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🤓 Latest Submissions
TowerShield Edge AI & Governance
TowerShield is an autonomous, privacy-preserving Edge-to-Cloud AI orchestrator engineered to revolutionize critical telecommunications infrastructure safety and complex structural anomaly detection. In operational environments where sensitive site telemetry and sensor data must remain secure within local perimeters, TowerShield bridges high-performance intelligence with rigorous enterprise governance. Powered by local DGX hardware, the platform deploys an intelligent three-tier vLLM routing architecture—utilizing 0.5B triage, 1.5B operational, and 7B-VL multi-modal models—managed via advanced capability-first routing logic. When complex multi-asset incidents, such as cascading windshear torsion or concealed structural joint stress fractures, exceed routine edge capacity, TowerShield engages an autonomous Claude-powered Model Download Advisor. This intelligent agent performs multi-dimensional gap analysis to evaluate model boundaries and propose necessary upgrades. Critically, TowerShield enforces an explicit Human-in-the-Loop (HITL) Safety Gate, intercepting recommendations and requiring manual operator authorization prior to any high-tier model fetching or container registration. This guarantees absolute operational oversight, complete data privacy, and zero unauthorized data egress for modern telecom operators.
19 Oct 2026
LeaderTrack
LeaderTrack Product Demo Summary 3-Step Core Workflow Track Progress & Streaks: Set reading goals, track book completion, and maintain daily reading streaks. Log Sessions & Habits: Record daily reading duration (15–60 mins) alongside associated reading habits. Capture Insights: Extract and tag key takeaways, actionable wisdom, and favorite quotes. Key Features Leadership Book Tracker: Organize your library, update status, and track completion visualizers. Quick Session & Time Logging: Seamlessly input time spent reading and build consistency. Structured Wisdom & Insight Notes: Categorize key learnings into actionable categories. Searchable Centralized Wisdom Feed: Instantly search, filter, and recall saved quotes and takeaways. Tech Stack Frontend: React, TypeScript, Tailwind CSS, Lucide Icons Data Storage: Client-side localStorage (offline-first, zero backend required) Hosting & Deployment: Vercel (via v0)
10 Aug 2026
AI Smart Warehouse Safety & Audit System
Core Architectural Components Edge Vision Processing Layer: Automates spatial downsampling across massive frame datasets, sampling sequential video frames dynamically to dramatically optimize hardware compute efficiency. Fine-Tuned Multimodal Inference Pipeline: Features a specialized LoRA adapter running natively on PyTorch ROCm infrastructure. The model dynamically interprets visual layout complexities, localized machinery activity, and granular safety variables (such as personnel tracking and PPE compliance metrics). Rugged Normalization Engine: A custom, robust post-inference parsing layer that scrubs syntactical anomalies, cleans inline VLM commentary via regex parsing, and unifies variant output schemas in flight to eliminate unparsed data losses. Transactional Data Ledger: Streams structured audit insights concurrently into high-fidelity JSON time-series ledgers and structured CSV alert records to maintain an immutable compliance log. Live Operational Dashboard: A responsive Streamlit web UI that ingests the transactional ledgers in real time, converting raw multimodal inferences into actionable risk heatmaps, key metrics, and immediate safety indicators for warehouse operators. Key Technical Achievements ROCm Hardware Optimization: Fully utilized local AMD compute kernels, managing stable multi-gigabyte VRAM bounds while keeping core execution times uniform across high-volume batches. Dynamic Content Extraction: Built-in resilience to edge-case model hallucinations and syntax shifts, turning brittle string parsing into a rugged, enterprise-grade validation layer. I generated the cover image with prompts tailored to my project in Gemini.
13 Jul 2026
