Reading through an online discussion today about someone discovering their passwords app had been opened after a phone repair left me genuinely concerned. The digital privacy implications are staggering, and it’s a scenario that plays out far too frequently in repair shops across the globe.

Let’s be brutally honest here - handing over your phone’s passcode to a repair shop is like giving a stranger the keys to your house, your bank accounts, and your entire digital identity. The thought makes me shudder, especially given how much of our lives are stored on these devices nowadays.

Running large language models locally has been a fascinating journey, especially for those of us who’ve been tinkering with these systems on consumer-grade hardware. Recently, I’ve discovered something quite remarkable about tensor offloading that’s completely changed how I approach running these models on my setup.

The traditional approach of offloading entire layers to manage VRAM constraints turns out to be rather inefficient. Instead, selectively offloading specific tensors - particularly the larger FFN (Feed Forward Network) tensors - to the CPU while keeping the attention mechanisms on the GPU can dramatically improve performance. We’re talking about potential speed improvements of 200% or more in some cases.

Interesting discussion caught my eye today about maintaining Australian mobile numbers while overseas, particularly for those essential OTP messages we can’t seem to escape. The quest for the most economical solution definitely resonates with my bargain-hunting spirit.

Back in 2019, I faced a similar dilemma when spending three months in Japan for a work project. The whole two-factor authentication landscape has become increasingly complex since then, with everything from banking to MyGov requiring that precious mobile number. It’s fascinating how our phone numbers have evolved from simple contact points to crucial digital identity anchors.