When AI Starts Using Less, Not More

"Take this rough idea and turn it into a clear 5-step execution plan with specific outputs for each step."

Batching:

Processing multiple inputs together in a single run instead of one at a time, improving efficiency, speed, and cost in AI systems.

Read: Measure What Matters by John Doerr

A practical guide to setting and tracking goals using OKRs. Especially useful for teams trying to stay focused and aligned while moving fast in uncertain environments.

Apple is reportedly doubling down on on-device AI, prioritizing privacy and speed over cloud dependency. The next AI battle may not be about who is smartest, but who runs locally.

What it is:

A no-code web automation tool that lets you extract and monitor data from any website without writing scripts or managing scrapers.

How you can use it:

Train a "robot" by clicking on elements on a webpage, and it will automatically extract data or track changes over time. Use it for competitor tracking, price monitoring, lead collection, or market research.

Good for you if:

You need structured data from websites regularly but don't want to deal with code, APls, or brittle scraping setups.

Google's latest breakthrough isn't just about better models, it's about using less memory to run them. That matters because the AI boom has so far been built on the idea that more compute and more memory equals better performance. News around semiconductor stocks reacting to this shift, alongside fresh activity in the chip ecosystem like new IPOs, shows how sensitive the market is to any change in that assumption. If AI systems need less memory, it directly challenges the demand narrative that has powered parts of the hardware industry.

What Ringg thinks:

This is where the story gets interesting. The AI wave has pulled in capital across the stack, from cloud to chips to new listings in the semiconductor space. But breakthroughs like this suggest the next phase may not be about scaling endlessly, but about optimising. If efficiency becomes the priority, the winners won't just be those building more hardware, but those reducing the need for it. For markets betting big on AI infrastructure, that's a shift worth watching closely.