Forem: Rudra Sarker

Nexus Agent — The Zero-Config, Self-Evolving Local AI Agent Framework

Rudra Sarker — Sat, 09 May 2026 13:31:48 +0000

What if your AI agent could improve itself?

Nexus Agent is an open-source AI agent framework that's zero-config, self-evolving, and runs 100% locally with Ollama.

What Makes It Different

Zero Config — Download and run. No API keys needed for local models
Self-Evolving — The agent learns from interactions and improves over time
Multi-Agent — Orchestrate multiple agents working together
GraphRAG — Graph-based retrieval augmented generation
IDE Integration — Works with your code editor
Docker Ready — One command to spin up
100% Local — Your data never leaves your machine

Architecture

Plugin-based system for adding capabilities
Built-in memory and context management
Supports local models via Ollama
Cloud sync (optional) for cross-device persistence

Quick Start

git clone https://github.com/rudra496/nexus-agent.git
cd nexus-agent
pip install -r requirements.txt
python -m nexus_agent

Who Is This For?

Developers building AI agents
Researchers exploring self-improving AI
Anyone who wants powerful AI without cloud dependencies

GitHub: github.com/rudra496/nexus-agent

If you're into AI agents, give it a star!

Built by Rudra Sarker — MIT License

ScienceLab 3D — 40+ Virtual Science Experiments in Your Browser (Free, No Install)

Rudra Sarker — Sat, 09 May 2026 13:31:15 +0000

What if every student could do science experiments at home, for free?

ScienceLab 3D is an interactive STEM education platform with 40+ virtual experiments — all running in your browser with Three.js and WebGL.

Try it: rudra496.github.io/science

What's Inside

3D Periodic Table — All 118 elements, click for details
Solar System Explorer — 8 planets + Sun with realistic orbits
DNA/RNA Visualizer — Double helix animation, replication, transcription
Cell Biology Lab — Animal/Plant cells, bacteria, 8 viruses, mitosis animation
Physics Lab — Double slit experiment, Rutherford scattering, pendulum, wave motion, optics
Robotics Academy — 6-DOF robotic arm, motors, sensors, controllers
Space Survival Game — 3D space combat

Why This Matters

Many schools — especially in developing countries — don't have science labs. ScienceLab 3D gives every student with a browser access to interactive experiments.

Tech Stack

Three.js — 3D graphics
Pure HTML/CSS/JS — No build tools needed
Responsive — Works on desktop, tablet, and mobile
3 files — index.html, style.css, script.js

Deploy Your Own

Fork the repo and enable GitHub Pages. That's it — your own virtual science lab.

GitHub: github.com/rudra496/science

If you care about science education, give it a star!

Built by Rudra Sarker — MIT License

SightlineAI — Open-Source Smart Glasses for the Visually Impaired (2.2B+ People Need This)

Rudra Sarker — Sat, 09 May 2026 13:30:42 +0000

Why This Matters

2.2 billion people worldwide have vision impairment. Most assistive devices cost thousands. What if open-source AI could help?

SightlineAI is an AI-powered assistive eyewear project that provides real-time object detection, text recognition, and environment awareness for visually impaired people.

What It Does

Real-time Object Detection — Identifies objects in the user's environment using computer vision
Text-to-Speech — Reads signs, labels, and documents aloud
Navigation Assistance — Helps users navigate their surroundings
ESP32 + IoT — Built with affordable, accessible hardware
Open Source — So anyone can build, modify, and improve it

The Hardware

ESP32 microcontroller with camera module
Audio output for text-to-speech
Designed to be 3D-printable and low-cost
Total component cost: under $50

Why Open Source?

Assistive technology shouldn't be expensive. By making SightlineAI open source, anyone with basic electronics skills can build their own smart glasses — students, hobbyists, NGOs in developing countries.

Quick Start

git clone https://github.com/rudra496/sightlineai.git

See the hardware setup guide in the repository.

GitHub: github.com/rudra496/sightlineai

Live Site: sightlineai website

If you believe in accessible technology, give it a star!

Built by Rudra Sarker — Open Source

MindWell — I Built a Free Mental Health Platform Because Therapy Shouldn't Cost $200/Session

Rudra Sarker — Sat, 09 May 2026 13:30:09 +0000

The Problem

1 in 4 people will experience mental health issues. But therapy costs $100-300/session. Many people — especially students and young professionals — can't afford it.

I built MindWell to provide free, private mental health tools that anyone can use.

What It Does

Mood Tracking — Log your mood daily with AI-powered insights
Self-Assessment — PHQ-9 and GAD-7 based screenings for depression and anxiety
Guided Mindfulness — Meditation and breathing exercises
Therapeutic Tools — Journaling, CBT exercises, thought reframing
PWA Support — Install it on your phone like a native app
100% Private — All data stays on your device

Tech Stack

Next.js + TypeScript — Full-stack React framework
Tailwind CSS — Beautiful, accessible design
PWA — Works offline, installable
Open Source — MIT License

Why Open Source?

Mental health tools shouldn't be paywalled. MindWell is free, open-source, and privacy-first. No accounts required. No data collection. Just tools that help.

Quick Start

git clone https://github.com/rudra496/mindwell.git
cd mindwell
npm install
npm run dev

GitHub: github.com/rudra496/mindwell

If mental health matters to you, give it a star and help spread the word.

Built by Rudra Sarker — MIT License

EdgeBrain — A Complete Edge AI Platform That Runs 100% Locally (Open Source)

Rudra Sarker — Sat, 09 May 2026 13:29:36 +0000

What if you could run a full AI-powered smart home system without any cloud dependency?

That's exactly what EdgeBrain does. It's an open-source edge AI platform that simulates, processes, and acts on IoT sensor data — all locally, all free.

Why Edge AI Matters

Cloud AI has problems:

Latency — round-trips to the cloud add 200-500ms
Privacy — your smart home data sent to third parties
Cost — API calls add up fast
Offline — no internet = no intelligence

EdgeBrain solves all of these by running everything on your local machine.

What It Does

11 Simulated IoT Devices — temperature, motion, energy, humidity, light sensors across 3 rooms
Multi-Agent AI System — Data Agent → Decision Agent → Action Agent pipeline
3 Decision Strategies — Threshold Rules (with hysteresis), Anomaly Detection (Z-Score + IQR + Gradient), No-Motion Timeout
Predictive Analytics — Linear regression forecasting, moving averages, anomaly scoring
Real-Time Dashboard — 5-page React dashboard with live charts, device states, agent pipeline visualization
MQTT + WebSocket — Full IoT communication stack
Docker Compose — 6 services, one command to start everything

Quick Start

git clone https://github.com/rudra496/EdgeBrain.git
cd EdgeBrain
docker compose up --build -d
# Open http://localhost:3000

Architecture

The system uses a clean multi-agent pipeline:

Sensor → MQTT → Data Agent → Decision Agent → Action Agent → Alert/Command

Each agent has its own metrics, error handling, and performance tracking. The decision engine uses a plugin architecture — just implement the DecisionStrategy interface and register it.

Tech Stack

Layer	Tech
Backend	FastAPI + Python 3.11
Frontend	React + Recharts
Database	PostgreSQL 16
Cache	Redis 7.2
Messaging	Mosquitto MQTT
Infra	Docker Compose
Hardware	ESP32 (optional)

Who Is This For?

IoT developers wanting a complete smart home template
Edge AI researchers testing decision strategies
Students learning about multi-agent systems
Anyone who wants AI without the cloud

GitHub: github.com/rudra496/EdgeBrain

If you're into edge computing or IoT, give it a star!

Built by Rudra Sarker — MIT License

I Built StealthHumanizer — A Free, Open-Source AI Text Humanizer (13 Providers, No Login)

Rudra Sarker — Sat, 09 May 2026 13:21:50 +0000

The Problem

AI detectors like GPTZero, Turnitin, and Originality.ai are everywhere. Students, writers, and professionals are getting flagged even when they write their own content. And the paid "humanizers" charge $10-20/month for something that should be free.

What I Built

StealthHumanizer is a completely free, open-source AI text humanizer that runs in your browser. No login. No limits. No server-side data storage.

Try it live: stealthhumanizer.vercel.app

Features

13 AI Providers — Gemini (free), OpenAI, Claude, Groq (free), Mistral, Cohere, Together, OpenRouter, Cerebras, DeepInfra, HuggingFace, Cloudflare, ZAI
4 Rewrite Levels — Light, Medium, Aggressive, Ninja (multi-pass auto-refinement)
6 Writing Styles — Academic, Professional, Casual, Creative, Technical, Humanize
13 Tone Presets — Conversational, Journalistic, Persuasive, Storytelling, and more
Built-in AI Detection Scoring — 12-metric analysis with confidence intervals
File Upload — PDF, DOCX support
16+ Languages — English, Chinese, Spanish, French, German, Japanese, Korean, Arabic, Hindi...
Batch Processing — Humanize multiple texts at once
100% Private — API keys stored browser-side only

How It Works

It uses a 4-layer humanization pipeline:

LLM Rewrite — Your chosen AI provider rewrites the text using anti-detection prompts
Non-LLM Post-Processing — 500+ synonym swaps, 230+ collocation replacements, AI vocabulary removal
Multi-Model Chain (Optional) — Pass text through multiple AI providers to mix statistical fingerprints
Final Polish — Light cleanup with readability guards

Quick Start

git clone https://github.com/rudra496/StealthHumanizer.git
cd StealthHumanizer
npm install
npm run dev

Comparison

	StealthHumanizer	QuillBot	StealthWriter	Undetectable.ai
Price	Free	$9.99/mo	$19/mo	$14.99/mo
Open Source	Yes	No	No	No
Providers	13	1	1	1
No Login	Yes	No	No	No
Self-Hostable	Yes	No	No	No

🤖 TermMind — A Free, Open-Source AI Terminal Assistant That Rivals Claude Code and Aider

Rudra Sarker — Fri, 27 Mar 2026 11:18:48 +0000

An AI-powered terminal assistant with 7 providers, 40+ commands, code analysis, file editing with undo, snippet management, and project templates — all in pure Python with zero cost options.

The Problem

AI coding assistants like Claude Code and Aider are incredible — but they're either:

Expensive (Claude Code: $20-200/month)
Locked to one provider (Claude Code → Anthropic only)
Heavy (Node.js, lots of dependencies)
No free tier for local/offline use

I wanted something that:

Works with any AI provider
Has a free tier (Gemini, Groq, Ollama)
Runs in the terminal with a beautiful UI
Is truly open source

What Is TermMind?

TermMind is a modern, open-source AI terminal assistant. Think Claude Code or Aider — but free, open-source, and lightweight.

pip install termmind
termmind init    # Select provider & API key
termmind chat    # Start chatting with AI

Supported Providers (7!)

Provider	Free Tier?	Setup
Google Gemini	✅ Yes (free API key)	aistudio.google.com
Groq	✅ Yes (free tier)	console.groq.com
Ollama	✅ Yes (fully offline!)	`ollama pull llama3.2`
OpenAI	❌ Paid	platform.openai.com
Anthropic Claude	❌ Paid	console.anthropic.com
Together.ai	❌ Paid	together.ai
OpenRouter	❌ Paid	openrouter.ai

Yes, you can use TermMind completely free with Gemini, Groq, or Ollama.

Core Features

🎨 Beautiful Terminal UI

Syntax highlighting
Markdown rendering
Streaming responses
Dark/Light themes

✏️ Smart File Editing

AI-powered file creation and editing
Side-by-side diff view in the terminal
Hunk-by-hunk confirmation (accept/reject individual changes)
Undo system — every edit is tracked

📁 Smart Context Engine

Automatically includes relevant files from your project
.termmindignore support (like .gitignore)
Persistent code index — function signatures, class definitions, imports
Supports 9 languages: Python, JS, TS, Go, Rust, Java, Ruby, C, C++

🐙 Git Integration

Status, diff, log
AI-generated commit messages

📊 Cost Tracking

Real-time token count and cost estimation

💾 Session Management

Save and load conversations

🔄 Provider Switching

Change models mid-conversation with /provider

40+ Commands

CLI Commands

termmind init        # Configure provider & API key
termmind chat        # Interactive chat session
termmind ask "q"     # One-shot question
termmind edit file   # Edit a file with AI
termmind review ./src  # Review code in directory
termmind explain file  # Explain a file
termmind test file     # Generate tests
termmind history       # Show saved sessions
termmind config        # Show current configuration

In-Chat Slash Commands

/edit <file> [instruction]  — Edit a file with AI
/run <command>              — Run a shell command
/files                      — List files in context
/search <query>             — Search project files
/tree                       — Show file tree
/save [name] / /load <name> — Session management
/model / /provider          — Switch mid-conversation
/cost                       — Show token usage & cost
/undo                       — Undo last file edit
/diff                       — Show session changes
/snippet save/search/load   — Code snippet management
/template list/use          — Project scaffolding
/refactor <op> <file>       — AI-powered refactoring

Standout Features

📦 Snippet Manager

Save code snippets from conversations
Search by name, description, tags, or code content
Template variables: {{filename}}, {{datetime}}, {{year}}, etc.
Auto-suggest relevant snippets based on context

🏗️ Project Templates

Scaffold projects from 8 built-in templates:

Python, FastAPI, React, Next.js, and more

🔧 Refactoring Engine

AI-powered refactoring with:

Diff preview
Confirmation prompt
Full undo support

🐳 Docker Support

# Build and run
docker compose up --build

# Fully offline with Ollama
docker compose --profile local-llm up --build

Shell Completions

Auto-generated completions for Bash, Zsh, and Fish:

termmind completions install

How It Compares

Feature	TermMind	Claude Code	Aider
Open Source	✅ MIT	❌	✅ Apache
Free Tier	✅ (Gemini/Groq)	❌	❌
Local/Offline	✅ (Ollama)	❌	✅
Multi-Provider	✅ 7 providers	❌	✅
Rich Terminal UI	✅	✅	❌
Undo Edits	✅	❌	✅
Cost Tracking	✅	❌	❌
Session Save/Load	✅	❌	❌
Dependencies	4 packages	Heavy	Moderate

The Architecture

CLI (click commands)
├── Commands Layer (chat, ask, edit, review…)
├── Diff Engine (side-by-side diffs)
├── Context Engine (smart file selection, code index)
│   ├── File Ops (read, write, edit)
│   ├── Git Module (status, diff, log)
│   └── API Client (streaming, multi-provider)
└── Plugin System (on_start, on_msg hooks)

Get Started

pip install termmind
termmind init      # Walks you through setup
termmind chat      # Start chatting!

Or for free usage:

# Get a free Gemini API key from aistudio.google.com
termmind init      # Select "gemini" as provider
termmind chat      # Start chatting — it's free!

⭐ GitHub: github.com/rudra496/termmind
📦 PyPI: pypi.org/project/termmind

Rudra Sarker — student researcher and full-stack developer from Bangladesh. Building free, open-source developer tools because great tools shouldn't cost a fortune.

🔗 Connect:

GitHub | LinkedIn | Twitter/X | ResearchGate | Personal Site

🗺️ DevRoadmaps — A Free, Open-Source Roadmap.sh Alternative with 17 Career Paths and 1,700+ Resources

Rudra Sarker — Fri, 27 Mar 2026 11:12:32 +0000

17 comprehensive developer roadmaps, 800+ topics, 1,700+ free resources, offline support, and community features — all in pure HTML/CSS/JS with zero dependencies.

The Problem

roadmap.sh is great — but it has limitations:

No interactive progress tracking
Limited resources
Print/poster is paid
No community features
No offline support
No resource filtering or ratings

I wanted something that goes further — truly free, feature-rich, and community-driven.

What Is DevRoadmaps?

DevRoadmaps is a free, open-source developer roadmap platform with 17 career paths, 800+ curated topics, and 1,700+ free resources.

Live Demo

🌐 rudra496.github.io/devroadmaps

17 Complete Roadmaps

#	Roadmap	Topics	Description
1	🎨 Frontend Developer	52	HTML, CSS, JS, React, Vue, Angular
2	⚙️ Backend Developer	50	Python, Node, Go, APIs, databases, Docker
3	🌐 Full Stack Developer	50	End-to-end web development
4	🧠 ML / AI Engineer	50	ML, deep learning, LLMs
5	☁️ DevOps / Cloud	50	Docker, K8s, AWS, Terraform, CI/CD
6	📱 Mobile Developer	50	Flutter, React Native, Swift, Kotlin
7	🔒 Cybersecurity	50	Pentesting, OWASP, cloud security
8	🗃️ Data Engineer	50	Spark, Kafka, Snowflake, Airflow
9	⛓️ Blockchain / Web3	50	Solidity, DeFi, smart contracts
10	🎮 Game Developer	50	Unity, Godot, Unreal, C#, C++
11	🔌 Embedded / IoT	50	Arduino, ESP32, RTOS, sensors
12	🛡️ DevSecOps Engineer	40	Container security, SAST/DAST, zero trust
13	🧪 QA / Test Engineer	43	Selenium, Playwright, BDD, TDD
14	✍️ Technical Writer	42	API docs, Docusaurus, Mermaid
15	⚡ Low-Code / No-Code	32	Bubble, Webflow, Zapier, Airtable
16	🏗️ Cloud Architect	42	AWS, GCP, Azure, IaC, FinOps

Plus pre-built learning paths like:

Full Stack in 6 Months (~22 topics)
Zero to ML Engineer (~12 topics)
DevOps Career Switch (~14 topics)

Features That Set It Apart

✅ Interactive Progress Tracking

Track your learning with clickable nodes. See your progress visually.

💬 Community Tips

Share and read tips for each topic. Learn from others' experiences.

⭐ Resource Ratings

Rate resources 1-5 stars. Find the best learning materials faster.

🔍 Resource Filtering

Filter by type: 📖 Docs, 🎥 Videos, 🎯 Courses, 🛠️ Tutorials, 🔧 Tools

📥 Export & Bookmarks

Export community tips as JSON
Bookmark topics across roadmaps
Export bookmarks as a study plan (Markdown)

🍅 Built-in Pomodoro Timer

Study timer with break reminders integrated into each roadmap.

🏆 Achievement System

Unlock badges as you learn:

🌟 First Steps (complete your first node)
🔥 Getting Serious (complete 10 nodes)
🏆 Roadmap Master (complete an entire roadmap)
🧠 Polymath (start 5 different roadmaps)

🌙 Dark/Light/Auto Theme

Three theme modes with smooth transitions. Your preference is persisted.

📱 PWA Support

Works offline as a Progressive Web App. Install it on your phone or desktop.

🖨️ Print as Poster or Checklist

Print any roadmap as a poster or a progress checklist.

How It's Built

HTML5 — Semantic markup
CSS3 — Custom properties, glassmorphism, animations
Vanilla JavaScript — Zero dependencies
GitHub Pages — Hosting
MIT License — Free forever

Just clone and open index.html — no build step needed:

git clone https://github.com/rudra496/devroadmaps.git
cd devroadmaps
python3 -m http.server 8000
# Open http://localhost:8000

Comparison with roadmap.sh

Feature	DevRoadmaps	roadmap.sh
Progress tracking	✅	❌
800+ curated topics	✅	~300
1,700+ free resources	✅	Limited
Community tips	✅	❌
Resource ratings	✅	❌
Resource filtering	✅	❌
Search within roadmaps	✅	❌
Print poster	✅ (free)	✅ (paid)
Offline PWA	✅	❌
100% free	✅	Partial

Who Is This For?

Self-taught developers who need structured learning paths
Students planning their career trajectory
Career switchers looking for clear roadmaps
Teams who want to standardize onboarding

Get Started

🌐 Live: rudra496.github.io/devroadmaps
⭐ GitHub: github.com/rudra496/devroadmaps

Rudra Sarker — student researcher and full-stack developer from Bangladesh. Building free, open-source tools for developers worldwide.

🔗 Connect:

GitHub | LinkedIn | Twitter/X | ResearchGate | Personal Site

🔍 CodeVista — A Code Analysis Tool That Generates Beautiful HTML Reports with Zero Dependencies

Rudra Sarker — Fri, 27 Mar 2026 10:54:42 +0000

CodeVista analyzes your codebase and generates a stunning single-page HTML report — health scores, security scans, architecture detection, code smells, decay tracking, and more. All in pure Python.

The Problem

Every developer has stared at a codebase and thought: "I wish I could see the big picture." How healthy is this code? Where are the security holes? What's the architecture? Which files are decaying?

Existing tools are either:

Heavy (require Node.js, servers, or databases)
Expensive (SonarQube, CodeClimate)
Limited (only do one thing — lint, or coverage, or complexity)

I wanted something that does everything and generates a single HTML file you can share with anyone.

Enter CodeVista

CodeVista is an open-source code analysis and security scanner that supports 80+ languages with 38 lint rules. Install it, point it at your project, and get a beautiful interactive report.

pip install codevista
codevista analyze ./my-project/
# Open report.html — that's it. No server needed.

What It Does

🏥 Health Scoring (0-100)

Composite health score across 6 categories:

Readability
Complexity
Duplication
Coverage
Security
Dependencies

🔒 Security Scanner

Detects:

Hardcoded secrets (AWS keys, GitHub tokens, Stripe, API keys, passwords)
Dangerous functions (eval, exec, shell=True, pickle)
Private key detection
Severity scoring (critical/high/medium/low)

🏗️ Architecture Detection

Automatically identifies patterns from your project structure:

MVC, MVVM, Clean Architecture, Hexagonal
Microservices, Event-Driven, CQRS
Repository, Service Layer, Singleton, Strategy, Observer
Includes architecture quality scoring

👃 19 Code Smell Categories

Goes beyond typical linters:

God Classes, Long Parameter Lists, Feature Envy
Shotgun Surgery, Dead Code, Magic Numbers
Copy-Paste Code, Missing Error Handling
Inconsistent Naming, Boolean Parameters, and more

📉 Decay Detection

Tracks how your codebase degrades over time using git history:

Complexity growth trends
Coupling growth
Duplication growth
Debt velocity (technical debt per week)
12-week predictions via linear regression
Identifies inflection points where quality shifted

🧬 CodeDNA Fingerprinting

Generate a unique fingerprint of your codebase structure for tracking and comparison.

📊 25+ Subcommands

codevista analyze ./project/      # Full analysis
codevista quick ./project/        # Fast analysis (~3 seconds)
codevista compare ./v1/ ./v2/     # Compare two codebases
codevista watch ./project/        # Re-analyze on file changes
codevista smells ./project/       # Code smell detection
codevista architecture ./project/ # Architecture patterns
codevista code-age ./project/     # File age, churn, risk
codevista health ./project/       # Health score only
codevista security ./project/     # Security scan only
codevista dna ./project/          # CodeDNA fingerprint
codevista decay ./project/        # Architectural decay
codevista team ./project/         # Team productivity analysis
codevista trends ./project/       # Health trends over time
codevista lint ./project/         # Language-specific lint rules

👥 Team Analysis

Metric	Description
Lines per Author	Added/removed/net per developer
Bus Factor	People needed to understand 50% of code
Code Ownership	Contribution share
Onboarding Complexity	How hard for a new contributor to ramp up
Time Zone Distribution	When the team commits

Zero Dependencies. Seriously.

CodeVista is built with pure Python stdlib. No external packages. Install it from PyPI and it just works.

CI/CD Integration

Export to SARIF for GitHub Actions, Checkstyle, and more:

codevista ci-output ./project/ -f sarif
codevista export ./project/ --all

Why I Built This

As a student researcher working on multiple projects, I needed a way to quickly understand codebases — my own and others'. Commercial tools were too expensive, open-source tools were too narrow. So I built the tool I couldn't find.

Get Started

pip install codevista
codevista analyze ./your-project/

⭐ GitHub: github.com/rudra496/codevista
📦 PyPI: pypi.org/project/codevista

Rudra Sarker — student researcher and full-stack developer from Bangladesh. Building ethical, impactful open-source tools.

🔗 Connect:

GitHub | LinkedIn | Twitter/X | ResearchGate | Personal Site

🤟 I Built the Most Comprehensive Open-Source Sign Language Dataset Catalog — 73+ Datasets, 26 Languages

Rudra Sarker — Fri, 27 Mar 2026 10:52:07 +0000

How I curated and verified 73+ sign language datasets covering 26 sign languages to help researchers, developers, and the deaf community build better assistive technology.

Why I Built This

Sign language recognition (SLR) is one of the most impactful applications of computer vision and AI — it directly helps millions of deaf and hard-of-hearing people communicate. But when I started working on assistive technology projects, I hit the same wall every researcher faces: finding reliable, verified datasets is painfully hard.

Most existing lists are:

❌ Full of broken links
❌ Missing sample counts
❌ No license or citation info
❌ Limited to 1-5 sign languages

So I decided to build what I wish existed: a single, verified, comprehensive catalog.

What Is SignLanguage-Dataset-Hub?

SignLanguage-Dataset-Hub is a curated, open-source catalog of 73+ verified sign language datasets covering 26 sign languages — the most comprehensive collection available anywhere.

Key Numbers

Metric	Count
Verified Datasets	73+
Sign Languages Covered	26
Modality Types	Video, Image, Sensor, Pose, RGB-D, Skeleton, Text
URL Verification	100% (all links checked)
Tutorials Included	9 (beginner to advanced)

Languages Covered

The catalog spans sign languages from around the world:

ASL (American), BSL (British), ISL (Indian), CSL (Chinese), LSM (Mexican)
ArSL (Arabic), BdSL (Bangla), JSL (Japanese), KSL (Korean), TİD (Turkish)
DGS (German), LSF (French), NGT (Dutch), LIS (Italian), RSL (Russian)
Plus 11 more including multilingual corpora and linguistic databases

What Makes It Different

Every Link is Verified

Not just collected — verified. Every URL resolves to HTTP 200 or an auth-gated page. No placeholder links. No fabricated data. If we couldn't verify a dataset, it's excluded.

From Original Sources

Sample counts come from the original publications, not estimated or copied from secondary sources.

Full License & Citation Info

Every dataset includes:

Source URL (verified)
Sample count
License information
Proper citation to original creators

Working Tools Included

This isn't just a list — it comes with tools:

# Load demo sensor data (4,824 samples)
from scripts.data_loader import BdSLSensorGloveDataset
dataset = BdSLSensorGloveDataset(split='train')
print(f"Loaded {len(dataset)} samples, 36 gesture classes")

# Visualize
python tools/visualize.py --data data/bdsl/BdSL-Sensor-Glove/

# Query the catalog
import pandas as pd
df = pd.read_csv('datasets_catalog.csv')
asl = df[df['language_code'] == 'ASL']

9 Step-by-Step Tutorials

From beginner to advanced:

Introduction to Sign Language Recognition
Loading and Exploring Datasets
Visualizing Sign Language Data
Building Your First Classifier
Hand Pose Estimation with MediaPipe
Data Augmentation Techniques
Real-time Recognition System
Continuous Sign Language Recognition
Multilingual Sign Recognition

Project Structure

SignLanguage-Dataset-Hub/
├── DATASETS.md              # Complete verified catalog (73 datasets)
├── datasets_catalog.csv     # Machine-readable catalog
├── STATISTICS.md            # Detailed statistics & breakdowns
├── data/bdsl/               # Demo sensor dataset (4,824 samples)
├── docs/
│   ├── BENCHMARKS.md        # Published accuracy numbers
│   ├── TUTORIALS.md         # 9 tutorials (beginner to advanced)
│   ├── QUICKSTART.md        # Quick start guide
│   └── LICENSE_ATTRIBUTION.md
├── scripts/
│   ├── data_loader.py       # PyTorch data loaders
│   └── download_datasets.py # Multi-source downloader
└── tools/
    ├── visualize.py         # Sensor data visualization
    └── generate_realistic_data.py

Who Is This For?

Researchers — Find datasets for your next SLR paper with proper citations
Developers — Build sign language apps with verified, working data sources
Students — Learn sign language recognition with guided tutorials
The Deaf Community — Better tools start with better data

Why Open Source Matters Here

Sign language technology shouldn't be locked behind paywalls. The deaf community deserves free, accessible tools — and that starts with free, accessible data. This project is licensed under CC BY 4.0 so anyone can use it.

Get Started

git clone https://github.com/rudra496/SignLanguage-Dataset-Hub.git
cd SignLanguage-Dataset-Hub
pip install -r requirements.txt

🌐 Live Demo: rudra496.github.io/SignLanguage-Dataset-Hub
⭐ GitHub: github.com/rudra496/SignLanguage-Dataset-Hub

Citation

@misc{signlanguage_dataset_hub,
  title = {Sign Language Dataset Hub: A Verified Catalog of Sign Language Datasets},
  author = {Sarker, Rudra and Contributors},
  year = {2026},
  url = {https://github.com/rudra496/SignLanguage-Dataset-Hub}
}

I'm Rudra Sarker, a student researcher and full-stack developer from Bangladesh, building ethical AI and assistive technology. If you found this useful, ⭐ the repo and share it with someone who needs it!

🔗 Connect with me:

GitHub | LinkedIn | Twitter/X | ResearchGate | Personal Site

I Built a Tool to Validate AI-Generated Code (Now with JS/TS Support + AI Auto-Fix)

Rudra Sarker — Wed, 25 Mar 2026 11:38:34 +0000

We've all been there - you paste code from ChatGPT, Claude, or Copilot, it looks perfect, you ship it... and then it breaks because the AI invented a package that doesn't exist, or snuck in an eval(), or used an f-string for SQL queries.

84% of developers use AI coding tools now, but only 29% actually trust the output. That trust gap is real.

So I built AI Code Trust Validator - a tool that catches AI code problems before they reach production.

---

## What It Detects

**Security Vulnerabilities**
- SQL injection, command injection
- eval(), exec(), innerHTML XSS
- Hardcoded secrets, API keys
- Prototype pollution (JS)

**Hallucinations**
- Fake npm packages, invented Python modules
- Made-up functions and methods
- Placeholder API URLs

**Logic Errors**
- Infinite loops, unreachable code
- Missing await/async issues
- Bare except clauses, mutable defaults

---

## Supported Languages

- Python
- JavaScript  
- TypeScript

---

## Example Output

text
Analyzing: generated_code.py

TRUST SCORE: 67/100

Category Score Issues
Security 72 2 medium, 1 low
Hallucinations 45 3 critical
Logic 85 1 minor
Best Practices 70 2 warnings

Critical Issues:
[HALLUCINATION] Line 12: Import 'fancy_lib' does not exist
[SECURITY] Line 24: Potential SQL injection via f-string

Suggestions:
Replace 'fancy_lib' with 'numpy' or 'pandas'
Use parameterized queries


---

## Quick Start

bash
pip install ai-trust-validator
aitrust validate your_code.py --min-score 75


---

## What's New in v0.4.0

### AI-Powered Auto-Fix

Let GPT-4, Claude, or local Llama fix issues automatically:

bash
export OPENAI_API_KEY="sk-..."
aitrust ai-fix buggy_code.py --apply


Supports:
- OpenAI (GPT-4, GPT-3.5)
- Anthropic (Claude)
- Ollama (local models like Llama 3)

### JetBrains Plugin

Now works in IntelliJ, PyCharm, WebStorm with:
- Real-time code analysis
- Trust score in status bar
- One-click AI fixes

### JavaScript/TypeScript Support

Now validates .js, .jsx, .ts, .tsx files with:
- Security analysis (eval, innerHTML, prototype pollution)
- Hallucination detection (fake packages, invented functions)
- Best practices (var vs let/const, == vs ===)

---

## More Features

- VS Code extension
- Docker support
- GitHub Action for CI/CD
- Pre-commit hooks
- REST API server
- HTML/SARIF/JSON reports
- Team analytics dashboard

---

## Links

- GitHub: https://github.com/rudra496/ai-code-trust-validator
- PyPI: https://pypi.org/project/ai-trust-validator/
- Release Notes: https://github.com/rudra496/ai-code-trust-validator/releases/tag/v0.4.0

MIT licensed, open source. Contributions welcome!

---

What's the worst AI code hallucination you've encountered? Drop it in the comments and I'll add detection for it!