Potluck: Dynamic documents as personal software

docuowl/docuowl

🦉 A documentation generator

Living documents and interactive schematics — Selin Jessa

Using LLMs to turn static figures from data analysis, schematics, and publications into interactive widgets as tools for thought

Embark: Dynamic documents for making plans

Gradually enriching a text outline with travel planning tools.

AI Focus

NotebookLM is one of my favorite applications in decades. If you haven’t experienced it before, it’s an application that lets you pull in sources from all around - Google Drive, PDFs, public links - collate them into a notebook, and then query or transform that content. Want to turn five research papers into a podcast? Done. Need to extract key takeaways from a collection of articles? Easy. It’s a fundamentally different way of interacting with information that wasn’t possible before large language models.

Visual-Meta

Visual-Meta is an approach to augmenting documents in order to enable rich interactions in a robust manner.

Denicek: Computational Substrate for Document-Oriented End-User Programming

As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.

Composer by DXOS

Introducing programmable documents. Blend the power of popular file formats (docs, sheets, sketch & tables) inside of a collaborative local-first workspace.

bliki: Ubiquitous Language

a bliki entry for Ubiquitous Language

Plaintext Productivity | Introduction

The Plaintext Productivity system is a smart way to get organized on Windows, using plaintext files as your trusted system and great software to make it easy.

the mud coders guild

:computer: text-based words in long-form format

Generative Agents: Interactive Simulacra of Human Behavior

Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.