Disclaimer: this article was written by an organic sentient being, me, and not by an AI. All typos, mistakes, and repetitions are mine, and I am proud of them.
I tend not to trust my own understanding of a topic if I have just read a description of it, or watched a video. My level of understanding is several orders of magnitude better when I’ve been learning a topic by actively engaging with the topic for example by trying to build something, because a lot of the deep understanding of something only happens when you interact with it, and when you observe it collide with the real world.
For example, I’ve written compilers to understand how programming languages work, I’ve written a persisted key-value store to understand large-scale data storage systems, and more recently, I wrote a crypto payment system to understand the Ethereal blockchain and its ecosystem.
Everyone nowadays is selling us their AI skills, AI best practices, and God’s truth on how we are supposed to use AI. I’ve myself drank the kool-aid only to wake up to the reality that all these people are only using persuasion and marketing tricks but not proving anything, and if I wanted to understand the impact of AI on software engineering, then I would have to develop my own system and workflow, and see it run in wild.
So my idea was simple: build an AI workflow, apply it to a few projects, and observe myself interact with it to see what new habits I’m adopting, what I’m learning, what works and doesn’t. After a few weeks, I would extract the guiding principles behind my decisions, as a way to gain a deeper understanding of how those systems work under the hood, and equip myself with some mental models of how one should interact with such systems in the future.
Nine weeks and 421 commits later, I’ve built a workflow which I’ve named Backlog Loop and released on GitHub, and in this article I am sharing what I’ve learned. I don’t recommend that you should use this tool, but rather, read about the issues and limitations with AI that it helped me uncover.