An Emacs frontend for the pi coding agent. Compose prompts in a full Emacs buffer, chat history as markdown, live streaming output, and more.
Learn how to equip your Microsoft Agent Framework agents with portable, reusable skill packages that provide domain expertise on demand using Agent Skills. This article covers what Agent Skills are, progressive disclosure, creating skills, connecting skills to an agent (with .NET and Python examples), use cases, and security considerations.
Researchers have demonstrated that large language models (LLMs) can identify pseudonymous users across different social media platforms with high accuracy, potentially undermining online privacy and opening users up to risks like doxxing and targeted advertising. The study highlights the growing capability of AI to deanonymize individuals based on their online activity, even with limited information.
This repository provides the official implementation of the STATIC (Sparse Transition-Accelerated Trie Index for Constrained decoding) framework, as described in Su et al., 2026. STATIC is a high-performance method for enforcing outputs to stay within a prespecified set during autoregressive decoding from large language models, designed for maximum efficiency on modern hardware accelerators like GPUs and TPUs.
Google AI introduces STATIC, a sparse matrix framework that accelerates constrained decoding for LLM-based generative retrieval. It addresses the inefficiency of traditional trie implementations on hardware accelerators by flattening the trie into a static Compressed Sparse Row (CSR) matrix, achieving up to 948x speedup and demonstrating improvements in YouTube video recommendations.
GenAI-based coding assistants are evolving towards agent-based tools that require contextual information. This paper presents a preliminary study investigating the adoption of AI context files (like AGENTS.md) in 466 open-source software projects, analyzing the information provided, its presentation, and evolution over time. The findings reveal a lack of established content structure and significant variation in context provision, highlighting opportunities for studying how structural and presentational modifications can improve generated content quality.
Zach Lloyd argues that we are moving beyond traditional apps towards “meta-apps” – AI-powered tools like Claude Code and Warp that directly fulfill user intent rather than requiring users to learn and adapt to specific applications. These meta-apps will access all of a user’s data, anticipate needs, and dynamically create tailored solutions, effectively eliminating the need for most standalone apps. He predicts a shift in software development, emphasizing data accessibility and agent-based systems over frontend development, and believes companies like Apple are uniquely positioned to lead this transition. Ultimately, Lloyd envisions a future where everyone can be a “digital god,” effortlessly creating software through simple interaction with these meta-apps.
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In this tutorial, we build a hierarchical planner agent using an open-source instruct model. We design a structured multi-agent architecture comprising a planner agent, an executor agent, and an aggregator agent, where each component plays a specialized role in solving complex tasks. We use the planner agent to decompose high-level goals into actionable steps, the executor agent to execute those steps using reasoning or Python tool execution, and the aggregator agent to synthesize results into a coherent final response. By integrating tool usage, structured planning, and iterative execution, we create a fully autonomous agent system that demonstrates how modern AI agents reason, plan, and act in a scalable and modular manner.
This article details the process of running a personal AI assistant on a low-cost microcontroller. It covers the use of Ollama for running large language models (LLMs) locally and MimicLaw for optimizing the model for resource-constrained devices. The author shares their experience with porting and running the models, along with the challenges and solutions encountered.
NanoClaw, a new open-source agent platform, aims to address the security concerns surrounding platforms like OpenClaw by utilizing containers and a smaller codebase. The project, started by Gavriel Cohen with the help of Anthropic's Claude Code, focuses on isolation and auditability, allowing agents to operate within a contained environment with limited access to system data.
