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The article describes building a centralized control room for managing AI agents including Claude, Codex, and local models.
The author describes building a set of components that allow locally running AI agents to have persistent identities, discover each other, and exchange messages across devices, forming an 'Internet for AI agents' beyond simple API calls.
A practical solution using AnySearch to enable local AI agents to efficiently query multiple specialized sources (CVEs, SEC filings) and return structured JSON/Markdown, avoiding rate limits and broken SDKs.
This article recaps the AI agent landscape in 2026, highlighting local agents like OpenClaw and Hermes, self-improvement loops, VLA models for physical AI, and the growing importance of infrastructure for trusted agent systems.
Discusses methods for monitoring long-running local coding agents when you step away from your computer.
Explains reasons to run local AI agents instead of cloud-based alternatives, highlighting privacy and control benefits.
An analysis of the key differences between cloud-based and local AI agents, arguing that local agents offer better user experience due to richer environmental access, while the LLM layer becomes commoditized.
OmniParse es un repositorio que ingiere casi cualquier archivo y lo convierte en Markdown limpio y estructurado, totalmente local y sin APIs externas, ideal para pipelines de RAG y agentes locales.
This paper introduces AgentWall, a runtime safety layer for local AI agents that intercepts actions before execution, enforces declarative policies, requires human approval for sensitive operations, and logs tamper-evident trails. It is open-source and works with multiple agent platforms.
This paper introduces AgentStop, a lightweight supervisor that predicts and preemptively terminates local AI agent trajectories unlikely to succeed, reducing energy waste by 15-20% with minimal impact on task performance.
Announces the ability to run fully local agent teams using NousResearch Hermes agents on systems with 24-128GB unified memory. Each agent has its own Hermes session and works collaboratively via a local orchestrator on long-running tasks.