Instagram Let a Fake Mia Goth Go Viral

Earlier this month, a photo appeared to show actress Mia Goth wearing a T-shirt referencing her divorce from Shia LaBeouf. It spread rapidly, according to The Verge. Entertainment outlets including People and OK! wrote it up as genuine. It was AI-generated.

The image came from an account called miagoth_, which carried Instagram's blue verified checkmark and had over a million followers. Goth's representatives say they filed multiple complaints with Instagram to have it removed. The account has since been wiped.

The checkmark is the crux of it. Meta now sells verified badges — anyone can pay for one, including fan accounts run by people who have nothing to do with the person they depict. A blue tick used to mean "this is the real person." It no longer does.

Meta's own rules make the situation worse. Photorealistic AI videos of real people may require a label. Still images do not. So an AI photo of a celebrity, posted by a paid-verified fan account, can spread freely with no disclosure required.

Goth's representative called it "incomprehensible that Instagram evidently prioritizes the dissemination of forged and manipulated images over the protection and integrity of a public figure's likeness."

What to do: when you see a celebrity post that feels timed a little too perfectly to a news story, check whether the account name matches the person's actual verified presence elsewhere before sharing it. The blue tick is not the signal it once was.

Gemini 4 Argon is Google's first major frontier model (a top-tier AI system competing at the cutting edge of the field) since Gemini 3 in late 2025, according to The Verge.

Google says Argon is built for complex, multi-step tasks — coding, finance, legal work, science, and cybersecurity. The company claims it outperforms rival models including GPT-6 Astra, Claude Fable 5.1, and Claude Opus 5.5 across a range of benchmarks, though those results come from Google's own testing, not independent evaluation.

It is live today for Pro and Ultra subscribers in the Gemini app and in AI Mode in Search. Ultra subscribers also get access via Antigravity, Google's agentic development platform. Free and Plus users have no confirmed timeline.

Pricing starts at $2 per million input tokens and $10 per million output tokens — roughly one-fifth the cost of GPT-6 Astra, according to Google. That introductory rate doubles at the end of November.

Honest read: Worth trying if you are already a Pro or Ultra subscriber — Google has something competitive here, though independent benchmarks will tell the real story.

What it is: A new open-weights model (one you can download and run yourself, though training data and code are not public) designed to run AI agents efficiently on a laptop or a single GPU, according to Liquid AI's Hugging Face blog.

What's new: Previous on-device models either reasoned slowly or required more hardware than most people have. LFM2.5-24B-A2B runs a full reasoning chain before answering while activating only 2 billion of its 24 billion parameters per token — meaning each thinking step costs a fraction of what a similarly sized model would spend.

How it works: The model uses a mixture-of-experts (MoE) architecture — think of it as 64 specialist sub-models, with the system routing each piece of input to whichever four specialists are most relevant. This is paired with a 131,000-token context window (the amount of text the model can hold in memory at once). After initial training on 33 trillion tokens of text, the model went through five post-training stages including reinforcement learning inside real agent environments, teaching it to call external tools and complete multi-step tasks reliably.

What it can't do: It trails larger models on knowledge-heavy benchmarks like GPQA Diamond and on complex coding tasks. Liquid AI recommends pairing it with web search for knowledge-intensive work.

Who should care: Developers building agents that need to run privately on a user's device, without sending data to a cloud server.

Try it / read it: Hugging Face model page

LFM2.5-24B-A2B is a free, open-weights model from Liquid AI — you can download and run it today on a capable laptop or desktop, with no cloud account required and no data leaving your machine.

It is designed for agentic tasks: give it a goal, and it reasons step by step before acting, calling tools along the way. On an Apple M5 Max, it runs at 184 tokens per second — fast enough to feel responsive.

To start, install Python and run:

pip install -U transformers

Then load the model using the code in the Hugging Face model card. It is compatible with llama.cpp and MLX, which offer simpler setups if you prefer not to use Python directly.

Worth knowing: You will need roughly 16GB of RAM to run this at usable speed — check your machine's specs before downloading.

Google published a safety report alongside today's Argon launch, according to DeepMind. It flags that Argon showed elevated capability in both cybersecurity and biology, prompting Google to add three layers of real-time protection: screening inputs for harmful intent, reading internal neural signals to catch disguised dangerous requests, and monitoring outputs as they generate.

Google gave a limited group of trusted security researchers early access before the public launch. That window turned up a critical bug in hospital software affecting hundreds of thousands of patient records.

For ordinary users, the report means Argon was not released on a press-release schedule — there was a deliberate safety review first.