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Flutter Dev

Flutter Dev

Technology, Information and Internet

Mountain View, California 116,076 followers

Google's framework for building beautiful, natively compiled, multi-platform applications from a single codebase.

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Google’s UI toolkit to build apps for mobile, web, and desktop from a single codebase.

Website
https://flutter.dev/
Industry
Technology, Information and Internet
Company size
10,001+ employees
Headquarters
Mountain View, California
Type
Public Company

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  • Flutter Dev reposted this

    View profile for Nozomi Koborinai
    Nozomi Koborinai

    Sr.Specialist / SRE

    TerraDart v0.34.0 is out. Two things kept blocking a full-stack Dart workflow: 1. IaC still started with "install an engine, then learn another language." 2. Teams already on Terraform could not try a typed Dart stack without throwing that away. So this release is a CLI, not just a library. terradart init → a typed Dart stack → validate / plan / apply - OpenTofu is managed. Nothing extra to install. - Already on Terraform? --engine terraform - dev / stg / prd, with typed outputs a Flutter app can consume - AWS: the hashicorp/aws provider, as typed Dart, region per environment The bet is that infrastructure can live next to the app, in the same language, without forcing a migration. Docs, GitHub, and the install command are in the first comment. Thanks to Abdallah Shaban and the Flutter Devs and Dart teams for the ecosystem this sits on. #Dart #Flutter #Terraform #AWS

  • Flutter Dev reposted this

    View profile for Viktor Lidholt
    Viktor Lidholt

    In 2016, I wrote the very first Flutter game while working at Google. Now, it took less than a day to remaster it for modern Flutter. 🤯 Flutter has come a long way, and is now a serious contender in the game-space! 🕹️ Read how I did it (and how you can build your own game). Link in comments.

  • Flutter Dev reposted this

    View organization page for Very Good Ventures
    Very Good Ventures

    19,390 followers

    A GenUI dashboard went from 54 seconds to 3.8, at zero generation cost, with the same completion rate. Andrew King plugged TypeSafe AI's Jev into a plant-monitoring demo. Jev is a decision model: you hand it a state and typed questions, it returns typed answers with confidence scores, in parallel. It generates no text and never touches a measured value. What he caught early was the tell. The data was right and the reasoning was right, but the frontier model was making zero tool calls. The data was already fetched. The model was retyping numbers into a template and deciding what goes on screen, one token at a time. That's classification work. Jev answers about 100 closed questions in half a second. Code fetches the data, assembles from a pre-approved component catalog, and fills in every number at render. Most of the 3.8 seconds is the query. The limits are real: synthetic plant data, prose from templates, and anything outside the catalog falls back to an LLM. In a regulated environment that hard ceiling on what can render is a feature. Before swapping models, list every decision your composer makes. If more than half are closed questions with a fixed answer set, you're paying generation prices for classification. Full breakdown with benchmark table at the link in comments! #vgv #jev #genui

    • Flow diagram titled "Decide first, then compose." A plain-language question goes to a decision model that routes it with about 100 closed questions in 0.5 seconds. Code fetches data from the historian and turns numbers into words, the decision model picks the layout in 140 to 370 ms, code assembles the widgets in under 10 ms, a catalog gate validates them, and the client binds values at render time. Questions the decision model can't classify fall back to a language model that composes as text in about 6 seconds through the same gate. The fast path runs end to end in about 1 second with 0 tokens generated.

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