oss_fan
Re:Geography of Claude Code
on Tuesday March 24, 2026 @12:16PM (#47504967)
This is an interactive visualization of Claude Code signed commits from its release in 2025 until last week.
We can see the diffusion curves at the city and country level - the data is based on activity of 2 million developers located to the city level from their profile information.
Re:Who is using AI to code? Global diffusion and impact of generative AI
on Sunday January 25, 2026 @08:50AM (#46754055)
This paper shows that experienced coders are getting more out of AI than early career devs, even though the latter use it more.
Food for thought for the software career ladder
Who is using AI to code? Global diffusion and impact of generative AI
Who is using AI to code? Global diffusion and impact of generative AI
Re:Who is using AI to code? Global diffusion and impact of generative AI
on Wednesday June 11, 2025 @03:04AM (#44244962)
"By December 2024, AI wrote an estimated 30.1% of Python functions from U.S. contributors."
Twitter: https://x.com/johannes_wachs/status/1932688738821296519
Anchor Sponsor Firms in Open Source Software Ecosystems
Modeling interconnected social and technical risks in OSS ecosystems
Re:Modeling interconnected social and technical risks in OSS ecosystems
on Monday February 19, 2024 @05:27PM (#39435590)
Open source software ecosystems consist of thousands of interdependent libraries, which users can combine to great effect. Recent work has pointed out two kinds of risks in these systems: that technical problems like bugs and vulnerabilities can spread through dependency links, and that relatively few developers are responsible for maintaining even the most widely used libraries. However, a more holistic diagnosis of systemic risk in software ecosystem should consider how these social and technical sources of risk interact and amplify one another. Motivated by the observation that the same individuals maintain several libraries within dependency networks, we present a methodological framework to measure risk in software ecosystems as a function of both dependencies and developers. In our models, a library’s chance of failure increases as its developers leave and as its upstream dependencies fail. We apply our method to data from the Rust ecosystem, highlighting several systemically important libraries that are overlooked when only considering technical dependencies. We compare potential interventions, seeking better ways to deploy limited developer resources with a view to improving overall ecosystem health and software supply chain resilience.
Are Large Language Models a Threat to Digital Public Goods?
Re:Digital traces of brain drain: developers during the Russian invasi...
on Sunday May 21, 2023 @01:44PM (#36023181)
Data on how many devs are leaving Russia and where they are going.
Digital traces of brain drain: developers during the Russian invasion of Ukraine
Evolving collaboration, dependencies, and use in the Rust OSS ecosystem
Re:Evolving collaboration, dependencies, and use in the Rust OSS ecosy...
on Tuesday November 22, 2022 @08:57AM (#33705811)
Large dataset on all aspects of Rust
Re:Digital Traces of Brain Drain: Developers During the Russian Invasi...
on Monday September 05, 2022 @04:30AM (#32721854)
New paper trying to measure brain drain from Russia using GitHub data.