based content
• Admin editing interface

5. Personalized Analytics / Recommendation UI

Data-heavy frontend. Features

• Filtering & lazy loading
• Large dataset handling
• User-specific insights

6. AI Chatbot App — “My House Plant Advisor”

LLM-powered assistant with production-quality UX. Core Features

• Chat interface with real-time updates
• Input normalization & validation
• Offensive content filtering
• Unsupported query detection
• Rate limiting (per user)
• Caching recent queries
• Conversation history per session
• Graceful fallbacks & error handling

Advanced Features

• Prompt tuning (beginner vs expert users)
• Structured advice formatting (cards, bullets)
• Local LLM support
• Analytics dashboard (popular questions)
• Voice input/output (speech-to-text, TTS)

✅ Final Advice

You do NOT need to build everything. Instead, pick 1–2 strong projects per role and focus on depth:

• Explain the architecture clearly
• Document trade-offs (why you chose X over Y)
• Show incremental improvements
• Prove you understand why, not just how

📌 Portfolio Quality Signals (Very Important)

• Have a large, organic commit history → A single or very few commits is a strong indicator of copy-paste work.
• Prefer 3–5 complex projects over 20 simple ones → Many tiny projects often signal shallow understanding.

🎯 Why This Helps in Interviews

Working on serious projects gives you:

• Real hands-on practice
• Concrete anecdotes (stories you can tell in interviews)
• A safe way to learn technologies you don’t fully know yet
• Better focus and long-term learning discipline
• A portfolio that can be ported to another tech stack later (Java → Go, Node → Python, etc.)

🎥 Demo & Documentation Best Practices

• Create a 2–3 minute demo / walkthrough video
• Show the app running
• Explain what problem it solves
• Highlight one or two technical decisions
• At the top of every README:
• Add a plain-English paragraph explaining what the project does
• Assume the reader is a complete beginner

🤝 Open Source & Personal Projects (Interview Signal)

Always mention that you have contributed to Open Source or built personal projects.

• Shows team spirit
• Shows you can read, understand, and navigate an existing codebase
• Signals that you can onboard into a real-world repository
• Makes you sound like an engineer, not just a tutorial follower

❄️ DSF WinterFest 2025: Global Online Summit ❄️

Join the global data celebration!

Monday 24th to Friday 28th November 2025 Online \| 2-3 sessions per day \| Theme: Innovating with Data

DSF WinterFest is back, and this year, it’s going global! Join our 50,000-strong community for a week of world-class talks, tutorials, and panels exploring how data, AI, and analytics are reshaping the world. Expect inspiring content, expert insights, and the cosy, welcoming DSF atmosphere we are known for, all from the comfort of your own space!

Why join? 🌍 A global stage with speakers and attendees from every corner of the world 🎟️ One ticket for the full week. Register once and access every session 💻 Easy access from anywhere. Join live or catch replays in your own time ☕ Cosy community vibe. No travel, no stress, just data and connection

➡️ REGISTER HERE FOR FREE! ⬅️

🎟️ Tickets:

Choose your experience and secure your spot today: Free Pass - Watch live and enjoy replays until 30 November 2025 Upgrade at Checkout - Get extended replay access until May 2026

Register on our website to receive your joining links, add sessions to your calendar, and tune in live from anywhere in the world.

Please note: Clicking “Attend” on Meetup does not register you for this summit. You must register via our website to receive your links.

🎁 Competition:

We’re spreading festive cheer! One lucky attendee will win a £300 Amazon gift voucher (or equivalent in your currency). Find out more here.

❄️❄️❄️

Session details:

💡 The Great Data Engineering Reset: From Pipelines to Agents and Beyond 🗓️ Friday 28th November ⏰ 15:00 PM GMT 🗣️ Joe Reis, Data Engineer and Architect

For years, data engineering was a story of predictable pipelines: move data from point A to point B. But AI just hit the reset button on our entire field. Now, we're all staring into the void, wondering what's next. While the fundamentals remain unchanged, data continues to pose challenges in traditional areas such as security, data governance, management, and modeling. Everything else is up for grabs.

This talk will cut through the noise and explore the future of data engineering in an AI-driven world. We'll discuss how our focus must shift from building dashboards and analytics to architecting for automated action. The reset button has been pushed. It's time for us to invent the future of our industry.

Joe Reis, a "recovering data scientist" with 20 years in the data industry, is the co-author of the best-selling O'Reilly book, "Fundamentals of Data Engineering." He’s also the instructor for the wildly popular Data Engineering Professional Certificate on Coursera, in partnership with DeepLearning.ai and AWS.

Joe’s extensive experience encompasses data engineering\, data architecture\, machine learning\, and more. He regularly keynotes major data conferences globally\, advises and invests in innovative data product companies\, writes at [Practical Data Modeling](https://practicaldatamodeling.substack.com/) and his [personal blog](https://joereis.substack.com/)\, and hosts the popular data podcast "[The Joe Reis Show.](https://open.spotify.com/show/3mcKitYGS4VMG2eHd2PfDN?si=3d3fde23fc1c4a33)" In his free time\, Joe is dedicated to writing new books and articles and thinking of ways to advance the data industry.

➡️ REGISTER HERE FOR FREE! ⬅️

❄️❄️❄️

🔗 How to join:

Once registered, you’ll receive your unique joining link by email, plus handy reminders one week, one day, and one hour before each session. Don't forget to add the sessions you are attending to your calendar. If you can’t make it live, don’t worry, your ticket includes replay access until 30 November 2025 (or May 2026 with the upgrade).

📘 Reminders:

Time zones: All sessions are listed in GMT - please check your local time when registering.

Recordings: Access replays until 30 November 2025 with a free pass, or until May 2026 with an upgraded ticket

Please note: Clicking “Attend” on Meetup does not register you for this summit. You must register via our website to receive your links.

➡️ REGISTER HERE FOR FREE! ⬅️

Join the Celebration ❄️ Five days. Global speakers. Cutting-edge insights. Free to join live - replays included. Upgrade for extended access. Register now and be part of the global data community shaping the future. #DSFWinterFest

Everyone is talking about agents; if you don't have agents already in production you are hopelessly behind. Wasn't vibe coding supposed to take care of everyone's computer problems? How am I behind if AI takes care of everything for me now? Maybe I need an MCP and then I'll be caught up. Wouldn't it be nice if there was a tool where I could just say what I wanted the agent to do and it would vibe it out for me? And then maybe I could tell it what I wanted the app to look like, and it would vibe that out too. That day is today, that tool is called gofannon.

In this talk we'll discuss our motivations for making gofannon, lessons learned that we've incorporated from prior iterations, and a (possibly live) demo where we'll go from idea to deployed app. See you there!

About the presenters Trevor Grant (IBM)

Andrew Musselman is a seasoned technology leader with 20+ years of experience building scalable platforms and data-driven solutions. He is a builder with business focus, and has helped companies realize multiple millions of annual savings and revenue through innovation and modernization. Andrew has been an Apache Software Foundation contributor for over 12 years, and brings together the community-driven value of open source work with concrete business results, ranging from data engineering and platform development to cutting-edge AI solutions.

About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.

Join the community Sign up for the AI Alliance newsletter (check the website footer) and join our new AI Alliance Discord.

Everyone is talking about agents; if you don't have agents already in production you are hopelessly behind. Wasn't vibe coding supposed to take care of everyone's computer problems? How am I behind if AI takes care of everything for me now? Maybe I need an MCP and then I'll be caught up. Wouldn't it be nice if there was a tool where I could just say what I wanted the agent to do and it would vibe it out for me? And then maybe I could tell it what I wanted the app to look like, and it would vibe that out too. That day is today, that tool is called gofannon.

In this talk we'll discuss our motivations for making gofannon, lessons learned that we've incorporated from prior iterations, and a (possibly live) demo where we'll go from idea to deployed app. See you there!

About the presenters Trevor Grant (IBM)

Andrew Musselman is a seasoned technology leader with 20+ years of experience building scalable platforms and data-driven solutions. He is a builder with business focus, and has helped companies realize multiple millions of annual savings and revenue through innovation and modernization. Andrew has been an Apache Software Foundation contributor for over 12 years, and brings together the community-driven value of open source work with concrete business results, ranging from data engineering and platform development to cutting-edge AI solutions.

About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.

Join the community Sign up for the AI Alliance newsletter (check the website footer) and join our new AI Alliance Discord.

Everyone is talking about agents; if you don't have agents already in production you are hopelessly behind. Wasn't vibe coding supposed to take care of everyone's computer problems? How am I behind if AI takes care of everything for me now? Maybe I need an MCP and then I'll be caught up. Wouldn't it be nice if there was a tool where I could just say what I wanted the agent to do and it would vibe it out for me? And then maybe I could tell it what I wanted the app to look like, and it would vibe that out too. That day is today, that tool is called gofannon.

In this talk we'll discuss our motivations for making gofannon, lessons learned that we've incorporated from prior iterations, and a (possibly live) demo where we'll go from idea to deployed app. See you there!

About the presenters Trevor Grant (IBM)

Andrew Musselman is a seasoned technology leader with 20+ years of experience building scalable platforms and data-driven solutions. He is a builder with business focus, and has helped companies realize multiple millions of annual savings and revenue through innovation and modernization. Andrew has been an Apache Software Foundation contributor for over 12 years, and brings together the community-driven value of open source work with concrete business results, ranging from data engineering and platform development to cutting-edge AI solutions.

About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.

Join the community Sign up for the AI Alliance newsletter (check the website footer) and join our new AI Alliance Discord.

Everyone is talking about agents; if you don't have agents already in production you are hopelessly behind. Wasn't vibe coding supposed to take care of everyone's computer problems? How am I behind if AI takes care of everything for me now? Maybe I need an MCP and then I'll be caught up. Wouldn't it be nice if there was a tool where I could just say what I wanted the agent to do and it would vibe it out for me? And then maybe I could tell it what I wanted the app to look like, and it would vibe that out too. That day is today, that tool is called gofannon.

In this talk we'll discuss our motivations for making gofannon, lessons learned that we've incorporated from prior iterations, and a (possibly live) demo where we'll go from idea to deployed app. See you there!

About the presenters Trevor Grant (IBM)

Andrew Musselman is a seasoned technology leader with 20+ years of experience building scalable platforms and data-driven solutions. He is a builder with business focus, and has helped companies realize multiple millions of annual savings and revenue through innovation and modernization. Andrew has been an Apache Software Foundation contributor for over 12 years, and brings together the community-driven value of open source work with concrete business results, ranging from data engineering and platform development to cutting-edge AI solutions.

About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.

Join the community Sign up for the AI Alliance newsletter (check the website footer) and join our new AI Alliance Discord.

Everyone is talking about agents; if you don't have agents already in production you are hopelessly behind. Wasn't vibe coding supposed to take care of everyone's computer problems? How am I behind if AI takes care of everything for me now? Maybe I need an MCP and then I'll be caught up. Wouldn't it be nice if there was a tool where I could just say what I wanted the agent to do and it would vibe it out for me? And then maybe I could tell it what I wanted the app to look like, and it would vibe that out too. That day is today, that tool is called gofannon.

In this talk we'll discuss our motivations for making gofannon, lessons learned that we've incorporated from prior iterations, and a (possibly live) demo where we'll go from idea to deployed app. See you there!

About the presenters Trevor Grant (IBM)

Andrew Musselman is a seasoned technology leader with 20+ years of experience building scalable platforms and data-driven solutions. He is a builder with business focus, and has helped companies realize multiple millions of annual savings and revenue through innovation and modernization. Andrew has been an Apache Software Foundation contributor for over 12 years, and brings together the community-driven value of open source work with concrete business results, ranging from data engineering and platform development to cutting-edge AI solutions.

About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.

Join the community Sign up for the AI Alliance newsletter (check the website footer) and join our new AI Alliance Discord.

Everyone is talking about agents; if you don't have agents already in production you are hopelessly behind. Wasn't vibe coding supposed to take care of everyone's computer problems? How am I behind if AI takes care of everything for me now? Maybe I need an MCP and then I'll be caught up. Wouldn't it be nice if there was a tool where I could just say what I wanted the agent to do and it would vibe it out for me? And then maybe I could tell it what I wanted the app to look like, and it would vibe that out too. That day is today, that tool is called gofannon.

In this talk we'll discuss our motivations for making gofannon, lessons learned that we've incorporated from prior iterations, and a (possibly live) demo where we'll go from idea to deployed app. See you there!

About the presenters Trevor Grant (IBM)

Andrew Musselman is a seasoned technology leader with 20+ years of experience building scalable platforms and data-driven solutions. He is a builder with business focus, and has helped companies realize multiple millions of annual savings and revenue through innovation and modernization. Andrew has been an Apache Software Foundation contributor for over 12 years, and brings together the community-driven value of open source work with concrete business results, ranging from data engineering and platform development to cutting-edge AI solutions.

About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.

Join the community Sign up for the AI Alliance newsletter (check the website footer) and join our new AI Alliance Discord.

Everyone is talking about agents; if you don't have agents already in production you are hopelessly behind. Wasn't vibe coding supposed to take care of everyone's computer problems? How am I behind if AI takes care of everything for me now? Maybe I need an MCP and then I'll be caught up. Wouldn't it be nice if there was a tool where I could just say what I wanted the agent to do and it would vibe it out for me? And then maybe I could tell it what I wanted the app to look like, and it would vibe that out too. That day is today, that tool is called gofannon.

In this talk we'll discuss our motivations for making gofannon, lessons learned that we've incorporated from prior iterations, and a (possibly live) demo where we'll go from idea to deployed app. See you there!

About the presenters Trevor Grant (IBM)

Andrew Musselman is a seasoned technology leader with 20+ years of experience building scalable platforms and data-driven solutions. He is a builder with business focus, and has helped companies realize multiple millions of annual savings and revenue through innovation and modernization. Andrew has been an Apache Software Foundation contributor for over 12 years, and brings together the community-driven value of open source work with concrete business results, ranging from data engineering and platform development to cutting-edge AI solutions.

About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.

Join the community Sign up for the AI Alliance newsletter (check the website footer) and join our new AI Alliance Discord.