AI agents needs tools, and the more the merrier. With AINIRO's Magic Cloud you can create any tool in 3 seconds!

We've done what nobody else could or wanted to do, we built a custom LLM, exclusively to solve tool usage for AI agents, and it's made from 50,000 files of Hyperlambda.

Your website is full of repeating information. When creating a RAG database, this hurts performance. In this article I teach you how to make it 'DRY'.

With our latest release of Magic Cloud, we can now deliver 'self evolving' AI agents, implying agents that creates tools on demand.

With our recent additions to our Hyperlambda generator, it is safe to assume that we've got the by far best commercially available web scraper in the world.

With our Hyperlambda Generator you can treat the web as an API, querying it using natural language, and return structured JSON

I just measure the performance of Lovable versus Magic Cloud, and Magic can do in 3 seconds what Lovable needs 3 minutes to accomplish.

What if I told you that traditional APIs taking input arguments in structured format could be 'obsolete' a couple of years down the road? Well, Natural Language APIs promises to do just that.

If you measure performance of N8N versus Magic Cloud with Hyperlambda, you'll see that Hyperlambda has roughly 7 times better performance.

I just measured the performance of Rust with Actix-Web versus Hyperlambda, and Hyperlambda is 2x faster.

Sha-Hulud is the name of a cyber attack that seems to have started with a Zapier developer machine, now having infected probably all AI software in the world, except ours.

LangChain is the by far most popular Python framework to build AI agents in. However, how does it compare to Magic Cloud with Hyperlambda?