Скачать или смотреть This AI Shrunk a 1,000-Page Knowledge Base 128x… and Got *Smarter* 🤯

Everyone's talking about AI that reads more data.
They're missing the real opportunity: AI that remembers more with less.
Here's what smart teams are doing instead ↓

Most AI today has a simple strategy.
Throw more documents, more context, more compute at the problem.
It’s powerful, but it’s also slow, expensive, and hard to scale.

Apple’s new CLaRa system flips that idea.
Instead of re-reading full documents, it compresses them up to 128x into dense “memory tokens.”
Then it retrieves and reasons entirely inside that tiny space.
And in many tests, it can match or even beat classic RAG systems that read the full text.

Think about what that means for you.
Faster copilots that don’t choke on large wikis.
Research tools that feel instant, not laggy.
Knowledge bases that don’t cost a fortune to query.

I see a clear pattern:
• The next edge in AI isn’t just bigger models.
• It’s smarter memory, cheaper retrieval, and tighter feedback loops.
• Teams that design for compression and retrieval-first will move faster than those who just “add more context.”

↳ If you build with AI:
Ask: how much of my system is wasted on re-reading vs truly remembering?
Where can compressed memory replace brute-force context?

The winners won’t just have more data.
They’ll have better memories.

What’s your experience: are you hitting the limits of context windows or cost in your AI projects?