logoalt Hacker News

wxwtoday at 7:45 PM1 replyview on HN

> Rapidly adopting newer, more efficient models delivers the largest cost wins of any technique.

I think the more interesting lever is the fourth they mention: token efficiency.

> By the time costly LLM inference occurs, the user's initial statement accounts for only a negligible fraction of the data fed into the AI system, meaning costs are dominated by context the user did not explicitly include.

I think there’s still lots of low hanging fruit in regards to monitoring and improving agent work. Look at your sessions. Look at how much time and context is being spent on, say, a web search returning dozens of results when one good single-pager doc would’ve been better.


Replies

ankitmathurtoday at 8:45 PM

100% - there's a lot to learn from traces from real-life sessions with coding tools! For example, I found it pretty eye-opening to see how wide the distribution of tasks truly is. There's also subtle things like how a poorly designed MCP API surface can cause a massive amount of token waste from the model just iterating on finding the right way to call it.