{ "schemaVersion": "1.0", "spec": "https://github.com/NITISH-R-G/Portfolio/blob/main/docs/standard.md", "meta": { "generatedAt": "2026-09-07T10:53:19.024Z", "generator": "portfolio-engine" }, "person": { "name": "Nitish R.G.", "headline": "Software Engineering Student", "summary": "Software engineering student at IIT Madras (B.S. Data Science) with strong foundations in CS, data structures, algorithms, and OOP. Ranked #73 globally for autonomous AI agent creation (HackerRank Orchestrate June 2026). HackerRank 5-Star Gold in SQL. Experienced in Python, TypeScript, JavaScript, and C++ with a focus on shipping production-quality software.", "location": "India", "avatar": "assets/profile.svg", "availability": { "status": "open", "label": "Open to collaboration and opportunities", "preferredRoles": [ "Software Engineer", "ML Engineer", "Full-Stack Developer", "Data Scientist" ], "preferredLocations": [ "Remote", "India" ], "responseTime": "Usually responds within 24 hours", "currentAffiliation": "B.S. Data Science @ IIT Madras" } }, "socials": { "github": "https://github.com/NITISH-R-G" }, "statistics": [ { "id": "repositories", "label": "Public repositories", "value": 39, "kind": "derived", "connectors": [ "github" ], "display": "39" }, { "id": "stars", "label": "Stars earned", "value": 59, "kind": "derived", "note": "across all public repositories", "connectors": [ "github" ], "display": "59" }, { "id": "forks", "label": "Forks", "value": 5, "kind": "derived", "connectors": [ "github" ], "display": "5" }, { "id": "languages-used", "label": "Languages used", "value": 8, "kind": "derived", "display": "8" }, { "id": "certifications", "label": "Certifications", "value": 5, "kind": "derived", "display": "5" }, { "id": "contributions", "label": "Contributions", "value": 4480, "note": "in the last year", "kind": "fetched", "display": "4,480" }, { "id": "followers", "label": "GitHub followers", "value": 104, "kind": "fetched", "display": "104" } ], "education": [ { "id": "sri-shakthi-institute-of-engineering-and-technology-b-e-computer-science-enginee", "institution": "Sri Shakthi Institute of Engineering and Technology", "degree": "B.E. Computer Science Engineering", "location": "Coimbatore, India", "dates": { "start": { "iso": "2025-09-01", "precision": "month" }, "end": { "iso": "2029-05-01", "precision": "month" } }, "description": "Coursework: Algorithms and Complexity, Operating Systems, Computer Networks, Software Engineering, Database Systems, OOP with Java." }, { "id": "indian-institute-of-technology-madras-b-s-data-science-and-applications", "institution": "Indian Institute of Technology Madras", "degree": "B.S. Data Science and Applications", "location": "Chennai, India", "dates": { "start": { "iso": "2025-07-01", "precision": "month" }, "current": true }, "description": "Coursework: Mathematics for Data Science, Statistics, Python, Computational Thinking, DBMS, Machine Learning. Core CS: Data Structures, Algorithms, OOP, Systems Design." } ], "experience": [ { "id": "codestreak-founder", "company": "CODESTREAK", "role": "Founder", "location": "Remote", "dates": { "start": { "iso": "2025-11-01", "precision": "month" }, "current": true }, "description": "Founded and scaled an international AI/data engineering community with members from IITs, Stanford, University of Toronto, and quant firms.", "highlights": [ "Founded and scaled community to 200+ members across 15 countries", "Designed technical curriculum covering AI/ML, data engineering, and systems design", "Organized weekly technical talks and monthly hackathons", "Built partnerships with researchers at IITs and Stanford" ], "technologies": [ "Community Building", "Technical Leadership", "Curriculum Design" ], "metrics": [ { "label": "Members", "value": "200+", "note": "across 15 countries" }, { "label": "Events", "value": "50+", "note": "talks and hackathons" } ] }, { "id": "infosys-springboard-ai-data-intern", "company": "Infosys Springboard", "role": "AI/Data Intern", "location": "Remote", "dates": { "start": { "iso": "2025-12-01", "precision": "month" }, "end": { "iso": "2026-02-01", "precision": "month" } }, "description": "Completed advanced AI/data science tracks; delivered production-style assignments covering deep learning, NLP, and applied ML pipelines.", "highlights": [ "Completed 3 advanced AI/ML engineering tracks", "Built production-style NLP pipelines for text classification", "Delivered technical assignments meeting production quality standards" ], "technologies": [ "Python", "TensorFlow", "NLP", "Deep Learning" ], "metrics": [ { "label": "Tracks Completed", "value": "3", "numeric": 3, "note": "advanced level" }, { "label": "Assignments", "value": "12", "numeric": 12, "note": "production-quality" } ] }, { "id": "evoastra-ventures-pvt-ltd-data-scientist-intern", "company": "EVOASTRA Ventures Pvt Ltd", "role": "Data Scientist Intern", "location": "Remote", "dates": { "start": { "iso": "2025-09-01", "precision": "month" }, "end": { "iso": "2025-11-01", "precision": "month" } }, "description": "Executed end-to-end data science workflows: EDA, feature engineering, model prototyping, and insight generation for applied business use cases.", "highlights": [ "Led EDA and feature engineering for 3 business datasets", "Prototyped and benchmarked 5 ML models for demand forecasting", "Generated actionable insights that informed business strategy" ], "technologies": [ "Python", "pandas", "scikit-learn", "EDA" ], "metrics": [ { "label": "Models Benchmarked", "value": "5", "numeric": 5, "note": "for demand forecasting" }, { "label": "Datasets", "value": "3", "numeric": 3, "note": "end-to-end workflows" } ] }, { "id": "scj-entertainments-junior-developer-ai-ml", "company": "SCJ Entertainments", "role": "Junior Developer — AI/ML", "location": "Remote", "dates": { "start": { "iso": "2025-08-01", "precision": "month" }, "end": { "iso": "2025-11-01", "precision": "month" } }, "description": "Implemented AI/ML components within production delivery cycles; applied engineering principles to model deployment and release activities.", "highlights": [ "Integrated ML models into production delivery pipelines", "Built automated testing for model inference endpoints", "Applied TDD practices to ML deployment workflows" ], "technologies": [ "Python", "Docker", "CI/CD", "ML Deployment" ], "metrics": [ { "label": "Models Deployed", "value": "3", "numeric": 3, "note": "to production" }, { "label": "Test Coverage", "value": "85%", "note": "on ML endpoints" } ] }, { "id": "encolink-front-end-development-intern", "company": "Encolink", "role": "Front-End Development Intern", "location": "Remote", "dates": { "start": { "iso": "2025-10-01", "precision": "month" }, "end": { "iso": "2025-11-01", "precision": "month" } }, "description": "Engineered responsive, accessible frontend components; improved UI consistency and performance using modern JavaScript and CSS.", "highlights": [ "Built 15+ responsive UI components with accessibility compliance", "Improved page load performance by 30% through code splitting", "Established CSS design token system for visual consistency" ], "technologies": [ "JavaScript", "CSS", "Responsive Design", "Accessibility" ], "metrics": [ { "label": "Components Built", "value": "15+", "note": "accessible & responsive" }, { "label": "Performance Gain", "value": "+30%", "note": "page load improvement" } ] }, { "id": "suvidha-foundation-machine-learning-intern", "company": "Suvidha Foundation", "role": "Machine Learning Intern", "location": "Remote", "dates": { "start": { "iso": "2025-08-01", "precision": "month" }, "end": { "iso": "2025-10-01", "precision": "month" } }, "description": "Built and validated ML pipelines: data preprocessing, feature selection, model training, and evaluation using scikit-learn.", "highlights": [ "Built end-to-end ML pipelines for 2 classification tasks", "Implemented hyperparameter tuning reducing error by 12%", "Delivered model evaluation reports with actionable recommendations" ], "technologies": [ "Python", "scikit-learn", "ML Pipelines", "Hyperparameter Tuning" ], "metrics": [ { "label": "Error Reduction", "value": "-12%", "note": "via hyperparameter tuning" }, { "label": "Pipelines Built", "value": "2", "numeric": 2, "note": "end-to-end" } ] } ], "projects": [ { "id": "github-svg-ambiguity-bench", "name": "Svg Ambiguity Bench", "description": "An evaluation methodology for context-augmentation experiments: a format-matched control that separates whether added context helps because of its information or its format. Pre-registered instrument, mutation-audited scorer, worked example in SVG reference resolution.", "technologies": [ "Python", "benchmark", "evaluation-methodology", "llm-evaluation", "null-results", "preregistration", "reference-resolution", "reproducible-research", "small-language-models", "svg" ], "repository": "https://github.com/NITISH-R-G/svg-ambiguity-bench", "liveUrl": "https://doi.org/10.5281/zenodo.21682240", "stars": 2, "forks": 0, "primaryLanguage": "Python", "topics": [ "benchmark", "evaluation-methodology", "llm-evaluation", "null-results", "preregistration", "reference-resolution", "reproducible-research", "small-language-models", "svg" ], "date": { "iso": "2026-07-28", "precision": "day" }, "updatedAt": { "iso": "2026-07-31", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/svg-ambiguity-bench", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 51, "featured": true }, { "id": "github-leadmorph-ai", "name": "Lead Morph AI", "description": "Intelligently import and normalize CSV lead data into GrowEasy CRM format — a production-grade TypeScript pnpm monorepo (Next.js + Express).", "technologies": [ "TypeScript", "crm", "csv", "csv-import", "data-normalization", "express", "lead-generation", "monorepo", "nextjs", "open-source", "pnpm" ], "repository": "https://github.com/NITISH-R-G/LeadMorph-AI", "liveUrl": "https://github.com/NITISH-R-G/LeadMorph-AI#readme", "stars": 1, "forks": 0, "primaryLanguage": "TypeScript", "topics": [ "crm", "csv", "csv-import", "data-normalization", "express", "lead-generation", "monorepo", "nextjs", "open-source", "pnpm", "typescript" ], "date": { "iso": "2026-07-07", "precision": "day" }, "updatedAt": { "iso": "2026-08-02", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/LeadMorph-AI", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 50, "featured": true }, { "id": "github-portfolio", "name": "Portfolio", "description": "Connect your profiles — your portfolio builds itself. An open, plug-and-play portfolio engine and standard: evidence-backed, provenance-tracked, conflict-resolved. Not just Nitish's portfolio — a fork away from being yours.", "technologies": [ "JavaScript", "career-tools", "data-provenance", "developer-portfolio", "developer-tools", "github", "open-source", "portfolio", "portfolio-builder", "portfolio-generator", "professional-profile", "react", "resume", "static-site-generator", "web-extraction" ], "repository": "https://github.com/NITISH-R-G/Portfolio", "liveUrl": "https://nitish-r-g.github.io/Portfolio/", "stars": 1, "forks": 0, "primaryLanguage": "JavaScript", "topics": [ "career-tools", "data-provenance", "developer-portfolio", "developer-tools", "github", "open-source", "portfolio", "portfolio-builder", "portfolio-generator", "professional-profile", "react", "resume", "static-site-generator", "web-extraction" ], "date": { "iso": "2026-07-13", "precision": "day" }, "updatedAt": { "iso": "2026-09-07", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/Portfolio", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 50, "featured": true }, { "id": "github-roadsos", "name": "Road SOS", "description": "Offline-first, Gemma-4-powered emergency response app for road accidents in India — crash detection, AI triage, BLE mesh, and multilingual first-aid guidance.", "technologies": [ "Dart", "ble-mesh", "emergency-response", "flutter", "gemma", "india", "offline-first", "on-device-ai", "road-safety", "supabase" ], "repository": "https://github.com/NITISH-R-G/RoadSOS", "stars": 4, "forks": 2, "primaryLanguage": "Dart", "topics": [ "ble-mesh", "dart", "emergency-response", "flutter", "gemma", "india", "offline-first", "on-device-ai", "road-safety", "supabase" ], "date": { "iso": "2026-04-17", "precision": "day" }, "updatedAt": { "iso": "2026-09-04", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/RoadSOS", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 50 }, { "id": "github-screensaathi", "name": "Screen Saathi", "description": "Open-source AI copilot for Android: understands the live screen via the Accessibility API, resolves the exact UI element you need in natural language (Hindi, Tamil, English), and visually guides you to it — built for people who find modern app UIs hard to navigate.", "technologies": [ "Kotlin", "accessibility", "agentic-ai", "ai", "ai-agent", "ai-copilot", "android", "android-accessibility-service", "android-ai", "assistive-technology", "elderly-accessibility", "hindi", "multilingual", "sarvam-ai", "screen-understanding", "tamil", "voice-assistant" ], "repository": "https://github.com/NITISH-R-G/ScreenSaathi", "stars": 3, "forks": 2, "primaryLanguage": "Kotlin", "topics": [ "accessibility", "agentic-ai", "ai", "ai-agent", "ai-copilot", "android", "android-accessibility-service", "android-ai", "assistive-technology", "elderly-accessibility", "hindi", "kotlin", "multilingual", "sarvam-ai", "screen-understanding", "tamil", "voice-assistant" ], "date": { "iso": "2026-07-26", "precision": "day" }, "updatedAt": { "iso": "2026-08-15", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/ScreenSaathi", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 49 }, { "id": "github-hackerrank-orchestrate-skills", "name": "Hackerrank Orchestrate Skills", "description": "Software for HackerRank Orchestrate: an evaluator, an engineering-memory CLI that remembers what you measured and rejected, a mentor that costs a change before you write it, an adaptive AI-judge interview simulator, and 34 auto-triggering Agent Skills.", "technologies": [ "Python", "agent-skills", "ai-agents", "ai-agents-framework", "ai-evaluation", "chakra", "claude-code", "coding-agent", "cursor", "developer-tools", "engineering-memory", "hackathon", "hackerrank", "hackerrank-orchestrate", "interview-prep", "llm-agents", "llm-evaluation", "llm-tools", "open-source", "prompt-engineering", "python-cli" ], "repository": "https://github.com/NITISH-R-G/hackerrank-orchestrate-skills", "stars": 3, "forks": 0, "primaryLanguage": "Python", "topics": [ "agent-skills", "ai-agents", "ai-agents-framework", "ai-evaluation", "chakra", "claude-code", "coding-agent", "cursor", "developer-tools", "engineering-memory", "hackathon", "hackerrank", "hackerrank-orchestrate", "interview-prep", "llm-agents", "llm-evaluation", "llm-tools", "open-source", "prompt-engineering", "python-cli" ], "date": { "iso": "2026-07-31", "precision": "day" }, "updatedAt": { "iso": "2026-08-12", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/hackerrank-orchestrate-skills", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 47 }, { "id": "github-intelli-credit-v2", "name": "Intelli Credit V2", "description": "AI Credit Decision Making Engine", "technologies": [ "TypeScript" ], "repository": "https://github.com/NITISH-R-G/Intelli-Credit-V2", "liveUrl": "https://intelli-credit-v2-phi.vercel.app/", "stars": 1, "forks": 0, "primaryLanguage": "TypeScript", "date": { "iso": "2026-04-15", "precision": "day" }, "updatedAt": { "iso": "2026-09-07", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/Intelli-Credit-V2", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 46 }, { "id": "github-composio-app-research", "name": "Composio App Research", "description": "Agent-driven research pipeline for evaluating API, auth, MCP, credential access, and toolkit feasibility across 100 apps.", "technologies": [ "HTML", "ai-agents", "api-research", "composio", "developer-tools", "llm", "mcp", "product-ops", "python", "web-research" ], "repository": "https://github.com/NITISH-R-G/composio-app-research", "stars": 2, "forks": 0, "primaryLanguage": "HTML", "topics": [ "ai-agents", "api-research", "composio", "developer-tools", "llm", "mcp", "product-ops", "python", "web-research" ], "date": { "iso": "2026-08-17", "precision": "day" }, "updatedAt": { "iso": "2026-08-17", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/composio-app-research", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 45 }, { "id": "github-agent-memory-protocol", "name": "Agent Memory Protocol", "description": "Portable, git-committed memory infrastructure for AI coding agents — survives switching models, providers, IDEs, and harnesses. Adopts the official MCP memory server; doesn't reinvent it.", "technologies": [ "Python", "agent-memory", "agent-skills", "ai-agents", "claude-code", "coding-agent", "cursor", "llm-tools", "long-term-memory", "mcp" ], "repository": "https://github.com/NITISH-R-G/agent-memory-protocol", "stars": 1, "forks": 0, "primaryLanguage": "Python", "topics": [ "agent-memory", "agent-skills", "ai-agents", "claude-code", "coding-agent", "cursor", "llm-tools", "long-term-memory", "mcp" ], "date": { "iso": "2026-08-01", "precision": "day" }, "updatedAt": { "iso": "2026-08-01", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/agent-memory-protocol", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 44 }, { "id": "github-skills-i-use", "name": "Skills I Use", "description": "A curated, cross-agent collection of 400+ Agent Skills for Claude Code, Cursor, Codex & more — reviewed, deduplicated, and fully attributed to original authors.", "technologies": [ "Python", "agent-skills", "ai-agents", "anthropic", "awesome-list", "claude-code", "codex", "coding-agent", "cursor", "developer-tools", "llm-tools", "mcp", "skill-md" ], "repository": "https://github.com/NITISH-R-G/skills-i-use", "stars": 1, "forks": 0, "primaryLanguage": "Python", "topics": [ "agent-skills", "ai-agents", "anthropic", "awesome-list", "claude-code", "codex", "coding-agent", "cursor", "developer-tools", "llm-tools", "mcp", "skill-md" ], "date": { "iso": "2026-07-31", "precision": "day" }, "updatedAt": { "iso": "2026-08-01", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/skills-i-use", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 44 }, { "id": "github-raven", "name": "RAVEN", "description": "Relational Verification Engine", "technologies": [ "TypeScript" ], "repository": "https://github.com/NITISH-R-G/RAVEN", "stars": 3, "forks": 0, "primaryLanguage": "TypeScript", "date": { "iso": "2026-05-23", "precision": "day" }, "updatedAt": { "iso": "2026-06-13", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/RAVEN", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 43 }, { "id": "github-graph-engineering-skills", "name": "Graph Engineering Skills", "description": "8 free AI agent skills on loop and graph engineering — verifier design, autonomy ladders, node/edge architecture, and reviewer nodes, sourced and evidence-tiered.", "technologies": [ "agent-skills", "agentic-workflows", "ai-agents", "claude-code", "coding-agent", "cursor", "langgraph", "llm-tools", "multi-agent" ], "repository": "https://github.com/NITISH-R-G/graph-engineering-skills", "stars": 1, "forks": 0, "topics": [ "agent-skills", "agentic-workflows", "ai-agents", "claude-code", "coding-agent", "cursor", "langgraph", "llm-tools", "multi-agent" ], "date": { "iso": "2026-08-01", "precision": "day" }, "updatedAt": { "iso": "2026-08-01", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/graph-engineering-skills", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 42 }, { "id": "github-future_ds_01", "name": "FUTURE DS 01", "description": "Future Interns", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/FUTURE_DS_01", "stars": 2, "forks": 0, "primaryLanguage": "Python", "date": { "iso": "2025-08-09", "precision": "day" }, "updatedAt": { "iso": "2026-09-07", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/FUTURE_DS_01", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 41 }, { "id": "github-intelli-credit-v1", "name": "Intelli Credit V1", "description": "intelli-credit", "technologies": [ "TypeScript" ], "repository": "https://github.com/NITISH-R-G/Intelli-Credit-V1", "stars": 1, "forks": 0, "primaryLanguage": "TypeScript", "date": { "iso": "2026-04-02", "precision": "day" }, "updatedAt": { "iso": "2026-06-06", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/Intelli-Credit-V1", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 40 }, { "id": "github-siet_cse_inceptron_landing_page", "name": "Siet Cse Inceptron Landing Page", "description": "Scroll Animation", "technologies": [ "TypeScript" ], "repository": "https://github.com/NITISH-R-G/siet_cse_inceptron_landing_page", "stars": 1, "forks": 0, "primaryLanguage": "TypeScript", "date": { "iso": "2026-04-20", "precision": "day" }, "updatedAt": { "iso": "2026-06-23", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/siet_cse_inceptron_landing_page", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 40 }, { "id": "github-amypo", "name": "Amypo", "technologies": [ "JavaScript" ], "repository": "https://github.com/NITISH-R-G/Amypo", "liveUrl": "https://amypo-backend-one.vercel.app/", "stars": 2, "forks": 0, "primaryLanguage": "JavaScript", "date": { "iso": "2026-03-12", "precision": "day" }, "updatedAt": { "iso": "2026-09-06", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/Amypo", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 39 }, { "id": "github-iportfolio-macos-style-portfolio-", "name": "I Portfolio Mac OS Style Portfolio", "technologies": [ "JavaScript" ], "repository": "https://github.com/NITISH-R-G/iPortfolio-MacOS-Style-Portfolio-", "liveUrl": "https://mac-os-portfolio-nine-beryl.vercel.app/", "stars": 2, "forks": 0, "primaryLanguage": "JavaScript", "date": { "iso": "2025-11-30", "precision": "day" }, "updatedAt": { "iso": "2026-09-07", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/iPortfolio-MacOS-Style-Portfolio-", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 39 }, { "id": "github-railatc", "name": "Rail ATC", "technologies": [ "TypeScript" ], "repository": "https://github.com/NITISH-R-G/RailATC", "liveUrl": "https://replit.com/@nitishrg8220psg/Alumni-Login-System", "stars": 2, "forks": 0, "primaryLanguage": "TypeScript", "date": { "iso": "2026-05-02", "precision": "day" }, "updatedAt": { "iso": "2026-06-12", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/RailATC", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 39 }, { "id": "github-intelli-credit", "name": "Intelli Credit", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/intelli-credit", "stars": 3, "forks": 0, "primaryLanguage": "Python", "date": { "iso": "2026-02-28", "precision": "day" }, "updatedAt": { "iso": "2026-07-20", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/intelli-credit", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 35 }, { "id": "github-multi-domain-support-triage", "name": "Multi Domain Support Triage", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/Multi-Domain-Support-Triage", "stars": 3, "forks": 0, "primaryLanguage": "Python", "date": { "iso": "2026-05-01", "precision": "day" }, "updatedAt": { "iso": "2026-09-05", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/Multi-Domain-Support-Triage", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 35 }, { "id": "github-rag-context-optimizer", "name": "Rag Context Optimizer", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/rag-context-optimizer", "stars": 2, "forks": 1, "primaryLanguage": "Python", "date": { "iso": "2026-04-08", "precision": "day" }, "updatedAt": { "iso": "2026-09-07", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/rag-context-optimizer", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 35 }, { "id": "github-ev-grid-oracle", "name": "Ev Grid Oracle", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/ev-grid-oracle", "stars": 2, "forks": 0, "primaryLanguage": "Python", "date": { "iso": "2026-04-25", "precision": "day" }, "updatedAt": { "iso": "2026-09-07", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/ev-grid-oracle", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 33 }, { "id": "github-palmplay", "name": "Palm Play", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/PalmPlay", "stars": 2, "forks": 0, "primaryLanguage": "Python", "date": { "iso": "2026-01-08", "precision": "day" }, "updatedAt": { "iso": "2026-09-07", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/PalmPlay", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 33 }, { "id": "github-skills-communicate-using-markdown", "name": "Skills Communicate Using Markdown", "description": "Exercise: Communicate using Markdown", "repository": "https://github.com/NITISH-R-G/skills-communicate-using-markdown", "stars": 1, "forks": 0, "date": { "iso": "2026-04-19", "precision": "day" }, "updatedAt": { "iso": "2026-04-19", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/skills-communicate-using-markdown", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 33 }, { "id": "github-skills-introduction-to-github", "name": "Skills Introduction To Github", "description": "My clone repository", "repository": "https://github.com/NITISH-R-G/skills-introduction-to-github", "stars": 1, "forks": 0, "date": { "iso": "2026-04-19", "precision": "day" }, "updatedAt": { "iso": "2026-04-19", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/skills-introduction-to-github", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 33 }, { "id": "clicky", "name": "clicky (YC-backed fork)", "description": "Ported a YC-backed macOS Swift app to Windows by rewriting the core in C# .NET with multi-provider AI SDK support.", "technologies": [ "C#", ".NET", "Swift", "Multi-provider AI SDKs" ], "repository": "https://github.com/NITISH-R-G", "status": "completed", "role": "Core Contributor", "context": "Open-source contribution to a YC-backed startup", "problem": "The application was locked to macOS, excluding Windows users. The Swift core had deep platform dependencies that couldn't be cross-compiled.", "approach": "Rewrote the core logic in C# .NET with a provider-agnostic AI SDK layer. Designed an abstraction layer that supports OpenAI, Anthropic, and local models through a unified interface.", "impact": "Successfully ported the application to Windows with feature parity. The abstraction layer now supports 3 AI providers, making the app provider-independent.", "responsibilities": "Rewrote core logic in C#, designed AI SDK abstraction layer, implemented provider-agnostic API.", "constraints": "Had to maintain behavioral parity with the Swift original while making the code cross-platform.", "lessons": "Abstraction layers pay off when you need to support multiple backends. C# .NET is excellent for cross-platform desktop apps.", "metrics": [ { "label": "Platform Support", "value": "2x", "note": "macOS + Windows" }, { "label": "AI Providers", "value": "3", "numeric": 3, "note": "OpenAI, Anthropic, Local" } ], "featureScore": 32 }, { "id": "github-dialectica", "name": "DIALECTICA", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/DIALECTICA", "stars": 1, "forks": 0, "primaryLanguage": "Python", "date": { "iso": "2026-04-26", "precision": "day" }, "updatedAt": { "iso": "2026-09-06", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/DIALECTICA", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 32 }, { "id": "github-discourse-rag-assistant", "name": "Discourse Rag Assistant", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/discourse-rag-assistant", "stars": 1, "forks": 0, "primaryLanguage": "Python", "date": { "iso": "2025-12-04", "precision": "day" }, "updatedAt": { "iso": "2026-09-06", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/discourse-rag-assistant", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 32 }, { "id": "discourse-rag", "name": "discourse-rag-assistant", "description": "Document-grounded RAG system with FAISS vector indexing, semantic chunking, and structured prompt pipelines. Benchmarked with MRR and Recall@K.", "technologies": [ "Python", "LangChain", "OpenAI API", "FAISS", "RAG" ], "repository": "https://github.com/NITISH-R-G", "status": "completed", "role": "AI/ML Engineer", "context": "Research-oriented project exploring RAG systems", "problem": "RAG systems often struggle with retrieval quality. Generic chunking loses context, and naive similarity search misses semantically relevant passages.", "approach": "Implemented semantic chunking that preserves document structure, FAISS vector indexing for fast retrieval, and structured prompt pipelines that ground LLM responses in retrieved context.", "impact": "Achieved top-quartile performance on custom benchmarks. MRR@10 of 0.78 and Recall@5 of 0.85 on a curated document set.", "responsibilities": "Designed the RAG pipeline, implemented semantic chunking, built evaluation benchmarks.", "constraints": "Chunk size vs. retrieval quality tradeoff. Embedding model limitations for technical documents.", "lessons": "Semantic chunking significantly improves retrieval quality over fixed-size chunking. Evaluation benchmarks are essential for RAG development.", "metrics": [ { "label": "MRR@10", "value": "0.78", "numeric": 0.78, "note": "retrieval quality" }, { "label": "Recall@5", "value": "0.85", "numeric": 0.85, "note": "on custom benchmarks" } ], "featureScore": 32 }, { "id": "github-face-emotion-detection", "name": "FACE EMOTION DETECTION", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/FACE-EMOTION-DETECTION", "stars": 1, "forks": 0, "primaryLanguage": "Python", "date": { "iso": "2025-08-13", "precision": "day" }, "updatedAt": { "iso": "2026-09-07", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/FACE-EMOTION-DETECTION", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 32 }, { "id": "github-future_ds_02", "name": "FUTURE DS 02", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/FUTURE_DS_02", "stars": 1, "forks": 0, "primaryLanguage": "Python", "date": { "iso": "2025-08-19", "precision": "day" }, "updatedAt": { "iso": "2026-09-07", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/FUTURE_DS_02", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 32 }, { "id": "github-future_ds_03", "name": "FUTURE DS 03", "technologies": [ "Jupyter Notebook" ], "repository": "https://github.com/NITISH-R-G/FUTURE_DS_03", "stars": 1, "forks": 0, "primaryLanguage": "Jupyter Notebook", "date": { "iso": "2025-08-19", "precision": "day" }, "updatedAt": { "iso": "2026-09-04", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/FUTURE_DS_03", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 32 }, { "id": "github-health-drop-surveillance-system-main", "name": "Health Drop Surveillance System Main", "technologies": [ "TypeScript" ], "repository": "https://github.com/NITISH-R-G/Health-Drop-Surveillance-System-main", "stars": 1, "forks": 0, "primaryLanguage": "TypeScript", "date": { "iso": "2026-02-20", "precision": "day" }, "updatedAt": { "iso": "2026-09-06", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/Health-Drop-Surveillance-System-main", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 32 }, { "id": "github-infosys-springboard", "name": "INFOSYS SPRINGBOARD", "technologies": [ "Dart" ], "repository": "https://github.com/NITISH-R-G/INFOSYS-SPRINGBOARD", "stars": 1, "forks": 0, "primaryLanguage": "Dart", "date": { "iso": "2026-01-09", "precision": "day" }, "updatedAt": { "iso": "2026-09-06", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/INFOSYS-SPRINGBOARD", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 32 }, { "id": "intelli-credit", "name": "Intelli-Credit", "description": "Three-generation AI credit decision engine: rule-based frontend → TypeScript REST API → Python ML risk model with logistic regression and gradient-boosted trees.", "technologies": [ "Python", "TypeScript", "scikit-learn", "REST APIs", "ML" ], "repository": "https://github.com/NITISH-R-G", "status": "completed", "role": "ML Engineer & Backend Developer", "context": "Academic project exploring ML in financial services", "problem": "Traditional credit scoring relies on static rules that miss nuanced risk patterns. ML models can capture non-linear relationships but need to be explainable for regulatory compliance.", "approach": "Built three iterative generations: (1) rule-based frontend for baseline, (2) TypeScript API layer for orchestration, (3) Python ML model with logistic regression and gradient-boosted trees for risk scoring.", "impact": "Achieved 15% improvement in risk classification accuracy over the rule-based baseline. The three-generation approach demonstrates iterative ML maturity.", "responsibilities": "Designed the three-generation architecture, implemented ML models, built the API orchestration layer.", "constraints": "ML models needed to be interpretable for financial compliance. Balanced model complexity with explainability.", "lessons": "Iterative development (rule → API → ML) is a practical path for ML adoption in regulated industries.", "metrics": [ { "label": "Accuracy Gain", "value": "+15%", "note": "over rule-based baseline" }, { "label": "Model Types", "value": "3", "numeric": 3, "note": "logistic, random forest, gradient boost" } ], "featureScore": 32 }, { "id": "github-palmplay1", "name": "Palm Play1", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/PalmPlay1", "stars": 1, "forks": 0, "primaryLanguage": "Python", "date": { "iso": "2026-01-08", "precision": "day" }, "updatedAt": { "iso": "2026-09-06", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/PalmPlay1", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 32 }, { "id": "github-pedagogyx", "name": "Pedagogy X", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/PedagogyX", "stars": 1, "forks": 0, "primaryLanguage": "Python", "date": { "iso": "2026-05-19", "precision": "day" }, "updatedAt": { "iso": "2026-09-07", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/PedagogyX", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 32 }, { "id": "railatc", "name": "RailATC", "description": "Automatic Train Control simulation with block signaling algorithms and collision-avoidance logic using graph traversal and state-machine design.", "technologies": [ "TypeScript", "Simulation", "Graph Algorithms", "State Machines" ], "repository": "https://github.com/NITISH-R-G", "status": "completed", "role": "Simulation Engineer", "context": "Academic project exploring safety-critical systems", "problem": "Train collisions are catastrophic but preventable with proper signaling. Simulating block signaling requires correct state management and graph-based track modeling.", "approach": "Built a simulation using graph traversal for track modeling, state machines for signal management, and collision-avoidance logic that prevents unsafe train movements.", "impact": "Successfully simulated a 50-station rail network with zero collisions in 10,000 simulated hours. The state-machine approach caught 3 edge cases in the signaling logic.", "responsibilities": "Designed the block signaling algorithm, implemented the state machine, built the graph-based track model.", "constraints": "Had to handle simultaneous train movements. State space exploded with more trains — needed pruning.", "lessons": "State machines are ideal for safety-critical systems. Formal verification concepts help catch edge cases early.", "metrics": [ { "label": "Simulated Hours", "value": "10k+", "note": "zero collisions" }, { "label": "Stations", "value": "50", "numeric": 50, "note": "in test network" } ], "featureScore": 32 }, { "id": "raven", "name": "RAVEN — Relational Verification Engine", "description": "Multi-layer financial fraud detection combining document ingestion, cross-document coherence scoring, and graph-based fraud ring detection using NetworkX.", "technologies": [ "TypeScript", "Python", "NetworkX", "Graph Algorithms", "Multi-Agent Systems" ], "repository": "https://github.com/NITISH-R-G", "status": "completed", "role": "Lead Engineer", "context": "Personal project exploring financial AI systems", "problem": "Financial fraud costs institutions billions annually. Existing single-document checks miss coordinated fraud rings that operate across multiple documents and accounts.", "approach": "Designed a multi-layer pipeline: document ingestion with entity extraction, cross-document coherence scoring, and graph-based fraud ring detection using NetworkX community detection algorithms.", "impact": "Built a working prototype that detects cross-document fraud patterns invisible to single-document analysis. Graph-based approach identifies fraud rings with 92% precision on synthetic benchmarks.", "responsibilities": "Designed the multi-layer architecture, implemented graph-based detection algorithms, built the entity extraction pipeline.", "constraints": "Had to balance detection precision with processing latency. Graph algorithms on large document sets required careful optimization.", "lessons": "Cross-document analysis reveals patterns that single-document checks miss. Graph theory is powerful for fraud detection but requires careful tuning.", "metrics": [ { "label": "Fraud Precision", "value": "92%", "note": "on synthetic benchmarks" }, { "label": "Documents Processed", "value": "10k+", "note": "in testing" } ], "featureScore": 32 }, { "id": "roadsos", "name": "RoadSOS", "description": "Cross-platform emergency roadside app with real-time GPS, SOS dispatch, nearest-responder matching, and offline-first architecture.", "technologies": [ "Dart", "Flutter", "GPS", "Real-time Systems", "Offline-first" ], "repository": "https://github.com/NITISH-R-G", "status": "completed", "role": "Mobile Developer", "context": "Personal project addressing real-world safety needs", "problem": "In roadside emergencies, finding help quickly is critical. Existing solutions require internet connectivity and don't optimize for nearest-responder matching.", "approach": "Built a Flutter app with offline-first architecture, real-time GPS tracking, and a nearest-responder matching algorithm that works without internet. SOS dispatch sends alerts via SMS when connectivity is available.", "impact": "Created a working prototype that can match responders within 5km radius in under 2 seconds. Offline-first design ensures functionality in low-connectivity areas.", "responsibilities": "Designed the matching algorithm, implemented offline-first data sync, built the GPS tracking system.", "constraints": "Had to work without internet. GPS accuracy varies by device. SMS dispatch has latency.", "lessons": "Offline-first is essential for emergency apps. Simple algorithms (nearest-neighbor) can be highly effective when well-tuned.", "metrics": [ { "label": "Response Match", "value": "<2s", "note": "nearest responder" }, { "label": "Coverage", "value": "5km", "note": "radius matching" } ], "featureScore": 32 }, { "id": "github-smart-task-tracker-macos-dark-edition-", "name": "Smart Task Tracker Mac OS Dark Edition", "technologies": [ "HTML" ], "repository": "https://github.com/NITISH-R-G/Smart-Task-Tracker-macOS-Dark-Edition-", "stars": 1, "forks": 0, "primaryLanguage": "HTML", "date": { "iso": "2026-01-02", "precision": "day" }, "updatedAt": { "iso": "2026-09-06", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/Smart-Task-Tracker-macOS-Dark-Edition-", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 32 }, { "id": "github-textbridge3d", "name": "Text Bridge3 D", "technologies": [ "Python" ], "repository": "https://github.com/NITISH-R-G/TextBridge3D", "stars": 1, "forks": 0, "primaryLanguage": "Python", "date": { "iso": "2026-03-21", "precision": "day" }, "updatedAt": { "iso": "2026-09-05", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/TextBridge3D", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 32 }, { "id": "github-nitish-r-g-leetcode-solutions", "name": "NITISH R G Leetcode Solutions", "technologies": [ "Java" ], "repository": "https://github.com/NITISH-R-G/NITISH-R-G-Leetcode-solutions", "stars": 0, "forks": 0, "primaryLanguage": "Java", "date": { "iso": "2026-06-29", "precision": "day" }, "updatedAt": { "iso": "2026-08-16", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/NITISH-R-G-Leetcode-solutions", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 29 }, { "id": "github-rti", "name": "RTI", "technologies": [ "JavaScript" ], "repository": "https://github.com/NITISH-R-G/RTI", "stars": 0, "forks": 0, "primaryLanguage": "JavaScript", "date": { "iso": "2026-08-26", "precision": "day" }, "updatedAt": { "iso": "2026-08-27", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/RTI", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 29 }, { "id": "github-iportfolio", "name": "I Portfolio", "repository": "https://github.com/NITISH-R-G/iPortfolio", "stars": 1, "forks": 0, "date": { "iso": "2026-01-01", "precision": "day" }, "updatedAt": { "iso": "2026-09-06", "precision": "day" }, "isFork": false, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G/iPortfolio", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "featureScore": 26 } ], "skills": [ { "name": "Python", "category": "Languages", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "21 projects", "count": 21, "connector": "github" }, { "label": "17 repositories", "count": 17, "connector": "github" }, { "label": "4 roles", "count": 4 }, { "label": "2 featured projects", "count": 2, "connector": "github" }, { "label": "1 topic", "count": 1, "connector": "github" }, { "label": "20 repositories", "count": 20, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 45 }, { "name": "TypeScript", "category": "Languages", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "10 projects", "count": 10, "connector": "github" }, { "label": "7 repositories", "count": 7, "connector": "github" }, { "label": "2 featured projects", "count": 2, "connector": "github" }, { "label": "1 topic", "count": 1, "connector": "github" }, { "label": "9 repositories", "count": 9, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 20 }, { "name": "JavaScript", "category": "Languages", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "4 repositories", "count": 4, "connector": "github" }, { "label": "4 projects", "count": 4, "connector": "github" }, { "label": "2 featured projects", "count": 2, "connector": "github" }, { "label": "1 role", "count": 1 }, { "label": "14 repositories", "count": 14, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 11 }, { "name": "AI Agents", "category": "Other", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "5 projects", "count": 5, "connector": "github" }, { "label": "5 topics", "count": 5, "connector": "github" }, { "label": "5 repositories tagged", "count": 5, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 10 }, { "name": "Developer Tools", "category": "Other", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "4 projects", "count": 4, "connector": "github" }, { "label": "4 topics", "count": 4, "connector": "github" }, { "label": "1 featured project", "count": 1, "connector": "github" }, { "label": "4 repositories tagged", "count": 4, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 9 }, { "name": "Agent Skills", "category": "Other", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "4 projects", "count": 4, "connector": "github" }, { "label": "4 topics", "count": 4, "connector": "github" }, { "label": "4 repositories tagged", "count": 4, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 8 }, { "name": "Claude Code", "category": "Other", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "4 projects", "count": 4, "connector": "github" }, { "label": "4 topics", "count": 4, "connector": "github" }, { "label": "4 repositories tagged", "count": 4, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 8 }, { "name": "Coding Agent", "category": "Other", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "4 projects", "count": 4, "connector": "github" }, { "label": "4 topics", "count": 4, "connector": "github" }, { "label": "4 repositories tagged", "count": 4, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 8 }, { "name": "cursor", "category": "Other", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "4 projects", "count": 4, "connector": "github" }, { "label": "4 topics", "count": 4, "connector": "github" }, { "label": "4 repositories tagged", "count": 4, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 8 }, { "name": "LLM Tools", "category": "Other", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "4 projects", "count": 4, "connector": "github" }, { "label": "4 topics", "count": 4, "connector": "github" }, { "label": "4 repositories tagged", "count": 4, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 8 }, { "name": "Open Source", "category": "Other", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "3 projects", "count": 3, "connector": "github" }, { "label": "3 topics", "count": 3, "connector": "github" }, { "label": "2 featured projects", "count": 2, "connector": "github" }, { "label": "3 repositories tagged", "count": 3, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 8 }, { "name": "Dart", "category": "Languages", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "3 projects", "count": 3, "connector": "github" }, { "label": "2 repositories", "count": 2, "connector": "github" }, { "label": "1 topic", "count": 1, "connector": "github" } ], "weight": 6 }, { "name": "mcp", "category": "Other", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "3 projects", "count": 3, "connector": "github" }, { "label": "3 topics", "count": 3, "connector": "github" }, { "label": "3 repositories tagged", "count": 3, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 6 }, { "name": "LLM Evaluation", "category": "Other", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "2 projects", "count": 2, "connector": "github" }, { "label": "2 topics", "count": 2, "connector": "github" }, { "label": "1 featured project", "count": 1, "connector": "github" }, { "label": "2 repositories tagged", "count": 2, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 5 }, { "name": "HTML", "category": "Languages", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "2 repositories", "count": 2, "connector": "github" }, { "label": "2 projects", "count": 2, "connector": "github" }, { "label": "14 repositories", "count": 14, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 4 }, { "name": "flutter", "category": "Frontend", "evidence": [ { "label": "2 projects", "count": 2, "connector": "github" }, { "label": "1 topic", "count": 1, "connector": "github" } ], "weight": 3 }, { "name": "Kotlin", "category": "Languages", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "1 repository", "count": 1, "connector": "github" }, { "label": "1 project", "count": 1, "connector": "github" }, { "label": "1 topic", "count": 1, "connector": "github" }, { "label": "4 repositories", "count": 4, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 3 }, { "name": "React", "category": "Frontend", "evidence": [ { "label": "1 project", "count": 1, "connector": "github" }, { "label": "1 topic", "count": 1, "connector": "github" }, { "label": "1 featured project", "count": 1, "connector": "github" } ], "weight": 3 }, { "name": "scikit-learn", "category": "AI & ML", "evidence": [ { "label": "2 roles", "count": 2 }, { "label": "1 project", "count": 1 } ], "weight": 3 }, { "name": "Java", "category": "Languages", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "1 repository", "count": 1, "connector": "github" }, { "label": "1 project", "count": 1, "connector": "github" } ], "weight": 2 }, { "name": "Jupyter", "category": "Languages", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "1 repository", "count": 1, "connector": "github" }, { "label": "1 project", "count": 1, "connector": "github" }, { "label": "6 repositories", "count": 6, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 2 }, { "name": "C#", "category": "Languages", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "1 project", "count": 1 }, { "label": "listed by you", "connector": "github" }, { "label": "1 repository", "count": 1, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 1 }, { "name": "CSS", "category": "Languages", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "1 role", "count": 1 }, { "label": "listed by you", "connector": "github" }, { "label": "12 repositories", "count": 12, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 1 }, { "name": "Docker", "category": "Infrastructure", "evidence": [ { "label": "1 role", "count": 1 } ], "weight": 1 }, { "name": "LangChain", "category": "AI & ML", "evidence": [ { "label": "1 project", "count": 1 } ], "weight": 1 }, { "name": "NLP", "category": "AI & ML", "evidence": [ { "label": "1 role", "count": 1 } ], "weight": 1 }, { "name": "OpenAI API", "category": "AI & ML", "evidence": [ { "label": "1 project", "count": 1 } ], "weight": 1 }, { "name": "REST APIs", "category": "Backend", "evidence": [ { "label": "1 project", "count": 1 } ], "weight": 1 }, { "name": "Swift", "category": "Languages", "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" }, "evidence": [ { "label": "1 project", "count": 1 }, { "label": "listed by you", "connector": "github" }, { "label": "2 repositories", "count": 2, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 1 }, { "name": "TensorFlow", "category": "AI & ML", "evidence": [ { "label": "1 role", "count": 1 } ], "weight": 1 }, { "name": "Agentic Systems", "category": "AI & ML", "weight": 0 }, { "name": "AWS", "category": "Infrastructure", "weight": 0 }, { "name": "Batchfile", "category": "Languages", "evidence": [ { "label": "1 repository", "count": 1, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "C", "category": "Languages", "evidence": [ { "label": "1 repository", "count": 1, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "C++", "category": "Languages", "evidence": [ { "label": "1 repository", "count": 1, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "CMake", "category": "Languages", "evidence": [ { "label": "1 repository", "count": 1, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "CNNs", "category": "AI & ML", "weight": 0 }, { "name": "Dockerfile", "category": "Languages", "evidence": [ { "label": "9 repositories", "count": 9, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "FastAPI", "category": "Backend", "weight": 0 }, { "name": "Flask", "category": "Backend", "weight": 0 }, { "name": "GitHub Actions", "category": "Infrastructure", "weight": 0 }, { "name": "Google Cloud", "category": "Infrastructure", "weight": 0 }, { "name": "GSAP", "category": "Frontend", "weight": 0 }, { "name": "Keras", "category": "AI & ML", "weight": 0 }, { "name": "Makefile", "category": "Languages", "evidence": [ { "label": "2 repositories", "count": 2, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "Mako", "category": "Languages", "evidence": [ { "label": "2 repositories", "count": 2, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "Mermaid", "category": "Languages", "evidence": [ { "label": "5 repositories", "count": 5, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "MySQL", "category": "Backend", "weight": 0 }, { "name": "Node.js", "category": "Backend", "weight": 0 }, { "name": "Objective-C", "category": "Languages", "evidence": [ { "label": "2 repositories", "count": 2, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "Oracle Cloud", "category": "Infrastructure", "weight": 0 }, { "name": "PLpgSQL", "category": "Languages", "evidence": [ { "label": "1 repository", "count": 1, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "PostgreSQL", "category": "Backend", "weight": 0 }, { "name": "PowerShell", "category": "Languages", "evidence": [ { "label": "6 repositories", "count": 6, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "RAG pipelines", "category": "AI & ML", "weight": 0 }, { "name": "Ruby", "category": "Languages", "evidence": [ { "label": "2 repositories", "count": 2, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "Shell", "category": "Languages", "evidence": [ { "label": "5 repositories", "count": 5, "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories" } ], "weight": 0, "source": { "connector": "github", "url": "https://github.com/NITISH-R-G?tab=repositories", "fetchedAt": "2026-09-07T10:53:06.927Z" } }, { "name": "SQL", "category": "Languages", "weight": 0 }, { "name": "SQLite", "category": "Backend", "weight": 0 }, { "name": "Vite", "category": "Frontend", "weight": 0 } ], "achievements": [ { "id": "award-1", "title": "HackerRank Orchestrate — Rank #73 Globally", "organization": "HackerRank", "date": { "iso": "2026-06-01", "precision": "month" }, "description": "Ranked #73 globally for autonomous AI agent creation in HackerRank's Orchestrate challenge, demonstrating proficiency in multi-agent system design and AI orchestration.", "metrics": [ { "label": "Global Rank", "value": "#73", "note": "out of 10,000+ participants" } ] } ], "certifications": [ { "id": "cert-2", "name": "Oracle Cloud Infrastructure 2025", "issuer": "Oracle", "date": { "iso": "2025-01-01", "precision": "year" }, "credentialId": "Generative AI Professional", "description": "Data Science Professional, AI Foundations" }, { "id": "cert-1", "name": "HackerRank 5-Star Gold — SQL", "issuer": "HackerRank", "credentialId": "#73 Globally", "description": "Ranked #73 globally — Orchestrate Autonomous AI Agents" }, { "id": "cert-3", "name": "Google Gemini Certified University Student", "issuer": "Google", "description": "Gemini for Google Workspace certification" }, { "id": "cert-4", "name": "AWS AI/ML Certifications", "issuer": "AWS", "credentialId": "Multiple Certificates", "description": "Generative AI Fundamentals, Foundations of Prompt Engineering, ML & AI Fundamentals" }, { "id": "cert-5", "name": "McKinsey Forward Program", "issuer": "McKinsey", "credentialId": "Completion Certificate", "description": "Problem-solving and leadership program" } ], "publications": [], "writing": [], "packages": [], "videos": [], "models": [], "hackathons": [], "talks": [], "competitions": [], "languages": [ { "name": "English", "level": 5 }, { "name": "Hindi", "level": 4 }, { "name": "Tamil", "level": 5 } ], "url": "https://nitish-r-g.github.io/Portfolio/", "capabilities": { "schemaVersion": "1.0", "spec": "https://github.com/NITISH-R-G/Portfolio/blob/main/docs/standard.md", "provenance": true, "evidence": true, "search": "hybrid-semantic", "embeddingModel": "Xenova/all-MiniLM-L6-v2" } }