AI Agents Are Hogging Your Vintage Finds

Meta's Muse — an AI agent (a piece of software that can browse the web, make decisions, and take actions on your behalf without constant supervision) — turned out to be surprisingly good at one thing nobody fully anticipated: being an inconsiderate shopper.

The Verge reported that Muse could be aimed at The RealReal, a secondhand luxury fashion retailer, to exploit a simple feature: placing an item in a cart puts it "on hold" for 20 minutes. A human might dither over a cashmere coat for a few cycles before giving up. Muse, instructed to keep re-adding items the moment each hold expired, could freeze a listing for hours. No purchase necessary. No intention required.

The RealReal's CTO confirmed the company has updated its bot defences so agents can no longer log in, add to cart, or check out with the same ease. Agents can still surface listings and send users to product pages — they just cannot act autonomously from there.

This is a small story with a large implication. Agents do not get tired, bored, or guilty. Every consumer behaviour that relied on human limits — impulsiveness, attention span, the basic sense of "this is a bit much" — may need rethinking. Amazon has already blocked agents from shopping its site. Others are working out their position in real time.

For now: if you shop secondhand luxury, availability should improve slightly. But the broader race between agents and the retailers trying to manage them is only starting.

Apple's iWork suite — its free productivity apps for writing, presentations, and spreadsheets — is getting a built-in AI assistant, according to The Verge.

The assistant is built on Apple's own models plus OpenAI's, and it understands the document you have open. In Pages it can draft and restructure text. In Numbers it suggests chart types and builds tables. In Keynote it can animate and redesign slides from a prompt.

It is rolling out in beta to devices running iOS 27, iPadOS 27, or macOS 27 that support Apple Intelligence — so not everyone has it today. The apps also now support the Model Context Protocol (a standard that lets third-party AI agents plug into other software) on macOS 27, meaning outside tools can also interact with your documents.

It is free, bundled with the apps that are themselves free.

Honest read: Worth trying if you are already in the Apple ecosystem and find yourself doing repetitive formatting or drafting work — but this is a beta, and Apple's description of its own capabilities is all the evidence available so far. No independent evaluation yet.

What it is: A family of small AI models designed to be customised and run on everyday hardware — phones, laptops, even a Raspberry Pi.

What's new: LFM3 fits six different model types (text, vision, audio, a combined "omni" model, and a decision model) onto a single shared backbone. The 1.7-billion-parameter base model outperforms Liquid's previous 2.6-billion-parameter model while using 35% fewer parameters — more capable, smaller footprint.

How it works: The architecture mixes three layer types in each pass: gated short convolutions (which spot local patterns quickly), sliding-window attention (which reads nearby context), and global attention (which connects distant parts of a sequence). A technique called partial RoPE — positional encoding (a way of telling the model where each word sits in a sequence) applied only to part of each attention layer — lets the model match content across long distances without losing speed. The text model fits in roughly 1GB at 4-bit precision (a compression method that shrinks model size at some quality cost) and handles a 256K-token context window (the maximum text it can hold in working memory at once).

What it can't do: The omni model and video support are new and untested at frontier scale. The Liquid team notes the architecture search covered 14 device combinations, but real-world performance across all of them has not been independently verified. Fine-tuning (adapting the model on your own data) still requires a single GPU — fine for a developer, not for someone without one.

Who should care: Developers building specialised tools who need a capable starting point they can run and adapt without cloud infrastructure.

Try it / read it: Hugging Face blog

OpenAI has released GPT-6.1 Terra, a model built for computer-use tasks: filling in forms, reading invoices, editing video, interpreting technical drawings.

The headline improvement is factuality. On OpenAI's own internal benchmark of error-inducing prompts, the error rate fell from 53.6% with the previous GPT-5.6 Terra to 16.7% — a meaningful drop, though these are OpenAI's own evaluations, not independent tests.

API pricing is 50% lower than GPT-5.6 Terra. For everyday users, it is available now through ChatGPT.

To try it: open ChatGPT, select GPT-6.1 Terra from the model picker, and give it a task involving a document or image — uploading a PDF and asking it to summarise or reformat works well as a first test.

Worth knowing: The computer-use benchmark scores — including comparisons to Claude Opus 5.5 and GPT-6 Astra — come from OpenAI's own testing. No independent evaluation of Terra has been published yet.

NIST (the US National Institute of Standards and Technology) has released NISTIR 8547, a roadmap for switching from today's encryption to quantum-resistant standards before powerful quantum computers can crack them.

Federal high-value systems must migrate by 2030–2031. Organisations working with the federal government will face the same pressure indirectly.

The practical threat is real now: attackers can collect encrypted data today and decrypt it later once quantum hardware matures — a strategy called "harvest now, decrypt later." If your organisation stores sensitive data, checking which encryption algorithms you rely on is a sensible first step.