Webtagr - Technology News Summarizer

Inner Loop Agents: LLMs Calling Tools Directly

2025-04-21

Traditional LLMs require a client to parse and execute tool calls, but inner loop agents allow the LLM to parse and execute tools directly—a paradigm shift. The post explains how inner loop agents work, illustrating the difference between them and traditional LLMs with diagrams. The advantage is that LLMs can concurrently call tools alongside their thinking process, improving efficiency. Reinforcement learning's role in training inner loop agents and the Model Context Protocol (MCP)'s importance in supporting diverse tool use are also discussed. Ultimately, while LLMs can currently use tools, achieving optimal tool use requires specialized model training for best results.

(timkellogg.me)

AI Inner Loop Agents

Regex Isn't Hard: Mastering the Core Concepts for Efficient Text Processing

2025-04-21

This article argues that regular expressions aren't as complex as many believe. By focusing on core concepts—character sets, repetition, groups, and the |, ^, $ operators—one can easily master the power of regex. The article explains these core concepts in detail and suggests ignoring less-used shortcuts to avoid unnecessary complexity. The author emphasizes that regex allows for a lot of text processing with minimal code, far more efficiently than traditional procedural code.

(timkellogg.me)

Development

$6 AI Model Shakes Up the LLM Landscape: Introducing S1

2025-02-05

A new paper unveils S1, an AI model trained for a mere $6, achieving near state-of-the-art performance while running on a standard laptop. The secret lies in its ingenious 'inference time scaling' method: by inserting 'Wait' commands during the LLM's thinking process, it controls thinking time and optimizes performance. This echoes the Entropix technique, both manipulating internal model states for improvement. S1's extreme data frugality, using only 1000 carefully selected examples, yields surprisingly good results, opening up new avenues for AI research and sparking discussion on model distillation and intellectual property. S1's low cost and high efficiency signal a faster pace of AI development.

(timkellogg.me)

AI low-cost training inference time scaling

Open-Source R1 Shakes Up the AI World: Accelerated Development!

2025-01-26

The AI landscape is exploding with new models. DeepSeek's open-source reasoning model, R1, matches the performance of OpenAI's closed-source o1, but at a fraction of the cost, sending shockwaves through the industry. R1 validates OpenAI's o1 and o3 approaches and reveals new trends: pretraining's diminished importance and the emergence of inference time scaling laws, model downsizing, reinforcement learning scaling laws, and model distillation scaling laws, all accelerating AI development. R1's open-source nature intensifies US-China competition, highlighting the massive geopolitical implications of AI's rapid progress.

(timkellogg.me)

AI