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13 items · 5 projects · 8 feed items
Co-founded an end-to-end GenAI platform that automates the innovation workflow for Consumer Packaged Goods companies, from idea prototype through to production-ready concept. Full-stack React + Python + LLM integration, shipped to early customers.
Built a Python validation library that turns sample events into editable contracts with reusable field-level quality rules.
Refactored a fragmented set of analytics pipelines into a reusable Python template with consistent ingestion, transformation, testing, and operational behaviour.
Led development of a configurable data-quality platform with reusable connectors for profiling, validation, and reconciliation across varied sources.
Built backend services and REST APIs for an enterprise user-management platform, working closely with product stakeholders in an Agile delivery process.
Three Python data libraries, three personalities. pandas is the API everyone knows. Polars is faster and stricter. DuckDB is the database masquerading as a library. Here's the decision framework I use, with the cases where each is genuinely the right pick.
Final part of the series. Generators turn an O(N) memory problem into an O(1) memory problem and let you compose pipelines that read like sentences. Five patterns: streaming files, paginated APIs, itertools chains, batching, and async generators.
Three tools that replaced six in my Python workflow. uv subsumes pip + venv + pip-tools + build + twine. ruff subsumes flake8 + black + isort + pyupgrade. pyright (or basedpyright) makes type checking instant. Here's how to migrate.
Part 2 of a 3-post series. Context managers earn their keep when you have a resource that must be set up and torn down — and `with open()` is barely scratching the surface. Five patterns: timing, transactions, async, ExitStack, and supressing exceptions.
Part 1 of a 3-post series. Decorators are easy to misuse and spectacular when they fit. Here are the four shapes I reach for in production AI/data work, with code: retry, timing, instrumentation, and feature-flagging.
Pydantic v2 isn't just dataclasses with validation, it's a contract layer for any boundary where untrusted data crosses into your code: HTTP, JSON files, LLM tool outputs, queue messages. Here are the patterns I reach for.
Pattern matching, structural type generics, exception groups, TaskGroup, the f-string self-doc =, and a couple of less-loved features. Code samples for each, with the use-cases I reach for them in real work.
The mental model behind asyncio that finally clicked, the production patterns I reach for (TaskGroup, Semaphore, run_in_executor), and the four mistakes that bite every async codebase eventually.