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12 items · 4 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.
A Pydantic-based data-validation library for the team that runs API analytics. Engineers point it at a JSON event, it generates a schema, they edit it to add field-level rules (null, range, allowed-values, custom), and the orchestrator runs validations every ingest. Distributed as an installable wheel.
Refactored the inconsistent set of ETL pipelines feeding TomTom's API analytics into a single OOP template, distributed as an internal Python package. Engineers subclass the base, get live Azure Data Explorer connections for free, and only write the business logic in extract / transform / load. Result: consistent, version-controlled pipelines across volume, response-time, and error-rate use cases.
First professional engagement: built REST APIs and backend services for Sainsbury's (UK's largest retailer) user-management platform via TCS. Python Flask, OOP design patterns, and direct collaboration with the Corporate Pingit Business Product Owners through an Agile SDLC.
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.