Python Automation
Workflow automation, data pipelines, web scraping, REST APIs, data apps with Streamlit, Flask web services, and ML-powered tooling — all in Python.
All
Your Flask App Accepts File Uploads — But Can It Survive a Malicious One?
A deep dive into building a secure file upload pipeline in Flask, covering extension whitelisting, memory-efficient size checks, path traversal prevention, and security headers.
One Dictionary, Seven Colors, and 200 Paragraphs: Engineering a Document Compliance Engine
A deep dive into the acronym sweep engine of STORM DAT, a Python-based document analysis tool that uses regex and state machines to enforce complex compliance rules across multi-page Word documents.
Your API Reloads the Model on Every Request: Here's the FastAPI Pattern That Fixes It for Good
An ML inference API has a constraint that a standard REST API does not: its most expensive resource is not a database connection or a network socket — it is the model artifact itself.
Trading Precision for Speed: Implementing Model Quantization and Format Conversion for Clinical Medical Imaging
In clinical environments, deploying deep learning models for medical imaging—brain tumors, strokes, cardiac anomalies—presents a unique constraint: the inference latency and memory footprint directly impact patient outcomes.
Stop Copying Response Boilerplate Across Every Endpoint: Build a Typed Generic Envelope in Pydantic v2
Learn how to use Python's `Generic[T]` protocol with Pydantic v2's `BaseModel` to create a single, typed response envelope that propagates concrete type information through to OpenAPI schema generation, provides consistent operational metadata across all endpoints without repetition, and enforces field-level constraints on both inbound and outbound data.
Stop Guessing Types at Runtime: Building a Dispatch-Based Parameter Parser
Learn how to build a clean, maintainable parameter parser using the command dispatch pattern.