Deep Dives
Case Studies
Problem, approach, and results with implementation notes.
AINA (AI Nutrition Assistant)
Mobile AI assistant with a clear data flow from capture to inference to actionable results.
Problem
Users need nutrition insights that feel immediate and usable, not buried in spreadsheets.
Approach
Define an inference boundary, keep UX fast, and make model outputs explainable enough to trust.
Result
A product-shaped experience with an architecture that supports iteration on models without rewriting the app.
FlutterPythonFastAPICV/MLStorageTelemetry
AI Agent Business Automation + Dashboard
Orchestrated workflows with audit logs and a dashboard that surfaces state and failures.
Problem
Business workflows fail silently: missed leads, inconsistent handoffs, and no visibility into why.
Approach
Treat workflows as products: retries, idempotency, logging, and a control surface for humans.
Result
Less manual chasing, fewer dropped tasks, and a dashboard that makes operations legible.
n8nTypeScriptPythonAPIsDashboardObservability
MCP Server + Local Codex/n8n Tooling
A tool registry boundary that lets agents call real capabilities safely and consistently.
Problem
Agents become unreliable when tool access is ad-hoc, insecure, or inconsistent across environments.
Approach
Centralize tool contracts, keep auth boundaries explicit, and document what tools do and do not do.
Result
More reliable agent execution and fewer ambiguous failures during automation runs.
MCPNode.jsPythonAuthToolingAutomation
Computer Vision Training Pipeline (YOLO/SAM)
Dataset to training to evaluation with reproducibility and deployment readiness.
Problem
CV work fails when data, training, and evaluation are not repeatable or measurable.
Approach
Standardize dataset flow, version configs, and enforce evaluation outputs as artifacts.
Result
A pipeline that supports iteration without losing track of what changed and why.
YOLOSAMPythonData pipelinesExperiment tracking