Investing in a server with mass storage would "pay for itself" in less than a year, compared to what I'm currently renting (I'm low key scared to look up the prices of DDR5 RAM and NVMe drives though). Since I plan to maintain TankieTube "forever", it seems like the best option.

I'm so ready to ditch BackBlaze because their timeout errors are causing ~90% of the current problems with the website (external storage move failures and buffering problems).

I have plenty of experience assembling computers and the thought of building a server is really fun, but I've never used colocation before.

Questions/Thoughts/Concerns:

1. Do datacenters let you walk inside to maintain your own server? There is a datacenter in my home city, which would be convenient, but using it would effectively soft-doxx my location. Right now "Burgerland" is as specific as I publicly reveal.

2. If I ship the server to a more remote location, how would I replace failed drives? Is that a commonly provided service? Would using a datacenter within ~2 hours driving distance be the best compromise between accessibility and location obfuscation?

3. Is paying with Monero an option? Is it a good idea? Could I mail replacement drives directly to the datacenter without revealing my home return address?

It looks like I'll need NVMe drives in something called the U.2 form factor (instead of M.2) in order to enable hot swapping. TIL.

https://archive.ph/lqg68

This paper discovered the continuous math equivalent of the digital NAND gate. It turns out that a single binary operation paired with the constant 1 can generate every single standard elementary function. That operation is defined as eml(x,y)=exp(x)-ln(y). You can reconstruct constants like pi and the imaginary unit alongside basic addition and complex calculus tools using nothing but this one function.

The implications for machine learning and symbolic regression are massive. Normally when artificial intelligence tries to discover mathematical formulas from data it has to search through a chaotic space of different operators and syntax rules. Because the EML operator turns every mathematical expression into a uniform binary tree of identical nodes the search space becomes perfectly regular. You can basically treat a mathematical formula like a neural network circuit. The paper shows that when you train these EML trees using standard gradient optimizers like Adam the weights can actually snap to exact closed-form symbolic expressions instead of just giving fuzzy numerical approximations.

This finding could change how we design analog circuits and specialized computing hardware. If you only need a single instruction to execute any complex mathematical function you could build physical hardware or single instruction stack machines optimized purely for the EML operation. The fact that this was discovered by computationally stripping down a calculator rather than through purely theoretical derivation highlights how much structural beauty is still hiding in basic math.

cross-posted from: https://lemmy.ml/post/45946938

Why are emerging economies rapidly choosing electrification over oil and gas?

In this interview, Markham Hislop speaks with Dan Walter of Ember Energy about a major shift underway in global energy systems. Across the global South, technologies like solar, electric vehicles, and heat pumps are no longer just climate solutions—they are often the cheapest and most practical energy options available.

The conversation explores how falling costs have triggered a tipping point, why decentralized energy (like balcony solar) is spreading, and how electrification is bypassing traditional grid constraints. It also examines the role of disruptive innovation, drawing on Clayton Christensen’s theory to explain how “good enough” technologies are reshaping entire energy systems.

cross-posted from: https://hexbear.net/post/8244184

https://archive.vn/Udfnb

OpenAI leaders horrified staffers after proposing an “insane” plan to enrich the company by pitting world governments against each other.

This anecdote of near comic-book-villainry comes from The New Yorker’s sweeping new investigation into CEO Sam Altman, which documents his alarming pattern of lying and manipulating to build his AI empire, a behavior that some insiders likened to that of an actual “sociopath.”