Sudharshan Ramesh
FDE, ML Engineer
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About
Sudharshan Ramesh works in AI engineering and ML & data science, based in New York/ United States (New York University). FDE, ML Engineer. Public projects on GitHub include Precedent (A triage agent for robot fleet video whose memory is CockroachDB: it files what it recognises, escalates…), Peercode Plugin (Claude MCP plugin to work together with your team members over short projects, without draining context loss…) and Twin_Gate (OpenClaw is an experimental platform that builds persistent digital twins of users and lets those twins…), mostly written in Python, TypeScript and Jupyter.
Projects 6
- github.com
Precedent
A triage agent for robot fleet video whose memory is CockroachDB: it files what it recognises, escalates what it does not, and one human correction cascades to every decision built on it.
- github.com
Peercode Plugin
Claude MCP plugin to work together with your team members over short projects, without draining context loss over time and over different claude sessions.
- github.com
Twin_Gate
OpenClaw is an experimental platform that builds persistent digital twins of users and lets those twins interact autonomously with each other in real-time social contexts. It explores a fundamental question in applied AI: how do you build an agent that accumulates enough context about a person to represent them authentically across time.
- github.com
StreamPal
A multi-agent AI system that watches your live stream and reacts in real time roasting bad plays, hyping victories, triggering sound effects, and firing visual overlays. Three autonomous agents coordinate via A2A (Agent-to-Agent) communication to create a fully automated co-host experience.
- github.com
Newtrition
Prevent Public Misinformation in reddit and source research documents backed by scientific data and provide alternates
- github.com
GhostBuster
AI-powered bug detection and auto-fix pipeline. A user reports a bug through a Chrome extension capturing a screenshot, DOM snapshot, and voice description and an autonomous agent diagnoses the issue, fixes the code in a sandboxed environment, commits the fix, deploys it, and closes the GitHub issue.