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18 following Java backend development, Spring Boot, Kafka, infrastructure and automation. Interested in local AI tools and practical developer workflows. Open-source developer tools: https://github.com/krotname.
replied to animesh1986 'spost 1 day ago I built a Quantum Information Agent 🔬, an LLM agent for photonic quantum information that you can trust with numbers.
Ask in plain English, e.g. “Apply global depolarizing noise with p = 0.3 to phi+ and compare it with local depolarizing noise on both photons.” The agent plans the work, calls tested physics tools, and answers with tables and figures.
What makes it different:
• The LLM never computes a number. 32 tools (NumPy/SciPy) do the physics, covered by 638 tests and 30 analytic checks.
• An answer checker compares the model’s text with the tool results before you see it. Unsupported numbers are sent back, and false physics statements are corrected or removed (e.g. an “azimuth” for circular light, CHSH values labelled CHSH/2, wrong comparisons between states).
• Open models through HF Inference Providers (DeepSeek V4 Flash, gpt-oss-120b), with free fallbacks and a daily cost cap.
• A Workbench tab runs the same tools without any model.
Built with LangGraph + Gradio. I’d love feedback, especially from people building agents for scientific domains.
🔗 Demo: https://huggingface.co/spaces/animesh1986/quantum-information-agent
💻 Code and tests: https://github.com/animeshQKD/quantum-information-agent replied to maratos 'spost 1 day ago LinnetTTS: a 41 M-parameter English text-to-speech model with voice prompts that streams from the first 80 ms and runs in real time on one phone CPU core. Code Apache-2.0; weights under the Linnet Weights License (free to use in your own products, commercial included).
Same machine, same three texts, each model through its own Python package. Speed only, quality not measured:
- Linnet 41 M: RTF 10.6 % on one core (3.4 % on four), first audio 87 ms, 187 MB RSS, voice prompts, streams every 80 ms
- pocket-tts 100 M: 23.8 % on two cores, 968 MB, voice prompts, streams every 80 ms
- Kokoro-82M: 51.7 %, 1.2 GB, whole utterance per call, 8 languages
- Piper medium 16 M: 5.8 %, 503 MB, no voice prompts, per-sentence chunks
On phones: iPhone 13 Pro Max 5.5 ms per 80 ms step, iPhone 11 7 ms, Snapdragon 662 14 ms on four cores.
🎧 Hear the same sentence from all four, side by side: the "Compare" tab of the demo Space https://huggingface.co/spaces/VoxRT/linnet-tts (type your own text for Linnet there too)
📦 Weights and model card: https://huggingface.co/VoxRT/linnet-en-35m
🦀 Code, Rust runtime, bindings for Linux C, Python (pip install linnet-tts), Android and iOS, and the benchmark script: https://github.com/VoxRT/LinnetTTS
📊 Method: https://github.com/VoxRT/LinnetTTS/blob/main/docs/benchmarks.md
📝 Write-up with the trade-offs and the samples: https://huggingface.co/blog/VoxRT/small-cpu-text-to-speech-models-compared-piper-kok
📚 Collection of small CPU TTS models and their demos: https://huggingface.co/collections/VoxRT/on-device-tts-small-models-that-run-on-a-cpu-6aec390f2f84a4407d227cf4
English only for now; no text normalisation, so write numbers as words. If it is useful to you, a like on the model or the Space helps other people find it. Questions welcome.
replied to maratos 'spost 3 days ago LinnetTTS: a 41 M-parameter English text-to-speech model with voice prompts that streams from the first 80 ms and runs in real time on one phone CPU core. Code Apache-2.0; weights under the Linnet Weights License (free to use in your own products, commercial included).
Same machine, same three texts, each model through its own Python package. Speed only, quality not measured:
- Linnet 41 M: RTF 10.6 % on one core (3.4 % on four), first audio 87 ms, 187 MB RSS, voice prompts, streams every 80 ms
- pocket-tts 100 M: 23.8 % on two cores, 968 MB, voice prompts, streams every 80 ms
- Kokoro-82M: 51.7 %, 1.2 GB, whole utterance per call, 8 languages
- Piper medium 16 M: 5.8 %, 503 MB, no voice prompts, per-sentence chunks
On phones: iPhone 13 Pro Max 5.5 ms per 80 ms step, iPhone 11 7 ms, Snapdragon 662 14 ms on four cores.
🎧 Hear the same sentence from all four, side by side: the "Compare" tab of the demo Space https://huggingface.co/spaces/VoxRT/linnet-tts (type your own text for Linnet there too)
📦 Weights and model card: https://huggingface.co/VoxRT/linnet-en-35m
🦀 Code, Rust runtime, bindings for Linux C, Python (pip install linnet-tts), Android and iOS, and the benchmark script: https://github.com/VoxRT/LinnetTTS
📊 Method: https://github.com/VoxRT/LinnetTTS/blob/main/docs/benchmarks.md
📝 Write-up with the trade-offs and the samples: https://huggingface.co/blog/VoxRT/small-cpu-text-to-speech-models-compared-piper-kok
📚 Collection of small CPU TTS models and their demos: https://huggingface.co/collections/VoxRT/on-device-tts-small-models-that-run-on-a-cpu-6aec390f2f84a4407d227cf4
English only for now; no text normalisation, so write numbers as words. If it is useful to you, a like on the model or the Space helps other people find it. Questions welcome.
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