Groundy, by Berry Mingus
AI, software, and the people behind them.
Groundy is Berry Mingus's publication about AI and large language models, developer tools, infrastructure, and software culture.
For people who build software, choose technology, and want to understand what changes when a new tool arrives.
Why Groundy exists
A release is the beginning of the story.
What does it let you do? Where does it fall short? And what does it change for the people using it?
I follow those questions beyond the announcement. A model score needs an evaluation setup. A cheaper runtime comes with hardware and maintenance constraints. A new developer tool changes more than the time it takes to write a function.
My aim is useful analysis: comparisons that explain a choice, guides that make a system understandable, and essays that connect technical decisions to work and culture. Not every release deserves a recommendation, and not every benchmark settles an argument.
What I write about
The tools, systems, and people are connected. So is the coverage.
AI & large language models
Model capabilities, evaluation methods, agent frameworks, and the gap between a promising result and a useful system.
Developer tools & open source
Editors, coding assistants, programming languages, and the projects that shape how software is built, maintained, and shared.
Infrastructure & security
Local inference, serving runtimes, hardware, networks, and the trust boundaries beneath the software people depend on.
Industry, policy & culture
The business decisions, working habits, laws, and communities that shape technology, and the people who live with its consequences.
I'm Berry Mingus. Groundy is where I explore how software works, what new tools change, and why those changes matter.
My background is in professional writing and communications. My programming interests include Haskell, data structures, and graphs: subjects where understanding the underlying model matters as much as getting something to compile.
This is where I bring those interests together: explaining technical ideas clearly, examining the evidence behind product claims, and keeping the human consequences in view.
Sourcing approach
I want you to be able to follow the reasoning and check the evidence for yourself.
- Put evidence beside the claim
- I link to supporting material where the argument is made, giving priority to original research, documentation, source code, release notes, and public records. Reference lists make those sources easier to revisit.
- Separate a claim from a finding
- A vendor's performance claim is not an independent result. A preprint is not a settled consensus. I distinguish between those kinds of evidence and explain what a source establishes and what remains uncertain.
- Keep comparisons in context
- Model versions, hardware, workloads, pricing assumptions, and evaluation methods can change the answer. A useful comparison explains those conditions and the limits of carrying a result into a different setup.
- Make the conclusion useful
- I look for the practical consequence: who a tool suits, what tradeoff matters, or which assumption deserves another look. A feature list or a leaderboard alone is not a recommendation.
Groundy Comments
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Keep exploring
Start with a question that matters to you.
Explore a topic, find a comparison, or read my latest articles.
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