StreamGrid – Open-Source Multi-Stream Viewer

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I’ve been working on an open-source project called StreamGrid. It’s a desktop app (Electron + React + TypeScript) that lets you watch and manage multiple streams in a grid you design yourself. The goal is to give complete control over how streams are arranged. For example, you can set up one big “main” stream surrounded by smaller feeds, or just lay everything out in an even grid. Drag and drop to rearrange, resize on the fly, and save/load different grid setups for different use cases. Features Flexible layouts: create any arrangement of streams you want (2, 5, or more). Stream management: add/remove streams, custom names/logos, save and export your setups. Grid management (new in v1.2.0): save multiple configurations, switch between them, rename and organize presets. Platform support: YouTube (video, live, shorts), Twitch (live with chat), HLS, MPEG-DASH, and now local video files. Chat integration: YouTube and Twitch chats in draggable, resizable windows. Performance: virtualized rendering for smooth 50+ stream grids, player pooling for lower memory, web workers for layout, debounced state updates, lazy chat loading. Cross-platform: available for Windows, macOS, and Linux. What’s new in v1.2.0 Virtual rendering and player pooling for much smoother performance. Grid management system with save/load. Local file playback support. Improved error handling and recovery. Cross-platform packaging with electron-builder (NSIS, DMG, AppImage, DEB, RPM). Try it Download prebuilt binaries from Releases: [GitHub Releases link] Or build from source (Node.js 18+, npm 9+). Repo: https://github.com/LordKnish/StreamGrid I’d be interested in feedback from anyone who’s tried building video-heavy Electron apps, or who has ideas for making the system more efficient.

Visit product View on Hacker NewsAug 28, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apps · Missing: agents, agent, cursor

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, video · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, efficient, builder · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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