The hardware in the pictures of the NYT article don't resemble what I am familiar with when it comes to mobile data farming, they look like traditional sim equipment for texting.

I'd be surprised if you had no one around you that didn't have an old android laying around collecting dust. I'm still on an old A52s and it works fine.

i might, but i wouldn't want to rely on something i'm uncertain exists.

I usually hit robots.txt when I want to make fetch requests to a domain from the console without running into CORS or CSP issues. Since it's just a static file, there's no client-side code interfering, which makes it nice for testing. If you're hunting for vulnerabilities it's also worth probing (especially with crawler UAs), since it can leak hidden endpoints or framework-specific paths that devs didn't expect anyone to notice.

In my experience a good aimbot is impossible to tell from a normal player when you play ranked at a high competitive level, at least not with any degree of certainty that I think is worth banning people for. The cheaters you lose to at that level are the ones calling out your positions to their team because they can see you on the minimap while they aren't supposed to, things like that.

The DMA cheaters are caught when riot gets access to the vendors firmware and ban the people that are using it, not by the cheats themselves. Colorbots run on the same PC so these can be caught in various ways.

This article reads like it was written by an LLM and it doesn't mention how these "undetected" DMA cheats are actually caught. The anti-cheat teams join discords of vendors to get access to the cheats and flag users based on the heuristics they observe from the vendors firmware (that DMA card / hardware has to show up as _something_). So yeah, your setup can work (as long as you’re sticking to the drivers and input methods they tolerate), and the same goes for private DMA cheats.

Yes the hallmarks of llm is there.

I think the idea of Computational Irreducibility fits here, prediction becomes either as hard or as costly as the process itself.

What editor do you use, and how did you set it up? I've been thinking about trying this with some local models and also with super low-latency ones like Gemini 2.5 Flash Lite. Would love to read more about this.

Neovim with the llama.cpp plugin and heavily quantized qwen2.5-coder with 500 (600?) million parameters. It's almost plug and play although the default ring context limit is way too large if you don't have a GPU.

I’ve tried creating similar solutions and feel LLMs still lack accurate control over (or understanding of) length, stress / accents, and phonetics for consistent name generation. For usernames for example I’ve yet to create a generator that uses LLMs that beats simple Markov chains. Maybe because results are subjective it makes rating / training a lot harder? I like the site and your approach though and great job on lookup speed! If anyone has any tricks or suggestions I'd love to hear them.

Thank you! Maybe keep generating using the Markov Chains and use a LLM to evaluate the results?

"What is an interface?" reminds me of one of my favorite interview questions: "What happens when you open a browser and visit a webpage?". There’s no single right answer and when asked in the right way I find it helps surface knowledge and skills candidates might not otherwise bring up. Some go into OS internals, others talk about networking, some focus on UI and page load performance. Even when candidates expect the question it still reveals how they think and what they spent time reading up on.

“324 unrelated companies are made aware of you visiting the webpage via a mountain of JavaScript, tracking pixels, cookies, and telemetry, and then your browser renders megabytes of code to display kilobytes of content, while prompting you to make an account or download an app”

Hope you just overlooked the auto-playing video ads due to the pressure of the interview. When can you start?