You actually have a lot of options in this space. You can take the prefill engine of just about any LLM and turn it’s transformer into a classifier by lobotomizing out the decoder. You can also use a diffusion model on single pass to surprisingly competent result.
If Jeff looks easy enough to deploy by all means start there, but don’t discount the idea if Jeff sucks; you have a lot of options.
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
MIT: I build my thing on top of your thing, it’s my thing now. I sell it, you have no rights.
GPL: I build my thing on top of your thing and your license forces me to distribute my thing under the GPL, including the source code.
There are legitimate arguments for both. Sometimes it sucks when proprietary things get built on top of MIT code but sometimes it takes money for innovation, and in the realm of code the world often benefits anyway by virtue of copycats or distillation.
+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
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
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).
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
Genuinely, pull requests being off platform without good integrated diff and merge conflict resolution tooling just doesn’t work at a certain scale.
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
I don’t understand, is suggesting someone write a 7 line bash script in a Linux forum post about using a cli downloader, unreasonable?
It would be more accurate to say they try and block the vpn providers and Tor exit nodes. In practice, they do have ~95% of Tor exit nodes blacklisted but major vpn providers it’s closer to 40%. Vpn providers go through a lot of effort to hide their exit traffic and it’s pretty simple to use a provider that has a reasonably robust cli to just rotate nodes until you find one that works
Connect
Test known-good video using yt-dlp
If fail, use vpn cli to connect to a new node
Reeat until success
If your provider let’s you connect to specific nodes you can even store the last-known-good and use it on first connect until it gets blacklisted, extending uptime considerably
I like the stable, but newer packages that Ubuntu offers, plus the first-party support you get from a lot of projects. I wish debian was the standard dpkg assumption for first-party support but it unfortunately is not. Ubuntu makes it pretty easy to build automation on top of that is good for 5+ years without touching.
Yes I have, admittedly it’s been about a decade but at the time I switched because getting hardware acceleration in chrome and Firefox was difficult on debian, trivial on Ubuntu. I like the stable, but newer packages that Ubuntu offers, plus the first-party support you get from a lot of projects. I wish debian was the standard dpkg assumption for first-party support but it unfortunately is not.
Ubuntu server (headless)
Install pulse, x11, awesomewm.
Building the gui stack yourself on top of Ubuntu server gives you all of the debian/Ubuntu stability and issue searchability with none of the gnome/canonical cruft.
Very cool project!
What was the motivation to make it cross platform? On the Linux side I see immediate value but I’m struggling to understand the usecase for win/Mac.
I have nothing to contribute; I’m scared of used drives. Interested to hear who folks have had good luck with though.