Agentic - Runloop Developer Blog

Learn how Retrieval-Augmented Fine-Tuning (RAFT) combines the strengths of RAG and fine-tuning to optimize LLMs for specialized domains, offering improved accuracy and efficiency.

Discover how Q-learning enhances Large Language Models (LLMs) by improving multi-step reasoning, decision-making, and alignment with human values. Learn how reinforcement learning optimizes AI for real-world applications like coding agents.

Learn how Runloop.ai DevBoxes enable Claude.ai's Computer Use capabilities to safely automate coding tasks in isolated environments.

A technical exploration of reinforcement learning's evolution from academic research to powering modern LLMs and Runloop.ai's innovative self-improving agent workflows.

Explore how Automated Program Repair (APR) has transformed from early academic experiments into advanced AI-driven debugging solutions. Discover how Runloop.ai’s agentic approach and reinforcement learning push APR into a new era of intelligent coding.

SWE-Bench, a cornerstone of LLM evaluation for software engineering, reveals more than just bug-fixing prowess; it exposes crucial limitations and hidden insights into AI's code generation. Discover why understanding these nuances is vital for building truly reliable AI-driven software development tools with Runloop's benchmarking tools.

Explore the complete spectrum of LLM fine-tuning methods, from PEFT and LoRA to RLHF and DPO. Learn how to optimize language models after pre-training with practical techniques for developers.

Read our technical deep dive into how the latency vs. tokens paradigm serves as an organizing framework for LLM research, with real-world examples and practical applications for developers.

AI-generated code is transforming software development, but how do we ensure its quality? Discover the critical differences between benchmarking and evaluation in AI-generated code. Learn why combining standardized benchmarks with real-world assessments is essential for ensuring code quality, security, and performance.

Discover how knowledge distillation transforms large language models by making them smaller, faster, and more efficient without sacrificing performance. Models like DeepSeek's R1 leverage distillation techniques to mimic the capabilities of larger models such as GPT-4, enabling deployment on mobile devices and edge technology. Learn about the history, applications, and future innovations in AI model optimization via distillation.

Struggling to assess the quality of AI-generated code? Explore 10 essential dimensions, including correctness, efficiency, and security, for comprehensive evaluation.

Discover the progression of AI code benchmarks from early single-function tests to modern, real-world frameworks like SWE-bench and LiveCodeBench. Learn how these comprehensive evaluations measure multi-file integration, system design, and broader engineering quality.

One of the most significant challenges lies in controlling and structuring the output of LLMs to meet business needs. Over time, two distinct approaches have emerged as leading solutions: function-calling and the Model Context Protocol (MCP). While both methods aim to make LLMs more predictable and production-ready, they differ in their design philosophies and use cases. Understanding these differences is critical for effectively implementing LLMs in real-world applications.

LLMs took a huge step out of the chat window and into the broader digital world with the release of Model Context Protocol (MCP) by Anthropic in November 2024. Sometimes described by Anthropic as a “protocol for seamless integration between LLM applications and external data sources,” MCP has already been adopted by crucial data stores from GitHub to Slack, as well as enterprise platforms like Cloudflare and Sentry.

Unlock the power of LLM function calling! Learn how large language models go beyond text generation to execute real-world actions, from ordering pizza to automating complex tasks. Explore JSON schemas and frameworks like LangChain.

Discover how Runloop Devboxes are revolutionizing software development with AI-optimized environments, advanced security features, and intelligent resource management for modern dev teams.

When building AI-powered software development tools, you face two key challenges: optimizing costs during periods of inactivity (whether awaiting human feedback or between agent tasks) and enabling sophisticated exploration of solution spaces.

What if AI could think like a human coder, but faster, more accurately, and in parallel? Let’s explore how artificial intelligence is not just mimicking human fast and slow thinking in software engineering, but potentially changing the entire development process. In his groundbreaking book "Thinking, Fast and Slow," Nobel laureate Daniel Kahneman introduced us to two modes of thought

As AI continues to revolutionize software engineering, developers need powerful tools to manage their AI-powered coding solution infrastructure. Enter the Runloop Dashboard – your hub for building, deploying, and monitoring your Devboxes at scale. 1. Command and Control: The Runloop