🤝 Open to Collab

Andrei Ovcharenko

Kreout

1 3

https://krot.name/

AI & ML interests

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.

Recent Activity

repliedto animesh1986's post about 22 hours 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

repliedto SeaWolf-AI's post 4 days ago

No GPU. No cloud. Just your phone. Most “small” models still beg for a datacenter. Ours fit in your pocket. VIDRAFT POCKET is a family of GGUF models that run on an iPhone and on a GPU-free PC, straight out of the box with llama.cpp, Ollama, LM Studio, and PocketPal. Seven weeks after launch they crossed 1 million downloads. Today the family is past 2.81 million and still climbing, every day. Three models, one pocket: 🟢 POCKET-35B — a 35B mixture-of-experts that runs on an iPhone and on a PC with no GPU. Balanced power. → https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF 🔵 POCKET-26B — a Gemma-4 based 26B MoE, tuned for on-device Korean. Lightweight and fast. → https://huggingface.co/FINAL-Bench/POCKET-26B-GGUF 🟣 POCKET-Darwin-180B — a 180B flagship running on a laptop in 4-bit GGUF. The datacenter, on your desk. → https://huggingface.co/FINAL-Bench/POCKET-Darwin-180B-GGUF The whole family lives here: https://huggingface.co/FINAL-Bench Try our models live in the browser: https://huggingface.co/spaces/FINAL-Bench/POCKET-Image-Studio Private by default. Offline by design. Yours to run. #OnDeviceAI #LocalLLM #llamacpp #GGUF #VIDRAFT #POCKET

reacted to crp4222's post with 🔥 5 days ago

I trained a small PII detector for PrivAiTe, my local proxy that masks personal data before a prompt reaches an LLM: crp4222/privaite-pii-minilm. It is a multilingual MiniLM (117M parameters, 15 types from names and contacts to IDs, bank details and secrets), trained only on datasets whose licenses allow commercial use, released under BSD 3-Clause, with PyTorch, ONNX fp32 and int8, and Core ML builds. On 9 public test sets it never saw, it masks as many person names as the best of GLiNER PII, NVIDIA GLiNER-PII, GLiNER2, Piiranha and OpenAI privacy-filter, or more, on 6 of them, usually with higher precision. It trails GLiNER PII on short lowercase voice requests and NVIDIA on court rulings. It is also 2.5 to 7 times smaller and much faster: 9 ms per 1,000 characters on an M1 Pro Neural Engine and 23 ms on CPU in int8, against 186 to 621 ms for the others on the same machine and documents. Numbers, test sets and limits are on the card: https://huggingface.co/crp4222/privaite-pii-minilm

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