I know that consuming something and not thumbing it up/down sort-of does that, but it's a vague enough signal (that could also mean "not close enough to keyboard / remote to thumbs up/down) that recommendation systems can't count it as an explicit choice.

IME deep thinking hgas moved from upfront architecture to post-prototype analysis.

Pre-LLM: Think hard → design carefully → write deterministic code → minor debugging

With LLMs: Prototype fast → evaluate failures → think hard about prompts/task decomposition → iterate

When your system logic is probabilistic, you can't fully architect in advance—you need empirical feedback. So I spend most time analyzing failure cases: "this prompt generated X which failed because Y, how do I clarify requirements?" Often I use an LLM to help debug the LLM.

The shift: from "design away problems" to "evaluate into solutions."

I have found out recently that Grok-4.1-fast has similar pricing (in cents) but 10x larger context window (2M tokens instead of ~128-200k of gpt-4-1-nano). And ~4% hallucination, lowest in blind tests in LLM arena.

gemini-3-flash stands well above gemini-2.5-pro

1. There is still night and day difference

2. Local is slow af

3. The vast majority of people will not run their own models

4. I would have to spend more than $200+ a month on frontier AI to come close the same price it would cost for any decent AI at home rig. Why would I not use frontier models at this point?

In most cases, e.g. with regular ML, evals are easy and not doing them results in inferior performance. With LLMs, especially frontier LLMs, this has flipped. Not doing them will likely give you alight performance and at the same time proper benchmarks are tricky to implement.

What I did splurge on was brief openai access for some subtitle translator program and when I used the deepseek api. Actually I think that $13 includes some as yet unused credits. :D

I'd be happy to provide details if CLIs are an option and you don't m ind some sweatshop agent. :)

(I am just now noticing I meant to type 2 years not 3 above. Sorry about that.)

It’s shocking to me how often it happens. Aside from just the necessity to be able to prove something works, there are so many other benefits.

Cost and model commoditization are part of it like you point out. There’s also the potential for degraded performance because of the shelf benchmarks aren’t generalizing how you expect. Add to that an inability to migrate to newer models as they come out, potentially leaving performance on the table. There’s like 95 serverless models in bedrock now, and as soon as you can evaluate them on your task they immediately become a commodity.

But fundamentally you can’t even justify any time spent on prompt engineering if you don’t have a framework to evaluate changes.

Evaluation has been a critical practice in machine learning for years. IMO is no less imperative when building with llms.