{ "schemaVersion": 1, "site": { "name": "Aakif Mudel — Portfolio", "url": "https://aakifmudel.xyz/", "description": "Portfolio of Aakif Moulasaheb Mudel — software engineer building AI systems, developer tools, and production web applications. Case studies across AI, systems programming, web, mobile, and enterprise software.", "person": { "name": "Aakif Moulasaheb Mudel", "title": "Computer Science Engineer", "url": "https://aakifmudel.xyz/", "sameAs": [ "https://github.com/itsaakif", "https://www.linkedin.com/in/aakifmudel" ] } }, "sources": [ "src/data/portfolio.ts", "src/data/dossiers.ts", "src/data/clientWork.ts", "src/data/resume.ts", "src/config/sections.ts" ], "counts": { "projects": 17, "byStatus": { "active": 9, "completed": 8, "archived": 0 }, "clientWork": 8, "patents": 1, "sections": 7 }, "projects": [ { "slug": "trash-the-test", "title": "TrashTheTest", "status": "active", "url": "https://aakifmudel.xyz/projects/trash-the-test", "category": { "key": "APPLICATIONS", "name": "Applications" }, "date": "October 2026", "datePublished": "2026-10", "subtitle": "TrashTheTest: AI Study Platform for Academic PDFs", "summary": "An open-source study platform that turns textbooks, lecture notes and syllabi into grounded notes, flashcards, practice questions, an AI tutor and a publishable study website.", "description": "TrashTheTest treats a document as a course: upload a textbook, notes, syllabus or previous-year papers and it builds a structured academic model of pages, topics, subtopics and concepts, each with page-level provenance. Everything it generates is grounded in retrieved chunks, validated before persistence and traceable to a citation, while unsupported material becomes an explicit gap. Notes, flashcards, practice questions and a project-scoped tutor all read from that same retrieval layer, ending in a shareable site. Ingestion runs as resumable PostgreSQL-backed jobs, with 777 backend tests.", "tech": [ "Python 3.13", "TypeScript", "FastAPI", "Pydantic v2", "SQLAlchemy 2 async", "Alembic", "PostgreSQL 16", "pgvector (HNSW)", "tsvector full-text search", "PostgreSQL SKIP LOCKED job queue", "Redis", "Celery", "React 19", "Vite", "Tailwind CSS", "TanStack Query", "wouter", "Radix UI", "Qwen3-Embedding-0.6B", "Reciprocal rank fusion", "bge-reranker-v2-m3", "Groq", "OpenRouter", "PyMuPDF", "pdfplumber", "PP-OCRv6 tiny", "ONNX Runtime", "ReportLab", "Docker Compose", "S3-compatible storage" ], "features": [ "Structured ingestion: page-level extraction, native text first, targeted OCR and semantic chunking.", "Grounded study notes: topic-by-topic notes with citation checks and a PDF with TOC and gaps report.", "Retrieval practice: source-anchored flashcards and multiple-choice questions with citations.", "Project-scoped AI tutor: academic questions answer with sources, and sessions persist.", "Publishable study sites: notes, cards and questions rendered into a sanitised site with public slugs." ], "tags": [ "Full Stack", "EdTech", "FastAPI", "React", "PostgreSQL", "pgvector", "RAG", "AI", "Docker", "Self-Hosted" ], "links": [ { "label": "Live", "url": "https://trashthetest.aakifmudel.xyz", "type": "live" }, { "label": "Source Code", "url": "https://github.com/ItsAakif/TrashTheTest", "type": "repository" } ], "images": [ { "path": "Project Info/TrashTheTest/0.webp", "url": "https://aakifmudel.xyz/Project%20Info/TrashTheTest/0.webp" }, { "path": "Project Info/TrashTheTest/1.webp", "url": "https://aakifmudel.xyz/Project%20Info/TrashTheTest/1.webp" }, { "path": "Project Info/TrashTheTest/2.webp", "url": "https://aakifmudel.xyz/Project%20Info/TrashTheTest/2.webp" }, { "path": "Project Info/TrashTheTest/3.webp", "url": "https://aakifmudel.xyz/Project%20Info/TrashTheTest/3.webp" }, { "path": "Project Info/TrashTheTest/4.webp", "url": "https://aakifmudel.xyz/Project%20Info/TrashTheTest/4.webp" }, { "path": "Project Info/TrashTheTest/5.webp", "url": "https://aakifmudel.xyz/Project%20Info/TrashTheTest/5.webp" }, { "path": "Project Info/TrashTheTest/6.webp", "url": "https://aakifmudel.xyz/Project%20Info/TrashTheTest/6.webp" }, { "path": "Project Info/TrashTheTest/7.webp", "url": "https://aakifmudel.xyz/Project%20Info/TrashTheTest/7.webp" }, { "path": "Project Info/TrashTheTest/8.webp", "url": "https://aakifmudel.xyz/Project%20Info/TrashTheTest/8.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/TrashTheTest/0.webp" }, { "slug": "gideon", "title": "Project Gideon", "status": "completed", "url": "https://aakifmudel.xyz/projects/gideon", "category": { "key": "SYSTEMS", "name": "Systems" }, "date": "June 2025", "datePublished": "2025-06", "subtitle": "A privacy-focused offline Rust desktop assistant with local LLM chat, voice, task, document, and automation capabilities.", "summary": "A fully offline desktop AI assistant built in Rust that provides local LLM chat, voice commands, task management, clipboard intelligence, and desktop automation through an AI agent system.", "description": "Project Gideon is a privacy-focused desktop assistant that runs entirely offline using local LLM inference via Ollama. It combines an AI agent system (ZeroClaw) with deep OS integration—offering task management, document parsing, clipboard monitoring, and cross-platform automation (mouse/keyboard control). The UI is built with Slint, providing a modern native experience with dark/light themes.", "tech": [ "Rust", "Slint UI", "Ollama", "Whisper.cpp", "Sled", "Tokio", "Enigo" ], "features": [ "Local LLM chat with on-device inference, conversation history, and configurable response behavior.", "Offline speech-to-text assistant with multilingual transcription support (EN/HI/UR) for voice workflows.", "Local document analysis pipeline for PDF, DOCX, and CSV with text extraction and quick insights.", "Desktop automation engine for mouse, keyboard, app control, and window management actions.", "Task scheduling and tracking with priorities, due dates, progress states, and embedded Sled storage.", "Clipboard monitoring with content classification, timestamped history, and automatic summary generation." ], "architecture": [ "Local LLM chat", "Speech-to-text", "Document analysis", "Desktop automation", "Task scheduling", "Clipboard intelligence" ], "tags": [ "Rust", "AI", "Desktop", "Automation", "Local LLM" ], "links": [ { "label": "GitHub", "url": "https://github.com/ItsAakif/Project-Gideon", "type": "repository" } ], "images": [ { "path": "Project Info/Project Gideon/Image1.webp", "url": "https://aakifmudel.xyz/Project%20Info/Project%20Gideon/Image1.webp" }, { "path": "Project Info/Project Gideon/Image2.webp", "url": "https://aakifmudel.xyz/Project%20Info/Project%20Gideon/Image2.webp" }, { "path": "Project Info/Project Gideon/Image3.webp", "url": "https://aakifmudel.xyz/Project%20Info/Project%20Gideon/Image3.webp" }, { "path": "Project Info/Project Gideon/Image4.webp", "url": "https://aakifmudel.xyz/Project%20Info/Project%20Gideon/Image4.webp" }, { "path": "Project Info/Project Gideon/Image5.webp", "url": "https://aakifmudel.xyz/Project%20Info/Project%20Gideon/Image5.webp" }, { "path": "Project Info/Project Gideon/Image6.webp", "url": "https://aakifmudel.xyz/Project%20Info/Project%20Gideon/Image6.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/Project%20Gideon/Image1.webp" }, { "slug": "toris", "title": "Toris", "status": "completed", "url": "https://aakifmudel.xyz/projects/toris", "category": { "key": "SYSTEMS", "name": "Systems" }, "date": "February 2026", "datePublished": "2026-02", "subtitle": "A Rust llama.cpp runtime that monitors token confidence and adapts decoding to recover unstable generation.", "summary": "A Rust-based llama.cpp runtime that monitors token confidence and entropy in real time, then adaptively adjusts decoding or rolls back KV cache to recover unstable generation.", "description": "ToRIS is a modular LLM inference runtime focused on token-level control during generation. It solves the reliability gap in standard decoding by detecting instability while tokens are being produced, instead of only checking outputs after completion. The system combines per-token metric capture, sliding-window instability detection, and adaptive control actions such as sampler adjustment and regeneration.", "tech": [ "Rust", "llama.cpp", "llama_cpp_sys", "Serde", "Tracing", "PyO3", "Criterion" ], "features": [ "Token-level LLM generation runtime with on-the-fly confidence and entropy monitoring for each decoded token.", "Sliding-window instability detection that flags low-confidence or high-entropy token patterns during inference.", "Adaptive decoding control that adjusts temperature and top-p dynamically based on real-time instability severity and trend.", "KV-cache rollback and targeted regeneration to replace unstable token segments without restarting full generation.", "Session metrics collection for token latency, regeneration events, adaptation count, and confidence summaries with export support.", "Rust core with llama.cpp-backed GGUF model loading plus Python benchmarking scripts for baseline vs adaptive evaluation workflows." ], "architecture": [ "Token metric capture", "Sliding-window instability detection", "Adaptive decoding control", "KV-cache rollback", "Session metrics export" ], "tags": [ "Rust", "AI", "LLM", "ML" ], "links": [ { "label": "GitHub", "url": "https://github.com/ItsAakif/Toris", "type": "repository" } ], "images": [ { "path": "Project Info/Toris/Image1.webp", "url": "https://aakifmudel.xyz/Project%20Info/Toris/Image1.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/Toris/Image1.webp" }, { "slug": "enamel", "title": "Enamel", "status": "active", "url": "https://aakifmudel.xyz/projects/enamel", "category": { "key": "APPLICATIONS", "name": "Applications" }, "date": "March 2026", "datePublished": "2026-03", "subtitle": "Enamel: AI-Assisted Dental Practice Platform", "summary": "A multi-tenant dental practice platform for scheduling, patient records and charting, imaging, treatment plans, billing, patient portal and analytics, with an evaluated AI assistant and PostgreSQL row-level security.", "description": "Enamel is a full-stack dental practice management platform for small clinics, bringing scheduling, public booking, patient records, odontogram charting, DICOM imaging, treatment plans, billing, a patient portal and analytics into one same-origin application. Tenant isolation is enforced by PostgreSQL row-level security with separate runtime roles for the staff API, worker, patient portal and maintenance commands. It includes a clinic-scoped AI assistant with citations and confirmation gates plus a no-show prediction pipeline, and is a synthetic-data demo, not clinically validated software.", "tech": [ "Python", "TypeScript", "Django", "Django REST Framework", "PostgreSQL", "pgvector", "Celery", "Redis", "React", "Vite", "Tailwind CSS", "TanStack Query", "scikit-learn", "pydicom", "Docker", "Docker Compose", "nginx", "Render", "Supabase", "Upstash", "GitHub Actions" ], "features": [ "Multi-tenant data isolation: enforced by PostgreSQL row-level security with separate runtime roles.", "Front desk and booking: provider and operatory scheduling, public booking with holds and recall.", "Clinical workspace: patient records, encounters, FDI odontogram, treatment plans and consent records.", "AI assistant: tool-calling with confirmation gates, clinic-scoped retrieval and cited answers.", "Billing and patient access: invoices, sandbox payments and a patient portal with one-time-code sign-in." ], "architecture": [ "Multi-tenant isolation", "Scheduling & booking", "Clinical workspace", "Imaging archive", "Billing & portal", "AI assistant", "Analytics & audit", "Production deployment" ], "tags": [ "Full Stack", "Healthcare", "Django", "React", "PostgreSQL", "Multi-Tenant", "Row-Level Security", "AI", "RAG", "Docker", "Render" ], "links": [ { "label": "Live", "url": "https://enamel.aakifmudel.xyz", "type": "live" }, { "label": "GitHub", "url": "https://github.com/ItsAakif/Enamel", "type": "repository" } ], "images": [ { "path": "Project Info/Enamel/0.webp", "url": "https://aakifmudel.xyz/Project%20Info/Enamel/0.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/Enamel/0.webp" }, { "slug": "freehaven", "title": "FreeHaven", "status": "active", "url": "https://aakifmudel.xyz/projects/freehaven", "category": { "key": "APPLICATIONS", "name": "Applications" }, "date": "November 2026", "datePublished": "2026-11", "subtitle": "Freehaven - a local-first reading app that turns any book into a narrated, chapter-by-chapter guide", "summary": "A React Native 0.88 / Expo 58 app that imports a PDF or EPUB, finds its chapters, writes 250 words per chapter, narrates them with on-device Kokoro and word-level timings, and hands the reader one chapter a day - with SQLite as the only source of truth and a bring-your-own-key model layer that degrades instead of failing.", "description": "Freehaven is a local-first reading app that starts from a file, not a store: import a PDF or EPUB, and it detects the real chapters, writes 250 words per chapter, and narrates each one with on-device Kokoro and word-level timings, one chapter a day. SQLite is the only source of truth, holding books, plans and progress across 33 tables, while generation is checkpointed per chapter and resumed by an Android foreground service. Its bring-your-own-key layer shares thirteen provider presets behind one transport that retries, repairs malformed JSON and falls back to an on-device extractive engine.", "tech": [ "TypeScript 6 (strict)", "React Native 0.88.1", "Expo SDK 58", "React 19.3", "Expo Router", "Zustand", "expo-sqlite (FTS5)", "Kotlin", "Apache PdfBox", "react-native-sherpa-onnx", "Kokoro-82M", "expo-audio", "BYOK (13 provider presets)", "pdf-lib", "JSZip", "fast-xml-parser", "pako", "iconoir-react-native", "Jest 30", "GitHub Actions" ], "features": [ "Import that handles real books: native PdfBox PDF extraction, EPUB parsing and a quality gate.", "Chapter detection with a review step: outline, heading and page-group detection plus manual controls.", "A full guide, not a summary: 250 words per chapter, one chapter a day and per-chapter audio.", "On-device neural narration: Kokoro-82M through sherpa-onnx with word-level subtitle timing.", "An AI layer that degrades honestly: thirteen provider presets, retries and an extractive fallback." ], "tags": [ "Mobile", "React Native", "Expo", "TypeScript", "SQLite", "Local-First", "Offline", "AI", "BYOK", "TTS", "On-Device ML", "PDF", "EPUB", "Android" ], "links": [ { "label": "Source Code", "url": "https://github.com/ItsAakif/freehaven", "type": "repository" }, { "label": "Beta APK", "url": "https://github.com/ItsAakif/freehaven/releases", "type": "release" } ], "images": [ { "path": "Project Info/Freehaven/1.webp", "url": "https://aakifmudel.xyz/Project%20Info/Freehaven/1.webp" }, { "path": "Project Info/Freehaven/2.webp", "url": "https://aakifmudel.xyz/Project%20Info/Freehaven/2.webp" }, { "path": "Project Info/Freehaven/3.webp", "url": "https://aakifmudel.xyz/Project%20Info/Freehaven/3.webp" }, { "path": "Project Info/Freehaven/4.webp", "url": "https://aakifmudel.xyz/Project%20Info/Freehaven/4.webp" }, { "path": "Project Info/Freehaven/5.webp", "url": "https://aakifmudel.xyz/Project%20Info/Freehaven/5.webp" }, { "path": "Project Info/Freehaven/6.webp", "url": "https://aakifmudel.xyz/Project%20Info/Freehaven/6.webp" }, { "path": "Project Info/Freehaven/7.webp", "url": "https://aakifmudel.xyz/Project%20Info/Freehaven/7.webp" }, { "path": "Project Info/Freehaven/8.webp", "url": "https://aakifmudel.xyz/Project%20Info/Freehaven/8.webp" }, { "path": "Project Info/Freehaven/9.webp", "url": "https://aakifmudel.xyz/Project%20Info/Freehaven/9.webp" }, { "path": "Project Info/Freehaven/10.webp", "url": "https://aakifmudel.xyz/Project%20Info/Freehaven/10.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/Freehaven/1.webp" }, { "slug": "clearance", "title": "Clearance", "status": "active", "url": "https://aakifmudel.xyz/projects/clearance", "category": { "key": "APPLICATIONS", "name": "Applications" }, "date": "November 2025", "datePublished": "2025-11", "subtitle": "Built a self-hosted job search and resume workspace with a 350k-posting faceted catalogue, an evidence-backed claims layer, and server-rendered Typst/LaTeX PDFs.", "summary": "Built a self-hosted job search and resume workspace with a 350k-posting faceted catalogue, an evidence-backed claims layer, and server-rendered Typst/LaTeX PDFs.", "description": "Clearance is a local-first job search and resume workspace that runs on the owner's machine or server, signs in with a single application password, and stores everything in the owner's own PostgreSQL database. The catalogue comes from a sweep of company ATS boards and supports full-text search plus facets for location, work mode, seniority, salary, company and source. A career evidence layer turns imported or entered statements into claims; only confirmed claims can enter a resume, and the studio shows what is still unsupported. Match compares a posting against confirmed evidence and returns named gaps instead of a score, and the project engine turns those gaps into two-day project suggestions. Applications are tracked on a board with per-application events, notes and the resume version sent, while catalogue insights report measured rollups. A GitHub Actions workflow publishes weekly catalogue snapshots that can be imported locally. AI is bring-your-own-key and optional; every non-AI feature works without a provider.", "tech": [ "TypeScript", "React", "Vite", "Tailwind CSS", "Express", "Prisma", "PostgreSQL", "Typst", "LaTeX", "Docker", "Docker Compose", "GitHub Actions", "Self-Hosted", "BYOK AI" ], "features": [ "Faceted catalogue search over ~350k postings with stable paging and a derived search document", "Resume studio with Typst and LaTeX templates, live preview, and server-rendered PDFs", "Evidence and claims layer: nothing unconfirmed can enter a resume", "Job match with named gaps instead of a score", "Two-day project suggestions generated from the named gaps", "Applications board and workspace with stages, events, notes and the resume version sent", "Catalogue insights with measured rollups for skills, work mode, seniority and salary", "Weekly catalogue snapshots published by GitHub Actions and imported through a local sync", "MIT licensed, Docker Compose deployment, optional BYOK AI provider keys" ], "tags": [ "Full Stack", "Web", "Self-Hosted", "Job Search", "Resume Tech", "AI", "PostgreSQL" ], "links": [ { "label": "Live", "url": "http://clearance.aakifmudel.xyz/", "type": "live" }, { "label": "Source Code", "url": "https://github.com/ItsAakif/clearance", "type": "repository" } ], "images": [ { "path": "Project Info/Clearance/01-jobs.webp", "url": "https://aakifmudel.xyz/Project%20Info/Clearance/01-jobs.webp" }, { "path": "Project Info/Clearance/02-studio.webp", "url": "https://aakifmudel.xyz/Project%20Info/Clearance/02-studio.webp" }, { "path": "Project Info/Clearance/03-profile.webp", "url": "https://aakifmudel.xyz/Project%20Info/Clearance/03-profile.webp" }, { "path": "Project Info/Clearance/04-match.webp", "url": "https://aakifmudel.xyz/Project%20Info/Clearance/04-match.webp" }, { "path": "Project Info/Clearance/05-projects.webp", "url": "https://aakifmudel.xyz/Project%20Info/Clearance/05-projects.webp" }, { "path": "Project Info/Clearance/06-applications.webp", "url": "https://aakifmudel.xyz/Project%20Info/Clearance/06-applications.webp" }, { "path": "Project Info/Clearance/07-application.webp", "url": "https://aakifmudel.xyz/Project%20Info/Clearance/07-application.webp" }, { "path": "Project Info/Clearance/08-insights.webp", "url": "https://aakifmudel.xyz/Project%20Info/Clearance/08-insights.webp" }, { "path": "Project Info/Clearance/09-interview-prep.webp", "url": "https://aakifmudel.xyz/Project%20Info/Clearance/09-interview-prep.webp" }, { "path": "Project Info/Clearance/10-settings.webp", "url": "https://aakifmudel.xyz/Project%20Info/Clearance/10-settings.webp" }, { "path": "Project Info/Clearance/11-preferences.webp", "url": "https://aakifmudel.xyz/Project%20Info/Clearance/11-preferences.webp" }, { "path": "Project Info/Clearance/12-qa.webp", "url": "https://aakifmudel.xyz/Project%20Info/Clearance/12-qa.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/Clearance/01-jobs.webp" }, { "slug": "pipeline", "title": "Pipeline", "status": "active", "url": "https://aakifmudel.xyz/projects/pipeline", "category": { "key": "SYSTEMS", "name": "Systems" }, "date": "October 2025", "datePublished": "2025-10", "subtitle": "An educational platform visualizing C compilation from tokenization through LLVM IR to binary execution.", "summary": "A browser-based educational platform that visualizes every stage of C compilation from source code to binary execution with real-time feedback and interactive simulation.", "description": "Pipeline is an interactive web application that demystifies how C code becomes machine instructions. Built with a Rust-Actix backend and TypeScript-SolidJS frontend, it captures the full compilation pipeline: tokenization, parsing, semantic analysis, LLVM IR generation, optimization passes, assembly generation, and binary encoding.", "tech": [ "Rust", "Actix Web", "TypeScript", "SolidJS", "Monaco Editor", "LLVM", "Docker" ], "features": [ "Interactive C compilation walkthrough with stage-by-stage visualization from tokenization and AST generation to LLVM IR, assembly, and binary output.", "Real-time browser code editor with C syntax highlighting, built-in templates, auto-save, and JSON import/export for repeatable learning sessions.", "Instruction-level execution simulator with register/stack state tracking and frame-by-frame memory events for low-level program behavior analysis.", "Decoded machine-instruction view that breaks bytes into prefixes, opcodes, ModR/M, SIB, displacement, and immediate fields with human-readable explanations.", "Educational diagnostics pipeline with clear compilation error reporting, symbol/scope insights, and side-by-side representations across compilation stages.", "Docker-ready full-stack deployment architecture with a Rust Actix backend and SolidJS frontend for local development and production hosting." ], "architecture": [ "Tokenization", "Parsing", "Semantic analysis", "LLVM IR generation", "Optimization passes", "Assembly generation", "Binary encoding" ], "tags": [ "Web", "Educational", "Compiler", "Full Stack" ], "links": [ { "label": "Live", "url": "https://pipeline.aakifmudel.xyz", "type": "live" }, { "label": "GitHub", "url": "https://github.com/ItsAakif/pipeline", "type": "repository" } ], "images": [ { "path": "Project Info/Pipeline/1.webp", "url": "https://aakifmudel.xyz/Project%20Info/Pipeline/1.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/Pipeline/1.webp" }, { "slug": "vault", "title": "Vault", "status": "completed", "url": "https://aakifmudel.xyz/projects/vault", "category": { "key": "SYSTEMS", "name": "Systems" }, "date": "October 2026", "datePublished": "2026-10", "subtitle": "A self-hosted personal command centre backed by your own Google Drive.", "summary": "A self-hosted command centre for notes, projects, job applications and a calendar in which every record is a plain markdown or JSON file inside the user's own Google Drive, with no vendor database of their content.", "description": "Vault is a personal information manager built around one constraint: the user's data must remain theirs. Notes, projects, job applications and calendar events live as markdown and JSON files in a folder tree inside the user's own Google Drive, while the application keeps no copy and a small SQLite database holds only the account, OAuth link and preferences. Because Drive is the only store, every surface is honest about failure: an unreachable route returns a typed 403 with a remedy, and a failed save keeps the draft. Lists are served from cached metadata without reading note bodies.", "tech": [ "TypeScript", "Next.js 15 (App Router)", "React 19", "Tailwind CSS 3.4", "Radix UI", "react-resizable-panels", "Socket.IO", "NextAuth", "Google OAuth", "SQLite", "Prisma", "Google Drive REST v3", "Vitest", "Testing Library", "Docker", "Docker Compose", "Railway", "ESLint" ], "features": [ "Drive-backed content, no vendor store: notes are markdown and jobs are JSON inside Google Drive.", "Type-aware filing: a note lands in the folder for its content type, recorded in the file itself.", "Drive-availability gate: data routes return a typed 403 with a connect link instead of failing.", "Lists without body reads: list metadata lives in Drive properties, so a list costs one call.", "Honest failure states: a failed save keeps the draft and reports the server's reason." ], "architecture": [ "Google Drive file store", "Type-aware filing", "Drive-availability gate", "Metadata list index", "Bounded caches", "Shared application shell", "Realtime over Socket.IO" ], "tags": [ "Full Stack", "Next.js", "TypeScript", "Google Drive API", "Self-Hosted", "Productivity", "Prisma", "SQLite", "NextAuth", "Socket.IO", "Tailwind CSS", "Docker" ], "links": [ { "label": "Source Code", "url": "https://github.com/ItsAakif/vault", "type": "repository" } ], "images": [ { "path": "Project Info/Vault/1.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/1.webp" }, { "path": "Project Info/Vault/2.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/2.webp" }, { "path": "Project Info/Vault/3.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/3.webp" }, { "path": "Project Info/Vault/4.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/4.webp" }, { "path": "Project Info/Vault/5.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/5.webp" }, { "path": "Project Info/Vault/6.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/6.webp" }, { "path": "Project Info/Vault/7.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/7.webp" }, { "path": "Project Info/Vault/8.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/8.webp" }, { "path": "Project Info/Vault/9.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/9.webp" }, { "path": "Project Info/Vault/10.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/10.webp" }, { "path": "Project Info/Vault/11.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/11.webp" }, { "path": "Project Info/Vault/12.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/12.webp" }, { "path": "Project Info/Vault/13.webp", "url": "https://aakifmudel.xyz/Project%20Info/Vault/13.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/Vault/1.webp" }, { "slug": "bitewise", "title": "BiteWise", "status": "active", "url": "https://aakifmudel.xyz/projects/bitewise", "category": { "key": "APPLICATIONS", "name": "Applications" }, "date": "October 2026", "datePublished": "2026-10", "subtitle": "A local-first nutrition and training diary for Indian home cooking.", "summary": "A React Native diary built around Indian home food rather than barcodes and restaurant portions: 174 foods in SI units, twelve micronutrients against ICMR-NIN 2020 reference intakes, and optional AI that only runs on a key the user supplies.", "description": "BiteWise is a nutrition and training diary built for people who cook, rather than barcodes and restaurant portions. Its catalogue ships 105 Indian dishes as first-class rows measured in grams, each carrying micronutrients, and any value can be corrected in place to match how a household cooks. The diary lives entirely on the device in AsyncStorage and works offline, and the app never invents a number: missing micronutrients are unknown, and AI only identifies a catalogue row while every calorie and macro is computed locally. It ships as a downloadable Android build with 121 behavioural checks.", "tech": [ "JavaScript", "JSDoc", "React Native 0.76", "Expo SDK 52", "Redux Toolkit", "React Navigation", "Firebase Authentication", "AsyncStorage", "OpenRouter", "OpenAI", "Open Food Facts", "schema.org JSON-LD", "expo prebuild", "Gradle", "assembleRelease", "GitHub Actions" ], "features": [ "Indian-first catalogue in SI units: 174 foods, 105 dishes, with per-user overrides for any value.", "Four ways to log: catalogue search, sentence parsing, barcode lookup and manual FSSAI entry.", "Insights that show their working: nutrient gaps and consistency trends with days examined.", "Optional AI, constrained: AI identifies a catalogue row and the app computes every number.", "Local-first privacy: no analytics, no account; the diary works offline and stays on the device." ], "architecture": [ "Indian-first catalogue in SI units", "Four logging paths", "Meal Memory templates", "Insights that show their working", "Adaptive energy expenditure", "Constrained bring-your-own-key AI", "Local-first AsyncStorage" ], "tags": [ "Mobile", "React Native", "Expo", "Redux Toolkit", "JavaScript", "HealthTech", "Nutrition", "Local-First", "Offline", "AI", "BYOK", "Android" ], "links": [ { "label": "Source Code", "url": "https://github.com/ItsAakif/Bitewise", "type": "repository" }, { "label": "Install APK", "url": "https://github.com/ItsAakif/Bitewise/releases/tag/v1.0.0", "type": "release" } ], "images": [ { "path": "Project Info/Bitewise/1.webp", "url": "https://aakifmudel.xyz/Project%20Info/Bitewise/1.webp" }, { "path": "Project Info/Bitewise/2.webp", "url": "https://aakifmudel.xyz/Project%20Info/Bitewise/2.webp" }, { "path": "Project Info/Bitewise/3.webp", "url": "https://aakifmudel.xyz/Project%20Info/Bitewise/3.webp" }, { "path": "Project Info/Bitewise/4.webp", "url": "https://aakifmudel.xyz/Project%20Info/Bitewise/4.webp" }, { "path": "Project Info/Bitewise/5.webp", "url": "https://aakifmudel.xyz/Project%20Info/Bitewise/5.webp" }, { "path": "Project Info/Bitewise/6.webp", "url": "https://aakifmudel.xyz/Project%20Info/Bitewise/6.webp" }, { "path": "Project Info/Bitewise/7.webp", "url": "https://aakifmudel.xyz/Project%20Info/Bitewise/7.webp" }, { "path": "Project Info/Bitewise/8.webp", "url": "https://aakifmudel.xyz/Project%20Info/Bitewise/8.webp" }, { "path": "Project Info/Bitewise/9.webp", "url": "https://aakifmudel.xyz/Project%20Info/Bitewise/9.webp" }, { "path": "Project Info/Bitewise/10.webp", "url": "https://aakifmudel.xyz/Project%20Info/Bitewise/10.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/Bitewise/1.webp" }, { "slug": "tasktide", "title": "TaskTide", "status": "completed", "url": "https://aakifmudel.xyz/projects/tasktide", "category": { "key": "SYSTEMS", "name": "Systems" }, "date": "December 2024", "datePublished": "2024-12", "subtitle": "A Rust Windows task manager with live process telemetry and deadline-based termination.", "summary": "A Rust-based Windows desktop task manager that monitors running processes, supports deadline-based termination, and attempts auto-save before closing user applications.", "description": "TaskTide is a native desktop utility for managing active Windows processes from a single interface. It addresses a common gap in basic task killers by adding timed shutdown controls and safer process termination behavior.", "tech": [ "Rust", "Iced", "sysinfo", "chrono", "Windows API", "Enigo" ], "features": [ "Real-time Windows process dashboard with live CPU and memory telemetry, searchable task discovery, and per-process status visibility", "Deadline-based process control with preset/custom timers, remaining-time tracking, and automatic termination when deadlines expire", "Safe shutdown workflow for productivity apps with window detection, simulated Ctrl+S save attempts, and delayed force-kill execution", "Native process management layer using Win32 APIs for handle acquisition, permission-aware termination, and cleanup of OS resources", "Executable icon extraction and rendering pipeline with shell/GDI integration, bitmap conversion, and lightweight in-table icon display", "Windows-ready desktop packaging with embedded metadata/icon resources, modern manifest settings, and high-DPI aware display behavior" ], "architecture": [ "Process monitoring", "Deadline-based termination", "Safe shutdown and auto-save", "Win32 process management", "Icon extraction" ], "tags": [ "Desktop", "Windows", "Rust", "Productivity", "Automation" ], "links": [ { "label": "GitHub", "url": "https://github.com/ItsAakif/Tasktide", "type": "repository" } ], "images": [ { "path": "Project Info/TaskTide/1.webp", "url": "https://aakifmudel.xyz/Project%20Info/TaskTide/1.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/TaskTide/1.webp" }, { "slug": "krezy-detection", "title": "Krezy Detection", "status": "completed", "url": "https://aakifmudel.xyz/projects/krezy-detection", "category": { "key": "INTELLIGENCE", "name": "Intelligence" }, "date": "November 2024", "datePublished": "2024-11", "subtitle": "A Python emotion-detection library for images and webcam streams with reusable app entry points.", "summary": "A Python emotion-detection library that analyzes images and webcam streams with DeepFace and exposes reusable web, desktop, and mobile integration entry points.", "description": "Krezy is a packaged Python library for facial emotion analysis across multiple app surfaces. It solves the problem of wiring emotion inference separately for each platform by centralizing detection in one EmotionDetector API.", "tech": [ "Python", "DeepFace", "OpenCV", "NumPy", "Flask", "Kivy", "CustomTkinter" ], "features": [ "Real-time emotion detection from webcam streams with live visual overlays and dominant-emotion highlighting", "Image emotion analysis for file paths or in-memory image arrays with per-emotion percentage breakdowns", "Configurable face-detection backends (OpenCV, SSD, MTCNN, RetinaFace) to balance speed and detection accuracy", "Structured inference output with success status, dominant emotion, full emotion scores, error details, and ISO timestamps", "Cross-platform integration layer with ready-to-use web, desktop, and mobile app entry points from one core detector API", "Flexible Python packaging with optional platform extras for targeted installs (core, web, mobile, desktop, or all)" ], "architecture": [ "Face detection", "Emotion inference", "Structured output", "Cross-platform integration" ], "tags": [ "AI", "Computer Vision", "Python", "ML" ], "links": [ { "label": "GitHub", "url": "https://github.com/ItsAakif/KrezyDetection", "type": "repository" }, { "label": "PyPI", "url": "https://pypi.org/project/krezy/", "type": "package" } ], "images": [ { "path": "Project Info/Krezy Detection/Image1.webp", "url": "https://aakifmudel.xyz/Project%20Info/Krezy%20Detection/Image1.webp" }, { "path": "Project Info/Krezy Detection/Image2.webp", "url": "https://aakifmudel.xyz/Project%20Info/Krezy%20Detection/Image2.webp" }, { "path": "Project Info/Krezy Detection/Image3.webp", "url": "https://aakifmudel.xyz/Project%20Info/Krezy%20Detection/Image3.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/Krezy%20Detection/Image1.webp" }, { "slug": "its-mocked", "title": "ItsMocked", "status": "active", "url": "https://aakifmudel.xyz/projects/its-mocked", "category": { "key": "INTELLIGENCE", "name": "Intelligence" }, "date": "January 2025", "datePublished": "2025-01", "subtitle": "A full-stack AI interview simulator with multimodal analysis and coaching feedback.", "summary": "A full-stack AI interview simulator that records candidate responses and generates multimodal performance analysis with actionable coaching feedback.", "description": "ItsMocked is a web application for practicing mock interviews through video or text responses. It solves the gap between generic interview prep and measurable communication feedback by scoring confidence, communication quality, emotional stability, and sentiment.", "tech": [ "Python", "Flask", "SQLAlchemy", "MediaPipe", "faster-whisper", "librosa", "Transformers", "Next.js" ], "features": [ "Session-based mock interview simulator with randomized question sets, progress tracking, and separate video/text practice modes", "Secure user account system with registration/login, protected routes, and built-in rate limiting for auth and submission endpoints", "Video interview pipeline with upload validation, optional S3 storage, asynchronous analysis triggering, and per-question interview persistence", "Multimodal analysis engine combining speech transcription, facial emotion signals, text emotion detection, and voice features into composite interview scores", "AI coaching feedback generation via LLM prompts using transcript and scoring context to produce strengths, improvement areas, and communication tips", "Performance dashboard with session-level analytics, interview history, aggregated confidence/overall metrics, and per-question analysis breakdowns" ], "architecture": [ "Interview session", "Video and text capture", "Multimodal analysis", "AI coaching feedback", "Performance dashboard" ], "tags": [ "AI", "Full Stack", "Computer Vision", "NLP", "Web" ], "links": [ { "label": "Live", "url": "https://itsmocked.aakifmudel.xyz", "type": "live" } ], "images": [ { "path": "Project Info/ItsMocked/Image1.webp", "url": "https://aakifmudel.xyz/Project%20Info/ItsMocked/Image1.webp" }, { "path": "Project Info/ItsMocked/Image2 (2).webp", "url": "https://aakifmudel.xyz/Project%20Info/ItsMocked/Image2%20(2).webp" }, { "path": "Project Info/ItsMocked/Image3.webp", "url": "https://aakifmudel.xyz/Project%20Info/ItsMocked/Image3.webp" }, { "path": "Project Info/ItsMocked/Image4.webp", "url": "https://aakifmudel.xyz/Project%20Info/ItsMocked/Image4.webp" }, { "path": "Project Info/ItsMocked/Image5.webp", "url": "https://aakifmudel.xyz/Project%20Info/ItsMocked/Image5.webp" }, { "path": "Project Info/ItsMocked/Image6.webp", "url": "https://aakifmudel.xyz/Project%20Info/ItsMocked/Image6.webp" }, { "path": "Project Info/ItsMocked/Image7.webp", "url": "https://aakifmudel.xyz/Project%20Info/ItsMocked/Image7.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/ItsMocked/Image1.webp" }, { "slug": "geo-vision", "title": "GeoVision", "status": "completed", "url": "https://aakifmudel.xyz/projects/geo-vision", "category": { "key": "INTELLIGENCE", "name": "Intelligence" }, "date": "2025", "datePublished": "2025", "subtitle": "An end-to-end geospatial AI pipeline detecting glacial lakes, roads, and drainage from satellite imagery.", "summary": "An end-to-end geospatial AI pipeline that detects glacial lakes, road centerlines, and drainage networks from satellite imagery and exports GIS-ready outputs.", "description": "GeoVision is a Python-based computer vision and geospatial processing system for automated feature extraction from multispectral raster data. It replaces manual mapping workflows by running preprocessing, model training, inference, and geospatial post-processing in one pipeline.", "tech": [ "Python", "PyTorch", "segmentation-models-pytorch", "Albumentations", "Rasterio", "GeoPandas", "Streamlit" ], "features": [ "Automated geospatial feature detection pipeline for satellite imagery, covering glacial lakes, road centerlines, and drainage networks in a single workflow", "Multi-stage CLI execution for preprocessing, model training, inference, and end-to-end runs with configurable YAML-driven settings", "Deep-learning segmentation stack using U-Net/DeepLab-style models with augmentation, validation, early stopping, and checkpointed model saving", "Large-raster inference engine with tiling and overlap blending, plus feature-specific post-processing for cleaner masks and network continuity", "Geospatial vectorization workflow that converts predictions into polygons/lines/points, exports GeoJSON outputs, and computes area/length statistics", "Temporal change analysis utilities for multi-date imagery to track feature growth, shrinkage, emergence, and overall trend summaries" ], "architecture": [ "Preprocessing", "Model training", "Large-raster inference", "Geospatial vectorization", "Temporal change analysis" ], "tags": [ "AI", "ML", "Computer Vision", "Geospatial", "Automation" ], "links": [ { "label": "GitHub", "url": "https://github.com/ItsAakif/geovision", "type": "repository" } ], "images": [ { "path": "Project Info/GeoVision/Image1.webp", "url": "https://aakifmudel.xyz/Project%20Info/GeoVision/Image1.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/GeoVision/Image1.webp" }, { "slug": "sahayak", "title": "Sahayak", "status": "active", "url": "https://aakifmudel.xyz/projects/sahayak", "category": { "key": "INTELLIGENCE", "name": "Intelligence" }, "date": "July 2025", "datePublished": "2025-07", "subtitle": "A React AI teaching assistant generating stories, worksheets, and lesson plans with Google Gemini.", "summary": "A React-based AI teaching assistant that helps teachers generate stories, worksheets, lesson plans, and image-driven classroom material using Google Gemini.", "description": "Sahayak is a web app designed for teachers who need to prepare quality classroom content quickly, especially in resource-constrained school settings. It centralizes multiple teaching workflows: content generation, student Q&A support, worksheet creation, visual-aid instructions, and textbook image analysis.", "tech": [ "JavaScript", "React 19", "Vite", "Framer Motion", "Gemini API", "react-dropzone", "jsPDF" ], "features": [ "AI-powered teaching assistant with prompt-driven generation of stories, worksheets, lesson plans, Q&A responses, and blackboard drawing guides", "Camera and image-analysis workflow with live capture or upload, textbook-content understanding, and automatic generation of questions, stories, and lesson activities", "Audio learning toolkit with browser-based voice recording, speech-to-text transcription, text-to-speech playback, and transcript-to-content conversion", "Interactive educational games module with playable math and language activities, score/streak tracking, and level progression for classroom engagement", "Configurable Gemini integration with API key validation, multi-model selection, request throttling/retry logic, and robust user-facing error handling", "Teacher-ready output pipeline with formatted result preview, copy/share actions, PDF export, and local lesson-plan persistence for reuse" ], "architecture": [ "Prompt-driven generation", "Camera and image analysis", "Audio learning toolkit", "Educational games", "PDF export" ], "tags": [ "AI", "Web", "NLP", "EdTech" ], "links": [ { "label": "GitHub", "url": "https://github.com/ItsAakif/Sahayak/", "type": "repository" }, { "label": "Live", "url": "https://sahayak-0.web.app/", "type": "live" } ], "images": [ { "path": "Project Info/Sahayak/Image1.webp", "url": "https://aakifmudel.xyz/Project%20Info/Sahayak/Image1.webp" }, { "path": "Project Info/Sahayak/Image2.webp", "url": "https://aakifmudel.xyz/Project%20Info/Sahayak/Image2.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/Sahayak/Image1.webp" }, { "slug": "yapbot", "title": "Yapbot", "status": "active", "url": "https://aakifmudel.xyz/projects/yapbot", "category": { "key": "APPLICATIONS", "name": "Applications" }, "date": "October 2023", "datePublished": "2023-10", "subtitle": "A full-stack AI document chat app with vector retrieval and streaming, citation-style answers.", "summary": "A full-stack AI document chat application that lets authenticated users upload PDFs, retrieve context with vector search, and receive streaming, citation-style answers in real time.", "description": "Yapbot is a web application for conversational Q&A over user-uploaded PDF documents. It solves the problem of manually searching long files by turning documents into semantic chunks and retrieving the most relevant passages per question.", "tech": [ "TypeScript", "Next.js", "Drizzle ORM", "Clerk", "Groq SDK", "PostgreSQL", "AWS S3", "Stripe" ], "features": [ "Authenticated multi-PDF chat workspace with Clerk-based user sessions, per-user chat ownership checks, and persistent chat history retrieval.", "PDF ingestion pipeline with S3 upload/download, server-side text extraction via pdf-parse, semantic chunking, and document-level storage for reuse.", "Vector retrieval layer using Hugging Face embeddings + PostgreSQL vector columns, with top-K similarity search to fetch relevant chunks per query.", "Streaming AI chat API powered by Groq, combining prior conversation context and retrieved document passages to generate grounded responses in real time.", "Document management experience with chat-scoped file attachment, sidebar chat navigation, and cascade-safe deletion of chats, documents, and messages.", "Subscription and billing foundation with Stripe Checkout, Billing Portal routing, webhook-driven subscription state sync, and Pro-plan period tracking.", "Abuse and reliability controls including Upstash Redis sliding-window rate limiting, request payload validation, and API-level unauthorized access handling." ], "architecture": [ "PDF ingestion", "Vector retrieval", "Streaming AI chat", "Document management", "Subscription billing" ], "tags": [ "AI", "Web", "Full Stack", "NLP", "SaaS" ], "links": [ { "label": "Live", "url": "https://yapbot.aakifmudel.xyz", "type": "live" } ], "images": [ { "path": "Project Info/Yapbot/Image1.webp", "url": "https://aakifmudel.xyz/Project%20Info/Yapbot/Image1.webp" }, { "path": "Project Info/Yapbot/Image2.webp", "url": "https://aakifmudel.xyz/Project%20Info/Yapbot/Image2.webp" }, { "path": "Project Info/Yapbot/Image3.webp", "url": "https://aakifmudel.xyz/Project%20Info/Yapbot/Image3.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/Yapbot/Image1.webp" }, { "slug": "zyra", "title": "Zyra", "status": "completed", "url": "https://aakifmudel.xyz/projects/zyra", "category": { "key": "APPLICATIONS", "name": "Applications" }, "date": "June 2025", "datePublished": "2025-06", "subtitle": "A React Native payment app that scans global QR formats and settles on Algorand.", "summary": "A cross-platform React Native payment app that lets users scan global QR payment formats, settle transactions on Algorand, and earn tokenized rewards with built-in auth, KYC, and subscription tiers.", "description": "Zyra is a cross-platform fintech app that converts global QR payments into blockchain-settled transactions from a single mobile workflow. Users can scan UPI, PIX, PayNow, Alipay, SEPA, and other regional formats without learning country-specific payment rails. It is built for real-world remittances, cross-border freelancer payouts, and merchant payments where traditional gateways are fragmented or expensive. Each payment computes fiat-to-crypto value, executes settlement through an Algorand wallet, and records auditable transaction data.\n\nThe system uses Expo Router for client navigation, with service modules handling auth, wallet state, payment orchestration, rewards, and compliance. Supabase backs identity, transaction storage, KYC status, achievements, and token metadata, while API routes encapsulate payment/KYC/exchange operations. On-chain interactions are isolated in Algorand services for account creation, balance tracking, asset transfers, and transaction history retrieval. A feature-toggle and demo-mode path preserves full UX continuity when external providers are unavailable, enabling reliable testing and presentations.", "tech": [ "TypeScript", "React Native", "Expo SDK", "Supabase", "Algorand SDK", "RevenueCat", "ElevenLabs API" ], "features": [ "Type-safe payment orchestration in TypeScript that validates QR payloads, computes fiat-to-crypto conversion, and returns structured success/error results for transaction flows", "TypeScript QR parsing engine that recognizes UPI, PIX, PayNow, Alipay, Zelle, Venmo, M-Pesa, and SEPA formats, then normalizes each into a unified payment object", "Algorand wallet services in .ts that handle account creation/import, secure key persistence, ALGO/asset transfers, and periodic balance synchronization through typed state listeners", "Auth/state layer in .ts that integrates Supabase session management, profile hydration, and reactive subscribers to keep client screens aligned with backend identity state", "KYC and rewards logic in TypeScript that processes verification status, issues identity-token records, computes Zyro incentives, and persists milestones for achievement tracking", "API route modules in .ts that separate HTTP handling from domain services for payments, exchange rates, voice, and analytics, improving maintainability and testability" ], "architecture": [ "QR parsing", "Payment orchestration", "Algorand settlement", "KYC and rewards", "API route modules" ], "tags": [ "Mobile", "Blockchain", "FinTech", "AI" ], "links": [ { "label": "Demo", "url": "https://youtu.be/BYGFUWHgRqk", "type": "demo" }, { "label": "GitHub", "url": "https://github.com/ItsAakif/Zyra", "type": "repository" } ], "images": [ { "path": "Project Info/Zyra/zyra.webp", "url": "https://aakifmudel.xyz/Project%20Info/Zyra/zyra.webp" } ], "image": "https://aakifmudel.xyz/Project%20Info/Zyra/zyra.webp" }, { "slug": "screens", "title": "Screens - Windows Wallpaper Manager", "status": "completed", "url": "https://aakifmudel.xyz/projects/screens", "category": { "key": "SYSTEMS", "name": "Systems" }, "date": "November 2025", "datePublished": "2025-11", "subtitle": "A WinUI 3 desktop wallpaper manager with live wallpapers and SQLite-backed favorites and history.", "summary": "Built a WinUI 3 desktop application that lets users browse local wallpaper libraries, apply static or live wallpapers, and persist favorites/history with SQLite-backed state.", "description": "Screens.WinUI is a Windows desktop wallpaper manager built with MVVM and dependency injection. It solves the friction of manually finding, applying, and tracking wallpapers by centralizing browsing, filtering, preview, and one-click desktop/lock-screen updates. 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