LLM

OpenAI

OpenAI Launches GPT-5.5: Agentic Model Tops Benchmarks

OpenAI's GPT-5.5 is a fully retrained agentic model scoring 82.7% on Terminal-Bench 2.0 and 84.9% on GDPval, signaling a major step forward in autonomous coding and real-world task execution capabilities.

LLM

Your LLM Is a Sampler, Not a Function: Rethinking AI

A technical exploration of why large language models behave as probabilistic samplers rather than deterministic functions, and why this distinction fundamentally changes how we should evaluate, deploy, and trust them.

LLM

Running PrismML Bonsai 1-Bit LLM on CUDA with GGUF & RAG

A technical walkthrough of deploying PrismML's Bonsai 1-bit LLM on CUDA using GGUF quantization, with benchmarking, structured JSON output, chat, and retrieval-augmented generation pipelines.

AI Agents

AI Agent Architectures: A Complete Technical Guide

From single-agent loops to multi-agent orchestration, a comprehensive overview of every major AI agent architecture pattern driving autonomous systems today.

RoPE

RoPE Explained: The Matrix Math Behind Every LLM

Rotary Position Embeddings power every major LLM, yet few tutorials show the actual matrix math. This deep dive walks through the linear algebra that makes modern transformers understand sequence order.

LLM

The 'Lost in the Middle' Effect: LLMs' Context Blindspot

Large language models struggle to use information placed in the middle of long contexts, favoring content at the beginning and end. This 'lost in the middle' effect has major implications for RAG systems and AI reliability.

LLM

Building Type-Safe LLM Pipelines with Outlines and Pydantic

Learn how to build reliable LLM pipelines with guaranteed structured outputs using the Outlines library and Pydantic schemas for type-safe AI applications.

LLM

LLM Quantization Explained: FP32, FP16, BF16, and INT8 Formats

Understanding numeric precision formats is crucial for deploying AI models efficiently. Learn how FP32, FP16, BF16, and INT8 quantization affects model performance, memory usage, and inference speed.

RAG

Why Your RAG System Fails: The Chunking Problem Explained

Most RAG failures aren't LLM issues—they're chunking failures. Learn why text segmentation strategies determine retrieval quality and how to fix common mistakes.

LLM

Reinforcement-Aware Knowledge Distillation Advances LLM Reasoning

New research combines reinforcement learning with knowledge distillation to improve how smaller language models learn complex reasoning from larger teacher models.

LLM

AutoQRA: Joint Quantization and LoRA for Efficient LLM Training

New research introduces AutoQRA, a framework that jointly optimizes mixed-precision quantization and low-rank adapters, enabling more efficient fine-tuning of large language models on limited hardware.

AI Agents

SlotBot: Building AI Agents That Impersonate Business Owners

A developer's deep dive into creating SlotBot, an AI agent that mimics solo business owners for scheduling tasks, revealing key lessons about agentic system architecture and the future of AI impersonation.