Declassified Missions
Technical breakdowns of engineering problems and system architectures, declassified from the development pipeline.
VitalGuard
In remote clinics, patients face critical referral delays due to the lack of immediate, expert radiological assistance. To solve this, I fine-tuned Convolutional Neural Network (CNN) classifiers on large chest radiographic datasets and deployed them in an asynchronous task pipeline. The system runs PyTorch models inside containerized GPU-capable worker queues, coordinated by a FastAPI broker that handles incoming scans and generates Grad-CAM activation saliency maps in under 1.5 seconds.
Executing high-resolution image processing directly in the web broker thread blocked concurrency event loops. Moving image decoding to background worker queues is vital for responsive HTTP endpoints.
EventSphere
During high-demand checkout rushes, ticketing platforms frequently suffer from double-booking race conditions and ticket bot fraud. EventSphere addresses this by establishing a secure relational schema with strict transaction isolation levels. The system manages transaction locks via an Express backend paired with a PostgreSQL database, while issuing check-in tickets protected by cryptographically signed QR codes and real-time websocket check-in checkpoints.
High-concurrency booking pools quickly degrade and time out without explicit database indexing on foreign keys and locking mechanisms. Structural indexing strategies are core database bedrocks.
Heimdall
High SIEM licensing costs prevent small local servers from securing network traffic visibility. Heimdall is a lightweight network sniffer designed to run locally, observing low-level socket interface packets. It feeds parsed header inputs to an offline-trained Random Forest classifier that identifies and logs volumetric network anomalies without expensive external log forwarder pipelines.
High socket packet throughput drops raw sniffer frames. Buffering network inputs to shared ring-buffers or moving packet classification closer to kernel spaces (e.g. eBPF) is required at scale.
Trust AI
Generative AI models face hallucination risks and lack real-time factual checks in production. Trust AI introduces automated validation pipelines by calculating the semantic vector distance between generated model outputs and static knowledge databases. The application evaluates sentence embedding cosine coordinates in parallel to verify factual consistency and output factual confidence ratios.
Evaluating text batches sequentially causes high response latencies. Shifting vector logic to matrix operations or asynchronous batch evaluation engines is key to maintaining sub-second speeds.
SkipQ
Physical retail checkout counters experience severe customer congestion and delays during peak hours. SkipQ bypasses the cashier queue entirely by providing a mobile web client capable of parsing barcodes directly through client-side cameras. The application manages local cart states and synchronizes transactions securely with relational SQL payment processors.
Intermittent cell service inside concrete store structures easily drops payment socket connections. Implementing a robust client-side offline storage state cache with transactional rollbacks resolved network drops.