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Comment by dakolli | Hacker News Reader

I call this junk "meta-agentic engineering", it reminds me of people who have the coolest nvim configs, spend hundreds of hours customizing it but ultimately get less work done than the guy with minimal workflows, if any at all.

I look on twitter and it's just people building tools for agents to use agents, some weird customization loop going on in the LLM space right now. Ultimately these are trends pushed on us from model providers because they 10x token consumption. Its literally just BS trends to increase revenue at these companies, most of it is largely useless.

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Some of this stuff is ludicrous. I finally tried /loop last week and discovered every loop iteration passes the entire context history to the model. So pretty quickly you're running a full 1M context window, without cache, likely just to check if something is ready or needs to be done. It's miserably terrible engineering unless your entire and only goal is to burn tokens.

Wait, why isn't context cached with /loop?

Because it's typically cached for 5min (1hr is a setting you have to explicitly opt into), and very few people run loops at a cadence of < 5 mins.

Oh! I thought Anthropic cached for one hour (by default), am I wrong about that? Or is this an OpenAI thing?

Anthropic changed their cache duration for some reason a while back.

https://code.claude.com/docs/en/prompt-caching#on-a-claude-s...

"On a Claude subscription, Claude Code requests the one-hour TTL automatically. Usage is included in your plan rather than billed per token, so the longer TTL costs you nothing extra and only affects how long your cache stays warm. If you’ve gone over your plan’s usage limit and Claude Code is drawing on usage credits, you are billed for that usage, so Claude Code automatically drops the TTL to five minutes."

I never liked the term "ricing" with window managers, but this is definitely veering into LLM ricing territory sometimes. And people are getting rich off of it.

I think many of us engineers (including myself) are suffering from AI-induced procrastination. Fixing that pesky bug in production isn't fun. Building a harness/workflow that detects and fixes pesky bugs in production on its own is way more fun (especially when your employer pays for tokens), and it feels just as important, if not more! So what if it didn't actually fix that pesky bug in production on its own...

I'd rather anyone using llms to fix pesky bugs in production gets fired and I work at a shop that cares about quality and craftsmanship. There are handmade swiss watchmakers, and then there are assembly line workers in China also making watches, which worker do you think goes home happier at night (and who's company is actually beloved), you have a choice.

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