Diego Pacheco Tech blog

Burning tokens is a double-edged sword. Powerful weapons can either give a lot of leverage or drag you down. It's easy to think that you are not shooting yourself in the foot with unlimited tokens. The thing is, you won't be shooting yourself in the foot; you will be expending a lot of company money and perhaps with lots of amplified waste. But only if you do it wrong or don't know what you are doing, and that's the very tricky part. Big Tech and big companies already have unsustainable token usage, like Uber . The list goes on and on: Microsoft , Walmart , Meta , JPMorgan , AT&T , and many others. For some, tokenmaxxing is over. Is it? Really depends on the cost of inference. Jev is showing that a classifier can be powerful and reliable, at least in the sense of low cost and speed, not saying anything about accuracy, which is not the score. The problem is not using tokens; that is the symptom. The problem is a lack of training; there is a huge AI enablement GAP ...

AI Slop is everywhere. Technical debt always existed, but sure, slop will surpass it; somehow we managed to make things worse: Human-generated, handcrafted tech debt blended with synthetic AI generations. I won't say productivity is up because that's hard to measure; for sure, output is up, but so are bugs and incidents. The less you look at the code, the less you pay attention, the more slop you get. Attention is all we have; it's precious. The catch is, to produce more, you need to look less. It takes 5 minutes for the agent to produce the code and 5 hours for you to validate and understand. Multiply that by several PRs a day, and we have a big attention problem and slop proliferation. AI Slop Definition I think it would be fair to ask frontier LLM models what they define as ai slop. I had to ask to be max 2 lines; otherwise, it would be slop 🙂 GPT has the most interesting definition for me, specially because it mention judgement and quality. IMHO what we are also missin...