AI Agents
New framework enables automated generation of synthetic tools for training AI agents at scale, addressing tool scarcity in agent development through systematic tool synthesis and validation methodologies.
Agentic AI
A technical guide to constructing autonomous AI agents that plan, execute, and verify their own data operations using locally-deployed Hugging Face models, eliminating dependency on external APIs while maintaining robust performance.
LLM Research
New research demonstrates LLMs can complete million-step reasoning tasks with zero errors through novel verification and correction methods, advancing AI agent capabilities for complex multi-step workflows.
Agentic AI
Small language models are outperforming larger counterparts in agentic AI workflows due to speed, cost efficiency, and specialized task performance. Technical analysis reveals why compact models excel at autonomous decision-making.
AI Agents
A technical walkthrough of creating an autonomous AI agent that can browse the internet, reason through tasks, and execute multi-step plans using Python, LLMs, and web scraping tools.
Agentic AI
New research shows that incorporating procedural knowledge into agentic LLM workflows significantly improves performance. The study demonstrates how structured procedures enhance agent reliability and task completion rates.
LangChain
A comprehensive technical guide to LangChain, the framework enabling developers to build sophisticated LLM applications with chaining, memory, and retrieval capabilities essential for modern AI systems.
LLM Safety
New research demonstrates how multi-agent debate frameworks can evaluate LLM safety more efficiently than traditional methods, reducing costs while maintaining accuracy in identifying harmful model behaviors.
AI Agents
Analysis of how AI agents handle human tasks reveals critical performance patterns: exceptional speed paired with systematic flaws in reasoning, tool use, and decision-making that impact real-world deployment.
AI Agents
Anthropic's Model Context Protocol (MCP) establishes a standardized infrastructure layer enabling AI agents to access external data and tools. This protocol architecture is transforming how AI systems interact with real-world applications and services.
AI research
New research introduces Jr. AI Scientist, an autonomous system that conducts scientific exploration from baseline papers. The study includes comprehensive risk assessment frameworks for AI-driven research automation.
AI Safety
Researchers introduce comprehensive evaluation framework for control protocols designed to manage untrusted AI agents, addressing key safety challenges as autonomous systems become more capable and potentially misaligned.