Freelancers using Hugging Face

Freelancers using Hugging FaceFreelancers using Hugging Face

Projects People

Lahore, Pakistan

ML AI | Backend | Computer Vision | GenAI | LLM Agents

New to Contra

ML AI | Backend | Computer Vision | GenAI | LLM Agents

Cover image for LakeShield - AI-Powered Video Monitoring

LakeShield - AI-Powered Video Monitoring and Vessel Intelligence Platform I led the development of LakeShield as the Senior AI/ML Engineer and Lead Developer, taking the platform from initial research and experimentation to a scalable production system. My responsibilities included: 🔹 Designing the end-to-end AI and video-processing architecture 🔹 Building YOLO-based boat and vehicle detection pipelines 🔹 Developing object tracking and movement-analysis workflows 🔹 Implementing OCR for extracting boat registration information 🔹 Creating scalable pipelines for processing thousands of surveillance videos 🔹 Developing FastAPI backend services and automated data workflows 🔹 Building a Next.js analytics dashboard integrated with Supabase 🔹 Deploying and operating the AI pipeline on cloud GPU infrastructure 🔹 Optimizing model accuracy, inference speed, infrastructure costs, and reliability 🔹 Managing production monitoring, troubleshooting, maintenance, and continuous improvements The platform transforms raw surveillance footage into structured operational insights, enabling automated vessel monitoring, vehicle activity analysis, registration extraction, and reporting. This project involved complete technical ownership across Computer Vision, AI/ML, backend development, cloud infrastructure, data engineering, MLOps, and production operations. #ComputerVision #VideoAnalytics #ArtificialIntelligence #ObjectDetection #OCR #MLOps #FastAPI #NextJS #Supabase #CloudEngineering

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Cover image for Shelfr - AI-Powered Retail Shelf

Shelfr - AI-Powered Retail Shelf Intelligence Platform I led the development of Shelfr as the Senior Computer Vision Engineer and Lead Developer, taking the platform from the initial idea and system architecture through development, deployment, and production operations. My work included: 🔹 Designing the complete computer vision and backend architecture 🔹 Building product detection, shelf analysis, OCR, and image-processing pipelines 🔹 Developing APIs and scalable data-processing workflows 🔹 Deploying and managing production systems on GCP cloud servers 🔹 Optimizing model accuracy, processing speed, and infrastructure performance 🔹 Managing production monitoring, reliability, troubleshooting, and ongoing improvements 🔹 Leading technical decisions across AI, backend, cloud infrastructure, and DevOps The platform converts real-world retail shelf images into structured product and shelf-level insights, helping automate retail auditing, product visibility analysis, and inventory workflows. #ComputerVision #RetailAI #LeadDeveloper #AIEngineering #GCP #MLOps #Python #CloudEngineering

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Cover image for AI-Powered PDF Data Extraction
My role:

AI-Powered PDF Data Extraction My role: AI Data Processing and Extracton Engineer Organizations often struggle to extract structured and useful information from large volumes of unstructured PDF documents. I developed a flexible AI-powered data extraction solution that allows users to define the specific entities and fields they want to retrieve. The system processes different PDF formats, identifies relevant information, and converts it into structured, usable data. The solution reduces manual document processing, improves retrieval accuracy, and can be adapted to different document types and business requirements. A working demo link is attached.

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Cover image for AI-Powered PDF Data Extraction

Organizations often

AI-Powered PDF Data Extraction Organizations often face challenges in extracting useful information from a large volume of unstructured data in PDF format. The lack of standardized data formats makes it difficult to efficiently retrieve relevant information, when dealing with documents. The goal of this solution was to develop a flexible automated solution that allows users to specify entities to extract, enabling quick and accurate data retrieval from diverse PDFs. The demo is available on request.

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Hugging Face(3)

Stephen Kisong'e

Cyber Security Analyst

Cyber Security Analyst

Cover image for AI-Assisted CTF Solving and Bug

AI-Assisted CTF Solving and Bug Bounty Hunting I integrated Cyber-Ornith Obliterated, an uncensored cybersecurity model running locally in LM Studio, with HexStrike AI through MCP to execute tools on Kali Linux. I demonstrated the setup by solving Root-Me’s HTML source-code CTF challenge. The model retrieved the page, inspected its HTML and identified a password exposed in a comment. I verified the command output and submitted the answer, successfully completing the challenge. Beyond CTFs, I built this project to support AI-assisted bug bounty hunting, including web response analysis and vulnerability investigation within authorized scope, with human oversight and verification of findings.

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Cover image for Adversarial Data Poisoning Attack on a Network Intrusion AI Mod…

Adversarial Data Poisoning Attack on a Network Intrusion AI Mod…

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Cover image for Performed a manual, black-box security

Performed a manual, black-box security assessment of a locally-hosted large language model served through LM Studio, using Burp Suite as the primary testing tool. The engagement began with no prior knowledge beyond a single IP address, mirroring how an external attacker would approach an exposed AI server discovered on a network. Worked through a full discovery-first methodology: confirming the target was live, enumerating the server's exposed endpoints by probing and reading response codes, extracting the loaded model name directly from the API, and proving the expected request format through the server's own error responses rather than assuming it. This reconnaissance phase converted an unknown target into a complete map of its attack surface. With the attack surface established, executed a series of manual techniques through Burp's Repeater and Intruder tools, including prompt injection, system prompt extraction, role and system-message injection, parameter manipulation, error-message disclosure, and authentication testing. Demonstrated additional Burp capabilities relevant to AI systems, including response comparison to prove behavioral changes under attack, randomness testing of server-generated identifiers, and live request tampering to illustrate a man-in-the-middle scenario against AI traffic. Organized all findings into a structured vulnerability map aligned to OWASP LLM risk categories, recording each technique's result, severity, and supporting evidence. Delivered the work as a complete, reproducible, beginner-accessible walkthrough covering environment setup, discovery, exploitation, and reporting.

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Cover image for Conducted an automated security assessment

Conducted an automated security assessment of a locally-hosted large language model, Dolphin 3.0 (Llama 3.1 8B), using Garak, NVIDIA's open-source LLM vulnerability scanner. The model was served through LM Studio's OpenAI-compatible REST API and tested from a Parrot OS environment running in VirtualBox. Configured Garak to target the model via a custom REST generator configuration, handling endpoint routing, Bearer token authentication, JSON response parsing, and extended timeouts for local inference. Executed nine distinct probe categories against the model, covering DAN jailbreaks, prompt injection, encoding-based bypass, malware generation, latent injection, real toxicity prompts, continuation attacks, grandma exploits, and package hallucination. The assessment identified that Dolphin 3.0, an uncensored model with no built-in safety guardrails, was vulnerable across the majority of probe categories. Findings were documented per probe, distinguishing where the model resisted versus complied, and translated into a clear, layered explanation of each attack type and its real-world security implications. Delivered the work as a complete, reproducible walkthrough, including environment setup, tool configuration, probe execution, and results interpretation, suitable for both technical practitioners and stakeholders newer to AI security.

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Hugging Face(5)

Senior FullStack & AI Engineer | Product-Driven Solutions

$25k+: Earned

7x: Hired

5.0: Rating

32: Followers

Senior FullStack & AI Engineer | Product-Driven Solutions

Cover image for MakeMyQuiz (AI Project)

MakeMyQuiz (AI Project)

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Cover image for Permission Aware AI Agents Suite for Hospitality Group

Permission Aware AI Agents Suite for Hospitality Group

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Cover image for Chronicler | An a16z funded Startup

Chronicler | An a16z funded Startup

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Cover image for AuditionAid - AI Career Platform for Actors (Web & Mobile)

AuditionAid - AI Career Platform for Actors (Web & Mobile)

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Hugging Face(1)

proDubai - United Arab Emirates

AI Engineer | Model Deployment | Fine-tuning | Inference

$10k+: Earned

6x: Hired

4.9: Rating

59: Followers

expert

AI Engineer | Model Deployment | Fine-tuning | Inference

Cover image for LLM Fine-Tuning & Evaluation for Financial Risk

LLM Fine-Tuning & Evaluation for Financial Risk

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Cover image for IOS Risk Data Foundry — Domain-Specific Financial Risk AI

IOS Risk Data Foundry — Domain-Specific Financial Risk AI

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Cover image for DomainOS is offering 10 businesses

DomainOS is offering 10 businesses a free build. Pick one task that costs you time, money or sales. Got a task that costs you time, money or sales? We’ll help you fix or automate it using your existing tools, then show you how to run it. Maybe leads go cold, invoices need chasing, customer onboarding gets messy, or your team keeps doing the same work by hand. DomainOS helps businesses automate their workflows, integrate AI agents, deploy their own open source models and manage it. As part of our end of the year giving back to the community program. We are offering one free workflow build out to Contra community members. Free consultation, build and handover. No obligation to buy anything. Open to the first 10 eligible businesses. Choose the one task that matters most. Apply here: https://ethercess.com/discovery/free-workflow-build/ We’ll be in touch within 24 hours.

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Cover image for Your first fine-tune should teach

Your first fine-tune should teach you the whole loop. I walk through it with Unsloth: data → baseline → training → evaluation → local model → Python. Including a mistake I made—and why a completed training run doesn't mean you have a better model. Full tutorial below 👇 https://youtu.be/YxXBojdD1as

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Hugging Face(2)

Creative code → Apps ✦ Research ✦ Events

$5k+: Earned

1x: Hired

113: Followers

Expert

Expert

Creative code → Apps ✦ Research ✦ Events

Cover image for Qrave.ai - Artistic QR Code Generator 2022

Qrave.ai - Artistic QR Code Generator 2022

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Cover image for Innovative App Template for Selling Physical Products Creati...

I built a way to sell physical products in an app that feels more like a gallery than a shop. 📱 What does it do? It’s a remix-able template for selling physical products inside an app. You browse through pieces in a horizontal carousel, then scroll vertically through a product to explore it through multiple images before buying instantly with Apple Pay. I designed it for galleries and artists first, but it works for anyone selling physical products creators, brands, influencers anything that needs to feel a bit more premium. Why I built this: I kept thinking about how intentional artwork is presented in physical spaces, and how transactional it feels online. This is an attempt to close that gap and making purchases feel as frictionless as possible. Another bonus to why people will use this app is that apple does not tax physical good transactions, so sellers can keep more of what they earn. 🎉 The template remix-potential? Works really well because users can swap in any product and it simply works! Art, jewellery, vinyls, collectibles, anything physical allowed on the app store. This was fun I plan on refining and making more templates so I would appreciate any comments and feedback Build Preview 👀 (https://www.anything.com/mobile-preview/68217499-f987-4843-8bcb-f7864c536abc)Build Link 🔗 (https://www.anything.com/build/aCF0mfmHSEOLy_eGTFNqvA?v=sBZM6bBFQ0CWHu4QlFbZpA)

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Cover image for To mark 122 years of

To mark 122 years of Ford Motors, this Super Bowl advertisement speaks directly to multi-generational audiences who value heritage, perseverance, and the powerful connection between sound and emotion. The story of the horse and carriage draws a parallel between the original Mustang and its modern counterparts, building towards anticipation before the unveiling of the new 2026 Mustang in Spring. The Location: Santa Clara, California played a huge role in the story as this year's Super Bowl is being hosted at the at Levi's Stadium. This made prompting the clothing easier, you'll notice each actor is wearing Denim. 'Horses dont Stop, they keep going' Brand: Mustang SFX: Morphic Images: Morphic Scripting: Morphic Voice: Eleven Labs Final: Premier Pro Each video was generated and sequenced entirely in Morphic *sound on*

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Cover image for I think I pushed my limits on this one.. 🛏️ My goal was to ...

I think I pushed my limits on this one.. 🛏️ My goal was to use all the tools in unison and learn how each tool could own a clear layer of the full product experience from code to marketing. Rork developed the app, Suno designed the audio, FLORA defined the visual language, Tolstoy for all the UGC, and Replit brought it online. After Christmas dinner, I sent my first prompt to Rork: “Design a digital members club for Gen-Z + working professionals committed to sleeping earlier in 2026.” The idea came from an old note I wrote after a health & wellness conference in Budapest: 'productivity revolves around a good night’s sleep' - marketing = Maslow's Hierarchy of Sleep (iOS app) ' 😬😂 And so? Pillow FM: A modern sleep experience for overstimulated minds. 'Guided Sleep meditations everyday at 10pm' Shall I continue this app?

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Hugging Face(1)

Bangkok, Thailand

Full-Stack Developer | Web Apps, Integrations & WordPress

5.0: Rating

18: Followers

Full-Stack Developer | Web Apps, Integrations & WordPress

Cover image for Adventures of Dexter & Mars: Building a Godot Dog Adventure

Adventures of Dexter & Mars: Building a Godot Dog Adventure

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Cover image for Maze Unlocker: An Android Maze Game Built with Godot

Maze Unlocker: An Android Maze Game Built with Godot

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Cover image for Take Two - Video Feedback That Stays with the Cut

Take Two - Video Feedback That Stays with the Cut

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Cover image for NikoCodes: Visual Review for AI-Suggested Links

NikoCodes: Visual Review for AI-Suggested Links

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Hugging Face(2)

Lahore, Pakistan

Top Rated Plus Freelancer & Top 1% Talent

$5k+: Earned

2x: Hired

5.0: Rating

17: Followers

Top Rated Plus Freelancer & Top 1% Talent

Cover image for AI Function Calling Agent

AI Function Calling Agent

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Cover image for Business Development AI Workflow

Business Development AI Workflow

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Cover image for ADHD-Friendly AI Automation Workflows

ADHD-Friendly AI Automation Workflows

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Cover image for Anchor Down - Transport & Logistics Platform

Anchor Down - Transport & Logistics Platform

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Hugging Face(1)

Md Rajibul Ahsan

proDhaka, Bangladesh

Full-Stack App & Web Developer | AI & Store Launch , UI/UX

22: Followers

Full-Stack App & Web Developer | AI & Store Launch , UI/UX

Cover image for 🚀 Run Your Own AI

🚀 Run Your Own AI Locally — No Cloud Required! Want to run an AI model like ChatGPT directly on your computer? Here's a simple setup using Llamafile and Qwen3 4B Thinking: Step 1: Download Llamafile Search for Llamafile GitHub and download the latest release. Step 2: Download the Model Get the Qwen3 4B Thinking 2507 GGUF model from Hugging Face. Step 3: Create a Batch File Create a .bat file in the same folder and add: .\llamafile-0.10.3.exe -m qwen3-4b-thinking-2507.Q4_K_M.gguf --server --gpu disable Step 4: Launch Your AI Double-click the .bat file to start the server. Step 5: Open in Browser Visit: http://127.0.0.1:8080/ That's it! 🎉 You now have a private AI assistant running entirely on your own machine—no subscriptions, no API keys, and no internet dependency after setup. ✅ Full local control ✅ Better privacy ✅ Works offline ✅ Easy to set up Have you tried running LLMs locally yet? Share your experience in the comments.

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Cover image for Ever struggled with testing payment

Ever struggled with testing payment QR codes? If you work in banking or fintech, you probably know how challenging QR payment testing can be. You generate an EMVCo QR code, but then you need to validate its structure, check the CRC, test invalid QR data, or encode and decode Consumer QR codes. Sometimes, even a small error in the QR data can cause a payment to fail. Finding that error can take a lot of time. We noticed that these tasks often require different tools. So we thought, why not build one app that can handle them all? That's how AttoQR came to life. At AttoExa Solutions, we built AttoQR to help banking professionals, fintech developers, and QA testers work with QR codes more easily. Here's what you can do with AttoQR: ✅ Generate and Scan QR Codes ✅ Generate EMVCo Merchant & Consumer QR Codes ✅ Validate and Verify QR Data ✅ Generate and Test CRC Values ✅ Create Valid & Invalid QR Codes for UAT ✅ Generate Encryption Keys for Consumer QR Encoding & Decoding No need to switch between different tools for these tasks. You can do it all from your phone. We're happy to share that AttoQR (Beta) is now live on Google Play. This is our first release, and we're looking forward to making it better with feedback from people who actually work with payment systems. If you're working in banking, fintech, digital payments, or software testing, give AttoQR a try. We'd love to hear what you think and what features you'd like us to add next. 📲 Search "AttoQR" on Google Play to download the app. Developed by AttoExa Solutions 🌐 https://attoexasolutions.com/

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Cover image for I built SkillForge, an AI-powered

I built SkillForge, an AI-powered Learning Management System (LMS) prototype using Base44, designed to simplify online education and course management. The platform is structured around three user roles: Admin, Instructor, and Student, with dedicated experiences for managing courses, organizing educational content, and supporting online learning. Key features designed for the platform: Admin dashboard for centralized platform management Course creation and management Instructor and student management Course enrollment workflows Student learning progress tracking Role-based access and dashboards Modern, responsive user interface The project demonstrates how AI-assisted development and low-code tools can accelerate the creation of a structured educational platform. I built SkillForge to explore how quickly a functional SaaS-style education platform could be developed using AI-powered development tools. Traditional LMS development often requires significant time for frontend development, backend integration, database configuration, and authentication. With Base44, my goal was to streamline that process, rapidly prototype essential LMS workflows, and demonstrate how AI can help turn a product idea into a usable web application. Note: The showcase video focuses specifically on the Admin Portal to provide a concise demonstration. The project was designed around a broader multi-role learning platform. Live Project: https://tough-skill-forge-learn.base44.app/

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Cover image for I created a reusable FireGuard

I created a reusable FireGuard brand promo animation template designed for fire safety brands, emergency services, and promotional campaigns. It combines animated branding, editable text, and replaceable visual assets in a clean, modern layout. The goal is to make professional animated promotions easy to customize, allowing anyone to replace the logo, headline, and images with their own brand content and create a personalized animation in just a few minutes.

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Hugging Face(1)