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Developer announces OpenLumara, a fully open-source, local-first webUI for chatting with local models, rewritten from scratch in Alpine.js to eliminate AI-generated code. It offers features like token efficiency, real-time toolcall viewing, and no extra requests to the model.
Prime Agent is an open-source coding and research harness that outperforms proprietary harnesses, scoring 95.5% on ARC-AGI-3 and improving models across benchmarks.
BetterWright is an open-source tool that improves browser automation by being more token-efficient, faster, and less prone to bot detection. It integrates with agent setups like OpenClaw to enhance agent capabilities.
This paper presents a framework for opinion summarization using LLMs that combines multidimensional classification and stratified sampling to reduce token usage while preserving semantic diversity and balance across viewpoints.
Released on Hugging Face, Qwopus-3.6-35B-A3B-Coder is a fine-tuned Mixture-of-Experts coding agent model designed for efficient, low-latency local execution with reduced token waste in agentic workflows.
OpenLumara is a new open-source AI agent framework designed for local models, emphasizing token efficiency, modularity, and security. It is written from scratch with a small system prompt and full control over AI capabilities.
id-agent is an open-source npm library that generates human-readable, token-efficient word-based IDs as a UUID alternative for AI agents, reducing token costs by ~40% while maintaining collision resistance.
nanobot v0.2.0 is released, featuring lightweight, token-efficient AI agents capable of long-horizon tasks. A demonstration shows building a Super Mario game with a single prompt for $0.1.
OpenUI is an open-source framework for generative UI that uses a streaming-first language to produce UI with up to 67% fewer tokens than JSON, including a React runtime and chat interfaces.
AdaGATE is a training-free evidence controller for multi-hop RAG that uses entity-centric gap tracking, micro-query generation, and utility-based selection to improve robustness under noisy retrieval, achieving state-of-the-art evidence F1 with fewer input tokens.