AI Engineering
Built Gemini-powered apps with prompt pipelines, structured outputs, fallback states, and user-review flows.
Priyanshu Chawda
AI-focused software engineer building LLM workflows, MCP tools, and production-grade web apps.
I build AI products with Next.js, TypeScript, Python, Gemini, typed APIs, evals, and deployment-focused engineering. My work focuses on turning AI demos into usable tools with clear UX, safety boundaries, and measurable behavior.
Pune, IndiaB.Tech Computer Science
Now Building
Building MCP serversBuilding AI toolsShipping public technical breakdownsWriting blogs on AI workflows
Engineering Evidence
Evidence across AI product engineering, full-stack delivery, developer tooling, and reliability.
Full-Stack Engineering
Shipped Next.js + TypeScript apps with SEO metadata, accessible UI, dynamic routes, reusable data models, and clean component architecture.
Developer Tools
Built tools for GitHub search, learning workflows, and automation where AI is used as a system component, not just a chatbot wrapper.
Reliability Mindset
I document architecture, edge cases, limitations, future improvements, and testing strategy so projects are easier to review and maintain.
Featured Projects
Impact-driven engineering. Code is the medium.
Native Android AI agent that turns WhatsApp notifications into a local actionable inbox with offline FunctionGemma tool-call suggestions.
Problem: Busy WhatsApp conversations create scattered follow-ups, priority messages, and reminders, while cloud assistants introduce privacy risk for personal or work chats.
Solution: I built a Kotlin Android app that captures notifications with user permission, parses WhatsApp message context, stores sensitive data locally with AES-GCM encryption, and uses on-device FunctionGemma through LiteRT-LM to propose user-confirmed actions.
Tech: Kotlin, Android 16, Jetpack Compose, Room SQLite, WorkManager, LiteRT-LM, FunctionGemma 270M, AES-GCM, Android Keystore, MVVM
GitHub repoNative Android appTechnical documentationNotification parser
Impact: Demonstrates a privacy-first mobile AI agent pattern where notification ingestion, task extraction, reminders, priority flags, and reply preparation stay on device unless the user explicitly acts.
View case studyStable C++20 Windows screen recorder with WGC capture, D3D11, hardware-first H.264, WASAPI audio, camera overlay, HQ mode, and diagnostics.
Problem: Reliable Windows screen recording needs low overhead capture, synchronized audio/video output, useful diagnostics, and graceful fallbacks across different GPU and power states.
Solution: I built a native Win32/C++ recorder that captures the full screen through Windows Graphics Capture, encodes MP4 output with Media Foundation and Intel Quick Sync when available, records system audio through WASAPI, and writes per-session diagnostics beside each recording.
Tech: C++20, Win32, Windows Graphics Capture, D3D11, Media Foundation, Intel Quick Sync, WASAPI, CMake, CPack, GitHub Actions
GitHub repoLive documentation siteNative Windows appCMake build
Impact: Ships a lightweight native recording pipeline with default low-resource capture, optional 1080p HQ output, camera overlay, audio controls, orphaned partial-file recovery, and a public Netlify documentation site.
View case studyPrivacy-first Windows work recorder for local evidence timelines and cited AI reports.
Problem: Developers, students, freelancers, and remote knowledge workers often need to reconstruct what they worked on, but manual notes lose context and many AI summaries cannot cite trustworthy session evidence.
Solution: I built a local-first Tauri + Python sidecar app that records allowed desktop activity evidence, builds deterministic local timelines, previews and deletes sensitive artifacts, and generates reports only from cited session evidence.
Tech: Tauri v2, React, TypeScript, Python 3.13, FastAPI, Rust, SQLite WAL, Local Ollama-compatible models, Vitest, Pytest
GitHub repoTauri desktop appFastAPI sidecarSQLite WAL storage
Impact: Proves a private-beta Windows workflow for first-run privacy onboarding, session recording controls, active-window and screenshot metadata capture, evidence search, share-safe exports, provider provenance, and local validation gates.
View case studyRead-only MCP server and skill for AI code audits and repository reviews.
Problem: Developers using AI agents need a safer way to inspect projects, improve code quality, review security risks, and plan issues or PRs before changes are made.
Solution: Exposes read-only MCP tools and a CodeAudit skill for project detection, skill routing, repository audits, documentation evidence checks, security review, and issue or PR planning.
Tech: TypeScript, Model Context Protocol, MCP, Node.js, npm, Streamable HTTP
npm packageskills.sh skillGitHub repoMCP server
Impact: Packages a production-style code audit MCP server on npm and skills.sh so agents can inspect repositories, improve code quality, and produce evidence-backed plans without write access or remote mutation tools.
View case studyTurns YouTube lectures into interactive flashcards for faster review.
Problem: Watching long educational videos is passive and manual note-taking slows learning.
Solution: Uses Gemini AI to transform transcripts into concise Q/A flashcards with a quick review flow.
Tech: Next.js, TypeScript, Tailwind CSS, Gemini API
Live demoGitHub repoArchitecture notesScreenshots
Impact: Converts passive lecture watching into a reviewable study workflow with transcript parsing, structured Gemini output, and editable user approval before cards are trusted.
View case studyAI-powered search for relevant GitHub issues and PRs using natural language.
Problem: Finding the right GitHub issues or PRs via the UI is slow and often incomplete.
Solution: Uses Gemini to interpret natural language queries and surface high-signal results quickly.
Tech: Python, Streamlit, GitHub CLI, Gemini API
GitHub repoArchitecture notesCLI integrationQuery validation
Impact: Turns vague developer questions into targeted GitHub searches, reducing manual filtering through issues and PRs while preserving direct links to original discussions.
View case studyAI secure browser concept with real-time phishing and malware analysis.
Problem: Phishing and malware evolve faster than traditional browser warnings can catch.
Solution: Applies Gemini-driven threat analysis to flag risky pages and downloads earlier.
Tech: TypeScript, Gemini AI, Threat analysis pipeline
GitHub repoArchitecture notesThreat-model notesScreenshots
Impact: Explores a defense-in-depth browser workflow where risky URLs and downloads are analyzed before user action, with explanations designed to reduce alert fatigue.
View case studyAI crowd navigation system for safer exits and better flow during events.
Problem: Large events suffer from dangerous crowding and inefficient exit timing.
Solution: Uses AI analytics to suggest safer routes and timing for crowd movement.
Tech: TypeScript, AI analytics, Simulation modeling
GitHub repoArchitecture notesSimulation modelScreenshots
Impact: Models crowd routing as an operator decision-support system with density signals, route tradeoffs, and safety-first recommendations instead of generic chatbot advice.
View case studyCapabilities
What I build across AI, developer tools, and full-stack systems.
AI Engineering
Built Gemini-powered apps with prompt pipelines, structured outputs, fallback states, and user-review flows.
Full-stack Development
Shipped Next.js + TypeScript apps with SEO metadata, accessible UI, dynamic routes, reusable data models, and clean component architecture.
Developer Tools
Built tools for GitHub search, learning workflows, and automation where AI is used as a system component, not just a chatbot wrapper.
Track Record
Consistent output across projects, problem-solving, and shipping.
Quick Answers
Concise entity and expertise answers for search engines, AI assistants, and visitors.
- Who is Priyanshu Chawda?
- Priyanshu Chawda is an AI-focused software engineer from Pune, India who builds agentic systems, MCP servers, developer tools, and full-stack AI applications with TypeScript, Python, Gemini, and Next.js.
- What does Priyanshu Chawda build?
- Priyanshu builds practical AI software: MCP servers for agent-tool integrations, LLM workflows for automation, developer utilities, and full-stack applications that turn AI capabilities into usable products.
- Which technologies does Priyanshu use?
- Priyanshu works with Next.js, React, TypeScript, Python, Tailwind CSS, Gemini AI, MCP, LLM orchestration, automation pipelines, and API integrations for AI-powered web products.
- How can someone contact Priyanshu Chawda?
- The fastest way to contact Priyanshu Chawda is by email at hellopriyanshu4@gmail.com or through LinkedIn at linkedin.com/in/priyanshuchawda for AI engineering, MCP tooling, and full-stack project work.
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Open for AI engineering, MCP tool development, and full-stack product work.