Done Software: The Future of Sustainable Stewardship

Lately I keep circling back to software that’s done.

Not dead. Not abandoned. Not deprecated. Done. Feature-complete. Stable. Doing exactly what it set out to do, with no urgent need to do anything else. The concept makes most engineers uncomfortable—our entire industry rests on the assumption that software must continuously evolve or die.

That assumption runs wrong, and burns people out.

The Cult of Continuous Development #

Open source has an update problem. We’ve internalized the idea that a healthy project ships frequently—new features, new releases, activity graphs that glow green. GitHub’s contribution graph literally visualizes this: green squares good, gray squares bad. A project with no commit in six months looks neglected, even when stable, secure, and doing the job perfectly.

OSI Releases Version 1.0 of Open Source AI Definition

Back in September I wrote about the headache of defining “open source” for AI models. The Open Source Initiative has now published their answer—OSAID v1.0, released October 28 at the All Things Open conference in Raleigh. I’ve spent the last ten days reading the definition, the endorsements, the criticism, and the reaction from companies whose models don’t qualify.

My verdict? It’s a necessary compromise that will make some people furious and make everyone’s procurement conversations slightly less painful.

Defining Open Source AI: Solving a Million Headaches

Last month I burned two days evaluating “open source” models for a production use case at DreamFlare. By the end I was more confused than when I started — not about the models themselves, but about what “open source” even means anymore.

Traditional open source is straightforward: you get the source code, you can modify it, you can redistribute it. That definition has been settled for decades. But AI models aren’t source code. They’re trained artifacts; the “source” is really the training data, the training code, the hyperparameters, and the weights. Calling a model “open source” because you released the weights is like calling a compiled binary “open source” because you published the .exe.

OpenTofu: The Community Fork of Infrastructure-as-Code

Back in August, I wrote about HashiCorp’s decision to switch Terraform from MPL v2.0 to the Business Source License. I said I was watching the fallout.

Three months later, the fallout has a name: OpenTofu — which arrived faster than anyone expected.

It also has a Linux Foundation home, 140+ organizational backers, 600+ individual pledges, 18 full-time developers, and a beta release that dropped just days ago. The community response moved fast — even by open-source drama standards.

HashiCorp Terraform License Shift: Strategy vs Community

Two days ago, HashiCorp announced that Terraform — and all their products — would switch from the Mozilla Public License (MPL 2.0) to the Business Source License (BSL 1.1). The news dropped on August 10th and the internet hasn’t stopped arguing about it since.

I have complicated feelings about this one.

What Actually Changed #

BSL isn’t proprietary, exactly. You can still read the source code. You can still use Terraform for your own infrastructure. What you can’t do is offer a competing commercial service built on HashiCorp’s code without their permission. After four years, the code converts to MPL 2.0 automatically — the “change date” mechanism.

OSPO Survey: The State of Enterprise Open Source

The 2023 TODO Group survey results dropped recently, and one number jumped out: 66% of surveyed organizations now have a dedicated Open Source Program Office or some formal open source initiative. That’s a 32% jump from the previous year.

Two years ago, OSPOs were something you’d find at Google, Microsoft, Meta — the usual suspects. Big tech companies with the resources and the open source exposure to justify a dedicated team. The idea that two-thirds of organizations would have formal open source governance would’ve seemed optimistic at best.

Stable Diffusion: The Open Source AI Explosion

Something big happens in generative AI, and it has nothing to do with who makes the best images.

Stable Diffusion—a text-to-image model built by Stability AI, RunwayML, CompVis at LMU Munich, EleutherAI, and LAION—prepares to release its model weights to the public. Not behind an API. Not through a Discord bot. The actual model, downloadable, runnable on your own hardware. About 10,000 testers held the beta, with a broader research release to roughly 1,000 researchers expected any day now.