🤝 Open to Collab
Andrei Ovcharenko
Kreout
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
upvoted an article 1 day ago
We Fine-Tuned a Memory Classifier for Our Agent. Here's Why We Didn't Ship It repliedto ZennyKenny's post 1 day ago
Trying to train a locally deployable model on a LoRa adapter that's just wicked strong at running sec evals on PRs because these general purpose code review solutions always seem to miss glaring security stuff.
The clear finding from the v1 training run was that "less code = more secure" lmfao.
Model basically scores functions better the less stuff they do. It's right I guess in a kind of demented way. Pack it up boys, we've got the solution. repliedto animesh1986's post 3 days 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-agentOrganizations
