The Latest in AI Development

A practical walkthrough of building production-ready AI agents for tax form document processing - with agentic architectural patterns you can apply to any compliance-sensitive use case.

Discover the general availability of our Object Storage SDK: a first-class way to ship binaries, datasets, and runtime resources onto devboxes.

We're releasing new Python and TypeScript SDKs that make working with Runloop easier and more full-featured.

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 accurately? This "fast and slow" thinking approach could reshape how we build software.

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 improves 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 latency vs tokenization trade-offs shape LLM design, with real examples and practical tips for developers.

Struggling to assess AI-generated code quality? Learn how to evaluate correctness, efficiency, and security for comprehensive results.

See the evolution of AI code benchmarks from simple tests to SWE-bench and LiveCodeBench, measuring integration, system design, and engineering.

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 make 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.