Thanks for the pointers. The pricing is free for now. In the future we’re guessing it will be a typical freemium pricing model. There is also a totally self-hosted version that people can license.
Like Access, we do aim to make development much easier, and in-house tools are an obvious first market. However, there are a lot of aspects of Dry (involving access permission, data tagging, moderation, and more) that aren't all together in any other platform. These make it easier to build social, collaborative, and messaging apps much more easily than other approaches.
The idea is that in many cases how an edge case is handle is up to the person requesting the software. So unless you build an AI that can read minds there needs to be an easy way to express what their preferences are.
Yes, any company that's serving a lot of web pages would be tempted to put ads on their pages. As the article and our website state, we are doing several things to preserve people's privacy. If we do ever serve ads, you and others can help us find a way to be super-transparent about it.
That is, until the VCs take over or it goes to private equity, and the new stockholders say it's much easier to monetize if you're super-opaque about it.
This isn't a snarky response, but a sincere question: how would you have titled the article or expressed our mission? It's difficult to come up with something that's clear and captures our intent while also fitting into a single sentence. We'll take any help on that we can get.
This is less about the title/headline and more about the overall approach.
I think to gain any attention in today’s environment, you’re going to have to do more “showing” and less “telling”.
Remember how Ruby On Rails got its amazing dev mindshare? I’d encourage you to hit archive.org and go back to like 2006 at rubyonrails.org. They had a compelling demo that was just a simple screen share, but it blew people’s minds. Their headlines and body copy were also excellent.
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.
There is a partial answer to this in another thread, but here is a response to your specific point:
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.
