I'm Boggavarapu Yuva Satya Kunaal — a B.E. AI & Data Science graduate from CBIT with a 9.16 CGPA.

I don't prototype and move on. I design, build, ship, and own end-to-end systems — from AI pipelines and data engineering to fully deployed SaaS products with real users. Over the last year, I have focused on turning ideas into practical, production-ready products.

From engineer → to founder. From builder → to shipper.

My most ambitious product yet. SQLumina is a large-scale SQL learning SaaS platform — designed from the ground up to be the definitive place where people go to master SQL. Not just a query tool, but a complete learning environment.

A browser-based GitHub repository analysis tool that turns any public repository into an instant, structured overview — without cloning the repo, installing anything, or running a single local command.

Beyond file counting, it provides a complete snapshot of how a repository is structured and maintained.

Key highlights: Full recursive repository file analysis · Code vs. non-code classification · Breakdown by file extension · Config/dotfile filtering · Large-tree truncation detection · Repository health snapshot · Full pull request history with progressive loading · PR velocity metrics including median merge time, merge rate & average open-PR age · GitHub-style PR search qualifiers (is:open, is:merged, author:, label:) · Markdown export · GitHub API usage tracking · Fully client-side architecture — GitHub tokens never leave the user's device

Stack: TanStack Start · TypeScript · React · Tailwind CSS · GitHub REST API

An open-source TypeScript/React SDK for embedding AI-scored interviews directly into your own product as a single component — your backend, your API keys, your database, end to end. The maintainers never see, store, or process a single candidate's answer.

Key highlights: One component, two modes — client for instant prototyping, server for production · Dynamic, depth-limited follow-up engine that scales difficulty to the candidate · HMAC-signed evaluation scores that can be verified, not just trusted · Interview Simulator & Bias/Consistency Harness for fairness testing before real candidates · Five provider adapters (OpenAI, Claude, Gemini, Deepgram, ElevenLabs) with automatic failover · Session persistence with persistKey and a typed event stream · Opt-in integrity signals (tab-switch/paste counts) — no webcam or emotion tracking

Stack: TypeScript · React · Node.js · OpenAI/Claude/Gemini/Deepgram/ElevenLabs adapters · Next.js examples · pnpm monorepo · CLI + local dashboard

A minimal, intentional habit tracking SaaS built for people who want clarity over clutter. No noise, no gamification gimmicks — just a clean daily rhythm that actually sticks. Live and growing.

A production-ready AI diagram generator that turns plain English descriptions into structured flowcharts, workflow diagrams, and system design visuals — powered by Gemini AI and a 4-stage Multi-Agent pipeline.

• Orchestrator Agent → validates if the prompt can be visualized as a flowchart
• Logic Parser Agent → decomposes text into a structured JSON graph of Nodes & Edges
• Generator Agent → converts the graph into optimized Mermaid.js syntax
• Syntax Validator → catches rendering anomalies before output reaches the browser

Key highlights: BYOK (Bring Your Own Key) privacy architecture · SSE real-time streaming pipeline status · 50-step undo/redo engine · Export to PNG (3x super-scaled), SVG, or Mermaid code · Dark/Light theming

Stack: FastAPI (Python) · Gemini AI SDK · Vanilla JS (ES6+) · Mermaid.js · html2canvas · Glassmorphic CSS3 · Vercel (frontend) · Render/Gunicorn+Uvicorn (backend)

A local-first AI Git assistant that eliminates the most repetitive part of every developer's workflow — writing commit messages. CommitAI reads your staged diff, sends a focused prompt to Ollama, and generates clean Conventional Commit messages.

Key highlights: AI-generated Conventional Commit messages · Automatic changelog generation · PR description generation (--pr flag) · prepare-commit-msg git hook integration · Multi-model support

Stack: Python 3.13+ · Ollama · Gemma 4 (gemma4:e2b) · Rich · Git hooks

Architecture:

Staged Git Diff → CommitAI → Ollama API → Gemma 4 → AI Commit Message → Git Commit

An open source Agent Skill that guides AI agents to generate clean, bizarre, hand-drawn in-text illustrations for English articles and knowledge content. Not a generic image prompt — a full visual instruction system.

Key highlights: Wide-composition hand-drawn illustrations · Pure white background, sparse red/orange/blue annotations · Reference Boy & Girl character IPs · Works with Antigravity + Gemini · Designed for readability and style consistency

Contributed to Agenta, an open-source platform for building and evaluating LLM applications.

My contribution focused on strengthening API reliability through regression and acceptance test coverage for the mounts file/archive endpoints.

Contribution highlights:

• Added regression coverage for mtime behavior
• Added acceptance coverage for mounts file/archive API endpoints
• Worked through maintainer and CodeRabbit review feedback
• Contribution reviewed and merged into the release/v0.106.1 branch
• Officially recognized in Agenta's All Contributors list

• Python — Advanced backend development, automation-first architecture, production systems
• SQL — Expert in query optimization, window functions, CTEs, complex JOINs, analytics pipelines
• FastAPI & Flask — High-performance, production-ready API services
• Git & GitHub — Strong version control, hooks, collaborative and open-source workflows

• HTML, CSS, JavaScript (ES6+) — Responsive, production-ready, aesthetically intentional interfaces
• Glassmorphic UI, Dark/Light Theming, SSE — Real-time UX, modern design patterns
• Vibe Coding — Rapid, intuitive product iteration with a sharp sense of feel and function

• Large Language Models — Claude, GPT, Gemini, Mistral, DeepSeek, LLaMA, Qwen, Gemma 4
• Multi-Agent Orchestration — Designing intelligent pipelines with layered, specialized agent roles
• Prompt Engineering — Schema-aware, deterministic, structured-output prompting at production scale
• RAG Systems — Retrieval-augmented pipelines for grounded, accurate, hallucination-resistant AI outputs
• Ollama — Local LLM deployment, model selection, offline-first AI system design

• Data Analysis — Pandas, NumPy, Matplotlib, business insight extraction, pattern detection
• Data Engineering — ETL pipelines, OCR-driven data extraction, end-to-end automated workflows
• Automation Systems — Certificate generation, attendance monitoring, HR workflows, Make.com

• End-to-end SaaS architecture — From database schema to CI/CD and live deployment
• BYOK (Bring Your Own Key) — Privacy-first product design philosophy
• Vercel + Render + Firebase + Supabase — Production hosting, CORS, CDN, ASGI workers
• LocalStorage-first, zero server retention — Data ownership for users by default

GodForge: A Hybrid Multi-agent Framework for Execution-Driven Workflow Automation in Visual Studio Code Yuva Satya Kunaal Boggavarapu 📚 Intelligent Strategies for ICT — ICTCS 2025, Springer LNNS, vol. 1983 (2026), pp. 369–386

GodForge closes the "suggestion-only" gap in AI coding tools by turning LLMs from passive assistants into accountable executors. It pairs deterministic logic for structural tasks (file edits, Git operations, commits) with narrowly-scoped LLM use, and adds a cross-agent, snapshot-based undo system so every AI-driven action stays reversible — built as a native VS Code extension for real developer workflows.

Keywords: Hybrid execution model · Multi-agent systems · LLM assistance · IDE integration · Undo safety mechanisms · Workflow automation

@incollection{boggavarapu2026godforge,
  title     = {GodForge: A Hybrid Multi-agent Framework for Execution-Driven Workflow Automation in Visual Studio Code},
  author    = {Boggavarapu, Yuva Satya Kunaal},
  booktitle = {Intelligent Strategies for ICT},
  series    = {Lecture Notes in Networks and Systems},
  volume    = {1983},
  pages     = {369--386},
  year      = {2026},
  publisher = {Springer, Cham},
  doi       = {10.1007/978-3-032-31119-1_35}
}

• 🎓 B.E. AI & Data Science — CBIT · Final CGPA: 9.16 / 10
• 💻 200+ LeetCode problems solved (Top 18%)
• ⭐ HackerRank Master — Python 5★ · Problem Solving 4★
• 🍴 CodeChef 2★ Rated Coder
• 🏅 dev.to Gemma 4 Challenge — Completion Badge Earned (CommitAI)
• 📜 Certified by HackerRank, IBM, Infosys, Salesforce, NASSCOM, IIT Hyderabad, CBIT GenAI Program

Target Roles

• GenAI / AI Engineer
• Data Engineer · Data Analyst
• SaaS Founder / Indie Builder
• Automation Engineer (Make.com / n8n)
• Full-Stack AI Product Developer

What I Bring

• End-to-end product ownership — idea → architecture → deployment → users
• Multi-agent AI system design and orchestration
• Production data pipelines and intelligent automation
• A sharp eye for UX, performance, and reliability
• Proven track record of shipping real, usable products

Boggavarapu Yuva Satya Kunaal · Building real systems. Shipping real products. Making real impact.