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A Slayer’s Guide to DOOM: The Dark Ages’ Shield Saw

DOOM: The Dark Ages introduces a versatile new weapon called the Shield Saw, emphasizing a 'stand and fight' approach to combat.
The Shield Saw serves as a means of defense and offense, allowing players to efficiently take down enemies while protecting their armor and health.
Players can utilize the Shield Saw for blocking, parrying, Shield Charge for high damage attacks, and Shield Throw for stunning enemies.
The Shield Saw is also used for puzzle-solving, destroying walls, unlocking secrets, and navigating levels.
Upgrades for the Shield Saw, such as Shield Runes and damage increases, enhance its capabilities in combat.
DOOM: The Dark Ages offers a dark fantasy setting, new weapons like the Shield Saw, engaging combat, and exploration of unknown realms.
Players can stand and fight against demon hordes, utilizing powerful weapons and tools like the Shield Saw to turn the tide of battle in their favor.
The game features a variety of editions offering different content, including early access, campaign DLC, and additional skins for the DOOM Slayer.

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Digitaltrends

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A foldable screen laptop to rival the ThinkPad X1 Fold is set to launch soon

Huawei is set to unveil the Huawei MateBook Fold Ultimate Design, a laptop with a foldable screen similar to Lenovo ThinkPad X1 Fold and Asus Zenbook 17 Fold.
The event announcing the MateBook Fold Ultimate Design will occur on May 19 at 2:30 PM CST, in collaboration with Hongmeng Computers.
The MateBook Fold Ultimate Design is expected to function like a large tablet without a built-in keyboard, potentially using a Bluetooth keyboard or an on-screen keyboard.
Further details about the MateBook Fold, including specs, will be revealed at the event; it is anticipated to focus on compact design and ease of transport resembling Asus Zenbook Fold 17.

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Guardian

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Trump says he has a ‘little problem’ with Tim Cook over Apple’s India production

Donald Trump has expressed displeasure towards Apple and Tim Cook over the reported plans to shift iPhone production to India for the US market.
Trump mentioned his conversation with Tim Cook, stating his preference for Apple to focus on building in the US rather than in India, where production has reportedly increased due to US-China trade tensions.
No iPhones are currently manufactured in the US, and experts warn that moving production there would significantly increase costs.
Trump also mentioned that India has offered a trade deal with no tariffs for American goods, as both countries aim to achieve a trade agreement during the 90-day pause announced by the US president in April.

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Sentinelone

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Prioritizing CVEs in the Cloud

Organizations often struggle to prioritize and remediate known CVEs in cloud environments due to gaps in visibility and the overwhelming number of vulnerabilities.
Data from Google Cloud and Unit 42 shows that CVE exploitation is a common root cause of cloud compromises, with large-scale exploitation attempts on the rise.
Cloud CVEs can be categorized based on whether they impact applications deployed by customers or vulnerabilities within Cloud Service Providers (CSPs).
Efforts to increase transparency around CSP security vulnerabilities, such as Microsoft and Amazon issuing CVEs, aim to enhance overall cloud security.
Real-world case studies highlight the impact of CVEs on cloud environments, emphasizing the need for proactive vulnerability management and prioritization.
Risk rating cloud CVEs can be informed by resources like CVSS, KEV, EPSS, and CWE to properly categorize and prioritize vulnerabilities for remediation.
By applying a structured prioritization approach to cloud CVEs, organizations can mitigate the risk of breaches and enhance their cloud security posture.
SentinelOne's Singularity Cloud Security provides an evidence-based approach to prioritization, offering an Offensive Security Engine for validating and responding to cloud risks.
The Offensive Security Engine helps differentiate between theoretical and exploitable risks, providing proof of exploitability to aid in effective vulnerability management.
By adopting advanced security solutions and leveraging prioritization strategies, organizations can effectively protect their cloud resources and respond to cloud risks.
Emphasizing the importance of proactive vulnerability management and prioritization, organizations can improve their overall cloud security posture in the face of evolving threats.

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Whizlabs

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How to Use AWS Step Functions for Machine Learning Pipelines?

AWS Step Functions are crucial for machine learning pipelines, offering embedded capabilities and seamless integration with AWS services.
It serves as a low-code, serverless orchestration service using state machines to automate processes, ideal for ML pipelines.
Benefits include serverless architecture, simplifying pipeline development, scalability, reliability, ease of integration, and monitoring capabilities.
Use cases include event-driven workflows, model training automation, end-to-end pipeline automation, serverless pipelines, data enrichment, and microservice orchestration.
Steps for creating an AWS ML pipeline with Step Functions involve data ingestion, preprocessing, model training, evaluation, deployment, and monitoring.
Integrating AWS Lambda with Step Functions aids in custom preprocessing, task orchestration, cost optimization, and post-processing predictions.
Amazon SageMaker integration with Step Functions allows for model training automation, hyperparameter optimization, and model deployment from workflows.
Best practices for secure and scalable pipelines include securing data, using reusable components, infrastructure scaling, error handling, and auditability.
AWS Step Functions are essential for the MLA-C01 Certification exam, automating ML workflows and focusing on scalability and repeatability.
Concepts discussed align with exam objectives related to data engineering, exploratory data analysis, modeling, and machine learning implementation and operations.

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Dev

300

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Instantly Export Live GCP Infrastructure to Pulumi – No Import, No Terraformer

Pulumi users facing challenges in migrating or starting Infrastructure as Code (IaC) in Google Cloud Platform (GCP) projects.
Introducing StackForge, a system that connects to GCP projects and instantly generates Pulumi Main.yaml, Pulumi state, and offers optional GCS upload without deployment requirements.
Use cases include agencies managing multiple GCP clients, internal platform teams onboarding cloud projects to Pulumi, and large organizations aiming for repeatable IaC in GCP.
Early access available for DevOps and platform teams to try out StackForge, provide feedback, and shape future developments.

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Scand

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Building Functional .NET Applications: a Guide for Choosing between F# vs C#

Functional programming provides a predictable and manageable code structure by focusing on simple, reusable functions.
Choosing between C# and F# impacts the ease of implementing functional programming principles in .NET applications.
F# is tailored for functional programming, while C# is multi-paradigm, accommodating various programming styles.
F# emphasizes immutability and is well-suited for data science, financial modeling, and cloud services.
C# is versatile with features like lambda expressions and LINQ, making it suitable for diverse projects within the .NET framework.
F# promotes immutability, compact syntax, type inference, and higher-order functions for efficient functional programming.
C# excels in web development, desktop applications, and mobile apps, offering a strong type system and compatibility with other languages.
While F# is ideal for a functional-first approach, C# provides flexibility to blend object-oriented, imperative, and functional code.
Choosing between F# and C# for functional .NET applications depends on project requirements and organizational preferences.
Both languages run on the .NET platform, offering compatibility with a wide range of tools and libraries for development.

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Tech Radar

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Valve confirms your Steam data wasn’t breached in major hack – here’s how to keep your account safe

A hacker claimed to have 89 million records of Steam users for sale.
Steam has denied that any of its systems were hacked.
The data probably isn’t as damaging as was originally feared.
Steam confirmed that leaked data did not include vital account data or personally identifiable information, still advises users to strengthen security measures such as changing passwords and enabling two-factor authentication.

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Silicon

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Waymo Recalls Vehicles After Minor Collisions

Waymo has recalled 1,212 self-driving vehicles due to a tendency to crash into road barriers such as chains and gates.
The National Highway Traffic Safety Administration (NHTSA) found that in 16 incidents, Waymo vehicles collided with stationary objects like gates and chains.
Waymo released software in November 2024 to reduce the likelihood of collisions with road barriers and deployed it fleet-wide by December 26, 2024.
Although the issue has been fixed, Waymo is required to issue a formal recall notice, while the self-driving car industry faces increased regulatory scrutiny.

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Livescience

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Quantum computing: What is quantum error correction (QEC) and why is it so important?

Quantum computing aims to surpass classical computing but faces high error rates, prompting the need for quantum error correction (QEC) methods.
Implementing QEC involves adding redundancies to quantum information to prevent errors from disrupting computations.
Quantum error rates are significantly higher compared to classical computing, emphasizing the importance of QEC for reliable quantum operations.
Google's Willow quantum processor demonstrated the scalability of QEC, showing successful error correction in large quantum systems.
Quantum computers leverage entanglement and superposition for processing power, but these states are fragile and prone to errors.
Logical qubits spread quantum information across physical qubits, enabling redundancy and error detection/correction processes.
QEC techniques like surface codes and LDPC codes help manage errors in quantum systems, but the challenge lies in reducing individual qubit error rates.
Error correction methods in quantum computing are more complex than in classical systems due to the inherent fragility of quantum states.
Lowering individual qubit error rates is crucial for effective error correction in quantum systems, enabling exponential improvements in error suppression with more qubits.
Successful QEC implementation can lead to significant advancements in quantum computing by mitigating errors and enabling practical applications.

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Tech Radar

309

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OpenAI just gave ChatGPT users a huge free upgrade – 4.1 mini is available today

OpenAI has rolled out ChatGPT 4.1 for paid subscribers and 4.1 mini for free users, emphasizing coding capabilities for the former and efficiency for the latter.
GPT-4.1 is a specialized model aimed at coding tasks and instruction following, while 4.1 mini is a fast and efficient small model for non-paid users.
Users with ChatGPT Plus, Pro, or Team access can try out these new models from the 'more models' section, with 4.1 mini available as a fallback after reaching GPT-4o limits.
As OpenAI's naming convention becomes more complex, there is a call for simplification to help average consumers better understand the significance of new models like 4o, 4.1, and others.

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Silicon

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US ‘Nears Deal’ With UAE On Advanced AI Chips

The US has a preliminary deal with the United Arab Emirates to import 500,000 advanced Nvidia AI chips per year starting in 2025.
Around 100,000 chips per year might go to UAE AI firm G42, with the rest destined for US companies like Microsoft and Oracle for AI data centers in the Emirates.
The deal is expected to run until at least 2027 but could extend to 2030, with an agreement for G42 to build a data center in the US for every one it builds in the UAE.
The deals signify a move towards the Middle East becoming a key provider of AI data centers, raising concerns in Washington about possible national security risks and ties with Chinese companies.

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Tech Radar

159

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Warning: today's Wordle answer is the fifth hardest ever – so be careful out there

Today's Wordle puzzle, #1,426, is considered the fifth hardest of all time, with an average score of 5.5.
Twitter is abuzz with discussions about the challenging Wordle, and it is gaining attention on the platform.
The Wordle answer for puzzle #1,426 is 'EAGER,' making it an ER word, known for being particularly difficult.
ER words like EAGER are challenging due to their high frequency in Wordle solutions, making it hard to narrow down other letters.
EAGER ranks as the equal fifth hardest Wordle ever, along with other ER words like PARER, MUMMY, CORER, and ROWER.
These ER words often lead to the 'too-many-answers problem,' where similar words like PAGER, WAGER, and LAGER are considered.
Wordle enthusiasts face difficulties with ER words due to their common endings and the varied possibilities for the initial three letters.
Strategic playing, like identifying patterns early and ruling out options, can improve Wordle success rates, especially with challenging words.
Despite the difficulty of puzzles like #1,426, Wordle offers a range of challenges for players to enjoy and improve their skills.
For players looking to maintain streaks or improve their gameplay, strategies such as early pattern recognition and elimination can be beneficial.

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Bigdataanalyticsnews

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AI Agent Architecture: Revolutionizing Intelligent Automation in 2025

AI agents play a crucial role in businesses across industries, relying on evolving architecture for autonomous operations and decision-making.
AI agent architecture encompasses key components like perception modules, decision engines, action modules, communication protocols, and security frameworks.
These agents automate processes in customer service, finance, healthcare, and supply chain management, enhancing efficiency and decision-making.
Trends in AI agent architecture include Event-Driven Architecture, Edge Computing, Federated Learning, and Composable Systems for improved scalability and privacy.
Implementing AI agent architecture involves identifying use cases, selecting appropriate tools, developing modular architecture, and thorough testing before deployment.
Challenges in AI agent architecture include data quality, integration with legacy systems, and ethical/legal compliance regarding data privacy and decision-making.
AI agents are designed to complement human efforts, automating repetitive tasks to enhance productivity and allow humans to focus on strategic functions.
Interaction with other systems is facilitated through APIs and communication protocols, ensuring seamless integration and data exchange.
Businesses can scale AI agent architecture by leveraging cloud platforms, edge computing, and modular designs for dynamic expansion.
AI Agent Architecture continues to drive innovation, reshaping automation with the latest technologies and trends for improved efficiency and cost reduction.

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Dev

378

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🐾 Setting Up the Raspberry Pi-Based IoT Cat Feeder (Day-27)

Day-27 guide covers setting up an IoT-based pet feeder using a Raspberry Pi with a servo motor controlled by Python and cron jobs.
Objective: Establish an automated pet feeder with a Raspberry Pi running a Python script to feed your cat daily.
Steps mentioned include flashing Raspberry Pi OS to a 32GB microSD card, enabling SSH access, configuring Wi-Fi for headless boot, and booting the Pi.
Despite successful OS flashing and SSH enablement, issues with SSH connection, network detection, and hostname access were encountered, setting up for the next steps in Day-28.

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