Ji

JibarOS, a shared inference runtime for Android

Hacker News

JibarOS, a shared inference runtime for Android

JibarOS is an Android 16 fork I’ve been building to explore a shared runtime for on-device inference. It adds: a system service in system_server a native daemon pluggable inference backends a Binder AIDL for capability-based calls across text, audio, and vision The goal is to centralize things that are otherwise handled independently by apps, like model residency, scheduling, fairness, and backend routing. The current interface exposes 12 capabilities including completion, translation, rerank, embeddings, transcription, synthesis, VAD, OCR, detection, and description. Repo: https://github.com/Jibar-OS/JibarOS Interested in feedback from anyone who has worked on Android framework/services, ML runtimes, or device-level resource scheduling.

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
81%81% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface, calls · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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