2.0 -//Pentabarf//Schedule//EN PUBLISH L7GZBW@@pretalx.com -L7GZBW Registration en en 20251030T080000 20251030T084500 004500 Registration TBD PUBLIC CONFIRMED Organization https://pretalx.com/pycon-sweden-2025/talk/L7GZBW/ Auditorium PUBLISH Y7YTQQ@@pretalx.com -Y7YTQQ Opening Session en en 20251030T084500 20251030T090000 001500 Opening Session Welcome to PyCon Sweden 2025! PUBLIC CONFIRMED Organization https://pretalx.com/pycon-sweden-2025/talk/Y7YTQQ/ Auditorium PUBLISH X3PLGX@@pretalx.com -X3PLGX AI is having its moment ... again en en 20251030T090000 20251030T100000 010000 AI is having its moment ... again This keynote will connect the cycles of AI’s past to the challenges and opportunities we face today. By drawing lessons from past AI hype cycles, we can gain perspective on how to navigate the current wave of AI with more clarity, context, and realism. PUBLIC CONFIRMED Keynote https://pretalx.com/pycon-sweden-2025/talk/X3PLGX/ Auditorium Jodie Burchell PUBLISH EQZQ9P@@pretalx.com -EQZQ9P The Magic of Self: How Python inserts self into methods en en 20251030T103000 20251030T110000 003000 The Magic of Self: How Python inserts self into methods This talk explores the "magic" behind Python's automatic insertion of self into methods. While beginners are told "don't worry, it just happens" and experienced developers stop noticing it, there's fascinating machinery underneath. I will start my talk by showing the automatic insertion of `self` in action. I will show you that we get an argument "for free" when calling a method: despite only passing a single argument, our method actually receives two values! After showing you the Magic of Self in action, we will discuss why this happens. No, it's not because we named one of the parameters `self` when we defined the method. No, it's not even the fact that we defined a function within the body of a class. In fact, the magic happens when we access the method. As it turns out, accessing attributes (including those that are bound to methods) isn't as simple as it looks at a glance. We can actually influence what happens when we access attributes by implementing something called a descriptor. That's why I will continue by introducing you to the descriptor protocol and showing you how to implement a descriptor of your own. Now that we know how descriptors work, we're finally ready to understand the magic of self. To understand it, we now turn our attention to functions and observe that all functions are, in fact, such descriptors. I will conclude my talk by providing a few examples of other descriptors that you may find in Python and the standard library. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/EQZQ9P/ Auditorium Sebastiaan Zeeff PUBLISH EMCDDD@@pretalx.com -EMCDDD Trust and the success of Python en en 20251030T110000 20251030T113000 003000 Trust and the success of Python Why has Python become the most used programming language in the world? Is it the syntax, with indentation as block markers? Is it the lists and the dicts and the comprehensions? Is it some specific packages like Numpy or Django? It certainly isn't the execution speed, though that is improving a bit. While some popularity comes from the factors above, my take is that trust plays a major role in the rise of Python. I will explain what I mean in the talk. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/EMCDDD/ Auditorium Jacob Hallén PUBLISH HRR98Q@@pretalx.com -HRR98Q Architecting Enterprise AI Agents: Secure, Governed, and Interoperable en en 20251030T113000 20251030T120000 003000 Architecting Enterprise AI Agents: Secure, Governed, and Interoperable ### The Problem: Why Simple Agents Fail in the Enterprise The initial excitement of building a generative AI agent quickly meets the messy reality of the enterprise environment. There is no single, clean "data layer." We face a fragmented landscape of technical catalogs, business glossaries, semantic ontologies, and multiple data sources. This presents two core challenges: 1. **Navigating the Data & Tool Maze:** With potentially hundreds of datasets and specialized tools (e.g., for visualization, data quality checks, forecasting), a primary challenge is selection and relevance. How does the agent understand a user's ambiguous query and identify the appropriate datasets and tools? 2. **The Governance & Security Mandate:** Equally critical is governance. How can an agent act on a user’s behalf without “god mode” access to all data? ### Our Solution: A Multi-Level Governance Architecture We'll share a real-world architecture for a structured-data AI agent that works inside a heavily governed environment, built on three pillars: 1. **Secure by Design** - Remove unnecessary autonomy and place explicit guardrails and failsafe mechanisms around critical steps like data retrieval and tool execution. 2. **Delegated Permissions & Authentication** - Users authenticate via SSO and provide the agent with a token. The agent uses this delegated authority to interact with other services. It operates under its own identity (service principal) but forwards the user's identity so downstream systems can enforce user-specific access rules and masking. 3. **Governed Interoperability & Discovery** - Using Model Context Protocol (MCP) as a universal bridge between AI systems and data sources, the agent dynamically discovers datasets and tools relevant to a query while respecting permissions. Governance is enforced at discovery. Tools are treated as first-class citizens alongside data. This dynamic approach decouples the agent from specific tool implementations, enabling a flexible, maintainable, and scalable ecosystem of reusable capabilities. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/HRR98Q/ Auditorium Antonino Ingargiola Irene Donato PUBLISH RKXS8D@@pretalx.com -RKXS8D A Practical Guide to Testing Django with Pytest, Factory Boy, and Hypothesis en en 20251030T130000 20251030T133000 003000 A Practical Guide to Testing Django with Pytest, Factory Boy, and Hypothesis This talk is for Python developers who want to write better, faster, and more reliable tests using modern Python tools. We’ll start by looking at what Pytest is and how it can replace Django’s built-in test runner, offering cleaner syntax, powerful plugins, and flexible fixtures. Next, we’ll use Factory Boy to create test data in a smarter way avoiding hardcoded values and repetitive setup. Finally, we’ll introduce Hypothesis to explore property-based testing, which helps discover unexpected bugs by generating test inputs automatically. You’ll see how these tools work together in real Django projects, and learn practical patterns that can improve your testing workflow. No deep testing knowledge is required, just basic Django experience and curiosity about better tools. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/RKXS8D/ Auditorium Kader Miyanyedi PUBLISH FRKBMQ@@pretalx.com -FRKBMQ Interactive Python Programming en en 20251030T133000 20251030T140000 003000 Interactive Python Programming Learning Lisp was an eye opener for me. The Interactive way of writing code is Amazing, and you have great support for this kind of flow in most code editors. What about development with Python? For Python, there's limitations and lack of tooling support - but I have found ways to make Python development more interactive (and fun). Python is what I do at work and in Open Source projects. It has lead me to develop some Code Editor features that are specific for Python: evaluating code with visual feedback, modify a running Python app without any app restarts, and also some LLM support. I will demo and talk about what I have developed to make Python development interactive and joyful. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/FRKBMQ/ Auditorium David Vujic PUBLISH R9N3MZ@@pretalx.com -R9N3MZ Building Harry Potter’s Mirror of Erised with Python and Generative AI en en 20251030T140000 20251030T143000 003000 Building Harry Potter’s Mirror of Erised with Python and Generative AI What if the Mirror of Erised from Harry Potter actually existed and you could look into it and see your deepest desires visualized? What if you could build it using Python? In this talk, I’ll introduce a creative, AI-powered project that transforms a user's personal hopes and dreams into a visual representation using NLP and image generation. The user writes freely about what they long for. A large language model (LLM) analyzes the text to extract emotional tone, symbols, and core themes. Then, this interpretation is converted into a visual prompt for a text-to-image model, which generates an image. The final image is dynamically displayed behind the user's webcam feed, turning their screen into a personalized, digital “Mirror of Erised.” I’ll walk through the full technical pipeline: from prompt engineering and emotion detection to visual generation and building the interactive UI with Python. This talk combines storytelling with code and aims to inspire developers to explore how Python and AI can bring deeply personal, emotional, and magical experiences to life. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/R9N3MZ/ Auditorium Özge Çinko PUBLISH 8ASAP7@@pretalx.com -8ASAP7 Shrinking Infrastructure: Task Queues on PostgreSQL en en 20251030T150000 20251030T153000 003000 Shrinking Infrastructure: Task Queues on PostgreSQL 1 · Queue tables and job flow We start by outlining the core tables: how jobs are stored, tracked through states, and enriched with metadata like priority and timestamps. This sets the stage for understanding how pgqueuer manages work safely inside Postgres. 2 · Concurrency and coordination Next, we’ll dive into the mechanics: workers leasing jobs with FOR UPDATE SKIP LOCKED, and why this pattern allows safe parallel processing without global locks. We’ll then look at LISTEN/NOTIFY for instant wake-ups, and why a lightweight polling fallback is still necessary when notifications fail. 3 · Reliability and observability We’ll explore how retries, heartbeats, and cancellation keep the system robust under load and failures. Finally, we’ll step into the CLI dashboard to see live throughput and error stats—giving operators immediate visibility without extra tooling. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/8ASAP7/ Auditorium Jan Bjørge PUBLISH Z39XBR@@pretalx.com -Z39XBR Extending SQL Databases with Python en en 20251030T153000 20251030T160000 003000 Extending SQL Databases with Python 1. Introduction (3 min) • Who this talk is for: devs, data engineers, extension hackers • Motivation: why embed Python in databases? • Overview of the 3 systems (SQLite, DuckDB, PostgreSQL) 2. SQLite & DuckDB: Python Functions via sqlite3.create_function (7 min) • How sqlite3.create_function() works in SQLite • Example: creating a simple text-processing SQL function in Python • Use cases: rapid prototyping, lightweight data pipelines 3. PostgreSQL with PL/Python (6 min) • Enabling and using the PL/Python extension • Writing SQL functions in Python • Pros and limitations (e.g., sandboxing, permissions, virtualenvs) 4. Advanced: Embedding Python into PostgreSQL Extensions (7 min) • Writing PostgreSQL extensions in C that embed Python • Use cases: integrating ML models, custom procedural logic • Short demo or diagram: C + Python working inside Postgres 5. Trade-offs and Comparison (3 min) • Performance, deployment, complexity, ecosystem • When to use which approach 6. Q&A (4 min) • Invite questions and deeper discussion from the audience PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/Z39XBR/ Auditorium Florents Tselai PUBLISH ZEQ9RX@@pretalx.com -ZEQ9RX Designing Exceptions as an API: Hierarchies, Interfaces, and Error Codes en en 20251030T160000 20251030T163000 003000 Designing Exceptions as an API: Hierarchies, Interfaces, and Error Codes Outline: The problem (3 min): messy, ad-hoc exception handling. Hierarchies (8 min): designing categories (UserError, InfraError, Bug) and how they map to real-world failures. Interfaces (8 min): duck-typed properties (status_code, retryable, context) for consistent handling across your codebase. Domain codes (7 min): creating human- and machine-friendly error identifiers, integrating them into logs/metrics. Best practices (4 min): wrapping 3rd-party errors, raise ... from ..., documenting error contracts. Audience: Intermediate Python developers who build or maintain applications, libraries, or services and want clearer, more reliable error handling. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/ZEQ9RX/ Auditorium Nikita Churikov PUBLISH SX7N7V@@pretalx.com -SX7N7V The serendipity factor: How community turns accidents into opportunities en en 20251030T164000 20251030T174000 010000 The serendipity factor: How community turns accidents into opportunities TBD PUBLIC CONFIRMED Keynote https://pretalx.com/pycon-sweden-2025/talk/SX7N7V/ Auditorium Lysandros Nikolaou PUBLISH VMT3WX@@pretalx.com -VMT3WX Closing - Day 1 en en 20251030T174500 20251030T180000 001500 Closing - Day 1 Clasing Remarks Day 1 PUBLIC CONFIRMED Organization https://pretalx.com/pycon-sweden-2025/talk/VMT3WX/ Auditorium PUBLISH DQSCFX@@pretalx.com -DQSCFX What We Can Learn from Exemplary Python Documentation (SEE DESCRIPTION FOR PREPARATION) en en 20251030T103000 20251030T120000 013000 What We Can Learn from Exemplary Python Documentation (SEE DESCRIPTION FOR PREPARATION) PLEASE PREPARE A FRESH PYTHON PROJECT BEFORE THE WORKSHOP (PREFERABLY USING PYCHARM OR AT LEAST AN IDE YOU ARE EXPERIENCED WITH; JUPYTER NOTEBOOKS DO **NOT** WORK): pip install --upgrade pip setuptools pip install jupyterlite-pyodide-kernel pip install jupyterlite-sphinx pip install matplotlib pip install numpy pip install pandas pip install pydata-sphinx-theme pip install sphinx-book-theme pip install sphinx-copybutton pip install sphinx-gallery pip install sphinxcontrib-kroki ---------------------------------------------------------------------------------------------------- If you’ve attended PyCon Sweden last year, you might have seen my talk “Documenting Python Code”, where I introduced attendees to the basics of Python documentation. This year, I will expand on that foundation by looking at what can be learned from exemplary Python documentation. Building on renowned examples from popular Python libraries such as NumPy, pandas, and Matplotlib, this workshop will delve into techniques and tools that help streamline documentation creation while improving clarity and usability. The topics covered include: Sphinx documentation generator • use reStructuredText as a markup language • simplify docstrings with the NumPy and Google format • generate function and method documentation with sphinx-apidoc How to • include code snippets as examples and display them in environment-specific tabs • adopt themes and implement appealing styles • use admonitions to highlight important content • write sophisticated mathematical formulas • create versatile diagrams with Kroki • generate interactive HTML documentation with embedded notebooks Comparison: AsciiDoc vs. Sphinx • explore their strengths and limitations for multi-language projects • see how AsciiDoc can document APIs by including tagged source code snippets This workshop provides practical insights and examples to help developers, technical writers, and maintainers create better documentation and improve their workflows. Whether you are just starting out or refining an established project, this session will provide actionable techniques to overcome common challenges and take your documentation to the next level. PUBLIC CONFIRMED Workshop https://pretalx.com/pycon-sweden-2025/talk/DQSCFX/ Tutorial Room Christian Heitzmann PUBLISH ZU8GRN@@pretalx.com -ZU8GRN Version Everything: From Chaos to Order in Reproducible Python Projects en en 20251030T130000 20251030T143000 013000 Version Everything: From Chaos to Order in Reproducible Python Projects This interactive 90-minute workshop provides hands-on experience building reproducible workflows using modern Python tools. Through practical exercises, you'll transform a typical "script chaos" project into a fully reproducible pipeline applicable to any data-driven Python discipline. What We'll Build Together: Starting with a working but messy data analysis project, we'll systematically add reproducibility layers. You'll set up dependency management, use git to version control the code, use DVC to version control the data and outputs, use configuration files to store the parameters and lastly containerize everything with Docker. Each module includes guided coding exercises where you'll apply concepts immediately. Key Modules: 1. Modern Dependency Management (20 min): Hands-on with uv/pixi, creating lock files, managing Python versions 2. Code & Configuration Versioning (20 min): Git for version control of the source code 3. Data Pipeline Versioning (20 min): DVC setup, pipeline definitions, experiment tracking 4. Hidden Reproducibility Challenges (10 min): Randomness and human error 5. Production Deployment (20 min): Docker for Python, artifact registries, deployment reproducibility This workshop focuses on the tools and practices that make models and analyses reproducible and well-documented. The underlying methods will be introduced only briefly for context, while most of the time will be devoted to practical, hands-on work with the tooling. Prerequisites: * A laptop (admin root privileges are needed to install the necessary tooling) * Basic knowledge of Python syntax The necessary tooling can be installed during the workshop. Installation of tooling - uv (https://docs.astral.sh/uv/getting-started/installation/) - git (https://git-scm.com/install) - DVC (https://dvc.org/doc/install) - Podman (https://podman.io/docs/installation) Repos https://github.com/anivorlis/pycon-workshop-uv https://github.com/anivorlis/pycon-workshop-dvc https://github.com/anivorlis/pycon-workshop-challenge PUBLIC CONFIRMED Workshop https://pretalx.com/pycon-sweden-2025/talk/ZU8GRN/ Tutorial Room Aris Nivorlis Nikos Chatzis PUBLISH ZW8BUA@@pretalx.com -ZW8BUA Building Privacy-Focused Vector Search Applications: Hands-On Guide to Gen AI en en 20251030T150000 20251030T163000 013000 Building Privacy-Focused Vector Search Applications: Hands-On Guide to Gen AI Detailed insights into generating embeddings with tools like Ollama and demonstrating how LangChain agents can perform tasks such as document summarisation and API interactions, all while maintaining data privacy in a Python application PUBLIC CONFIRMED Workshop https://pretalx.com/pycon-sweden-2025/talk/ZW8BUA/ Tutorial Room Shivay Lamba Gaurav Pandey Kishor Deshpande PUBLISH VERK9X@@pretalx.com -VERK9X The Fraud Keynote (…Or Maybe Not) en en 20251031T090000 20251031T100000 010000 The Fraud Keynote (…Or Maybe Not) TBD PUBLIC CONFIRMED Keynote https://pretalx.com/pycon-sweden-2025/talk/VERK9X/ Auditorium Iulia Feroli PUBLISH CZZGT3@@pretalx.com -CZZGT3 Empowering Geospatial Workflows with Python en en 20251031T103000 20251031T110000 003000 Empowering Geospatial Workflows with Python In this talk, we will showcase how Python can be used to automate everyday spatial analysis tasks, using real examples from the professional field of environmental and infrastructure mapping. We will demonstrate lines of scripts utilizing basic python libraries for geospatial analysis that support the quality of geospatial data and compare spatial layers to gain useful insights. The talk begins with an introduction to the main formats used in geospatial data and some fundamental geospatial concepts. Aftwerwards, we will discuss on common challenges encountered when working with data in traditional GIS software, and how Python provides cleaner, more scalable solutions. Whether run independently or integrated with existing GIS software, Python scripts can save time, reduce errors, and improve data quality. Attendees will leave with actionable insights and inspiration to create their own GIS tools using Python libraries. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/CZZGT3/ Auditorium ELENI TOKMAKTSI PUBLISH MYK8DB@@pretalx.com -MYK8DB Quantum Computing in Python: Why Qiskit Matters for Developers en en 20251031T110000 20251031T113000 003000 Quantum Computing in Python: Why Qiskit Matters for Developers Quantum computing is often seen as complex and distant, but in reality it’s becoming a practical field where Python is the gateway language. This talk introduces the core ideas of quantum computing and demonstrates how they are implemented through Qiskit, the leading open-source quantum SDK. I will cover: Why quantum computing matters and how it differs from classical computing. The role of Python and Qiskit in making quantum accessible. Concrete examples of applications in optimization, AI, finance, and Health Care and Life Sciences. The current state of quantum hardware and software—and what’s next. On the example of IBM's road map. Why developers and organizations need to prepare now for a quantum future. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/MYK8DB/ Auditorium Anna Elisabeth Kazakova Lindegren PUBLISH HMPNFQ@@pretalx.com -HMPNFQ 18 Years of Falling Objects: Lessons from Maintaining Physics Library Pymunk en en 20251031T113000 20251031T120000 003000 18 Years of Falling Objects: Lessons from Maintaining Physics Library Pymunk Pymunk was created to easily let me write a game with 2D physics for the PyWeek 5 game competition. The game itself wasn't very successful, but Pymunk has since grown into an established library used in for example games, education and research, with 5 million total downloads and 1,000 stars on GitHub. During Pymunk's 18 years many things have happened, and I plan to cover some of the more impactful and memorable of them. Partly key technical "happenings" (Python 2-to-3 anyone?), but the main focus will be on the less technical side of Pymunk's development. This session is for anyone interested in open source, software longevity, or just a good story. No deep math or expert-level Python skills are required. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/HMPNFQ/ Auditorium Victor Blomqvist PUBLISH NUZXMZ@@pretalx.com -NUZXMZ Embeddings - LLMs - vector/semantic search - GenAI - RAG - agents - MCP (buzzword bingo) en en 20251031T130000 20251031T133000 003000 Embeddings - LLMs - vector/semantic search - GenAI - RAG - agents - MCP (buzzword bingo) TBD PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/NUZXMZ/ Auditorium Iulia Feroli PUBLISH YEA33H@@pretalx.com -YEA33H Agents need good developer experience too en en 20251031T133000 20251031T140000 003000 Agents need good developer experience too So what does “agent-friendly DX” actually look like in practice? At Modal, we set out to make cloud infrastructure feel like writing ordinary Python scripts: quick to iterate on, easy to test, and intuitive when things go wrong. A guiding principle was to delight developers who use our Python SDK. Along came AI powered coding agents, and it turns out some of our choices made these agents very efficient users of the SDK too! We’ll walk through a few design choices that turned out to matter just as much for machines as for humans, and share what we’ve learned about building SDKs that work well for both. If you care about APIs, infrastructure, or the future of programming, this talk will give you a fresh lens on what “good DX” means when the developer on the other side isn’t always human. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/YEA33H/ Auditorium Elias Freider PUBLISH U7ATWN@@pretalx.com -U7ATWN Gamified User Journeys with Pygame en en 20251031T140000 20251031T143000 003000 Gamified User Journeys with Pygame When this idea first took shape, I was entering a new chapter in life. Recently married and about to become a father. In the heat of it all, I thought it was a good idea to start experimenting with spring simulations and particle systems. These were topics I’d long avoided, but during PyconSE 2024, a colleague shared an idea that stuck: what if we could visualize how users interact with our web, not just as data points, but as living, moving entities? Armed with Pygame, some basic physics, math, and a stream of real user behavior data, I built a playful and unconventional way to display our customer journey—one that’s more alive, more visual, and maybe even a little fun. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/U7ATWN/ Auditorium Eric Rishmüller PUBLISH PQNNPP@@pretalx.com -PQNNPP Providing a Python library — the whole shebang en en 20251031T150000 20251031T153000 003000 Providing a Python library — the whole shebang First we look at the general problems one typically faces when creating Python dependencies with native components. Once that is settled we examine the fundamentals of the library we aim to provide. This includes both build setup for the library as well as the design to make the end result as easily usable from Python (or, in fact, from any scripting language). Once we have the library working we can do some performance measurements to verify that the end result is working as expected. Finally we examine the tooling needed to create fully compliant PyPI packages. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/PQNNPP/ Auditorium Jussi Pakkanen PUBLISH 9GH3SQ@@pretalx.com -9GH3SQ Mastodon and Python en en 20251031T153000 20251031T160000 003000 Mastodon and Python As free software alternative to business social media networks, that push you propaganda - like it or not - there is the fediverse. And within the fediverse, the Mastodon. This talk will show the interconnection between these federated universe and walk through how to use with python. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2025/talk/9GH3SQ/ Auditorium Helio Loureiro PUBLISH 9LNXZY@@pretalx.com -9LNXZY The Python Quiz! en en 20251031T160000 20251031T163000 003000 The Python Quiz! The Python Quiz! PUBLIC CONFIRMED Organization https://pretalx.com/pycon-sweden-2025/talk/9LNXZY/ Auditorium Aris Nivorlis PUBLISH YN87EW@@pretalx.com -YN87EW Python in the age of Agents en en 20251031T164000 20251031T174000 010000 Python in the age of Agents Python is gaining ground in AI - it is now the language of both AI engineering and the language of agents. But is it losing ground as the language of programmers? Python made its name as the programming language that minimizes the impedance mismatch with natural language. However, with coding agents, it is now possible to go from natural language to working Python code. Where do we go from here? I will place the evolution of coding agents in the history of programming language development. We will see that LLMs are more than just compilers that translate natural language into programs. I will also talk about the return of the waterfall lifecycle development process. I will also talk about the effect of agents on developers. How are coding agents being received by developers? I will talk about three types of developers: those who supercharge their coding with agents, those who feel threatened by them, and those who weren’t developers but now are developers. Finally, I will talk about developing agents. From model context protocol to application context protocol. Building AI systems has never been easier or more fun. PUBLIC CONFIRMED Keynote https://pretalx.com/pycon-sweden-2025/talk/YN87EW/ Auditorium Jim Dowling PUBLISH F7EFGU@@pretalx.com -F7EFGU Lighting Talks en en 20251031T174500 20251031T181500 003000 Lighting Talks TBD PUBLIC CONFIRMED Organization https://pretalx.com/pycon-sweden-2025/talk/F7EFGU/ Auditorium PUBLISH WDHJRB@@pretalx.com -WDHJRB Closing - Day 2 en en 20251031T181500 20251031T183000 001500 Closing - Day 2 Clasing Remarks Day 2 PUBLIC CONFIRMED Organization https://pretalx.com/pycon-sweden-2025/talk/WDHJRB/ Auditorium PUBLISH 7ZJVH7@@pretalx.com -7ZJVH7 Building Production-Ready AI Agents: The 4Ds for Scalable Python Systems en en 20251031T103000 20251031T120000 013000 Building Production-Ready AI Agents: The 4Ds for Scalable Python Systems Setup (10 min): Install Hatchet SDK, configure local environment against hosted infrastructure, verify connectivity. The Problem (15 min): Run a "typical tutorial" document classifier that works until it doesn't. Experience rate limiting, network failures, and resource contention. Discuss why demo-ware fails: no parallelization, no error recovery, no observability. Distributed Processing (15 min): Transform sequential processing into parallel task execution. Write Hatchet workflows that fan-out document classification across workers, then fan-in results. Process 100 documents in parallel, observing throughput improvements. Durable Execution (15 min): Add automatic retries, timeouts, and stateful checkpoints. Simulate infrastructure failures and watch workflows resume from the last successful step without data loss. Experience how durability prevents expensive recomputation. Deterministic Behavior (15 min): Replace unpredictable LLM outputs with structured schemas using Pydantic validation. Implement confidence thresholds and automatic fallbacks. See how constraints reduce debugging time and improve reliability. Debuggable Systems (15 min): Instrument workflows with structured logging, metrics, and distributed tracing. Use Hatchet's dashboard to identify bottlenecks, track success rates, and diagnose failures across execution graphs. Production Integration (5 min): Review complete example integrating all patterns. Discuss deployment strategies and monitoring best practices. Target Audience: Python developers building AI applications who need reliable, scalable infrastructure patterns. Especially valuable for teams transitioning from prototypes to production. Interactive Elements: Live coding with dashboard feedback, simulated failure scenarios, real-time parallel execution metrics, complete working system to take home. Takeaways: Production-ready code patterns, infrastructure best practices, and immediately applicable solutions for AI workflow scaling challenges every Python developer eventually faces. PUBLIC CONFIRMED Workshop https://pretalx.com/pycon-sweden-2025/talk/7ZJVH7/ Tutorial Room Gabriel Ruttner PUBLISH SSVHYH@@pretalx.com -SSVHYH Connecting IoT Devices with LavinMQ – Hands-On Workshop en en 20251031T130000 20251031T143000 013000 Connecting IoT Devices with LavinMQ – Hands-On Workshop In this workshop you will learn how to connect an IoT device to publish messages. Understand what LavinMQ is and why it’s valuable in IoT environments. Create a LavinMQ instance on CloudAMQP. Write a consumer to process messages. Scale consumers and test reliability. Lovisa will not be the one holding it. It will be Christina Dahlén and Erica Weistrand. PUBLIC CONFIRMED Workshop https://pretalx.com/pycon-sweden-2025/talk/SSVHYH/ Tutorial Room Christina Dahlén Erica Weistrand PUBLISH LMW9LS@@pretalx.com -LMW9LS Job Fair en en 20251031T150000 20251031T160000 010000 Job Fair Are you searching for a job? Our lovely Sponsors are hiring. Join this session to learn about new opportunities! PUBLIC CONFIRMED Organization https://pretalx.com/pycon-sweden-2025/talk/LMW9LS/ Tutorial Room