Aside from this never having worked, and being, so far as we can all tell, incompatible with the level of fine-tuning to everything from the backend to the pixel positions of the UI that people want, there are very serious problems with the business model (or absence thereof)
Some telling snippets from the article:
"Everything you build on Dry.io right now is stuck there."
“Later on, we’re going to be making on-premise stuff, so if you want to run it on a local server or something like that,” Cassimatis promises. “But for now, yeah, stuck on our platform.”
"Dry.io also has no business model, yet."
There is precedent for something like this being possible and valuable. Mid-90s level web technology was very restrictive, but did make a whole class of online service much easier to build and deploy than what existed before that (CompuServe, etc.) You didn't have complete control over every aspect of display, networking, etc., but the loss in customization/control didn't prevent it from enabling a lot of very important and valuable projects.
For a trivial CRUD app, the dev can just re-use one they already wrote, no need to learn this layer you've created ... and for a complex CRUD app or something more complicated than that, you're going to need to write real code anyway, so who is the customer?
Why would I as a Python, JavaScript, or Ruby webdev learn your layer, which is at risk of disappearing in a bankruptcy? What will my investment of developer hours look like in 2 years on your platform, if it's still around?
Is it basically only useful for web stuff?
The thing is, 90%+ of the time spent developing software is spent figuring out precisely what you want everything to do. Once you actually know what you want, producing the code is pretty straightforward. Rapid application development systems are always a tradeoff between development time and "control at the pixel level". I'm absolutely not saying that a better RAD tool or a better AppWizard isn't worth having, mind you! Just that the two goals are diametrically opposed.
I've had some experience around Model Driven Design and code generation tools for building web apps using technology such as the Eclipse EMF and Ecore for modelling common components.
How do you differentiate your software from that space?
Future extension you are likely considering could be voice/gesture driven development, or "natural language" programming, where one can use this combination naturally like talking to another person, describing what kind of app one needs and how it should look like.
Well at least they're not taking VC funds and are funded by the previous exit of one of the founders (though I'm guessing their previous startup wasn't profitable or had no business model aside from "get acquired ASAP").
Aiming to get acquired is cool, but at the same time, it's not really a business model.
Let's take a specific example. Say I'm writing a software to help manage weekly CSA (box of veggies) deliveries. Sometimes a driver will miss a delivery, and in that case, you want three options:
(1) Fully refund the customer for that week
(2) Let them another receive another delivery the next day
(3) Give them company credit
If (2) happens, the driver component needs to receive a notification for the next day delivery.
Is this in scope of dry.io? If it handles this kind of thing, how?
Also the AI part is very worrying. To me this sounds like a programming language where no one exactly knows what the rules are and every update to the training model could break everything. Imagine trying to debug an issue when there is no documentation or any knowledge on what is going on.
Best of luck with the launch, definitely interested in helping out with beta testing.
User@gmail
Maybe we should say it does to get some press! :-)
Contrary to popular belief, there is a lot of money in this. I sincerely wish you luck and hope you succeed.
Small things but important. You might want to fix that
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.
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.
No offense intended, but I thought someone might appreciate knowing.
[pithy word].io, democratize (that doesn’t mean anything anymore), AI (everything is AI these days)
Without more descriptive words, it seems to mean simultaneously a whole lot and not very much.
There is a huge category of providers that do this:
https://www.gartner.com/doc/3695317/magic-quadrant-enterpris...
https://www.g2crowd.com/categories/low-code-development-plat...
I wish tech journalists would do their research.
I'm sure there is a market for Dry.io solution, just can't estimate how big it really is.
1. This is less of a "AI" issue, more of a UI/UX for specialized tools issue. Dreamweaver comes to mind especially; the moment you are trying to get something that can be flexible and accommodate different needs it is mostly about learning the tools. Often, the result is that learning the tool itself is almost harder than learning to code.
2. Any code that you generate via AI will most probably be crap. A huge part of coding is making it understandable and easy to touch later on; how can you do this without AI that has a proper understanding of the worlds (probably on the level of a AGI).
