100 Days is Too Long

100 Days of Code (https://www.100daysofcode.com/), and many subsequent inspirations, became famous for motivating people to get up to speed as a programmer, and we applaud those efforts.

With the emergence of AI tools to help people create software code, writing software can be accelerated by 2X, 3X, maybe even 5X. Even after working around the AI hallucinations (which are rapidly decreasing), using those tools to help us write code is truly a game-changer.

It follows, then, that getting up to speed on creating AI applications (meaning: software that uses AI tools) shouldn’t take us the same 100 days that it used to take just to learn a single programming language and toolset.

Could it take longer? Should it take less time?

So yeah, the pure “coding” part should be way faster these days, but we’re talking about figuring out how to create software applications that use AI under the covers. This will be more difficult (and require different skills) than manually slinging programming code.

• To avoid spending cycles debating the future, we take inspiration from Douglas Adams, who famously suggested that the answer to The Ultimate Question is 42.

Our Motivation

It’s impossible to escape the relentless hype about how AI will either 1) revolutionize our world, or 2) destroy it and us (probably us first). None of us can know what will happen, but all of us who are around software can recognize that this current tidal wave of AI innovations is making fundamental changes in the way software is built and maintained. We don’t accept the popular hallucinations by many C-suite executives about being able to fire all their programmers before the next quartely numbers are reported, but we do have the following positions:

1. People who are now learning how to create software should use AI tools instead of learning to code manually.
2. The biggest payoff from AI will be creating AI applications that take business process automation to the next level while also providing transparency to and oversight by their human owners.

These two assertions drive our core objective to get people who participate in this adventure proficient in creating (“coding”) AI applications that are meaningful and manageable.

Too Ambitious?

Time will tell. What we don’t want people to experience is yet another “toy” tutorial experience, so we go deeper than the typical tutorial series. This includes real-world issues like production deployments, and it means real-world considerations like the limits—and governance—of AI.

We can’t promise that you will get a high-paying AI job, but our goal is for all of us to become proficient at transforming ideas into working/deployed AI applications that make us (and our bosses and clients) at the leading edge of the pragmatic (and responsible) use of AI.