I'm excited to share that I’ve been selected as an IOY Ambassador for 2026–2027! Looking forward to connecting with fellow leaders and contributing to the movement. @iycforyouth#IOYAmbassador
The rule for the 6 am hour that changed my career:
Do not check anybody else's demands on your time before 8.
No email. No WhatsApp. No news.
For those two hours, work on the thing you would work on if nobody else existed. That is your actual life. The rest is the world's version of you.
Two hours a day, six days a week, ten years. Nobody who does that stays where they started.
I am building a website that helps you practice IELTS exam for FREE.
The website will have a lot of practice questions and you can even practice in real time.
RT if you want this.
RAG vs. CAG, clearly explained!
RAG is great, but it has a major problem:
every query hits the vector DB. even for static information that hasn't changed in months.
this is expensive, slow, and unnecessary.
Cache-Augmented Generation (CAG) fixes this by letting the model keep static information in its key-value (KV) memory, which is what the model builds internally for every token it reads.
in fact, you can combine RAG and CAG for the best of both worlds.
here's how it works:
RAG + CAG splits your knowledge into two layers.
↳ static data (policies, documentation) gets cached once in the model's KV memory
↳ dynamic data (recent updates, live documents) gets fetched via retrieval
you get faster inference, lower costs, and less repeated work.
the trick is being selective about what you cache.
only cache static, high-value knowledge that rarely changes. cache everything and you'll hit context limits. separating "cold" (cacheable) and "hot" (retrievable) data keeps this system reliable.
you can start today. OpenAI and Anthropic already support prompt caching in their APIs.
one thing to know before you scale it.
prompt caching matches on an exact prefix, byte for byte. your cached layer only gets reused when it sits at the very front of the context in the same order every time.
↳ reorder two cached policy documents and both turn into a miss
↳ cache document A alone and document B alone, then query both, and the second one misses because the model computed its cached state without ever seeing the first
in production this looks like a small fraction of your cached blocks serving almost all the hits. the rest just sits there.
the way out comes from how attention behaves. tokens attend mostly to their own local neighborhood, and only a few reach across document boundaries. CacheBlend recomputes those few and reuses everything else from the separately cached documents.
multi-document queries run two to four times faster, quality holds, and order stops mattering.
it ships in LMCache, which is fully open source.
repo: https://t.co/TXlaLLu04a
(don't forget to star 🌟)
below, i have quoted my article on KV cache management. it covers where prefix caching stops working and how a proper caching layer fixes it.
give it a read.
cheers! :)