Hey Flutter devs,

We’re excited to announce Appwrite 1.9, which adds full support for MongoDB as the underlying database. This release also includes a new GUI-based installation wizard to help you choose your database and configure your setup more easily.

This release marks the beginning of our recently announced partnership with the MongoDB team. It’s an important step for Appwrite and brings us closer to our vision of building a complete open source development platform that gives developers the flexibility to make their own choices.

As a next step, we plan to deepen our MongoDB integration and build on the recent TablesDB refactor in Appwrite to introduce additional database options for developers in future self-hosted and cloud releases.

You can learn more about this release on our blog: https://appwrite.io/blog/post/self-hosting-appwrite-with-mongodb

Or get started right away on GitHub: https://github.com/appwrite/appwrite

Thank you, and as always, we’d love to hear feedback from the Reddit community and answer any questions about this release or what’s coming next.

A few weeks ago I posted about Koolbase, a BaaS built for mobile developers (Flutter + React Native). Got great feedback from this community, thank you all.

Since then I've shipped:

- Analytics: funnels, retention cohorts, DAU/WAU/MAU

- Cloud Messaging: FCM push notifications via the SDK

- Onboarding checklist: cleaner first-run experience

- Flutter SDK v2.4.0 on pub.dev

- React Native SDK v1.4.1 on npmjs.com

Still free to start, no credit card required.

If you tried it before and hit a wall, would love to know what blocked you. If you haven't tried it yet, would love to know why.

koolbase.com | docs.koolbase.com

hi i'm Farook. i just love building things for others.

lately, i’ve been shipping random things to see what sticks. the latest is zpl_kit.

if you’ve ever worked with zebra printers, you know it's hell. you’re stuck writing raw strings and doing manual coordinate math. if you move one text block, you have to recalculate everything else. it’s 2026, we shouldn't be doing math to print a label.

so, i built a declarative layout engine for it. i used vibe coding to speed through the initial architecture and it turned out great.

the architecture: it’s inspired by flutter’s own layout protocol:

1. constraints down: parent tells child the max space available.
2. sizes up: child calculates its size and reports back.
3. compile: the engine takes those sizes and automatically injects the ^FO coordinates for you.

instead of raw ZPL strings, you build labels like this:

dartZplColumn(
  children: [
    ZplRow(
      children: [
        ZplExpanded(child: ZplText('SHIP FROM: $name')),
        ZplExpanded(child: ZplText('UPS', textAlign: ZplTextAlign.right)),
      ],
    ),
    ZplDivider(),
    ZplCenter(child: ZplBarcode('123456')),
  ],
)

it turns a 4-hour math session into a 5-minute UI build.

it's open source and i've been testing it on industrial GK420T printers. would love for you to try it and tell me where it breaks.

package: https://pub.dev/packages/zpl_kit 

Hey everyone,

I’ve been working on a Flutter package called SafeText for profanity filtering and phone number detection. Originally it started as a small utility (~1.7K English words, simple matching), but recently I pushed a v2.0.0 update where I:

- Expanded dataset to 55K+ words across 75 languages
- Switched to Aho–Corasick for multi-pattern matching (~20x faster)
- Added support for multi-word phrases and better normalization

What surprised me is the traction after the update, in about 24 days:
- Downloads went from ~2.3K → 3.7K/month
- Likes increased from 48 → 62

Thanks to the community for there support.

So I’m trying to take this a bit more seriously now and open it up for contributors. I’ve added a bunch of issues, including:
- Dataset validation & cleanup (duplicates / false positives)
- Performance improvements (trie build, caching, memory)
- Chunk-based filtering (for streaming input)
- Better obfuscation handling (f@ck, f u c k, etc.)
- Documentation improvements (pub.dev API docs are pretty minimal right now)

Repo: https://github.com/master-wayne7/safe_text

If anyone’s interested in contributing, whether beginner or experienced, feel free to pick up an issue or suggest improvements.

Also open to feedback on the approach/architecture.

Thanks!

Hi everyone!

I’ve been working on a project called Neomage, and I wanted to share it with the community. It’s an "Agentic" AI engine built entirely in Dart/Flutter that goes beyond simple chat interfaces.

The goal is to enable Sovereign AI Agents that can actually do things on your machine—not just talk about them.

What makes it different? It's "Clawless".

A lot of existing AI integration in mobile/desktop feels "clawed": it's a web-based chat-bot reaching down into the system from the browser or a remote API.

Neomage is different. It's Native and Clawless. The agent exists as an agent in your Flutter app, operating directly on the system.

Key Features:

• Native, Clawless Tool Execution: Seamlessly run Bash commands, manage file systems, and interact with hardware without intermediaries.
• Multi-Provider Support: Plug and play with Gemini, OpenAI, Anthropic, or local LLMs via Ollama.
• MCP Integration: Supports the Model Context Protocol for extended toolsets.
• Reactive State: Built on top of Sint & Daemon Bridge for fluid, real-time agent responses.
• True Multi-platform: One codebase for macOS, Windows, Linux, Android, and iOS.

Why "Agentic"?

Because it treats the LLM as a controller. It can reason through a task, call the necessary native tools, and update the UI reactively as it executes.

I’d love to get some feedback from the community on this direct, Clawless approach. Are you guys exploring Agentic workflows in your Flutter apps?

Check it out on

pub.dev: https://pub.dev/packages/neomage

Looking forward to hearing your thoughts!