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React for Two Computers — overreacted
15 hours ago
- The article explores the conceptual differences between function calls (verbs/recipes) and tags (nouns/blueprints), showing how tags represent potential computations that are passive and open to interpretation.
- It introduces a framework for splitting computations across time and space, using 'Early' and 'Late' worlds to represent distributed computation across two computers, with tags serving as a bridge between them.
- Key concepts include 'Components' (capitalized functions that embed rather than introspect arguments) and 'Primitives' (lowercase functions that require eager evaluation), along with mechanisms like 'import tag' and 'import rpc' for crossing computer boundaries.
- The article demonstrates how to split a React-like program into Early and Late Components, with interpret() dissolving components outside-in and perform() handling primitives inside-out, enabling client-server architecture without traditional network calls.
- It concludes with advanced ideas like 'use client' and 'use server' directives, streaming execution, stateful Late worlds, and caching, showing the practical implications for React Server Components and modern web development.
A SETI Home for AI-Assisted Research
16 days ago
- SETI@home was a project using home computer idle compute for distributed signal analysis in the Search for Extraterrestrial Intelligence.
- Now, with AI subscriptions and local agent software, unused token allowances could be contributed to crowdsourced scientific or mathematical efforts.
- Small teams and individuals combining domain expertise with AI systems have achieved research successes like solving Erdős problems.
- A key challenge is aligning the frontiers of unknown problems with current AI system capabilities to effectively use increased compute.
- A public ledger tracking compute, methods, and results could help assess AI's contribution to knowledge as a common asset.
- Implementing this would require new commercial arrangements, checkpointing systems, and agent architectures for research collaboration.
- The goal is to pool AI inference capacity into a supercomputer-scale effort where resulting knowledge remains a common good.
Mesh LLM: distributed AI computing on iroh
17 days ago
- Mesh LLM is a distributed AI computing system that pools GPUs and memory across multiple machines, presenting them as a single OpenAI-compatible API.
- It offers three methods for serving requests: running locally on a GPU, routing to a peer with the model loaded, or splitting large models across machines as a pipeline.
- The system uses iroh endpoints for node identity and networking, handling NAT traversal and relay fallback, with two relays maintained for open internet reliability.
- Communication rides on QUIC's ALPN negotiation, with separate protocols for mesh management, control, and split model activations.
- Inside the main connection, various stream types (e.g., gossip, HTTP tunnels, route queries) are demuxed by a leading byte, enabling efficient peer-to-peer operations.
- Users can install lightweight software (~18 MB) to join the public mesh or set up private deployments, accessible via localhost:9337/v1 for standard OpenAI clients.

Hasty Briefsbeta

distributed computing

React for Two Computers — overreacted

A SETI Home for AI-Assisted Research

Mesh LLM: distributed AI computing on iroh