🚀 Take a look at our newly upgraded Enterprise POS architecture in action! We completely re-engineered this system, migrating from a Python CustomTkinter GUI to a lightning-fast Tauri front-end paired with a robust Python FastAPI backend.
🔄 The Architecture Upgrade: CustomTkinter to Tauri
Modern Web UI: We stripped away the rigid CustomTkinter framework and rebuilt the front-end using standard HTML, CSS, and vanilla JavaScript. This shift allows for pixel-perfect styling, fluid animations, and a much cleaner, responsive layout.
Decoupled FastAPI Backend: Instead of weaving the UI and database logic together, Python now acts as a standalone, high-performance API. The FastAPI backend handles all the heavy SQLite database operations, keeping the application incredibly snappy.
Seamless API Communication: The front-end and back-end now communicate flawlessly via local HTTP endpoints, allowing the UI to fetch live inventory and process checkouts asynchronously.
The Tauri Magic: Tauri wraps this entire modern web stack into a highly optimized, low-memory native desktop executable, automatically spawning the Python backend as a silent background sidecar process.
🛒 Live Sales Terminal: Watch how seamlessly the checkout flow operates. Users can quickly select products from a searchable catalog, append them to the live billing ledger, and instantly complete transactions with zero lag.
📦 Master Inventory Manager: Total control over stock is just a few clicks away. We showcase the live adjustment capabilities by selecting an item in the manual correction terminal, inputting an intervention magnitude of 55 units, and securely updating the database through a clean confirmation modal.
📊 Deep Analytics & Logs: Data drives decisions. The walkthrough highlights the comprehensive transaction ledger tracking real-time daily metrics—like total revenue, transaction counts, and top-selling products—alongside a detailed, modal-based Monthly Sales Summary.
⚡ This project proves that robust, localized SQLite databases and powerful Python backends can be perfectly paired with beautifully sleek, modern web-based user interfaces.
Proud to officially add the AWS Certified Machine Learning Engineer - Associate certification to my toolkit!
Building robust machine learning pipelines and scaling AI workloads securely on the cloud has always been a key focus of mine. Earning this certification has been a great deep-dive into mastering AWS ML infrastructure, MLOps best practices, and production-grade architectures.
Ready to bring these advanced capabilities to upcoming data science and engineering projects! Let's build something impactful. 💡💪
Congratulations on earning the AWS Machine Learning Engineer certification, Hazem khaled! Robust pipelines and cloud automation make all the difference for production-grade AI systems.
clean layout, the story cards read well. does the admin side have drafts and scheduled posts? that's usually the first thing people ask for once they start writing regularly
Building software with AI requires more than a good prompt.
I've been developing my own Autonomous Engineering Harness to structure how I build software with AI.
The idea is simple: instead of relying on AI to generate code one task at a time, I wanted a more structured process that connects specifications, task planning, implementation, code review, and QA.
The harness organizes the workflow into clear stages:
- Specifications: define what needs to be built.
- Task planning: break requirements into manageable units.
I'd carry acceptance criteria from the spec into QA as explicit checks, with a result attached to each one. When the spec changes mid-build, does the workflow flag which completed tasks and QA results need to be revisited?