Browsing: agents

Deploying AI agents safely in regulated industries is challenging. Without proper boundaries, agents that access sensitive data or execute transactions can pose significant security risks. Unlike…

Stanford researchers have introduced OpenJarvis, an open-source framework for building personal AI agents that run entirely on-device. The project comes from Stanford’s Scaling Intelligence Lab and…

class MetaAgent: def __init__(self, llm: Optional[LocalLLM] = None): self.llm = llm or LocalLLM() def _capability_heuristics(self, task: str) -> Dict[str, Any]: t = task.lower() needs_data = any(k…

The race to build autonomous AI agents has hit a massive bottleneck: data. While frontier models like Claude Code and Codex CLI have demonstrated impressive proficiency…

Andrej Karpathy released autoresearch, a minimalist Python tool designed to enable AI agents to autonomously conduct machine learning experiments. The project is a stripped-down version of…

AI development is accelerating fast. Advances in hardware, software optimization, and better datasets now allow training runs that once took weeks to finish in hours. A…

In this tutorial, we build a complete cognitive blueprint and runtime agent framework. We define structured blueprints for identity, goals, planning, memory, validation, and tool access,…

Integrating Google Workspace APIs—such as Drive, Gmail, Calendar, and Sheets—into applications and data pipelines typically requires writing boilerplate code to handle REST endpoints, pagination, and OAuth…

Organizations increasingly deploy custom large language models (LLMs) on Amazon SageMaker AI real-time endpoints using their preferred serving frameworks—such as SGLang, vLLM, or TorchServe—to help gain…

Summary created by Smart Answers AIIn summary:OpenAI’s GPT-5.4 introduces groundbreaking agentic AI capabilities that allow it to perform computer actions like clicking a mouse and editing…