As part of a growing anti-tech movement, startup dumb.co is pushing flip phones as a way for young people to find ‘social and spiritual freedom’

The Chinese company is adapting to the demise of Moore's Law, which guides chip production. It could complicate US chip dominance. Tingbo He, president of Huawei's chip-design subsidiary HiSilicon, says her company's engineers have developed a novel way to optimize semiconductors--and she believes it will close the performance gap between Chinese and Western chips over the next few years. Huawei's method, in short, focuses on speeding up computations across chips, circuits, and entire computing systems, rather than squeezing ever-more components onto a single piece of silicon. "We found a new path," He said at the IEEE International Symposium on Circuits and Systems in Shanghai last weekend.

We study the evolution of hidden-weight spectra in wide neural networks trained by (stochastic) gradient descent. We develop a two-level dynamical mean-field theory (DMFT) that jointly tracks bulk and outlier spectral dynamics for spiked ensembles whose spike directions remain statistically dependent on the random bulk. We apply this framework to two settings: (1) infinite-width nonlinear networks in mean-field/$μ$P scaling and (2) deep linear networks in the proportional high-dimensional limit, where width, input dimension, and sample size diverge with fixed ratios. Our theory predicts how outliers evolve with training time, width, output scale, and initialization variance. In deep linear networks, $μ$P yields width-consistent outlier dynamics and hyperparameter transfer, including width-stable growth of the leading NTK mode toward the edge of stability (EoS). In contrast, NTK parameterization exhibits strongly width-dependent outlier dynamics, despite converging to a stable large-width limit. We show that this bulk+outlier picture is descriptive of simple tasks with small output channels, but that tasks involving large numbers of outputs (ImageNet classification or GPT language modeling) are better described by a restructuring of the spectral bulk. We develop a toy model with extensive output channels that recapitulates this phenomenon and show that edge of the spectrum still converges for sufficiently wide networks.

SoftBank Group has reported a surge in quarterly profit due to valuation gains on its OpenAI investment, boosting confidence at the Japanese company to bet even more on the ChatGPT-maker. The gains on OpenAI outweighed lackluster investment gains elsewhere in the Tokyo-based technology group's portfolio while war in the Middle East roiled markets. That points to growing reliance on the U.S. startup, which faces rising competition from Anthropic and Google and is reportedly trailing its highest internal targets. SoftBank earned a net income of ¥1.83 trillion ($11.6 billion) in its fiscal fourth quarter, compared with the average analyst estimate of ¥295.2 billion. The profit could be attributed entirely to its booking $25 billion in valuation gains on OpenAI in the quarter, according to Bloomberg Intelligence analyst Kirk Boodry. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Launching next week on T-Mobile's network, the cell plan takes a nuclear approach to online safety. A new US-wide cell phone network marketed to Christians is set to launch next week. It blocks porn, which experts in network security say marks the first time a US cell plan has used network-level blocking for such content that can't be turned off even by adult account owners. It's also rolling out a filter on sexual content aimed at blocking material related to gender and trans issues, which will be optional but turned on by default across all plans. The network, which is currently being tested ahead of its May 5 launch date, will be run by Radiant Mobile, a newly launched mobile virtual network operator (MVNO). These operators don't own cell towers but buy bandwidth from the big providers (in this case, T-Mobile) and sell to specific demographics (President Trump announced his own MVNO last year called Trump Mobile; CREDOMobile sends donations to progressive causes).

HTTP/3 is a new application layer protocol supported by most browsers. It uses QUIC as an underlying transport protocol. QUIC provides multiple benefits, like faster connection establishment, reduced latency, and improved connection migration. Hence, popular browsers like Chrome/Chromium, Microsoft Edge, Apple Safari, and Mozilla Firefox have started supporting it. This paper presents an HTTP/3-supported browser dataset collection tool named H3B.

Say hello (Moto) to price hikes on all three of Motorola's latest Razr flip phones. Like clockwork, Motorola is back with a new set of Razr folding flip phones . The formula is the same as last year, with three phones differing in specs and price: the Razr Ultra, Razr+, and Razr. But alongside these models, Motorola is finally launching its first-ever book-style folding phone, the Razr Fold, which it first teased at CES 2026 . The company announced the new handsets at an event in Los Angeles, where it also revealed a new pair of Moto Buds 2 Plus wireless earbuds that look eerily like Apple's AirPods Pro, but in blue; these will retail for $150 and will be available on April 30.

Triplet sampling was implemented based on the temporal vicinity of samples. Since the input is sequential, for each sample (called anchor) in the sequence, we consider a small and a large temporal window with predefined fixed widths. These two temporal windows are centered at the timestamp of the anchor. Any sample inside the smaller temporal window can be considered as a positive sample and any sample outside the small window but inside the large window can be considered as a negative sample. The widths of the temporal windows roughly depend on the speed of the observer in the environment.

We study downlink beam and rate adaptation in a multi-user mmWave MISO system where multiple base stations (BSs), each using analog beamforming from finite codebooks, serve multiple single-antenna user equipments (UEs) with a unique beam per UE and discrete data transmission rates. BSs learn about transmission success based on ACK/NACK feedback. To encode service goals, we introduce a satisficing throughput threshold $τ_r$ and cast joint beam and rate adaptation as a combinatorial semi-bandit over beam-rate tuples. Within this framework, we propose SAT-CTS, a lightweight, threshold-aware policy that blends conservative confidence estimates with posterior sampling, steering learning toward meeting $τ_r$ rather than merely maximizing. Our main theoretical contribution provides the first finite-time regret bounds for combinatorial semi-bandits with satisficing objective: when $τ_r$ is realizable, we upper bound the cumulative satisficing regret to the target with a time-independent constant, and when $τ_r$ is non-realizable, we show that SAT-CTS incurs only a finite expected transient outside committed CTS rounds, after which its regret is governed by the sum of the regret contributions of restarted CTS rounds, yielding an $O((\log T)^2)$ standard regret bound. On the practical side, we evaluate the performance via cumulative satisficing regret to $τ_r$ alongside standard regret and fairness. Experiments with time-varying sparse multipath channels show that SAT-CTS consistently reduces satisficing regret and maintains competitive standard regret, while achieving favorable average throughput and fairness across users, indicating that feedback-efficient learning can equitably allocate beams and rates to meet QoS targets without channel state knowledge.