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š„ I just shipped something most developers never attempt:
An AI system that talks to industrial machines.
Specifically ā a hospital autoclave monitoring system integrated with PLC controllers, built with Django + Machine Learning + Computer Vision.
Here's what makes this interesting:
ā Most hospital sterilization equipment runs on manual checks
ā Human error = contamination risk = patient danger
ā We eliminated that with real-time AI anomaly detection
The system reads temperature, pressure and cycle data directly from PLC hardware, runs it through an ML model, and fires alerts before anything goes wrong.
No more manual checks. No more guessing.
šŖšµš®š š šÆšš¶š¹š:
āļø Django + WebSocket real-time dashboard
š PLC integration via Modbus protocol
š¤ ML anomaly detection model
šļø Computer Vision monitoring with OpenCV
š³ Docker deployment
šØ Automatic alert system
šŖšµš®š š š¹š²š®šæš»š²š±:
Bridging hardware and software is one of the most undervalued skills in tech. Every factory, every hospital, every industrial facility needs this ā and almost no one builds it.
That's the gap I work in.
š github.com/Davoadeivai/autoclave-ai-monitor
š¼ Currently available for:
ā Industrial AI & monitoring systems
ā Django REST API backends
ā RAG-based AI chatbots with LangChain
ā Computer Vision applications
DM me if you're building something that needs serious backend or AI engineering.
#Django #Python #PLC #IndustrialAI #MachineLearning #ComputerVision #HealthcareAI #OpenToWork #Freelance
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Most AI-generated SaaS products look production-ready.
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Is authentication secure?
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What happens when third-party services fail?
That idea inspired me to design "Agence Mission Control," an Agentic Software Due Diligence platform where 24 specialized AI engineering agents review every critical layer of a software product before launch.
Live link: https://agence-missioncontrol.vercel.app/
Instead of static checklists, I imagined an experience where founders receive an executive engineering review through an intuitive, enterprise-grade interface.
Instead of static checklists, every engineering domain is represented by its own AI specialist, including:
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ā¢ System Architecture Agent
ā¢ Operational Readiness Agent
ā¢ Database & API Agent
ā¢ Product Experience Agent
ā¢ Infrastructure & DevOps Agent
ā¢ Code Quality Agent
ā¢ AI Systems Agent
ā¢ Security Agent
ā¢ Reliability & SRE Agent
ā¢ Cross-Platform Agent
ā¢ Accessibility & Compliance Agent
ā¢ Testing & QA Agent
ā¢ Data Engineering Agent
ā¢ Performance Optimization Agent
ā¢ Developer Experience Agent
ā¢ Open Source Governance Agent
ā¢ FinOps Agent
ā¢ Product Analytics Agent
ā¢ Payments Agent
ā¢ Authentication & Identity Agent
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Each agent reviews a different layer of the product, identifies production risks, and contributes to an overall engineering readiness score.
For the Wonder Ć Contra Challenge, I used Wonder to rapidly iterate on layouts, refine the interface, generate visual elements, and transform a complex engineering framework into a polished product concept.
The result is a design that makes software due diligence feel visual, interactive, and accessible.
This submission is a high-fidelity UI prototype designed in Wonder to visualize the product experience. The complete agentic system, including AI agents, audit workflows, backend services, and a reporting engine, is planned as the next phase of development.But it explores a question I've been thinking about for a while:
What if every AI-generated product had to pass an engineering review before going live?
I'd love to hear your thoughts and feedback.
Built with Wonder for the Wonder Ć Contra Design Challenge.
#Wonder #Contra #AgenticAI #AIEngineering #ProductDesign #UIDesign #SaaS #SoftwareEngineering #DesignChallenge #AIProducts #Startup #Founders #DesignSystems
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