Jacob Tomlinson

I’ve spent the majority of my professional career working on software libraries, chunks of reusable code that other people stack together to make their code.

Agents are powerful because they can reason their way towards a goal using tools to interact with the world.

This post is a companion to a talk I gave at PyData London 2026.

As coding agents grow in popularity, open source project documentation is increasingly consumed by LLMs.

AI tools have radically changed note taking.

This is a slightly different kind of post than my usual tech ramblings.

Many open source maintainers have noticed an uptick in low-effort AI generated PRs recently, myself included.

Some quick notes on how to make a backport Conda Forge release.

When I talk to people about Python package management in 2025 I see the following tools in active use; uv, pixi, pip, conda, mamba, micromamba and poetry.

It’s hacktoberfest time again!

The majority of your users don’t read your changelog.

Version numbers are hard to get right.

In today’s world of ever-growing data and AI, learning about GPUs has become an essential part of software carpentry, professional development and the education curriculum.

Version numbers are hard to get right.

Accelerating Python using the GPU is much easier than you might think.

In October 2015 I gave a talk on Kubernetes at Tech Exeter (back when it was called the Exeter Web Meetup).

In kr8s I have a GitHub Actions workflow which runs a script nightly on a cron job.

I write a lot of markdown.

I consider myself an advanced Linux and macOS user.

If you want to point tools like kubectl to a config file other than ~/.kube/config you can set the environment variable KUBECONFIG.

The PyData software ecosystem is made up of many open-source software libraries that are used heavily in Python Software Development, Data Science/Engineering, Traditional Sciences, Artificial Intelligence and beyond.

If you’ve been around open source projects on GitHub you may have encountered a project with a stale bot.

Debugging software itself is a hard task, but debugging GPU software environments can be even more challenging.

Writing GPU code in Python is easier today than ever.

Accelerating Python using the GPU is much easier than you might think.

Dask is a popular Python framework for scaling your workloads, whether you want to leverage all of the cores on your laptop and stream large datasets through memory, or scale your workload out to thousands of cores on large compute clusters.

Abstract Since joining NVIDIA I’ve gotten to grips with the fundamentals of writing accelerated code in Python.

Abstract Since joining NVIDIA I’ve gotten to grips with the fundamentals of writing accelerated code in Python.

In PEP440 Python introduced Version Epochs as a mechanism to allow projects to change versioning scheme.

GitHub Releases is a feature where you can create a page associated with a git tag that contains a description of the changes in that tag along with build artifacts for users to download.

By leveraging cloud computing resources, you can pay for just the computing power you need, when you need it.

Managing Kubernetes resources with Python has never been easier thanks to the kr8s Kubernetes client for Python.

Databricks is a very popular data analytics platform used by data scientists, engineers, and businesses around the world.

Pandas is flexible, but often slow when processing gigabytes of data.

Sometimes you want to write some code using Dask which can then be run against multiple different cluster backends.

Version numbers are hard to get right.

From time to time I give talks and run workshops and folks will ask me for a bio.

Sometimes when writing code in a blog post I want to emphasize a couple of lines in particular.

I love using typer for creating CLI tools in Python.

I recently read Loving and hating the Streak by Cassidy Williams.