LLM

Agentic AI

AccelOpt: Self-Improving AI Agents Optimize GPU Kernels

New research introduces AccelOpt, an LLM agentic system that autonomously optimizes AI accelerator kernels through self-improvement, achieving significant performance gains on GPU workloads through iterative code generation and testing.

LLM

Mastering LLM Controlled Generation: Beam Search & More

Deep dive into controlled generation techniques for LLM inference, from beam search to constrained decoding. Learn how these methods shape AI output quality, coherence, and computational efficiency in production systems.

LLM

TALE Framework Cuts LLM Costs with Adaptive Thinking

Researchers introduce TALE, a framework that optimizes LLM performance by dynamically adjusting reasoning depth. The system reduces costs while maintaining accuracy through adaptive test-time compute allocation.

Agentic AI

New Planner Framework Improves LLM Tool Use Beyond ReAct

Researchers propose a planner-centric framework that enhances how language models use external tools for complex reasoning tasks, showing improvements over the widely-used ReAct approach through better planning and execution separation.

LLM

Build Custom GPT Conversational AI Locally with Hugging Face

A comprehensive technical guide to building GPT-style conversational AI systems locally using Hugging Face Transformers, covering model selection, memory optimization, and deployment strategies for privacy-focused implementations.

LLM

Gradient-Aware Data Selection Tackles LLM Forgetting

New research introduces gradient-aware approach to select training data that helps large language models retain prior knowledge while learning new information, addressing catastrophic forgetting through intelligent sample selection.

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

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.