The Latest in AI Development

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.

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

Building AI dev tools means cutting costs when the system sits idle and giving it room to explore different solutions.

What if AI could code like a human but faster and more accurate? This idea of “fast and slow” thinking in software development may reshape how code gets built.

Runloop Dashboard makes managing AI coding simple. Build, deploy, and monitor Devboxes in one place, scaling your setup fast and without hassle.

RAFT mixes RAG and fine-tuning to boost LLM performance in specific fields. It improves accuracy and makes models faster and more useful for real tasks.

Q-learning helps LLMs make choices and handle tasks. It uses reinforcement learning to improve reasoning and make AI more useful in jobs like coding agents.

How reinforcement learning moved from research labs to powering modern LLMs and Runloop.ai’s self-improving agent workflows.

Learn LLM fine-tuning methods like PEFT, LoRA, RLHF, and DPO, with practical tips to improve models after pre-training for real use.

Knowledge distillation makes big AI models smaller and faster without losing much quality. It helps run powerful tech on phones and other small devices.

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

Learn how the latency vs tokens idea shapes LLM research, guiding model design and performance trade-offs, with real examples and practical tips for developers.

Trends in AI benchmarking from recent white papers, showing how methods, metrics, and datasets are changing in machine learning research.

How AI agents can fix their own code over time. A look at the tools, the wins, and what still holds them back.

Function-calling and MCP help shape LLM output for real use. They share the goal of making AI more reliable, but each has different strengths and use cases.

MCP lets LLMs plug into real tools and data. With support from GitHub, Slack, Cloudflare, and Sentry, it makes AI way more useful in real work.

Function calling lets LLMs do real actions, not just text. It can order stuff or automate tasks using JSON schemas and tools like LangChain.