The open content licensing standard for the AI-first Internet
What is RSL? | RSL: Really Simple Licensing
The open content licensing standard for the AI-first Internet
OpenAI strikes Reddit deal to train its AI on your posts
Reddit’s signed AI licensing deals with Google and OpenAI.
The Agentic Web and Original Sin
Microsoft is putting forth compelling proposals for the Open Agentic Web. However, the proposal needs digital payments, which will be key to creating a new content marketplace for AI.
Exclusive: Multiple AI companies bypassing web standard to scrape publisher sites, licensing firm says
Multiple artificial intelligence companies are circumventing a common web standard used by publishers to block the scraping of their content for use in generative AI systems, content licensing startup TollBit has told publishers.
AI proxy: fostering a more open ecosystem - Blog - Braintrust
Introducing Braintrust's latest feature: an AI proxy that lets you use open source models like LLaMa 2 and Mistral, as well as all of OpenAI's and Anthropic's models, behind a single interface with caching, security, and API key management built in.
Open Source Beyond Licensing - The Evolution Ahead
Boris Mann's digital garden and personal site.
Content Independence Day, one year on- building the business model for the agentic Internet
One year after declaring Content Independence Day, a dynamic market for monetized content has officially emerged. In this report, we examine how the rise of autonomous AI agents is upending traditional search referrals and detail the new infrastructure required to support a sustainable web economy.
Data Streaming for AI: From Extractive Training to Sovereign Infrastructure DWeb Camp 2026
AI systems are consuming the world's content without compensating its creators. This session explores data streaming as a new paradigm — where content flows to AI in real time, with built-in rights management, usage tracking, and fair compensation — and asks what it would take to make this infrastructure decentralized, sovereign, and governed by the communities it serves.
Licenses – Open Source Initiative
The content on this website, of which Opensource.org is the author, is licensed under a Creative Commons Attribution 4.0 International License.Opensource.org is not the author of any of the licenses reproduced on this site. Questions about the copyright in a license should be directed to the license steward. Read our Privacy Policy
Reimagining OSS Licensing and Commercialization with Fair Source - Adam Jacob, System Initiative
OpenAccess.ai — Rigorous Open Access Publishing
$20 to submit, free to read. AI peer review. Open to human and machine authors. All articles CC-BY 4.0.
Permissive-Washing in the Open AI Supply Chain: A Large-Scale Audit of License Integrity
Permissive licenses like MIT, Apache-2.0, and BSD-3-Clause dominate open-source AI, signaling that artifacts like models, datasets, and code can be freely used, modified, and redistributed. However, these licenses carry mandatory requirements: include the full license text, provide a copyright notice, and preserve upstream attribution, that remain unverified at scale. Failure to meet these conditions can place reuse outside the scope of the license, effectively leaving AI artifacts under default copyright for those uses and exposing downstream users to litigation. We call this phenomenon ``permissive washing'': labeling AI artifacts as free to use, while omitting the legal documentation required to make that label actionable. To assess how widespread permissive washing is in the AI supply chain, we empirically audit 124,278 dataset $\rightarrow$ model $\rightarrow$ application supply chains, spanning 3,338 datasets, 6,664 models, and 28,516 applications across Hugging Face and GitHub. We find that an astonishing 96.5\% of datasets and 95.8\% of models lack the required license text, only 2.3\% of datasets and 3.2\% of models satisfy both license text and copyright requirements, and even when upstream artifacts provide complete licensing evidence, attribution rarely propagates downstream: only 27.59\% of models preserve compliant dataset notices and only 5.75\% of applications preserve compliant model notices (with just 6.38\% preserving any linked upstream notice). Practitioners cannot assume permissive labels confer the rights they claim: license files and notices, not metadata, are the source of legal truth. To support future research, we release our full audit dataset and reproducible pipeline.
15 Open-Source Tools for Digital Sovereignty (2026) | Comparisons & Alternatives | Vucense
Own your digital stack. The 15 best open-source tools for privacy, security, and full control over your data — reviewed and ranked for 2026.
