MCP tool server
Calendar, notes, vision, memory and autonomous-loop tools with strict schema validation and a full audit trail.
Model Context Protocol · Groq LPU · Hybrid RAG · CNN Vision
A ReAct agent that searches your knowledge with dense + lexical retrieval, reads scanned images through a CNN/OCR pipeline, manages your schedule via MCP tools, and fact-checks itself before it answers.
Runs 100% in your browser. Your data and Groq key never leave your device (stored in localStorage).
Calendar, notes, vision, memory and autonomous-loop tools with strict schema validation and a full audit trail.
Dense semantic vectors + BM25/tsvector lexical search merged by Reciprocal Rank Fusion for precise context.
Denoise, contrast, Sobel conv-feature maps, adaptive threshold, ROI boxes and OCR on whiteboards and scans.
Reason → call tools (in parallel) → observe → answer, running on Groq's ultra-fast Llama models.
A second model pass checks every answer for hallucinated details and timezone/schedule mistakes.
Short-term, long-term and episodic memories recalled semantically across sessions.
Notes, events and documents as an interactive Three.js constellation linked by similarity and tags.
TypeScript MCP server, Postgres + pgvector, Python CNN worker, all via Docker Compose.
AI-written briefings, conflict radar and a free-slot finder that books focus blocks for you.
Talk to your assistant and hear answers back using the browser's speech engines.
Summaries, action items, flashcards, auto-tags and k-means topic clusters.
Ask in your language — the agent replies in kind while staying grounded in your data.
Everything below is live. Add a Groq key in Settings for the full agent; without one, chat falls back to local hybrid search.
Schedule, conflicts and top priorities from your data.
Offline k-means over note embeddings.
AI/ML & Data Engineering · Agentic AI builder
📍 Thorrur, Telangana, India
● Open to collaboration & opportunities
3D spatial digital twin with ViT/CNN vision, Graph-RAG and an agent swarm.
Visual diagnostics platform: CNN vision, LangGraph agents, hybrid RAG + Neo4j.
Multimodal distillation, QLoRA and CNN-based model routing.
Low-latency Go gateway with MCP, semantic cache and rate limiting.
LoRA fine-tuning pipeline for Llama 3 8B / Mistral 7B.
Agentic translation app with RAG glossary, OCR and voice.
cp .env.example .env # add GROQ_API_KEY
docker compose up --build
Then open Settings → set “Backend API URL” to http://localhost:4000 to route chat through the TypeScript MCP server and Postgres.
Repo → Settings → Pages
Source: Deploy from a branch
Branch: main /docs
It is pure static HTML/JS — no build step.
In this browser's localStorage only. Use Audit → Export data to back it up, or Delete all data to wipe it.
It is kept in localStorage and sent only to api.groq.com. Anyone with access to your browser profile could read it, so use a restricted key and don't enter it on shared devices.
Offline mode runs local hybrid search over your notes, documents and upcoming events. The full ReAct agent with tool calls needs a Groq key.
Canvas grayscale → contrast stretch → box-blur denoise → Sobel convolution + ReLU + pooling feature maps → adaptive threshold → Tesseract.js OCR with line regions. The Docker version uses OpenCV + Tesseract.
The browser build uses hashed word/character n-gram vectors fused with BM25, which is strong on keywords and fuzzy matches but not deep semantics. Plug a real embedding model into the backend for that.