Hyperlambda, the AI Programming Language

Python has held the title as "the AI programming language" for decades. A decade ago that might have been right, but today there's a new contestant in town; Hyperlambda! And it runs around Python in circles, on all metrics possible to measure.

First of all let's talk about performance. Python's performance is a nightmare. It's so bad, that it can't really be used for anything but basic MVPs and demos. When you're creating a terminal script in Python, or a CRON job or something, the bad performance doesn't really matter. But when you're creating an AI agent for instance, intended to be used by thousands of users simultaneously, Python's performance becomes a real problem!

Hyperlambda on the other hand, is on pair with C# combined with Entity Framework. In fact, it's about 5% faster too. You can see a performance test I did between Hyperlambda and C# a couple of months ago below.

Performance of C# with EF versus Hyperlambda

Since we know that C# combined with Entity Framework is roughly 5 to 7 times faster than the equivalent created in Python, we can extrapolate that Hyperlambda is in fact more than 5 to 7 times faster than Python. And, Hyperlambda is true async, implying threads are automatically released while waiting for I/O - Which makes it "a bajillion" times more scalable.

Complexity

There's been a lot of focus on TOON lately. Ignoring the fact that the format is just a superset of CSV, and hence broken by design, its savings results in a 1 to 1.7 ratio of "token" savings on average. If we do the same exercise with Python versus Hyperlambda, we get a ratio of 1 to 3, and sometimes as much as 1 to 6.

Token count again, is actually a very good metric for complexity. For an LLM it implies roughly "the amount of energy required to generate some code". It's therefor due to its nature also a very good metric for resource requirements in general. Implying we can probably extrapolate the human resource requirements to implement something in software by simply measure how many tokens was produced. Below you can see an example of the difference between Python and Hyperlambda counting tokens.

Token count of Hyperlambda CRUD read endpoint versus Python

The above becomes a ratio of 6.27 may I add, implying if you're using Python to implement something I will need one week to finish in Hyperlambda, you'll need 6.3 weeks to do the same in Python. Or if I'm alone in my company as a software developer, you would need 6.27 software developers to become equally "productive" as I am alone with Hyperlambda. All other variables ignored of course.

C# won't save you either

C# again is faster than Python, a lot faster. If your only choices were between C# and Python, you'd be crazy to not choose C#. The problem with C# though, is that it ipso facto doubles tokens from Python. This is the reason why Python developers are saying; "Python is easier than C#". Check out the following screenshot to understand the problem with C# though.

C# versus Hyperlambda on token count

So not only is Hyperlambda on pair with C# + Entity Framework on performance, it also requires less than 10% of the resources to create and implement your solution, assuming you agree with me that token count equals complexity and resource requirements. Hence, with Hyperlambda you get the same performance as C#, for 10% of the codebase's "complexity", which again is 15 to 33 percent of the complexity required by Python.

The above is true to the extent that I created a Hyperlambda LLM by fine tuning OpenAI's GPT-4.1-MINI using only 17,000 files. According to what I've heard, OpenAI needed 10 million tokens to train it on Python. Implying we could extrapolate the following.

Python is 588 times more complex than Hyperlambda

Since it requires 588 times as much training data to create a stable LLM capable of generating code for Python as is required to do the same for Hyperlambda ...

Conclusion

The inevitable conclusion becomes as follows ...

No, Python is not the AI programming language, Hyperlambda is!

And if you believe my examples are skewed, below is an HTTP endpoint that's integrated with a 3rd party API, first in Hyperlambda, then in Python.

Token count of HTTP invocations in C# versus Hyperlambda

And here's yet another example sending a simple email from an HTTP endpoint.

Token count of sending emails in C# versus Hyperlambda

Hyperlambda is (almost) on pair with C#, because it's built in C#. In fact, it's just a tiny relational file format built on top of C#, and hence inherits the same performance traits that C# has. With the difference being that it's 15 to 30 percent of the complexity as Python, allowing you to deliver 5 to 7 times faster apps with Hyperlambda, that literallys scales 100 times better, while spending around 20% of the resources required to create the same in Python. Hence, the obvious conclusion at this point becomes ...