There are hidden rules in every system.
Rules to govern the sentences we write, the software we code,
and the music we play. These rules aren’t created to subjugate,
but to integrate. Without them, the different parts of a system
can’t work together to form something greater.
Some believe that these rules reflect our unique way of thinking and conceiving the world.
But for computers alike, without structure there is chaos.
Keywords and variable names, out of order, don’t make a program.
You need a grammar —
a syntax — to make the system work.
For almost a century, we’ve been teaching our machines the
grammars of our world to help us work, play, and create better.
But as of yet, our AIs remain stubbornly resistant to learning
them. While they can string together sentences to emulate human
syntax, they’ve yet to learn the syntax of computer, and of the
applications that run our world.
Without more
predictable outputs, LLMs simply can’t be
integrated into our systems and businesses. It’s their
fundamental flaw.
We believe that this demands a new programming paradigm, which
allows LLMs to respond in a way that software understands. Until
then, our generations are fun. But they are not transformative.
Until then, it’s just the beginning for how AI will change our
world.
Our mission is to help AI speak the language of every
application
This is why we built
.txt. A tool to make LLMs reliable
enough for the world to build on. A spellcheck for programming
the future. And more broadly, an ecosystem where developers can
design, execute, deploy, and evaluate LLM applications.
But how? LLMs are probabilistic by nature, so we believe the
only solution is implementing guard rails at the outset based on
your unique criteria… taking all potential probabilities and
setting all the useless ones to zero. In other words,
.txt stops
LLMs from making errors before they even make them.
Fewer tokens. Less money. More efficiency. We are not the
average AI engineers.
We cut our teeth in applied statistical modeling — a job where
you make bespoke models specially designed to give only correct
answers. To us, applying this field to LLMs is the logical next
step. The missing layer that will make AI truly useful, cutting
out the noise and uncertainty endemic to LLMs.
It’s the thing that makes LLMs act like
computers.
Finally. If your goal is to sound conversational and believable
enough, the everyday language works just fine. But to be good
enough to
compute with, the level of clarity must be far
higher. In this sense, LLMs are, ironically, a regression!
So, let’s create the new rules for AI and invent a new way to
program with them… together.
Join the discussion on
Discord.
Or just
tweet at us. We can’t wait
to see what you create!
-Remi, Brandon, and Dan