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After a year of nights and weekends, I shipped Lucidcast — an Android podcast player I built because every mainstream app I tried was missing the same three things. The app went live on Google Play this week. This post is for indie devs thinking about shipping their own Android project — six lessons I learned the expensive way. What Lucidcast does (90-second context) A podcast player with on-device AI: Whisper transcribes downloaded episodes locally (audio never leaves the phone) AI episode summaries via Gemini, with a live progress ring "Podcast" wake-word voice commands that work on the lock screen and in Android Auto Smart Pause auto-pauses on loud noises or when someone talks to you Plus the usual: chapters, transcripts, value tags (Podcasting 2.0), 22 languages, Android Auto, live radio, no accounts, no ads. Free includes the full podcast player. Pro one-time unlocks the audio intelligence engine. AI Pack subscription enables Whisper + summaries. Made in EU by a one-person Czech indie team (Prismatic s.r.o.). Play Store | Landing OK, lessons. 1. On-device Whisper is harder than it looks (NDK 29 patch needed) I wanted local transcription so audio doesn't leave the device. whisper_ggml is the only Flutter binding I found that works end-to-end. Catch: it needs Android NDK 29.0.13113456 while most other plugins are still on 27. Setting ndkVersion = "29..." in android/app/build.gradle.kts works for new builds, but the plugin's auto-detected version was sometimes off — needed a small patch script ( flutter/tool/patch_whisper_ggml.sh ) to enforce it during CI. Battery cost is real: I gate transcription to charging-only mode by default with a configurable battery threshold (10-80 %, default 50 %). When the user toggles "transcribe downloaded episodes," I queue the work but only execute when the phone is plugged in. Users on r/podcasts would otherwise notice a 5-10 % overnight battery drain. 2. Sharing the microphone is a contract nobody documents Smart Pause uses noise

TL;DR: Your AI coding assistant is a generalist. It writes Flutter that looks right but quietly reaches for 2022 patterns. Agent Skills are a new, official way (from the Dart and Flutter teams) to hand your agent task-specific, battle-tested workflows it loads on demand. Two repos, flutter/skills and dart-lang/skills , ship ready-to-use skills for responsive layouts, routing, testing, localization, static analysis, and more. Install in one command: npx skills add flutter/skills --skill '*' --agent universal npx skills add dart-lang/skills --skill '*' --agent universal This post breaks down what they are, how they differ from rules files and MCP, the full catalog, what a real skill looks like under the hood, and whether they actually move the needle. (Spoiler: mostly yes, with one honest caveat.) Let me tell you about a fight I have almost every day. I ask my AI agent to make a screen adapt to tablets. It confidently hands me code that switches layout based on MediaQuery.orientationOf(context) . It looks clean. It compiles. It even runs . And it's wrong, because device orientation has nothing to do with how much window space your app actually has on a foldable, in split-screen, or in a resizable desktop window. The model isn't dumb. It's a generalist trained on a giant pile of Flutter code, much of it old. And here's the uncomfortable truth the Flutter team said out loud when they launched this feature: Flutter and Dart ship new features faster than LLMs can update their training data. That lag has a name, the knowledge gap , and it's why your agent keeps writing rookie Flutter with a straight face. Agent Skills are the Flutter team's answer to that gap. I've been running them on real projects, and they're one of the few "AI workflow" things in 2026 that earned the hype instead of borrowing it. Let's get into it. Table of Contents The real problem: your AI is a generalist What are Agent Skills, exactly? Skills vs Rules vs MCP: who does what The full catalog: every of

Up front, so there's no confusion: the app I'm describing (Nightmare TV) is a player only . You bring your own M3U / Xtream Codes playlist — it ships with no channels and no content. This post is about the playback engineering, not about where streams come from. Think "VLC for IPTV," not a content service. The problem that started it I watch a lot of live content on my PC — sports, mostly. And every IPTV player I tried on Windows fell into one of two buckets: An Android app running in an emulator. TiviMate and the good mobile players are Android-only, so on a desktop you end up in an emulator or a VM. Input lag, no real HDR path, fans spinning. A thin ExoPlayer / libVLC wrapper. These run natively, but most of them treat HDR as "pass the HDR10 metadata to the display and hope." On an SDR panel — or even a lot of HDR panels — bright skies in a football match blow out to a flat white blob, and 4K HEVC with a DTS track stutters because the decode path isn't doing what you think it is. I wanted the thing that didn't exist: a native Windows player with a reference-grade video path. So I built it on libmpv — the same playback core mpv uses — with a Flutter desktop shell on top for the UI. This post is the part I actually find interesting: the HDR tone-mapping pipeline. Why HDR "just passing through" isn't enough HDR10 content is mastered in the PQ (ST.2084) transfer function against a mastering display — often 1000 nits, sometimes 4000. Your screen is whatever it is: a 350-nit SDR laptop, a 600-nit "HDR400" monitor, an 800-nit OLED. If you map PQ straight to the panel, everything above the panel's peak just clips — all the highlight detail collapses to maximum white. Tone-mapping is the process of intelligently compressing the mastering range into the display range so you keep highlight detail instead of clipping it. The naive version (a fixed curve, or clipping) is what most wrapper players ship. The good version adapts to both the content and the display. The pipeline H

One unified API for Gemini, OpenAI, Anthropic, HuggingFace, Ollama, on-device Gemma, and GGUF models — with tool calling, memory, and safety guardrails built in. The Problem Building AI agents in Flutter is fragmented. Every provider has a different API shape. There's no standard way to switch between cloud and on-device inference. Tool calling, persistent memory, and safety guardrails are always custom implementations. The result: developers rebuild the same plumbing for every project. What It Is genesis_ai_sdk is a universal Flutter SDK for building AI agents that run locally and in the cloud. One clean API. Seven providers. Zero vendor lock-in. Supports: Gemini (Google) OpenAI (GPT-4o) Anthropic (Claude) HuggingFace (any public model, no download needed) Ollama (local server, no API key) On-device Gemma (fully offline) On-device GGUF via llama.cpp (fully offline) Switch providers by changing one line. Your agent code stays the same. Quick Start — 10 Lines of Code import 'package:genesis_ai_sdk/genesis_ai_sdk.dart' ; final agent = GenesisAgent ( provider: GeminiProvider ( apiKey: 'YOUR_KEY' ), systemPrompt: 'You are a helpful assistant.' , tools: [ GenesisTools . calculator , GenesisTools . dateTime ], ); final response = await agent . chat ( 'What is 1337 * 42, and what day is it?' ); print ( response ); The agent figures out which tool to call, executes it, and returns the answer. No prompt engineering needed. The Features That Actually Matter Real Tool Calling — Not Just Text The ReAct loop is fully implemented. The agent reasons → calls tools → observes results → repeats until it has a complete answer. An onStep callback fires for every intermediate step — perfect for building a "thinking…" UI. Custom tools are five lines: final weatherTool = GenesisTool . define ( name: 'get_weather' , description: 'Returns weather for a city.' , params: { 'city' : ToolParam . string ( description: 'City name' )}, execute: ( args ) async = > fetchWeather ( args [ 'city' ]), )

I was looking at my bank statement one day and realised I was paying for 4 things I completely forgot about. Combined it was around 40 euros a month just silently leaving my account. I'm a 17 year old developer from Cyprus and I spent the last few weeks building Capsule, a simple subscription tracker that shows you everything you pay for, alerts you before renewals, and tracks how much you save by cancelling things. No bank connection required. You just add your subscriptions manually. Privacy first. It's not on the Play Store yet but the waitlist is live at capsule.crickdevs.com if anyone wants early access. Would genuinely love feedback from real people before I launch.