Autumn's here, and so is a fresh edition of my newsletter, packed with the latest in Flutter and AI:
- Riverpod 3.0
- The ultimate guide to migrating to Flutter
- Liquid Glass: what it means for you
- AI rules for Flutter and Dart
- Latest from the Flutter community
- Best AI Coding Agents and my takeaways
Let's dive in!
Riverpod 3.0 is here
The long-awaited Riverpod 3.0 has landed!
This new release brings experimental support for offline persistence and mutations, plus quality-of-life improvements like automatic retry, Ref.mounted, generics support, new testing utilities, and more.
Get a full summary of the changes here:
But what about migrating from Riverpod 2.x? After updating some of my own apps, I found some easy changes (like replacing .valueOrNull with .value). But I also hit unexpected bugs, such as this one.
While the official docs cover migrating from 2.0 to 3.0, it might be wise to hold off until the initial bugs are ironed out.
Migration to Flutter: The Ultimate Guide
Flutter has a strong value proposition: high-quality, multi‑platform apps from a single codebase. But for enterprises with legacy codebases, the path to migration is rarely simple or certain.
This guide by LeanCode is a practical, battle‑tested playbook for navigating that complexity, born from real-world transformations at Virgin Money, Crédit Agricole Bank Polska, Sonova, NOS, and other large-scale programs.
I did get early access to the guide, and I highly recommend it. You can get your copy here:
Liquid Glass is here: what it means for you
Apple's latest device lineup is out, bringing iOS 26 and its new Liquid Glass UI.
But what does this mean for us Flutter developers? And how hard will it be to update our iOS apps to Liquid Glass?
According to this GitHub issue, official work on the iOS 26 UI will only begin after the core Material and Cupertino libraries are moved outside the Flutter SDK.
Meanwhile, the Serverpod team had an idea: leverage Platform Views and method channels to bring pixel-perfect Liquid Glass UI to Flutter on iOS.
The result is a new package called cupertino_native, which works well and is very performant (note: this was vibe-coded with Codex and GPT-5, so it's not ready for production yet).
AI rules for Flutter and Dart
When building with AI, clear rules and guidelines are crucial for enforcing best practices in code style and design.
Good news: the Flutter team has released an official set of AI rules for Flutter and Dart. This includes a rules.md template that you can feed directly to your AI model.
Find all the usage instructions here:
Just like you'd start with a default set of lint rules for your projects, you can view this as a "sensible defaults" template for Flutter AI coding, which can be further customized to your own project needs.
Latest from the Flutter community
As I write this, FlutterCon EU is taking place in Berlin. I couldn't make it this year, but I'm keen to catch up on the session videos once they're available.
The Flutter community has also been active on r/FlutterDev and the official Flutter forum. Beyond the usual Google Play Store complaints, some cool posts can also be found, like this one: 👇
📱 Flutter vs Web Wrappers
Can you spot a web wrapper masquerading as a native mobile app? Common giveaways include slow loading screens, sluggish UX, and poor offline support.
Take the HomeDepot app, for instance – it's a web wrapper for their official site. While browsing r/FlutterDev, I found this post a developer who rebuilt it in Flutter. They then shared this cool video showing the two apps side-by-side. What a difference! 💪
Check out the full post:
Latest AI News
The past month in AI has been wild! Here's my rundown of the most relevant news.
🤖 Best AI Coding Agents with some crazy upsets
We've seen new, high-performant coding LLMs emerge, including GPT-5-codex, Qwen3 Coder, and Grok Code Fast 1.
The AI coding tool landscape is also expanding, with Codex, Qwen code, Roocode, and Claude code router (which enables selecting different models under the same Claude Code CLI), among many others.
So, which ones should you use for AI-assisted coding? This video offers an excellent summary, complete with benchmarks from real-world evaluations:
Here are some key takeaways, complemented with observations from my own experience:
- The gap at the top is narrowing; Claude Code is no longer the only game in town.
- Model choice matters a lot. It's crucial to balance speed, cost, and output quality.
- If you want to get good results consistently, prompting and context engineering remain as important as ever.
Model selection also hinges on another critical factor: reliability. 👇
⚠️ AI Service Issues
While leading AI companies battle for model and tool supremacy, they've also been experiencing significant service issues, as confirmed by the OpenAI status and Claude status pages.
Anthropic's situation has been particularly problematic, leading them to share this recent postmortem:
This is the current reality: models are improving, demand is soaring, and AI companies are struggling to guarantee high uptime. Many users on monthly subscriptions have already switched vendors, and I expect this trend to continue.
While uptime guarantees may be less concerning during development, they're absolutely critical when AI is built into production systems (think automotive or medical applications). It will be interesting to see how companies will account for and mitigate downtime.
Until Next Time
Having recently moved countries, some things are taking longer than expected, so I'm not yet back at full speed.
That said, I've started some new projects, which are proving to be a great way to experiment with the latest AI tools.
Despite their unreliability, I'm using these tools for increasingly complex tasks, including planning, refactoring, test generation, and more.
Keep an eye on my YouTube channel; I'll be sharing some agentic AI coding videos soon!
Happy coding!
