The Latest in AI Development

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.

Runloop is the safe and comfortable cage for OpenClaw 🦞

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.

Pass@K is everywhere in AI coding benchmarks. Is it really the best metric? Here's a critical look at Pass@K, its strengths, limitations, and more.

MBPP made AI code generation easier by using simple Python tasks. It changed how models are judged and helped more people get into coding benchmarks.

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

SWE-bench tests AI on real GitHub issues and pull requests. See how models handle real software tasks like bug fixes and code changes.

Learn how to interpret AI benchmark results and use them to improve model performance and guide deployment decisions.

HumanEval showed when AI started handling code for real, how benchmarks pushed progress, and what it means for software development going forward.

Discover the hidden flaws in SWE-bench, a widely used benchmark for AI coding agents. Learn why deeper evaluation matters for real-world performance.

AI-generated code needs more than benchmarks. Real tests and evaluation help check quality, security, and performance in real use.

Learn how to verify AI-generated code with real tests, logs, and observability. Go beyond pass/fail to ensure reliability in production systems.

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.