3 posts tagged with "generative ai"

Introduction​

Modern large language models face a fundamental limitation: context windows. While frontier models now reach 1 million tokens (Nova Premier, Claude Sonnet 4.5), workloads analyzing entire codebases, document collections, or multi-hour conversations can easily exceed 10 million tokens—far beyond any single model's capacity.

This post demonstrates Recursive Language Models (RLMs), an inference strategy from MIT CSAIL research that enables scaling to inputs far beyond context windows. What makes this implementation special: Strands Agents and Amazon Bedrock AgentCore reduce what could be weeks of glue code and deployment work to just a few hours of development.

Achieving sustained growth is the ultimate dream for many businesses, but how to realize that dream is often elusive. One proven way is to leverage the "flywheel effect," a concept that advocates for creating a self-perpetuating growth cycle through customer satisfaction and word-of-mouth referrals. And as we move further into the age of AI, the potential for supercharging your flywheel becomes even more palpable. Here's a look at how incorporating Generative AI into your flywheel model can boost your business.

The Emergence of Generative AI and Large Language Models​

The world has witnessed a meteoric rise in the use of artificial intelligence (AI) technologies over the past few years, with generative AI and large language models (LLMs) standing at the forefront. Generative AI, which includes the likes of LLMs, can generate creative and unique content, ranging from artwork to complex textual narratives. The idea of AI systems autonomously producing human-like content has transformed the AI landscape, opening up a plethora of possibilities.