Dran

You could (and probably should) use a system-one style inference system for spam classification. Much cheaper and the structured output means it’s impossible to go rogue and curl some malware or whatever. It can absolutely misclassify but its output is programmatically structured and just ranks a pre-selected set of output tokens.

In your case that’s

Spam

Not_spam

What’s remarkable is that they can extend the trace length without DDR5 timings shitting the bed lol. Can’t even put 4 sticks in most boards without spooky interposers.

The unfortunate answer is because it’s cheaper to not give a shit. Sending a request and waiting for a timeout costs next to nothing, and scales linearly in terms of compute cost. The overwhelmed server on the other end slows exponentially with each concurrent request. The crawlers are set to maximize the efficiency of local resources, which include both wall-clock time and developer time. Why send one request at a time when your server can handle tens of thousands?

Try x; wait 60 seconds, if fail: put on a list to try again later.

Costs nothing to write and nothing to run. And if you own the hardware, and are paying for power already, may as well extract maximum dollar per watt.

+1 for pikvm. Expensive, but very powerful and very portable. My favorite part of them is their APL that lets me noninteractively, programmatically, interact with hardware buttons

Sittin’ outside of a Wendy’s for free wifi lookin’-ass

Win 11 IOT enterprise LTSC already exists and doesn’t even require a TPM.

Unless this is a legitimate channel for regular users to get IOT enterprise LTSC, this is a non-story. The thing we want already exists, they just refuse to sell it.

I don’t understand what you’re proposing that nagios/chrck_mk doesn’t already do

Copyparty is what I’ve been using. Seems very similar in philosophy.

+1 though for anti-discouragement. Competition breeds competence!

Only if you pay for premium. Regular users it refuses to play in the background.

You can absolutely train a nontrivial task model on a gaming gpu (image classifier, sound classifier, text model with a very structured input and output, etc). You can also post-train addon layers on top of existing open-weight models (LORA).

Ubuntu and bad updates

This is the only drama in your list I’m not familiar with already. What/when did they last break?

I would argue that Linux kernel scale basically doesn’t work. It is a huge barrier to would-be developers to get into. You might argue that’s a feature, but it’s pretty hard to argue that it doesn’t add a lot of friction to the process

I’m glad I’m not the only one who is confused

Genuinely, pull requests being off platform without good integrated diff and merge conflict resolution tooling just doesn’t work at a certain scale.

I have no knowledge about the economics of live poultry, but rotisseries are generally loss leaders at grocery stores. They’re so commonly loss leaders that they’re often cited as one of the primary examples to explain the concept of a loss leader.

https://thehustle.co/the-economics-of-costco-rotisserie-chicken

https://en.wikipedia.org/wiki/Loss_leader

Is this hopium or did they release a statement suggesting an 3.8 35b a3b was in development for release?

I’m not aware of any public frontier LLM provider that uses a static seed for inference. Meaning, even with an identical prompt and identical model you will not get the same output. Seeds should absolutely come back with the streaming metadata on requests imho, but they don’t in any api/harness I’m aware of.

my guess is something like this

https://330k.github.io/misc_tools/unicode_steganography.html

A model could hypothetically be trained to insert zero-width characters (I doubt any have though). But any other layer could also very trivially insert these codes. The inference engine could be designed to delay output streaming by however many tokens is required to embed their coding and ninja-insert them during the decode stream. A proxy between the inference engine could insert them. A harness could insert them. Hell, even the rendering javascript frontend in your browser could insert them.

Either the inference engine or proxy would be the prime target if they want to enable this on api responses as well as copy/paste from a chat interface. They could also do a combination of the above depending on final output mode.

You are definitely correct though that it’d be trivial to detect and strip by someone aware of it.

Not necessarily, there are valid “characters” that are not rendered in most text parsers, or are rendered as whitespace. A great example of this is the byte-order-mark (BOM). You can embed a BOM code in the whitespaces of a text string and it looks exactly the same to a human as one without.

I assume that’s what they’re talking about here

Here’s an example of a steganography encoding technique using this method. https://330k.github.io/misc_tools/unicode_steganography.html