Big AI money, thin defences, and labs writing their own rules

Yesterday we saw that hacking with AI is getting cheaper. Now the walls are going up. On r/LocalLLaMA, a user says a tool called REA ("Reverse Engineer Anything") was blocked by Claude one day after launch. People used it to study smart TVs that spy on their owners. The post says OpenAI blocks this kind of use too. Another user says cloud models can even close your account for reverse engineering requests.

There is also news on Kimi K3.1, the Chinese model we covered yesterday. Its open-weight release (model files anyone can download) reportedly removes cyberattack training data. The Reddit poster says this is mainly because of new Chinese export restrictions. Meanwhile, a "root shell on Samsung TV" post (full control of the TV) got 507 points on Hacker News. A GitHub repo with 35 ready-to-paste jailbreak prompts for 25+ models got 540 stars in a few days.

These are community reports, not official statements. But the direction is clear: labs and governments now decide what AI may help you hack.

PHP is an old programming language, but it still runs a big part of the web. A serious PHP flaw led the AI safety newsletter Transformer to write: "The cyberpocalypse is here — sort of." The "sort of" matters. It is a warning, not a disaster.

The follow-up came from Reuters. The director of the PHP Foundation says it is "under-funded," with only one full-time staff member. One person, for software that so many websites depend on. The story got 137 points on Hacker News.

The same Transformer edition covers Anthropic's coming IPO, OpenAI's amendments to SB 53 (California's AI safety law), and AI safety advocates turning on Anthropic. The open question now: who pays to protect shared code like PHP?

A new study asked AI models to choose between candidates in nearly 500 US election races. When forced to pick, xAI's Grok overwhelmingly recommended Republicans. OpenAI's GPT favored Democrats.

The two models disagreed in more than 90% of the races. TechCrunch reported the study, and it was also discussed on r/artificial.

The models were forced to choose, so this is not how most people use them. Still, millions of people now ask chatbots for advice on everything, and election questions will come.

America has lost about 200,000 jobs in media, film and the arts, according to an analysis from Apricitas. The piece asks the hard question: how much of this is because of AI? It sparked a big discussion on Hacker News, with 368 points and 366 comments.

Job losses usually have many causes, so there is no simple answer. But AI tools for text, images and video are an obvious suspect, and the article tries to measure it.

Some creators are pushing back with authenticity. The Verge reports that BBC's Blue Planet III keeps less-than-perfect shots on purpose, because too-polished footage now looks like AI. Imperfection is becoming a sign of human work.

On Thursday, Pennsylvania Governor Josh Shapiro signed the Artificial Intelligence Deceptive Advertising Act. Any ad that "has been significantly modified or generated by artificial intelligence" must now carry a label. That covers images, video, audio and text, and the label must be in the same format as the ad.

The timing makes sense. On Reddit, a user complained about AI-made photos on Facebook Marketplace: a "pristine" leather couch turned out to be very worn. And TorrentFreak found an AI-assisted paper on Z-Library that cites a TorrentFreak article that does not exist.

Labels will not fix everything. But it is one more state saying: if AI made it, tell people.

A new open-source tool called gpu-unlock says it raises the speed of some AI math on Nvidia RTX 3000 cards. It got 248 points on Hacker News. On r/LocalLLaMA, people say a similar trick works on RTX 30 and 40 cards, with up to 2x faster "prefill" (the step where the model reads your prompt). One headline even says prices for these cards are surging.

At the same time, people run big models on normal hardware. One project runs Qwen3.8-Flash-Next, a 125-billion-parameter model, at about 20 tokens per second (tokens are small pieces of words) with 16 GB of video memory, by reading parts from a fast SSD. Others run it on 8 GB cards.

These are early community results, not official tests. But if they hold, a used gaming PC can run serious AI at home, with your data staying on your machine.