Jinfer – AI inference engine for the JVM. AI in a jar

Hacker News

Hi HN, Some would call AI on the JVM quixotic. And so, Quixotic AI was born. jinfer is an inference engine for the JVM: chat, vision, audio, embeddings, reranking, and TTS. No Python runtime, no ONNX, no Docker containers, no sidecar process, no IPC. Finally, AI in a jar. The stack underneath is built for the JVM rather than bolted onto it: toknroll: pure-Java tokenizers, zero dependencies gguf / safetensors: read and write llama.cpp and HuggingFace model formats jam: quantized matmul kernels, competitive with llama.cpp on CPU jota: Tensor API targeting Java, C, CUDA, HIP, Metal, OpenCL, and Mojo It ships with integrations for Spring AI and LangChain4j, and is compatible with GraalVM Native Image for low-overhead, self-contained binaries with millisecond startup. This is an early release (CPU only): I'd especially like feedback on the API surface. Site: https://qxotic.ai Repo: https://github.com/qxoticai/qxotic

Visit product View on Hacker NewsSep 15, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, dock, open · Missing: mac, agents, macos

83%83% 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.

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, 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.

TrustMRRLess likely to generate early MRR · 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 · Missing: plus, platform, intuitive

29%29% 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

26%26% 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, audio · Missing: web3, crypto, cryptocurrency

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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