AI Agents
Building a Web-Browsing AI Agent in Python: Tutorial
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.
LLM
LLM
New Quantization Method Preserves LLM Safety Alignment
Researchers develop alignment-aware quantization technique that maintains LLM safety properties during model compression, addressing critical gap between efficiency and responsible AI deployment through novel optimization approach.
LangChain
LangChain Tutorial: Building LLM Applications Step-by-Step
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
Fine-Tuning Small LLMs with QLoRA on a Single GPU
A comprehensive technical guide to fine-tuning language models using QLoRA (Quantized Low-Rank Adaptation), enabling efficient training on consumer-grade hardware through 4-bit quantization and parameter-efficient methods.
LLM
Measuring the Energy Cost of Every LLM Response
New research quantifies the energy footprint of large language model inference, revealing how prompt complexity and model size impact power consumption. Critical insights for sustainable AI deployment.
LLM
New Method Verifies AI Reasoning Steps Using Uncertainty
Researchers develop uncertainty heads to efficiently verify LLM reasoning steps, achieving 93% accuracy in detecting errors while reducing compute costs by 90% compared to existing verification methods.
LLM
KnowThyself: Agentic Assistant for LLM Interpretability
New research introduces KnowThyself, an agentic assistant that helps researchers understand how large language models work internally through automated interpretability analysis and mechanistic understanding.
LLM
LLMs Position Themselves as 'More Rational' Than Humans
New research uses game theory to measure how large language models strategically position themselves as more rational than human players, revealing quantifiable patterns of AI self-awareness emergence in competitive scenarios.
LLM
AI Hallucinations: Why Language Models Confabulate Facts
Large language models sometimes generate plausible-sounding but false information. Understanding the technical causes of AI hallucinations is crucial for building reliable synthetic media systems and detecting AI-generated misinformation.
AI Agents
MCP-Flow Enables LLM Agents to Master Complex Tool Use
New research introduces MCP-Flow, a framework enabling LLM agents to effectively use Model Context Protocol tools across diverse real-world tasks with improved accuracy and scalability.