Artificial Intelligence will solve our problem. Lot's of entrepreneurs have a grand vision of how AI can solve any difficult problem for their business. This doesn't mean they're wrong, but the fields of AI and ML (machine learning) have many sub categories and areas of active research that allow us to make progress on quite specific computational tasks. Applying the wrong form of learning or problem solving to a task can produce underwhelming results. Tweaking and tuning a model or method is the way to get solid results and outpace your competitors in business use cases. I'd like to go through a few ML methods and what they're used for maybe to inspire some new ideas.
There are web automation tools that help with website acceptance testing, and user flow testing like selenium. There is an underutilized area of development that can leverage these technologies that abuses their power. The intended use case is to open a website that I own and test functionality that I programed into the website to ensure it's working correctly. However, this tool, and many like it can crawl and interact with any website, and you can inject python (or other language) logic in the middle of actions taken on your behalf on the website. I want to explore a tool I created to utilize this, and brainstorm some other uses that might create new business use cases.
Artificial intelligence and machine learning are disciplines that cover many methods and systems. There are a ton of powerful tools and libraries that have been built by technologists that are ripe for application to business tasks. There are places where these novel methods can be applied in profitable sectors.
A common use case to build an essentialist MVP is to get more support for your idea. Taking the functional demo and pitching it to an investor, so you get more funds to flesh it out further, and recruit more team members to mentor, give resources, ideas, and connections to the project. Here I'm going to discuss some considerations around building an MVP for this purpose.
When facebook pulled together a simple app for dating, it was immediately valuable. This is the power of network effects. Not the most popular dating app in the world, but a viable, useful, and profitable tool. When you launch your dating app, while more innovative, it suffers from the cold start problem. You have zero users, so who am I going to match with? There are two main solutions to the cold start problem, and your MVP must keep these in mind while building to be successful.
There are a number of ways to build your MVP, and each might have a different pricing structure. But here I try to lay out some rules of thumb on how to get budget ballparks for certain features.
The year is 1996. My third grade teacher has five brand new Dell computers and is teaching the advanced math students Logo, an education programming language...
There are a number of ways to talk about essentialist MVPs. Minimum Viable Tests and Minimal Minimum Viable Products are popular. Here I want to explore this concept a bit further and compare a standard approach to focused one.
When constructing the backend architecture for a system, keep the concerns separated. This is a general principle followed in containerized designs, but holds true down to smaller components. Even if the component isn't far away and different, keep responsibilities relegated to separate files or directories.
Finding the right fit for your engineering staff is important, and there's a few things to keep an eye out for on resumes to share here. Screening out bad fits is much more important than being sure to hire the perfect person. So screening out good candidates should be seen as a necessary mistake to ensure the wrong employees don't get hired.