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Runloop provides infrastructure for building and deploying AI coding agents at scale. Explore tutorials, insights, and the future of AI-assisted development

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Product
Runloop Devbox: The Future of AI-Driven Development Environments
Discover how Runloop Devboxes are revolutionizing software development with AI-optimized environments, advanced security features, and intelligent resource management for modern dev teams.
January 22, 2025
Enhancing AI Code Understanding with MCP
We created an MCP server that enables you to load repos in Runloop’s isolated sandboxes and create various indices to try and inform repo navigation. Connect to it and try it out!
May 19, 2025
Product Update: Introducing Suspend/Resume and Snapshots
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.
November 13, 2024
View All Product

Model Performance

AI Ecosystem

AI Ecosystem
Latency vs. Tokenization: The Fundamental Trade-off Shaping LLM Research
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.
February 12, 2025
Evaluation for Functional Correctness: Ensuring AI-Generated Code Works as Intended  
Discover how to ensure AI-generated code works flawlessly—boost reliability, prevent costly errors, and build user trust with proven testing methods.
April 20, 2025
View All AI Ecosystem

Benchmarks

Coding Agents

Coding Agents
Model Context Protocol (MCP) - Understanding the Game-Changer
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.
January 26, 2025
Self-Improving AI Agents: The Next Evolution of Automated Program Repair
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.
February 24, 2025
Function-Calling vs. Model Context Protocol (MCP): Choosing the Right Approach for LLM Integration
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.
January 28, 2025
View All Coding Agents

Product

Discover the latest Runloop product updates, feature releases, and improvements designed to power AI-driven software development.

Enhancing AI Code Understanding with MCP

We created an MCP server that enables you to load repos in Runloop’s isolated sandboxes and create various indices to try and inform repo navigation. Connect to it and try it out!
May 19, 2025

Runloop DevBoxes Safely Unleash Claude.ai's Computer Use

Learn how Runloop.ai DevBoxes enable Claude.ai's Computer Use capabilities to safely automate coding tasks in isolated environments.
March 4, 2025

Runloop Devbox: The Future of AI-Driven Development Environments

Discover how Runloop Devboxes are revolutionizing software development with AI-optimized environments, advanced security features, and intelligent resource management for modern dev teams.
January 22, 2025

Product Update: Introducing Suspend/Resume and Snapshots

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.
November 13, 2024

More Human Than Human: Fast, Slow, and Parallel Thinking in AI

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
October 24, 2024

Product Update: The Runloop Dashboard

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
October 1, 2024

Model Performance

Explore analyses, benchmarks, and insights on AI model performance to optimize reliability, speed, and accuracy.

RAG in an Era of Fine-Tuning: Understanding RAFT's Evolution

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.
March 6, 2025

Q-Learning for LLMs: Smarter AI with Reinforcement Learning

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.
March 5, 2025

Remember Reinforcement Learning? It's Never Been More Relevant

A technical exploration of reinforcement learning's evolution from academic research to powering modern LLMs and Runloop.ai's innovative self-improving agent workflows.
February 25, 2025

LLM Fine-Tuning Methods: A Complete Guide to Post-Training Optimization Techniques

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.
February 17, 2025

How Knowledge Distillation Powers Efficient AI Models

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.
February 3, 2025

AI Ecosystem

Stay up to date with the latest developments in the AI ecosystem, from research breakthroughs to emerging tools and platforms.

Evaluation for Functional Correctness: Ensuring AI-Generated Code Works as Intended  

Discover how to ensure AI-generated code works flawlessly—boost reliability, prevent costly errors, and build user trust with proven testing methods.
April 20, 2025

Latency vs. Tokenization: The Fundamental Trade-off Shaping LLM Research

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.
February 12, 2025

Benchmarks

Learn how Runloop uses public and custom benchmarks to measure AI models, coding agents, and real-world performance.

HumanEval: When Machines Learned to Code

Discover how HumanEval showcases the moment machines began mastering code. Explore AI's coding evolution, benchmarks, and what it means for the future of software development.
July 24, 2025

SWE-Bench Deep Dive: Unmasking the Limitations of a Popular Benchmark

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.
February 22, 2025

Evaluation != Benchmarking: Critical Distinction in Assessing AI Generated Code

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.
February 6, 2025

Making Sure AI-Generated Code Actually Works

February 3, 2025

Assessing AI Code Quality: 10 Critical Dimensions for Evaluation

Struggling to assess the quality of AI-generated code? Explore 10 essential dimensions, including correctness, efficiency, and security, for comprehensive evaluation.
February 2, 2025

Understanding LLM Code Benchmarks: From HumanEval to SWE-bench

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.
February 1, 2025

Coding Agents

Dive into the future of AI coding agents, automation, and intelligent tools that streamline software development.

Self-Improving AI Agents: The Next Evolution of Automated Program Repair

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.
February 24, 2025

Function-Calling vs. Model Context Protocol (MCP): Choosing the Right Approach for LLM Integration

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.
January 28, 2025

Model Context Protocol (MCP) - Understanding the Game-Changer

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.
January 26, 2025

Mastering LLM Function Calling: A Guide to Enhancing AI Capabilities

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.
January 24, 2025

Case Studies

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