Is Craigslist the Last Real Place on the Internet? Millennials are still using Craigslist to find jobs, find love, and even to cast creative projects--eschewing other AIand algorithm-dominated online spaces. The writer and comedian Megan Koester got her first writing job, reviewing internet pornography, from a Craigslist ad she responded to more than 15 years ago. Several years after that, she used the listings website to find the rent-controlled apartment where she still lives today. When she wanted to buy property, she scrolled through Craigslist and found a parcel of land in the Mojave Desert.

Elon Musk's Grok AI appears to have made child sexual imagery, says charity The Internet Watch Foundation (IWF) charity says its analysts have discovered criminal imagery of girls aged between 11 and 13 which appears to have been created using Grok. The AI tool is owned by Elon Musk's firm xAI. It can be accessed either through its website and app, or through the social media platform X. The IWF said it found sexualised and topless imagery of girls on a dark web forum in which users claimed they used Grok to create the imagery. The BBC has approached X and xAI for comment.

Criminals have claimed to have used Grok to create the imagery on a dark web forum. Criminals have claimed to have used Grok to create the imagery on a dark web forum. Online criminals are claiming to have used Elon Musk's Grok AI tool to create sexual imagery of children, as a child safety watchdog warned the technology risked bringing such material into the mainstream. The UK-based Internet Watch Foundation (IWF) said users of a dark web forum boasted of using Grok Imagine to create sexualised and topless imagery of girls aged between 11 and 13. IWF analysts said the images would be considered child sexual abuse material (CSAM) under UK law.

We were fired, and we're owning it - here's how to find a new job that works for you The new year is a natural time to reflect, and for many of us, that involves thinking about our careers. Kristina O'Neill and Laura Brown are both editors who lost their jobs after restructures, and they initially thought it was the end of the world. I poured my heart into the role... I believed in the values we promoted. Yet, when it came to me, those values weren't there, says Laura.

Trump calls for US military spending to rise more than 50% to $1.5tn President Donald Trump has called for US defence spending to be increased to $1.5tn (£1.1tn) in 2027 for what he called these very troubled and dangerous times. That would be more than 50% higher than this year's $901bn budget, which was approved by Congress in December. This will allow us to build the Dream Military that we have long been entitled to and, more importantly, that will keep us SAFE and SECURE, regardless of foe, Trump said on social media on Wednesday. In separate posts, the president said he would crack down on payouts to bosses and shareholders of major US defence contractors unless the firms speed up deliveries of armaments and build new manufacturing plants. Economists have previously warned that the gap between US spending and its income has reached unsustainable levels.

It looks like a golden chandelier and contains the coldest place in the universe. What I am looking at is not just the most powerful computer in the world, but technology pivotal to financial security, Bitcoin, government secrets, the world economy and more. Quantum computing holds the key to which companies and countries win - and lose - the rest of the 21st Century. In front of me suspended a metre in the air, in a Google facility in Santa Barbara California, is Willow. Frankly, it was not what I expected.

President Trump's favourite word is tariffs. He reminded the world of that in his pre-Christmas address to the nation. With the world still unwrapping the tariffs gift from the first year of his second term in office, he said they were bringing jobs, higher wages and economic growth to the US. What is less debatable is that they've refashioned the global economy, and will continue to do so into 2026. The International Monetary Fund (IMF) says that although the tariff shock is smaller than originally announced, it is a key reason why it now expects the rate of global economic growth to slow to 3.1% in 2026.

Class imbalance significantly degrades classification performance, yet its effects are rarely analyzed from a unified theoretical perspective. We propose a principled framework based on three fundamental scales: the imbalance coefficient $η$, the sample--dimension ratio $κ$, and the intrinsic separability $Δ$. Starting from the Gaussian Bayes classifier, we derive closed-form Bayes errors and show how imbalance shifts the discriminant boundary, yielding a deterioration slope that predicts four regimes: Normal, Mild, Extreme, and Catastrophic. Using a balanced high-dimensional genomic dataset, we vary only $η$ while keeping $κ$ and $Δ$ fixed. Across parametric and non-parametric models, empirical degradation closely follows theoretical predictions: minority Recall collapses once $\log(η)$ exceeds $Δ\sqrtκ$, Precision increases asymmetrically, and F1-score and PR-AUC decline in line with the predicted regimes. These results show that the triplet $(η,κ,Δ)$ provides a model-agnostic, geometrically grounded explanation of imbalance-induced deterioration.

We analyze neural scaling laws in a solvable model of last-layer fine-tuning where targets have intrinsic, instance-heterogeneous difficulty. In our Latent Instance Difficulty (LID) model, each input's target variance is governed by a latent ``precision'' drawn from a heavy-tailed distribution. While generalization loss recovers standard scaling laws, our main contribution connects this to inference. The pass@$k$ failure rate exhibits a power-law decay, $k^{-β_\text{eff}}$, but the observed exponent $β_\text{eff}$ is training-dependent. It grows with sample size $N$ before saturating at an intrinsic limit $β$ set by the difficulty distribution's tail. This coupling reveals that learning shrinks the ``hard tail'' of the error distribution: improvements in the model's generalization error steepen the pass@$k$ curve until irreducible target variance dominates. The LID model yields testable, closed-form predictions for this behavior, including a compute-allocation rule that favors training before saturation and inference attempts after. We validate these predictions in simulations and in two real-data proxies: CIFAR-10H (human-label variance) and a maths teacher-student distillation task.

Motivated by recent work on the experts problem in the streaming model, we consider the experts problem in the sliding window model. The sliding window model is a well-studied model that captures applications such as traffic monitoring, epidemic tracking, and automated trading, where recent information is more valuable than older data. Formally, we have $n$ experts, $T$ days, the ability to query the predictions of $q$ experts on each day, a limited amount of memory, and should achieve the (near-)optimal regret $\sqrt{nW}\text{polylog}(nT)$ regret over any window of the last $W$ days. While it is impossible to achieve such regret with $1$ query, we show that with $2$ queries we can achieve such regret and with only $\text{polylog}(nT)$ bits of memory. Not only are our algorithms optimal for sliding windows, but we also show for every interval $\mathcal{I}$ of days that we achieve $\sqrt{n|\mathcal{I}|}\text{polylog}(nT)$ regret with $2$ queries and only $\text{polylog}(nT)$ bits of memory, providing an exponential improvement on the memory of previous interval regret algorithms. Building upon these techniques, we address the bandit problem in data streams, where $q=1$, achieving $n T^{2/3}\text{polylog}(T)$ regret with $\text{polylog}(nT)$ memory, which is the first sublinear regret in the streaming model in the bandit setting with polylogarithmic memory; this can be further improved to the optimal $\mathcal{O}(\sqrt{nT})$ regret if the best expert's losses are in a random order.