Lifestreams: a storage model for personal data

Plan B: an operating system for ubiquitous computing environments

The conventional approach for building pervasive environments relies on middleware to integrate different systems. Instead, we have built a system that can deal with these environments by exporting system resources through distributed virtual file systems. This requires no middleware, simplifies interoperation, and permits applying general purpose tools to any system resource. A constraint-based file system import mechanism allows the system to adapt to changes in the environment and permits users to customize the environment and tailor adaptation according to their needs. The system has been in use for over a year to carry out our daily work and is underlying the smart space that we built for our department

The Interfaces With Which We Think

The concepts in modern operating systems — apps, windows, desktops, notifications, and so on — have so permeated our understanding of personal computing, it’s hard to imagine anything else, let alone believe there could be anything better.

Basic | The Personal Computing Platform

We build products, tools, and standards to make computers more personal.

Unclutter for Mac

A new space on your desktop to store and organize your Files, Notes and Clipboard data.

Home

Platform created by the community to programmatically author, schedule and monitor workflows.

neodb-social/neodb

🧩 a self-hosted server tracking what you read/watch/listen/play, powering a global distributed community federating via ActivityPub and ATProto.

LiveStore: SQLite-based data layer for local-first apps

Introducing: LiveStore - the client-centric, local-first data layer for high performance applications.

~𝔐𝔦𝔩𝔰𝔢𝔱 on Twitter / X

To catch up on the current state of @urbit, check out Hawk: a sort of "namespace computer", rethinking how you manage data on your personal cloud. Over the next 15 short videos, you'll see how it unlocks a fully programmable environment you control. pic.twitter.com/gcB4l1iY1Z— ~𝔐𝔦𝔩𝔰𝔢𝔱 (@0xsyru) December 21, 2024

Lexicon Embeds Overview

ATProto’s inside out structure provides an opportunity for open, permissionless building on top of any component in the network, including its extensible data schema definition system, Lexicons. One of the opportunities this presents is users sharing their data and records with multiple apps. Those apps may be direct substitutions (different clients for microblogging say), or apps that use that data in a variety of ways, such as Popfeed an app which aggregates posts about a variety of media ty...

surrealdb/surrealdb

A scalable, distributed, collaborative, document-graph database, for the realtime web

Roomy and community organizing for system change

Roomy is a group messaging app that gives online communities tools for autonomous collaboration — designable spaces not just for chat, but for cultivating a web of shared understanding together. We show how cheap, user-controlled, interoperable data on ATProto can enable online communities to channel impassioned mobilizing into sustained organizing in the shadow of Big Tech's empire.

Designing better file organization around tags, not hierarchies

Computer users organize their files into folders because that is the primary tool offered by operating systems. But applying this standard hierarchical model to my own files, I began to notice shortcomings of this paradigm over the years. At the same time, I used some other information systems not based on hierarchical path names, and they turned out to solve a number of problems. I propose a new way of organizing files based on tagging, and describe the features and consequences of this method in detail.

File Over App: A Philosophy for Digital Longevity

My take on the 'file over app' philosophy and why it’s essential for keeping my data resilient and built to last.

DREAM — Dynamic Retention Episodic Architecture for Memory

Modern AI systems lack persistent, user-specific episodic memory. Existing approaches rely on short-term context windows, shallow preference storage, or static conversation logs that do not scale and cannot preserve meaningful long-term continuity. This paper introduces DREAM (Dynamic Retention Episodic Architecture for Memory), a scalable, opt-in, episodic memory framework designed to work with current LLM and agent architectures. DREAM integrates episodic summarization, user-controlled opt-in memory, semantic retrieval via per-user vector indexes, an adaptive retention mechanism that expands TTL based on user engagement, and horizontal sharding of orchestrators and storage for large-scale deployments. The paper details the architecture, components, data flows, and implementation examples, and argues that DREAM provides a practical path toward AI systems capable of consistent, privacy-aligned long-term reasoning. This project has been extended with a conceptual analysis and simulation of a "DREAM-as-a-Support" (DaaS) hybrid layer. This extension demonstrates DREAM's architectural extensibility, reframing it from a standalone framework into a foundational platform component. The DaaS model provides core memory governance such as adaptive retention (ARM) and user-centric opt-in as an on-demand service to complementary cognitive systems, validating the original four-pillar design through a scalable, internal API. Reference Implementation A reference implementation of the DREAM architecture is available as an open-source Python framework:Official Reference Implementation This implementation is intended for experimentation and architectural validation and does not represent a production-ready system DREAM Architecture — Official GitHub Repository