Adaptive systems for evolving ideas – Zensical creates scalable Open Source systems for technical writing that always keep you in the flow
AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances
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Nenex: A Neural Personal Wiki Idea
Proposal for a personal wiki built on neural nets: all edits are logged & used to finetune a NN assistant in realtime.
From OSS to Open Source AI: an Exploratory Study of Collaborative...
AI development is embracing open-source paradigm, but the fundamental distinction between AI models and traditional software artifacts may lead to a divergent open-source development paradigm with...
jedisct1/Qwen3.6-35B-rust.mlx · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
The Transformation of Documents: Repositories Are the New Unit of Knowledge Work
How will documents evolve when AI agents become ubiquitous? In a world of AI agents, does the repository become the source of truth—where humans declare intent, agents turn it into executable artif…
(iterate think thoughts): Painting with Gaussians
Dmitri's blog about programming, Clojure, and software development
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.
AI creates asymmetric pressure on Open Source
How Open Source communities can adapt to AI-generated contributions without overwhelming Open Source maintainers
Just-in-Time Ontological Reframing: Teaching Gemini to Route Around …
For any given AI system, there is a set of euphemisms and dual use framings that will allow it to construct nearly any output. This jailbreak teaches Gemini 3 Pro to construct and step into such …
Andrej Karpathy Stopped Using AI to Write Code. He’s Using It to Build a Second Brain Instead
His new workflow turns raw research into a self-maintaining wiki.No vector databases, no RAG pipelines, just markdown files and an LLM that…
Chasing RATs: Tracing Reading for and as Creative Activity
Creativity research has privileged making over the interpretive labor that precedes and shapes it. We introduce Reading Activity Traces (RATs), a proposal that treats reading -- broadly defined to include navigating, interpreting, and curating media across interconnected sources -- as creative activity both for future artifacts and as a form of creation in its own right. By tracing trajectories of traversal, association, and reflection as inspectable artifacts, RATs render visible the creative work that algorithmic feeds and AI summarization increasingly compress and automate away. We illustrate this through WikiRAT, a speculative instantiation on Wikipedia, and open new ground for reflective practice, reader modeling, collective sensemaking, and understanding what is lost when human interpretation is automated -- towards designing intelligent tools that preserve it.
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.
AI Data Centers Will Be Obsolete (Geometric Reasoning Explained)