GoKawiilAn Honest Review of AI Programming They gave me a Claude subscription and told me to get tokenmaxxing, so I tried to give it a fair shot.
It’s getting hard to avoid LLMs these days. Even if you avoid social media (or at least curate a follow list that avoids the bulk of the slop factory) and shrug off grandiose marketing statements that end up taken at face value in the news, it will likely come and find you at your place of work. Unlike the silver bullets of the past (like microservices or NoSQL), AI adoption seems to have been mandated in many places from the top layer of management, regardless of how many of them ever worked (or studied for) an engineering job.
I do admit that this approach immediately triggered my contrarian side and made me very defiant of any AI tool. I don’t believe someone who has never written a line of code in their life should be telling me what to use for my engineering job. This sounds to me like the most terminal case of micro-management, and that’s never a good thing (on top of being personally insulting).
Either way, over the past 3 months I got to use Claude and friends for work and I have to admit I found it somewhat useful. As long as you don’t ask it to write code. Please don’t ask it to write code. But I’m getting ahead of myself.
Artificial “Intelligence”
You probably heard this a million time by now, but artificial intelligence really isn’t that intelligent. It’s all marketing and buzzwords. All we really we have here is a (very) large neural network specialized in natural language processing.
As it turns out a lot of what we humans do on the computer is use text to communicate both ways, that model can be used to parse queries, generate textual1 responses based on a probabilistic heuristic or output command lines that can be then executed the old fashioned way and their results fed back into the model to make a loop until we reach some exit condition.
That’s not to say this is inherently bad. But it’s not magical either. “Agentic workflow” (or whatever they’re calling it at the time you’re reading this article) is just the realization that every software problem can be solved by adding another layer of indirection, and LLMs are no exception. If the neural network output can be improved by providing more input, then attach more input. And if the best way to figure out which input that would be is to query the model to generate a command and then pipe it through system() , so be it.
But let’s focus on the user point of view for the rest of this article.
We have Google at home…
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