15:[[47484203,{"by":"habosa","descendants":11,"id":47484203,"kids":[47486551,47489340,47484686,47484713,47512242,47534749,47499390,47497550,47484221,47493009,47486057,47486708,47485664,47501447],"score":7,"text":"Jensen Huang recently said that he thinks an engineer who makes $500k should spend at least $250k a year on “tokens” which is an astounding figure. I personally don’t know how I could spend that much if I tried. Obviously he has a huge financial incentive to convince people that $250k per engineer is reasonable, but it got me thinking that it’s time for a survey.

How much are you and your coworkers spending on AI coding tools at work? I’m talking about Cursor, Claude Code, etc. Not all AI-powered SaaS, just the stuff that’s metered by token.","time":1774227744,"title":"Ask HN: How much are you spending on AI coding at work?","type":"story"}],[47479124,{"by":"kaliades","descendants":1,"id":47479124,"kids":[47645679,47490414,47479225],"score":4,"text":"Every semantic cache library - RedisVL, LangChain, LiteLLM - targets Redis and assumes RediSearch wire compatibility. Point any of them at Valkey Search 1.2 and they break silently in multiple places. There's no library that actually handles Valkey natively.\nThis does.

@betterdb/semantic-cache is a standalone semantic cache backed by valkey-search. No framework coupling - works with any LLM client. Every check() and store() emits an OTel span and increments Prometheus counters and histograms out of the box. No instrumentation code required.\nMIT. Optional LangChain and Vercel AI SDK adapters. Works on self-hosted Valkey, ElastiCache for Valkey, and Memorystore for Valkey.

https://www.npmjs.com/package/@betterdb/semantic-cache

The Valkey Search 1.2 divergences from RediSearch that required explicit handling are in the changelog.","time":1774196669,"title":"Show HN: Valkey-native semantic cache with OTel and Prometheus built in","type":"story"}],[47484047,{"by":"FinDevOps","descendants":0,"id":47484047,"kids":[47490409,47485067],"score":2,"text":"Hey,

I’ve been thinking a lot about how hard it is to understand and compare costs across cloud providers (AWS, GCP, OCI, Azure) and now also AI/LLMs.

Most tools show historical spend, but not really:

what the same workload would cost elsewhere\nor how different AI providers compare for the same usage

Curious how people here handle this today.

Do you rely on internal tooling, spreadsheets, or something else?

I’ve been experimenting with a small tool for this, but still trying to understand if this is a real pain or just my bubble.

Would love to hear how you approach this.","time":1774226562,"title":"Ask HN: How do you compare cloud and AI costs across providers?","type":"story"}]]

user:	rolo_1992
created:	March 23, 2026
karma:	2