Author: Carter

Every cloud business outperformed. Every capital expenditure forecast went up. That’s the two-sentence summary of the biggest earnings day of 2026, and it reveals nearly everything you need to know about the current state of Big Tech’s AI infrastructure investment.Microsoft, Alphabet, Meta, and Amazon together pledged roughly US$630 billion to US$650 billion in capital spending for 2026. Q1 was the first real test of whether those investments are paying off. The verdict, across all four earnings calls, was affirmative. The follow-up, echoed on every call, was: we’re increasing our spending even further.Microsoft: Azure speeds up again, capex outlook climbs to…

Here is the paraphrased version of the article: Key takeaways:For Bitcoin to keep climbing toward $84,000, it must turn the $80,000 mark into a support level.Several leading altcoins are seeing buyers step in at lower prices, but they need to break through overhead resistance before a fresh upward move can begin.Bitcoin (BTC) has pushed past $78,000, building on its 11.87% April surge, according to CoinGlass data. The April recovery was fueled by strong demand in US spot BTC exchange-traded funds, which recorded $1.97 billion in inflows, per SoSoValue data.The rally is likely to face selling pressure in the range between…

TxPert architectureTxPert forecasts how genes will respond to perturbations (represented as vectors in ℝⁿ) based on a collection of perturbation identifiers (P ⊆ 𝒫 := {1, …, N}) and a baseline cellular state derived from a control gene expression profile (x ∈ 𝒳 ⊂ ℝⁿ). This control profile has been carefully matched to the target cell in terms of cell line and experimental batch to minimize technical variability. Here, n ∈ ℕ denotes the number of genes measured in the experiment, while N ∈ ℕ refers to the total number of distinct perturbations observed across the dataset. These perturbation identifiers…

Criminal IP has teamed up with Securonix to embed its threat intelligence directly into the ThreatQ platform. This collaboration lets organizations seamlessly weave external IP intelligence into their current security processes, empowering analysts to investigate and respond to threats faster with richer, more actionable insights. Going beyond conventional threat feeds, Criminal IP offers deep visibility into how an organization’s digital assets and infrastructure are exposed on the public internet. Integrating this capability into ThreatQ means teams gain real-world context during investigations—without having to alter their established workflows. ThreatQ acts as a central hub for aggregating and ranking threat data from…

The Stuxnet worm is widely acknowledged as the first verified cyberattack intended to damage critical infrastructure. Identified in 2010 but deployed as early as 2009, it targeted uranium enrichment systems at Iran’s Natanz Nuclear Facility, resulting in the physical destruction of centrifuges. Moving ahead to the post-IT/OT convergence surge of the mid- to late-2010s, attacks on operational technology and critical infrastructure have grown considerably more frequent and damaging, fueled by greater connectivity between IT and OT environments that has broadened the attack surface and allowed attackers to penetrate industrial systems via enterprise IT networks. TXOne Networks, a cybersecurity firm,…

Here’s the paraphrased HTML, keeping the structure intact while making the text clearer and easier to read: Adam Doud/ZDNETFollow ZDNET: Add us as a preferred source on Google. ZDNET’s key takeaways If you’d rather not lug a laptop everywhere, there are plenty of alternatives.These options vary widely in size, from AR headsets down to your smartphone.Here’s a look at several approaches and which ones work best for different scenarios.I cover mobile technology for a living, and I do a lot of my writing on the go. That means I frequently find myself without a laptop—or in situations where using one…

A phase regulator can make AMR fleet coordination more reliable and foreseeable. Source: VisualNest AI, Adobe Stock Predictability isn’t the same as stability. In an earlier piece, I introduced a priority-focused framework designed to make autonomous mobile robots—AMRs—more predictable in their actions. Before diving into the concept of phase regulation, let’s quickly recap the foundational ideas I put forward: Tiered mission structures Well-defined entities for interaction Decision-making levels shaped by context Neutral-autonomous status to ensure legal predictability The aim was structural clarity—determining who makes decisions, which mission they serve, and under what constraints. That said, having a clear structure on…

Written by: Ahsaas Bajaj and Benjamin S. Knight ? We conducted 134,400 simulations using real-world ML models to uncover the answer. It hinges on your specific optimization goals and a straightforward diagnostic you can calculate before even training your model. If you’ve ever built a linear model in scikit-learn, you’ve likely encountered this dilemma: should you choose RidgeCV, LassoCV, or ElasticNetCV? Perhaps you went with whatever a guide suggested, relied on a teammate’s recommendation, or simply tested all three and picked the one with the highest cross-validation score. Our goal was to swap guesswork for solid, data-backed decisions. We ran…

Under the latest proposal addressing the most debated segment of cryptocurrency market regulation, earning yield simply by holding stablecoins would be banned—an approach closely aligned with discussions since the beginning of the year.Newly unveiled text from the Digital Asset Market Clarity Act, released Friday, shows that Senators Thom Tillis (R-N.C.) and Angela Alsobrooks (D-Md.) reached a compromise that would prohibit stablecoin issuers from offering returns to investors who merely hold their stablecoins. The provision argues that “depository institutions deliver financial services critical to the health of the American economy,” and should stablecoin providers offer comparable services, it “may hinder” those…

In this tutorial, we delve into the lambda/hermes-agent-reasoning-traces dataset to uncover how agent-based models reason, interact with tools, and craft responses throughout multi-turn dialogues. We begin by loading and exploring the dataset, reviewing its layout, categories, and conversation structure to gain a solid understanding of the data at hand. Next, we develop straightforward parsers to isolate essential elements—such as reasoning traces, tool invocations, and tool outputs—enabling us to distinguish between internal deliberation and external tool interactions. We then examine patterns including how often tools are used, how long conversations tend to be, and how frequently errors occur, all to build…