The advancements in this space have moved so fast, it's hard to extract a predictive model on where we'll end up and how fast it'll get there.

Meta releasing LLaMA produced a ton of innovation from open source that showed you could run models that were nearly the same level as ChatGPT with less parameters, on smaller and smaller hardware. At the same time, almost every large company you can think of has prioritized integrating generative AI as a high strategic priority with blank cheque budgets. Whole industries (also deeply funded) are popping up around solving the context window memory deficiencies, prompt stuffing for better steerability, better summarization and embedding of your personal or corporate data.

We're going to see LLM tech everywhere in everything, even if it makes no sense and becomes annoying. After a few years, maybe it'll seem normal to have a conversation with your shoes?

Dirt bikes get WD40 on the chain every ride. Just keeps the water off.

For the really old stuff, I used to do NetBSD. I'm sure their 32bit x86 support is still top notch.

Halls of Torment. $5 game on steam that is like a Vampire Survivors clone, but with more rpg elements to it.

It's not usually enough power to make a big difference, and it can raise the noise level to a point where you might not like how it sounds on longer rides. Some can provide weight reduction, but again not a lot.

Take it through some gravel right away to scrub that release agent off.

Exhaust pipe is just a consumable

lol same. Nightmare scenario for off-road riders is just sending it into a pond with some throttle oopsie.

It's a great series, highly recommend.

These are amazing. Dell, Lenovo and I think HP made these tiny things and they were so much easier to get than Pi's during the shortage. Plus they're incredibly fast in comparison.

I've got a background in deep learning and I still struggle to understand the attention mechanism. I know it's a key/value store but I'm not sure what it's doing to the tensor when it passes through different layers.

Subscribed. That last episode of AAA was heartbreaking.

I'm on lemmy.world and the sidebar shows 401 subscribers. Is that just a sub count from the local instance or global?

Also not sure how that would be helpful. If every prompt needs to rip through those tokens first, before predicting a response, it'll be stupid slow. Even now with llama.cpp, it's annoying when it pauses to do the context window shuffle thing.

Which one is better? ;)

Any data sets produced before 2022 will be very valuable compared to anything after. Maybe the only way we avoid this is to stick to training LLMs on older data and prompt inject anything newer, rather than training for it.

If you live near a track or mountains or canyons, sport bike will be fun. If you want to do longer rides, they will be kinda crappy unless they're sport-touring.

Yes the desire for fast bike is hard to ignore. I find I get beat up and exhausted on the faster bikes, the KLR is chill mode.

I had the CRF250L for about 8 years, and I loved that little thing. But no, long trips were not great. The KLR is WAY more comfortable.

And it's still pretty comfy to tour on the back roads. They use a nice thick seat, which is unusual for anything that is marketed for dualsport use.