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Project Xent – A native C++ UI framework in KBs

Hacker News

Project Xent – A native C++ UI framework in KBs

Modern UI frameworks (WinUI, Flutter, Electron) are bloated. Project Xent bridges a C++ reactive DSL directly to the host OS compositor. The "FluXent" (Windows) Demo: Binary size: ~300KB .exe (No heavy runtimes required) RAM: <15MB idle Stack: DComp + D2D + Yoga The core architecture separates shared C++ logic from platform-optimal rendering. Instead of painting widgets, Xent bridges a reactive DSL directly to the native OS compositor: - Windows: DirectComposition (FluXent) - Linux: Wayland/EFL (LuXent - Planned) - macOS: SwiftUI/Metal (NeXent - Planned) I am a high school student. I acted as the architect, orchestrating this zero-bloat vision in 11 days of work using Claude and Gemini. GitHub: < https://github.com/Project-Xent >

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Actual performance

3points
1comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, claude · Missing: agents, agent, cursor
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: widgets, way, para · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: para, gemini · Missing: supports, reddit linkedin, podcasting
34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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