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Building a full-stack Municipal Complaint Management System that allows citizens to register complaints and enables administrators and municipal workers to manage, assign, and resolve them efficiently.
Features include:
• Role-based authentication (Citizen, Admin, Worker)
• Complaint registration and tracking
• Admin dashboard for complaint management
• Worker dashboard for assigned tasks
• Secure login and authorization
• Responsive user interface
Tech Stack:
React.js • Node.js • Express.js • MongoDB • JWT • Tailwind CSS
The network for creativity
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I had this idea stuck in my head for a while...
What if scrolling through a website felt more like controlling the camera in a cinematic Ferrari commercial?
So I decided to build it.
The hero section starts inside the cockpit. As you scroll, the camera moves through the windshield, circles around the Ferrari, and finishes behind the car all controlled by your scroll.
Getting it to feel smooth was the biggest challenge. Scrubbing a video directly didn't deliver the experience I wanted, so I switched to a frame by frame approach instead. It took a bit more work, but the result feels much more natural and responsive.
This is the kind of experience I love building where motion isn't just there to look cool, it becomes part of the storytelling.
You can try the live demo here:
https://pure-velocity-sculpt.base44.app
I'd genuinely love to hear your thoughts. 👇
#WebDesign #LandingPage #UIDesign #UXDesign #MotionDesign #CreativeCoding #ScrollAnimation #AI #WebDevelopment #Awwwards
Okay this is such a fun concept, scrolling as camera movement instead of just scroll-triggered fades. Frame by frame instead of scrubbing video is a smart workaround. Did you run into any performance issues getting it smooth on lower-end devices?
Found a bug that only happened in production.
The code looked fine locally. The real problem? A missing environment variable.
Lesson: Always check your production environment variables before rewriting working code. 😅
Have you ever spent hours debugging something that turned out to be a simple configuration issue?
Hype, hype and hype... It's the first page of the notebook that I write in tells me.
I was building automated workflows around Signalwatch, and it got me thinking about all the hype.
Hype around just one piece of the puzzle, LLMs. They just predict the next word while generating... No magic.The better they are trained, the better the output. Super helpful in a lot of contexts...
Tool calling. Helpful, but if LangChain and RAG + LLMs, for example, are not included where appropriate, ta ta, bye bye.
Automation. Someone bringing me a glass of water, for example, isn't something to be automated or waste resources or tokens on...
This brings me back to structural understanding, especially python + machine learning basics where you train a model based on a small piece of dataset you download from hugging face, for example... a lot becomes clear here
