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@Rcidshacker
Rcidshacker
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Ruchit Das Rcidshacker

Teaching AI agents to finish what they start. ☕ Deep in the rabbit hole of agentic systems, building verified, wonderful things with Python, FastAPI, LangGraph.

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Rcidshacker /README.md

About

AI engineer focused on agentic systems — multi-agent orchestration, local-first RAG, and the infrastructure that makes LLM agents actually reliable. I build across the stack: Python backends, LangGraph pipelines, React/Next.js frontends, and the occasional Rust tool.

  • 🔭 Now: neuro-symbolic RAG, multi-agent trading systems, cross-lingual speech ML
  • 🌱 Practicing: Harness Engineering — treating the agent's environment as the product
  • 💬 Ask me about: RAG, LangGraph, MCP servers, multi-agent design

How I build — Harness Engineering

"When things fail, don't swap the model — fix the harness."

A harness is everything outside the model weights. Same model, different harness, fundamentally different results — so I engineer five subsystems around every agent I run:

Subsystem What I ship
Instructions Short, routing-oriented AGENTS.md — a map, not a manual
Environment init.sh, lockfiles, ≤3-min rebuild from scratch
State progress.md + git checkpoints — the repo is the spec
Execution WIP=1: one feature active, next unlocks only after E2E passes
Feedback Executable definition of done — verification decides, not agent confidence
objective (AGENTS.md) → init → run task ⟲ runtime feedback
 → verify: lint → tests → full E2E (fail → loop back)
 → clean handoff (progress recorded, junk deleted, git green)

Mantras I work by: done = verification passed, not "code written" · someone in the crew must not believe you · do less but finish · give the agent a methodology, not a task.


Featured work

Neuro-symbolic RAG agent, fully local. Hybrid memory (vector + knowledge graph), HyDE retrieval, self-correcting web fallback.
Python · LangGraph · DSPy · Ollama

Four agents — research, write, review, publish — that ship finished tech articles to Dev.to autonomously.
Python · LangGraph · Llama 3.1 · Flutter

Cross-lingual Parkinson's detection from voice. 88-feature eGeMAPS pipeline, 7-model evaluation across Spanish/English/Italian datasets.
Python · scikit-learn · openSMILE

Multi-agent trading platform where 8 investor personas debate every trade before execution.
Next.js · TypeScript · Python

🎵 Symphony

AI-powered terminal music player for developers who never leave the shell.
Rust

MCP server giving Claude full read/write access to an Obsidian vault — notes as agent memory.
TypeScript · MCP SDK

33 more projects — agents, trading, CV, full-stack — on the repos tab.

Stack

Tech stack

+ LangGraph · DSPy · Ollama · MCP · Neo4j · pgvector


Stats

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  1. Air-canvas-new Air-canvas-new Public

    Unleash your inner artist without a single brush! 🎨 Air Canvas lets you draw in thin air using just your hand gestures, turning your webcam into a magical canvas.

    Python

  2. FarmAI FarmAI Public

    FarmAI is an AI-driven pest management system for custard apple farming. It uses CNNs for disease detection, XGBoost for pest forecasting, and RL for spray scheduling. Built on FastAPI and React, i...

    Python

  3. local-clara-agent local-clara-agent Public

    A local Neuro-Symbolic RAG agent inspired by Apple's CLaRa. Built with the "2026 Stack" (DSPy, LangGraph, Ollama), it features Hybrid Memory (Vector + Knowledge Graph), HyDE retrieval, and self-cor...

    Python 5

  4. Mission-control---Yes-Bank Mission-control---Yes-Bank Public

    A high-performance React dashboard for real-time inventory tracking and analytics, designed with a premium dark UI to streamline business operations and data visualization.

    TypeScript

  5. Multi-AI-Agent Multi-AI-Agent Public

    🤖 Intelligent multi-agent system built with LangGraph & Llama 3.1 that automates tech blog creation. Four AI agents collaborate to research, write, review & auto-publish to Dev.to. Features modern ...

    C++

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