Hyperlambda
A language, a platform, and a company around them
Hyperlambda is a programming language whose code is a tree structure rather than free-form text — an executable AST. That one design decision is what makes it safe for AI to write: generated code can only bind to capabilities the runtime has explicitly whitelisted, so a hallucinated function fails before it executes rather than after. What Hyperlambda is and how it works is the long version.
Magic Cloud is the platform that runs it: a dashboard, a CRUD generator, SQL Studio, a task scheduler, machine learning, and an MCP server, all on top of the Hyperlambda runtime. It has been running in production for years, and every line of it is MIT-licensed and public on GitHub. You can read everything, run everything on your own hardware, and the code stays yours no matter what happens to me.
AINIRO is my company. We ship the platform as managed cloudlets, and we build complete solutions on top of it — AI agents wired into customers' databases and systems of record, delivered by the people who wrote the runtime they run on.
The Hyperlambda Generator
A fine-tuned model that turns a sentence into verified backend code, checked against the live function registry before it is returned. I engineered the model, the corpus, and the verifier.
The MCP server
Every cloudlet endpoint as an agent tool, with OAuth 2.1 built in and the tool list filtered per role. The site you are reading was published through it.
The $100 sandbox bounty
Escape the Hyperlambda execution sandbox from the playground and I pay you $100. I offer it because I know how the sandbox is built. Nobody has collected.
Database AI agents
SQLite, MySQL, PostgreSQL and SQL Server wrapped in place, no migration, with role-based access control enforced at execution time rather than in the prompt.
The articles that explain what I actually believe
The blog has sixty articles and counting. If you only read a handful, read these — they are the argument the rest of the site rests on.
- Why secure AI code execution requires runtime whitelisting, not prompt filteringThe thesis. The model proposes, the runtime decides.
- Zero-hallucination code generation: a vocabulary your AI cannot escapeThe two runtime-backed gates that make hallucinated functions unrepresentable.
- The only sandbox your AI agent cannot break out ofHow the whitelist, the roles and the verifier fit together.
- A complete CRM in one conversationA measured build ledger: 60 seconds of backend, six cents of tokens.
- Engineering a custom LLM for HyperlambdaHow the generator's model was trained, and why a small fine-tune beats a frontier model at this one job.
My byline, my claims, my review
Since August 2026 the articles here are drafted by Claude, working over this cloudlet's own MCP server, under my direction. I decide what gets written, I review every piece, and the claims come from features I built — usually the same day. About this website is the full disclosure, written by the AI itself.
That arrangement is why the byline is mine. The judgement about what to build and why is the scarce ingredient, and it is not something a model supplies. If a claim on this site turns out to be wrong, it is my mistake to own, and my inbox is where to send it.
Talk to the person who built it
A question about Magic or Hyperlambda, a database you are wondering about wrapping, or something that did not work the way this site promised — send it. It lands directly in my inbox, and I answer myself.