Collaborative editing for Discourse. Contribute to gdpelican/collude development by creating an account on GitHub.
Lies I was Told About Collaborative Editing, Part 2: Why we don't use Yjs / Moment devlog
In part 1 of this series, we found that users generally view the most popular collaborative text editing algorithms (including the most popular library, Yjs) as silently corrupting their documents when the algorithms resolve direct editing conflicts. We argued that, while this is potentially ok for live collaborative editing (since presence cursors help users to avoid direct editing conflicts), this property makes them generally wholly inappropriate for the offline case, as users will have no ability to avoid such conflicts.
Metagov x Future of Science Seminar - Discourse Graphs with Matt Akamatsu
Collaborative Editor Demo App
Steps Towards an Infrastructure for Scholarly Synthesis
Sharing, reusing, and synthesizing knowledge is central to the research process, both individually, and with others. These core functions are not supported by our formal scholarly publishing infrastructure: instead of the smooth functioning of functional infrastructure, researchers resort to laborious "hacks" and workarounds to "mine" publications for what they need, and struggle to efficiently share the resulting information with others. Information scientists have proposed an alternative infrastructure based on the more appropriately granular model of a discourse graph of claims, and evidence, along with key rhetorical relationships between them. However, despite significant technical progress on standards and platforms, the predominant infrastructure remains steadfastly document-based. Drawing from infrastructure studies, we locate the current infrastructural bottlenecks in the lack of local systems that integrate discourse-centric models to augment synthesis work, from which an infrastructure for synthesis can be grown. Through 3 years of research through design and field deployment in a distributed community of hypertext notebook users, we elaborate a design vision of what can and should be built in order to grow a discourse-centric synthesis infrastructure: a thriving "installed base" of researchers authoring local, shareable discourse graphs to improve synthesis work, enhance primary research and research training, and augment collaborative research. We discuss how this design vision -- and our empirical work -- contributes steps towards a new infrastructure for synthesis, and increases HCI's capacity to advance collective intelligence and solve infrastructure-level problems.
Steps Towards an Infrastructure for Scholarly Synthesis
Sharing, reusing, and synthesizing knowledge is central to the research process, both individually, and with others. These core functions are not supported by our formal scholarly publishing infrastructure: instead of the smooth functioning of functional infrastructure, researchers resort to laborious ”hacks” and workarounds to ”mine” publications for what they need, and struggle to efficiently share the resulting information with others. Information scientists have proposed an alternative infrastructure based on the more appropriately granular model of a discourse graph of claims, and evidence, along with key rhetorical relationships between them. However, despite significant technical progress on standards and platforms, the predominant infrastructure remains steadfastly document-based. Drawing from infrastructure studies, we locate the current infrastructural bottlenecks in the lack of local systems that integrate discourse-centric models to augment synthesis work, from which an infrastructure for synthesis can be grown. Through 3 years of research through design and field deployment in a distributed community of hypertext notebook users, we elaborate a design vision of what can and should be built in order to grow a discourse-centric synthesis infrastructure: a thriving “installed base” of researchers authoring local, shareable discourse graphs to improve synthesis work, enhance primary research and research training, and augment collaborative research. We discuss how this design vision — and our empirical work — contributes steps towards a new infrastructure for synthesis, and increases HCI’s capacity to advance collective intelligence and solve infrastructure-level problems.
NLnet; Nanoarguments
Scientific knowledge is currently scattered across papers, repositories, and disconnected platforms, with no structured way to trace how claims connect to evidence or how arguments develop. Nanoarguments builds a framework and tools for creating, browsing, and contributing to a global, federated graph of scientific discourse and evidence. Researchers and their communities can collaboratively structure claims, evidence chains, and discussion as nanopublications, which are small, cryptographically signed Linked Data snippets with precise provenance and authorship, published to a decentralized peer-to-peer network. The project builds upon the Nanodash interface to help users browse, edit, and aggregate discourse and evidence graphs, and integrates with dokieli to enable in-context authoring of nanopublications as inline annotations while reading or writing a document. A bidirectional ActivityPub connector bridges the nanopublication network and the fediverse, allowing discourse threads to start as social exchanges and crystallize into persistent, machine-readable evidence records. The project will be piloted with early adopter research groups in discourse and evidence modeling. All components will be released as open-source modules that other systems can build upon.
GitHub - Responsible-Dataset-Sharing/easy-dataset-share: A CLI tool that helps AI researchers share datasets responsibly.
A CLI tool that helps AI researchers share datasets responsibly. - Responsible-Dataset-Sharing/easy-dataset-share
GitHub - collect-intel/osccai: Open-source Collective Constitutions for AI
Open-source Collective Constitutions for AI. Contribute to collect-intel/osccai development by creating an account on GitHub.
sourcehut - the hacker's forge
sourcehut is a network of useful open source tools for software project maintainers and collaborators, including git repos, bug tracking, continuous integration, and mailing lists.
DeepWiki | AI documentation you can talk to, for every repo
DeepWiki provides up-to-date documentation you can talk to, for every repo in the world. Think Deep Research for GitHub - powered by Devin.
Embracing ATProto, part 2: Tangled Knots and social coding
You thought Github was a social coding platform? Think again, and get ready to tangle! Built on atproto, tangled allows you to use your Bluesky/atproto identity on a (not quite yet) fully feldged git platform!
Is GitHub Cooked?
Track GitHub service incidents and outages. Real-time stats, incident history, and downtime analytics.
GitHub · Change is constant. GitHub keeps you ahead.
Join the world's most widely adopted, AI-powered developer platform where millions of developers, businesses, and the largest open source community build software that advances humanity.
GitHub - github/awesome-copilot: Community-contributed instructions, prompts, and configurations to help you make the most of GitHub Copilot.
Community-contributed instructions, prompts, and configurations to help you make the most of GitHub Copilot. - github/awesome-copilot
Getting started - Docs
Discourse Graphs are a tool and ecosystem for collaborative knowledge synthesis, enabling researchers to map ideas and arguments in a modular, composable graph format.
Universal Version Control
Building tools to help people explore alternatives, keep track of history, and collaborate better, across all kinds of media.
