One question I have been thinking about in the context of reproducible research is this: Why is all stable software technology old, and all recent technology fragile? Why is it easier to run 40-year-old Fortran code than ten-year-old Python code? A hypothesis that comes to mind immediately is growing code complexity, but I'd expect this to be an amplifier rather than a cause. In this pose, I will look at another candidate: the dominance of Open Source communities in the development of scientific software.
Mx. Aria Stewart (@aredridel@kolektiva.social)
I knew it was this way but it's really hitting me today how much the Open Source movement and copyright maximalism supplanted the idea of free software, and again how much the Free Software movement turned from a close ideological cousin of the remix and open culture movement into a culture of legalism. At the same time, copyright law itself has been extended to be near-immortal copyrights rather than brief monopolies to spur creation by enabling profit from creating works.
The Missing Middle of Open Source
Why serious, maintainer-driven projects struggle to sustain themselves—and what it will take to fix it.
AI and the Destruction of the Creative Commons
The balance of software copyright protection and openness has always been fraught with minutiae and detail that bores all but the most nerdy of pedants. Yet, through much effort and 40 years of debate we had reached an equilibrium. Now AI has thrown that out the window.
How open source projects need to adapt to the AI coding era | We Love Open Source • All Things Open
A new Carnegie Mellon study shows AI coding tools boost velocity by 281% — then leave codebases harder to work with. Here's what open source communities need to do before the sugar rush wears off.
The Few, the Tired, the Open Source Coders
The open source movement runs on the heroic efforts of not enough people doing too much work. They need help.
Linking the world's research to the code it runs on - OpenAlex blog
Research relies on software. Software written by scientists, for science, runs through the entire modern research stack: NumPy and SciPy, R and ggplot2, Jupyter, BLAST, ImageJ, AlphaFold. Yet in the scholarly record, that software is nearly invisible. Software is not usually cited formally in publications and is usually just mentioned in the text, which means […]
AI creates asymmetric pressure on Open Source
How Open Source communities can adapt to AI-generated contributions without overwhelming Open Source maintainers
Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community
Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.
Roadmap: Open Source
After years of investing in open source software, we’re releasing our thinking on what positions these companies as emerging technology giants.
Ten FOSS Development Fallacies For User Facing Software
This is a list, inspired by the Five Geek Social Fallacies, of common patterns or behaviours that developers of free/open source software (FOSS) fall into when they try to develop software for users who are not technical, or for whom non-technical users would be the most obvious userbase. Hopefully writing these down (which frankly is cathartic more than anything) is helpful to some in recognising these thought patterns and avoiding them when developing software - I’ve got some of my own thoughts on this at the bottom.
History of free and open-source software
The history of free and open-source software begins at the advent of computer software in the early half of the 20th century. In the 1950s and 1960s, computer operating software and compilers were delivered as a part of hardware purchases without separate fees. At the time, source code—the human-readable form of software—was generally distributed with the software, providing the ability to fix bugs or add new functions. Universities were early adopters of computing technology. Many of the modifications developed by universities were openly shared, in keeping with the academic principles of sharing knowledge, and organizations sprung up to facilitate sharing.
Reframing “Open Source AI” with Meredith Whittaker: AI Impact Summit 2026
Aaron Boodman on Twitter / X
There is this tension in dev tooling right now: Lots of people want to self-host, and not depend on a service. Lots of people also want the vibes of open source - being part of an open development community, having the code, etc.But builders still need to make a living...— Aaron Boodman (@aboodman) January 23, 2024
I can see a whole lot of people agreeing with this. Kinda the same with open source in general, but few open source projects are social protocols seeking widespread adoption. It's a particular flavor of narrative disconnection from reality we have to bridge out of to get out of our own way.
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Every @proto developer sounds like this to me:
When (not if) the Tech Monopolies come for the Atmosphere, can we create the institutional structures to protect what we have? Right now we're nowhere near ready. No company harmed the web more than Google, not even close, while claiming to help which many believed. What can we learn and how fast?
kottke.org
“The Metric Is Not the Mission is a ten-part examination of how Big Tech moved from building and expanding the open internet to increasingly shaping it around its own metrics, incentives and assumptions.” techdirt.com/2026/09/21/the-metric-is-not-…
Wild to see that @npmx.dev was the fastest-growing emerging open source organization by number of contributors in Q1 2026 according to osscar.dev. What is even more interesting: it was the only non-AI tool in the top 10.
