Dominik Šimoník
Technical Delivery Lead @ Very Good Ventures | Firebase, Flutter GDE
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Technical Delivery Lead at Very Good Ventures who sets a high bar, pairing clean architecture and strong Flutter skills with AI to build smarter and faster. Working in mobile since 2016, he loves helping startups turn a rough idea into a production-ready product. As a GDE for Firebase and Flutter and a GDG organizer, he mentors, speaks, and organizes events. Outside work, he's into gaming, volleyball, and a good beer.
Best Practices on Autopilot: How the Very Good AI Flutter Plugin Makes AI Write Code Your Way
AI coding assistants are powerful, but they don't know your team's conventions — until you teach them.
The Very Good AI Flutter Plugin is a Claude Code plugin that injects production-grade Flutter and Dart best practices directly into the AI's context. Covering 12 skill areas — from BLoC state management and layered architecture to accessibility, Material 3 theming, GoRouter navigation, testing, and security — it ensures every line of AI-generated code follows VGV standards out of the box. This talk explores how each skill provides architecture patterns, code examples, testing strategies, and anti-patterns to avoid.
We'll also cover MCP integration with Very Good CLI for project scaffolding and license auditing, and the automatic hooks that run dart analyze and dart format on every file edit.
Pigeon Over the Wire: Type-Safe Native Communication in Flutter
Every Flutter plugin lives or dies at one seam: the boundary between Dart and native code. Most of us cross it with MethodChannel, which trades away everything Dart's type system gives us. Method names become magic strings that fail at runtime. Arguments become hand-rolled maps. Return types become hopes. Multiply that across iOS, Android, and desktop, and one plugin becomes three fragile contracts that drift apart the moment someone adds a field.
This talk is about Pigeon, the Flutter team's code generator that turns the native boundary into a single type-safe contract: write one Dart interface, and Pigeon generates the Dart, Swift, and Kotlin sides in agreement. We'll cover both directions of communication, async calls and error propagation, and what Pigeon deliberately can't do. The takeaway: the native boundary should fail at compile time, not in production — so start type-safe rather than graduate to it.
Building an Integrated AI-Powered Development Stack
Most teams adopt AI coding assistants and discover the same thing within a month. The output is fast, the output is plausible, the output is wrong in ways that take longer to fix than writing it yourself. The gap isn't the model. It's everything around the model. This talk is about what fills that gap.
We'll walk through the architecture of a production AI engineering system, drawn from our experience building, shipping, and rebuilding our own at Very Good Ventures. The frame is layered. At the bottom, contextual knowledge that teaches the AI your standards. In the middle, structured workflows that replace ad-hoc prompting with brainstorm, plan, and build phases. At the top, guardrails through hooks, MCP integrations, and automated analysis that catch problems before they reach a PR. Each layer answers a different failure mode, and skipping any of them is where most teams stall.
The talk covers the design decisions that actually mattered. Why we moved from monolithic prompts to discrete skills. Why brainstorming and planning needed to be separate phases instead of one step. What we got wrong about tool-agnosticism the first time, and what changed when we tried again. How to know when a convention belongs in AI context versus a linter versus a code review checklist. The patterns generalize beyond Flutter, but we'll ground every principle in real examples from Flutter work so the lessons are concrete, not abstract.
You'll leave with a model for how the pieces fit together, a clearer sense of which layer to invest in first for your team, and a few opinionated takes on what to avoid.
Key Learnings
- A layered model for AI-assisted development, contextual knowledge, structured workflows, and automated guardrails, and why each layer matters
- How to decompose a feature into brainstorm, plan, and build phases that produce better output than direct prompting
- How to encode team standards as contextual skills the AI actually reaches for, versus standards that get ignored
- Where hooks and MCP integrations belong in the loop, and where they add friction without value
- Practical guidance for sequencing adoption so quality and velocity move together instead of trading off
Building a Production Viral Video Feed in Flutter
Video playback in Flutter looks simple until you ship it. The framework gives you the building blocks, but a production-quality video feed demands answers to questions you didn't know to ask.
When we built Divine, a short-form video app, we hit every wall the ecosystem has to offer. This talk covers the architecture that got us past them.
Key takeaways:
- How Flutter's video player ecosystem actually stacks up, and why the choice of player library shapes everything that follows
- An architecture pattern for managing native video resources in a scrollable feed without hitting device limits
- Preloading and caching techniques that make swipe transitions feel instant
- Real-world platform divergence between iOS and Android that you won't find in documentation
DevFest.cz 2025 Sessionize Event
2025 | Berlin | fluttercon Sessionize Event
DevFest Pisa 2023 Sessionize Event
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GDG DevFest Kenya 2020 Sessionize Event
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