Author: Carter

Managing Physical AI is growing more complex as autonomous AI systems are increasingly embedded in robots, sensors, and industrial machinery. The central concern is no longer simply whether AI agents can carry out tasks, but rather how their actions are tested, overseen, and halted when they interact with real-world systems.Industrial robotics already forms a substantial foundation for this conversation. According to the International Federation of Robotics, 542,000 industrial robots were deployed globally in 2024—more than twice the annual figure recorded ten years prior. The organization forecasts installations will climb to 575,000 units in 2025 and surpass 700,000 units by 2028.Market…

During Paris Blockchain Week, BeInCrypto conducted an exclusive interview with Aleksandr Vat, Head of Business Development at Ethplorer.io, to explore the company’s newly launched Aggregated Ethereum Rich List. Ethplorer contends that conventional Ethereum rich lists have grown increasingly deceptive because they rank wallets solely based on ETH holdings. The new ranking instead evaluates the total USD value held by each address, factoring in ETH, ERC-20 tokens, and stablecoins. Photo provided by Aleksandr Vat According to Vat, this shift reshapes the understanding of Ethereum wealth, liquidity, and risk. It also leads to one of Ethplorer’s more striking conclusions: altseason may have…

Published on May 4, 2026 by Marina Moore, Evan Anderson, and Sherine Khoury, CNCF Technical Advisory Group CNCF projects featured in this article RecipeGitHub Actions CI dependenciesTarget audience (the chef)Project maintainers and developers looking for clear, actionable steps to secure CI dependencies in their GitHub Actions workflowsScope (ingredients)Dependencies within GitHub Actions, GitHub Action runner images, selecting CI dependencies, and responding to vulnerabilities in CI dependenciesOut of scope (do not add)Creating custom actions, other CI tools, other dependency types (code, test, container, etc.), transitive dependencies, SBOMs – provenance TLDR: 🗹 Source the Best Ingredients: Assess before adopting / Trust the provider:…

# Introduction Claude Code is incredibly handy, but the costs can escalate surprisingly quickly. The reason is straightforward: you’re not just paying for the prompt you typed. More often than not, Claude also carries along previous messages, files it has accessed, tool outputs, memory documents like CLAUDE.md, and embedded instructions. So when token usage rises, it’s rarely just about poorly written prompts. The real culprit is usually overstuffed context. A lot of the general advice floating around isn’t very actionable. Saying "keep chats brief" is accurate, but it doesn’t explain what actually makes a real difference. What truly helps is…

A botnet consists of numerous “zombie” computers and can be used, for instance, to carry out DDoS attacks. Here’s what you should know about the topic. FOTOKINA | shutterstock.com Cybercriminals are constantly looking for ways to spread malware on a large scale or launch Distributed Denial of Service (DDoS) attacks. A botnet is particularly well-suited for this purpose. Botnet – Definition A botnet is a collection of internet-connected devices that have been compromised by an attacker to carry out DDoS attacks and other tasks as a coordinated “swarm.” The core idea is simple: every computer that joins the botnet becomes…

Sabrina Ortiz/ZDNETStay updated with ZDNET: Add us as a preferred source on Google.While the Android smartphone space may not be as fiercely competitive as it once was, buyers still enjoy a wide variety of choices. Options range from traditional slab-style phones and book-style foldables to flip phones and even devices with physical keyboards. Although iPhones hold a commanding 69% market share in the U.S., Android continues to offer greater feature depth and more flexibility in hardware design.Related: Why I suggest this $450 Samsung phone over similar models from Google and OnePlusWhen shopping for a new Android device, your decision often…

On June 4, 1996, Ariane 5 completed its maiden flight — a new European heavy-lift rocket engineered to place payloads into low Earth orbit. The vehicle blew up less than 40 seconds after launch. The root cause was specification and design flaws embedded in the software of the inertial reference system. A piece of software had been carried over from the earlier Ariane 4 without confirming that its assumptions still held in the updated environment. The failure went on to rank among the most costly software bugs ever recorded. Why bring up an incident from three decades ago in a…

Over the past couple of months, improvements to long-standing IT automation platforms will start linking agentic AI directly to core enterprise systems — including ERP platforms and mainframes. For years, businesses have relied on workload automation and orchestration tools, which originated long before cloud computing and DevOps became mainstream. These tools have since been updated to support newer trends and, more recently, have been enhanced to work with generative AI and agentic AI. Platforms in this space automate routine tasks and integrate varied, complex software systems into unified business processes with strong dependability, according to Dan Twing, an analyst at…

Extensive field tests carried out throughout Japan have delivered one of the most thorough real-world assessments yet of Wi-Fi HaLow — a long-range, energy-efficient version of Wi-Fi purpose-built for Internet of Things (IoT) applications. The trials were organised by the Wireless Broadband Alliance alongside industry partners, and examined performance across four distinctly different settings: a public park, an urban school campus, a multi-storey residential building, and a water treatment facility. At each site, engineers typically used a single access point to evaluate performance, tracking connectivity range, signal strength (RSSI), data throughput, latency, and packet loss as devices were positioned at…

class CellSignalingSimulationAgent: def run(self, df_signal: pd.DataFrame) -> AgentResult: peak_receptor = float(df_signal[“receptor_active”].max()) peak_kinase = float(df_signal[“kinase_active”].max()) peak_tf = float(df_signal[“tf_active”].max()) t_receptor = float(df_signal.loc[df_signal[“receptor_active”].idxmax(), “time”]) t_kinase = float(df_signal.loc[df_signal[“kinase_active”].idxmax(), “time”]) t_tf = float(df_signal.loc[df_signal[“tf_active”].idxmax(), “time”]) final_state = df_signal.iloc[-1].to_dict() summary = { “peak_receptor_activity”: round(peak_receptor, 4), “peak_kinase_activity”: round(peak_kinase, 4), “peak_tf_activity”: round(peak_tf, 4), “time_to_peak_receptor”: round(t_receptor, 4), “time_to_peak_kinase”: round(t_kinase, 4), “time_to_peak_tf”: round(t_tf, 4), “final_state”: {k: round(float(v), 4) for k, v in final_state.items()}, } return AgentResult(name=”CellSignalingSimulationAgent”, summary=summary) class PrincipalInvestigatorAgent: def __init__(self, client, model=OPENAI_MODEL): self.client = client self.model = model def synthesize(self, results: List[AgentResult]) -> str: payload = {r.name: r.summary for r in results} prompt = f””” You are a principal investigator…