How did you manage to uncover the operation? Is there any things that tipped you off? Is it like, accounts only posting on a precise subset of subs, or how much a network of accounts reply to each other? Or too much linking/references to products, or perfect spelling/identical consistent typos?

I'm curious because it feels like it could be built into a tool to analyse - even if it does become a bit of an arms race.

It's harder now that post history is hidden but I did something similar in the past.

Start with a single comment you think is a shill. Maybe 80% of their post history is vague inane generalities (like "aww so cute" in r/cats, or a reply to a top rated post that only paraphrases the existing context without adding anything new). You can use an LLM to identify every comment or post from that account that mentions a product or service. Take note of everyone who replies to that comment as well as the parent comment. Then use an LLM to identify every post from that original account asking for recommendations (hey r/bidet, what's your favorite bidet), and look at who responds. If you build this graph, draw directional edges based on who replied to who. The accounts with edges both ways across different posts are bots. Rinse and repeat by examing the post history of THOSE accounts. You will end up with a graph with a few loosely connected nodes (maybe false positives) but a tight web of spam accounts that frequently engage with each other.

That's your bot farm. This would be relatively trivial for reddit to implement, if they cared about reducing spam. I got a POC working in a few hours, back before they limited API access

It's a good idea. But this would no longer work now that Reddit hides post history, right? Or does the API still provide a user's post history?

You'd basically need to be pulling all the data for all the subreddits, and then recreate a user's partial post/comment history from that.

It's hidden for users who opt in (not all spambots do, believe it or not) but there are some 3rd party databases that index comments- no idea how complete they are

Ah, I thought it was opt-out and hidden by default now. (For new accounts, that is)

I think he must have sworn off any v3 after MS-DOSv3.

It's also a pretty silly thing to say difficulty = tokens. We all know line counts don't tell you much, and it shows in their own example.

Even if you did have Math-like tokenisation, refactoring a thousand lines of "X=..." to "Y=..." isnt a difficult problem even though it would be at least a thousand tokens. And if you could come up with E=mc^2 in a thousand tokens, does not make the two tasks remotely comparable difficulty.

Is this sarcasm? Not that Trump's word means anything, but Trump has been against it since his first term. Having cancelled it temporarily in that first term, has said that he'll end H1B if he gets reelected, and that US shouldn't have the H1B program.

It's only since 2025 when Elon was in his good books and told Republicans to not vote for a bill that Trump woke up that day and decided he'd be pro-H1B.

English isn't your first language right?

Could do something funky like convert it to grayscale, add a 4th "colour" channel and put the grayscale image it that

I'm actually trying to reduce the 'funkyness', initially the idea was to start from a child's sketch and bring it to life (so kids can safely use it as part of an exhibit at an art festival) :)

There's a world of possibilities though, I hadn't even thought of combining color channels.

I think they were suggesting that it might be possible to inject the initial sketch into every image/frame such that the model will see it but not the end user. Like a form of steganography which might potentially improve the ability of the model to match the original style of the sketch.

What are the quotes? I don't understand, some of them are just a single word like "Art", "Autoimmune", "Cartoons"; or innocuous phrases with no context.

What am I supposed to understand from this?

The beginning is a list of ## chapters because there's many. A woman was in the psych ward for more than a decade. Luckily a doctor tested for autoimmune, which cured her.

For example you can search the page for ## False Claims Act. It will show the FBI jailing psychiatrists who illegally over billed Medicaid for fake or unnecessary services.

The first two talks are in the "Ethics, Society & Politics" category, and the third in the "Art & Beauty" category. Why would they need to be about computing?

It's a big organisation, and politics is wrapped up in what they do, along with the post-WWII Antifaschism culture in Germany.

Even if it weren't the case, I don't get why attack them for helping stand up for democracy, something in dire need of advocacy these days

No I don't think it's the bits. I would say it's the computation. Inference requires performing a lot of matmul, and with more tokens the number of computation operations increases exponentially - O(n^2) at least. So increasing your context/conversation will quickly degrade performance

I seriously doubt it's the throughput of memory during inference that's the bottleneck here.

Nitpick: O(n^2) is quadratic, not exponential. For it to “increase exponentially”, n would need to be in the exponent, such as O(2^n).

To contrast with exponential, the term is power law.

Typically, the token generation phase is memory-bound for LLM inference in general, and this becomes especially clear as context length increases (since the model's parameters are a fixed quantity.) If it was pure compute bound there would be huge gains to be had by shifting some of the load to the NPU (ANE) but AIUI it's just not so.

It literally is. LLM inference is almost entirely memory bound. In fact for naive inference (no batching), you can calculate the token throughput just based on the model size, context size and memory bandwidth.

Prompt pre-processing (before the first token is output) is raw compute-bound. That's why it would be nice if we could direct llama.cpp/ollama to run that phase only on iGPU/NPU (for systems without a separate dGPU, obviously) and shift the whole thing over to CPU inference for the latter token-generation phase.

(A memory-bound workload like token gen wouldn't usually run into the CPU's thermal or power limits, so there would be little or no gain from offloading work to the iGPU/NPU in that phase.)

What do you mean? Is mRNA not used to produce the enzyme that these comments mentioned? I don't think they were saying mRNA is gene editing itself. Just commenting on a modified mRNA helping the process compared to normal mRNA. Might be misunderstanding though so correct me if I am

I dunno, I think they are being sloppy and conflating things. We can induce manufacture of proteins and can design proteins that carry out gene editing, so we can stack that knowledge together to induce cells to manufacture proteins that carry out gene edits, but it's the payload that is the gene editing, not the instruction to make the protein.

Given the merry movement to call the COVID vaccines gene editing, it rankles.

Hey, yeah, I'm not the most up to date on the current methods. Most of my knowhow is a bit out of date here. So thanks for piping up to correct things.

Do you know of any good resources that I can use to get up to speed on the exact methods they used for the baby?

My understanding, outdated as it is, is that we're using the mRNA to go in and create CRISPR-CAS9 slicers/dicers and additionally to that, the correct genes (not mRNA) to get stitched in. I would love to know more about how I am wrong here, as I am sure I'm not even close to really understanding it.

Thanks!

I think you're replying to someone edgelord about covid who got confused about some mrna statement and then back pedalled re-affirming what the article was about.