nickcassimatis

Many apps we won't be able to handle at first can easily be handled by our approach with more work.

For some capabilities, we will have ways of filling gaps in the platform. We have some easy-to-use and fairly flexible hooks where you can add a lot of UI customization. We've also discussed mechanisms for catching events in dry and triggering external code developers can write. But that's almost certainly not going to make v1.

I was the founder of SkyPhrase, a natural language processing startup that Yahoo acquired in 2013. Natural language was also a focus of my AI research. We have some ideas on how to apply that to this project, but they will probably have to wait at least a few months.

Thanks for the curiosity.

However, once you do know exactly what you want, it is still often a lot of work to actually build it in many cases. Say you wanted to build an exact clone of Slack (even v1 of Slack) It would still take weeks of effort at at the very least to build and deploy that.

That's a link to a widely-used AI textbook. The methods that most people today associate with AI (i.e., the learning-based inference methods), are only a fraction of the overall content.

It depends what you mean by inference. In statistical machine learning and deep learning, inference means predicting things using large amounts of data. Philosophers call that inductive inference.

But there is also deductive inference. Given some general knowledge (e.g., "All men are mortal") and some facts ("Socrates is a man"), you infer other facts ("Socrates is Mortal"). There is a huge amount of work in AI that has developed algorithms that do very complex and powerful versions of this kind of inference. You can use those to infer from a brief description of what you want a computer to do what the sequence of actions the computer can take to achieve that goal. You can use these kinds of methods to generate software behavior without explicitly programming the behavior in advance.

Part of how Dry works is that you give it a data model for your application (which can be quite complex) and we automatically generate the code for storing, retrieving, sharing, etc. that data. Normally for any cloud app that scales, this takes a fair amount of thought and expertise. We automate a lot of that without the programmer even needing to know it's happening.

One way AI factors in involves finding a core set of primitives that can describe all software functionality. Programming languages and web frameworks are more granular than Dry and so they require you to write a lot more code. Dry has a higher level of abstraction than concepts like arrays, servers, divs, database tables, etc.

On the other hand, Dry is not just a template system that gives you a predefined set of application types like messenger, social network, discussion group, etc. that you can just specify. That's a higher level of abstraction and can be fairly limiting and brittle.

Dry's abstractions are in at a level that is between those two extremes; it lets your write a lot of apps without needing to be too granular.

There is research in AI called "Knowledge Representation and Reasoning" that identifies fundamental primitives that can express a wide variety of concepts and ideas. I've been a researcher in this field my whole career and was a professor in that area once. We've translated those results into software and have used them to develop a core set of primitives that let you describe the behavior of a lot of software.

I know that is all vague, but we'll start to be more precise and concrete over the next few weeks.