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Project Cherub – TempleOS Fork. Early Build (ISO) & Future Plans

Hacker News

Project Cherub – TempleOS Fork. Early Build (ISO) & Future Plans

Hello! I, Rubinosław from REParadoxy, I'm presenting Project Cherub - a modernized version of TempleOS, very well known operating system made by Terry A. Davis. My vision is to create a minimalist, fully-functional and secure operating system. For home users, for programmers, for artists, for everyone! Where THE USERS matter the most! I've already changed FPS rate from 30 to 120 via modifying "KernelA.HH.Z" file, a part of the system kernel. I've changed WINMGR_FPS in that file from "(30000.0/1001)" to "(120000.0/1001)", and WINMGR_PERIOD from "(1001/30000.0)" to "(1001/120000.0)". I've also changed the system name from "TempleOS" to "Project Cherub". As I said - its just a very early build. But my plan is to add also 32-bit colors support, support for SATA AHCI, NVMe, Blu-ray, videos, GPU and USB. But I'll keep the 640x480 screen resolution, you know why. *THERE WILL BE MONTHLY PROGRESS UPDATES ON YOUTUBE AND LIVESTREAMS OF OSDEVING, SO YOU SHOULD SUBSCRIBE! https://www.youtube.com/@REParadoxy** Full presentation of an early build with 120 FPS, future plan, and installation showcase: https://www.youtube.com/watch?v=NNq_4vG5Src More information: https://github.com/Rubinoslaw/Project-Cherub - GitHub repository (with ISO!)

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Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, month, monthly · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: sde · Missing: arr, mrr, revenue
15%15% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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