Lol I instinctively freaked out when I saw the post preview assuming it was going to be a post about a major data breach or exploit of some sort relating to Let’s Encrypt.

I probably need more positivity in my life 😂

When you consider the price of a used android (ie. Oneplus 6T for $80 on ebay) and compare it spec for spec with a raspberry pi, it’s actually a really good deal. Like you get:

• Built in backup power supply (battery)
• 8-core power-efficient CPU (SDM845)
• Embedded sensors (microphone, magnetometer, gyro)

The way I set mine up is to run the server directly on Android using Termux, having an app autostart Termux on boot, and making sure to disable battery optimizations on the app. And then I just had the phone always plugged into the outlet to maintain the battery (and of course android would just trickle charge / disable once full charged).

Of course this isn’t perfect because you still have much more variability in play (at the OS level) than an RPi (along with not having a standard environment like debian unless you use proot), but it overall is a very powerful setup that works quite well.

I’m c1 in 2s and still cannot flick, dribble, flip reset, or pretty much anything else in that domain 😂.

I would say speedflips are probably the only important mechanic imo. The rest is just game sense and not committing when not beneficial (faking until last resort).

Fair. I guess in my case I’m actually looking for boring titles because I see reducing my engagement as a whole to the platform a good thing even if it means I don’t watch some genuinely interesting / informative content. Basically I am less likely to fall into a rabbit hole of watching “just one more video” by some creator I enjoy when I should be doing something else.

I respectfully disagree. Any high quality creator is tangibly penalized by YouTube’s recommendation algorithm for not optimizing their titles and thumbnails. A rare few choose to take this penalty but I don’t blame the many quality creators who choose to take part in the game that YouTube has made for everyone.

Yes, the alternate titles may not be perfect, but I’d take any random person’s attempt at a title over the hyper optimized ones any day because I’d rather make an informed decision to watch something even if there is some degree of inaccuracy than to make a completely uninformed decision based on what an algorithm predicted would most likely get me to click and get hooked on a video irregardless of my own will and whether I am satisfied at the end of watching it.

Sorry but has anyone in this thread actually tried running local LLMs on CPU? You can easily run a 7B model at varying levels of quantization (ie. 5 bit quantization) and get a generalized prompt-able LLM. Yeah, of course it’s going to take ~4GB of RAM (which is mem-mapped and paged into memory), but you can easily fine tune smaller more specific models (like the translation one mentioned above) and have surprising intelligence at a fraction of the resources.

Take, for example, phi-2 which performs as well as 13B param models but with 2.7B params. Yeah, that’s still going to take 1.5GB RAM which Firefox wouldn’t reasonably ship, but many lighter weight specialized tasks could easily use something like a fine tuned 0.3B model with quantization.