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Alvinashcraft

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Dew Drop – June 20, 2025 (#4444)

16 billion passwords exposed in record-breaking data breach, opening access to Facebook, Google, Apple, and any other service imaginable (Vilius Petkauskas & Jurgita Lapienytė)
Announcing Warp Preview (Hong Yi Chen)
GIF export in Snipping Tool begins rolling out to Windows Insiders (Dave Grochocki)
Copilot Compared: Advanced AI Features in Visual Studio 2022 vs. VS Code (David Ramel)
Fine-Tuning LLMs with C#: A Practical Guide to Customizing Models with ML.NET and Introduction to Semantic Kernel: The .NET Developer’s Guide to Building Powerful AI Agents (Sudhir Mangla)
Best practices for administering GitHub Copilot with Luis Pujols | Beyond the Commit (GitHub Team)
Mastering iOS Development: Your Essential Resource List (Coder)
Strengthen mobile device security with Edge for Business, the secure enterprise browser (Patrick Brosset)
SQL Server 2025: Secure by Default | Data Exposed (Anna Hoffman, Pratim Dasgupta & Pieter Vanhove)
freeCodeCamp Podcast #176 – From Therapist to six figure freelance dev (Beau Carnes)

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Medium

163

"The AI Revolution: How Artificial Intelligence is Changing Our World"

Artificial Intelligence (AI) is evolving from mere assistants like Siri to creators like DALL-E and ChatGPT.
AI adoption in businesses has surged by 270% in the last 4 years, with a projected $15.7 trillion contribution to the global economy by 2030.
AI offers various opportunities in healthcare, education, and work by enhancing convenience and fostering innovation.
However, challenges such as job displacement, biases in AI systems, and privacy concerns persist.
The ethical implications of AI raise a crucial question: Will AI be a friend or a foe to humanity?
It's essential to develop and regulate AI responsibly to ensure it complements human creativity.
The AI revolution is reshaping the world, emphasizing the need to harness its potential while managing associated risks.

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Medium

311

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The Complete Guide to AWS Machine Learning Tools and Services

Amazon SageMaker is the cornerstone of AWS’s machine learning offerings, covering the entire ML lifecycle.
AWS provides pre-trained AI services that can be easily integrated into applications without ML expertise.
Selecting the appropriate AWS ML service depends on factors like skill level, use case, and budget constraints.
Managing costs is crucial when using AWS ML services, involving right-sizing resources and leveraging cost-effective options.
Monitoring and optimization, storage optimization, security, performance, reliability, and operational excellence are important considerations.
AWS offers a wide range of machine learning tools for users of all skill levels and use cases.
Understanding the capabilities and limitations of each service helps in making informed decisions for specific requirements.
AWS continues to update and expand its ML offerings, requiring users to stay informed about new features and services.
Following best practices for security, performance, and cost optimization maximizes the value of AWS ML investments.

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Cloudwards

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What Is Salesforce & How Does It Work? CRM Software Explained

Salesforce is a cloud-based CRM platform offering scalable solutions for business across sales, service, and marketing.
Top global companies utilize Salesforce for increased productivity and customer satisfaction through features like Einstein and Lightning.
Salesforce controls a significant portion of the global CRM market with 21.8% share and employs a multi-tenant architecture for scalability.
Einstein AI in Salesforce uses a distributed training model for personalized customer solutions.
Salesforce is a modular cloud-based platform that centralizes customer information, automates tasks, and generates insights for sales, marketing, and service teams.
Despite its wide adoption, Salesforce may pose challenges such as a steep learning curve and complex pricing tiers for small teams.
Salesforce's architecture allows for customization, easy integration with third-party apps, and real-time data access.
Salesforce's services include Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud, Experience Cloud, CRM Analytics, MuleSoft, and more.
Salesforce benefits include enhanced team collaboration, intelligent automation, scalability, customization, and improved decision-making through analytics.
Challenges of using Salesforce include complex setup, steep learning curve, high costs, data migration challenges, and data quality control issues.

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Medium

159

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Mastering Automated Machine Learning with Amazon SageMaker Autopilot

Amazon SageMaker Autopilot is an automated machine learning (AutoML) capability within the Amazon SageMaker ecosystem.
It allows users to quickly build high-quality models by automating key steps in the machine learning workflow.
Autopilot provides transparency into its decision-making process, offering insights for users to learn from.
It works best with datasets that have clearly defined target variables for supervised learning tasks.
To start an Autopilot experiment, users need to specify the dataset, target variable, and objective metric.
Key steps in Autopilot include data analysis, feature processing, candidate generation, model training, tuning, and evaluation.
After the experiment, Autopilot ranks models based on specified objective metrics for users to select the best model.
Comprehensive explanations of models generated by Autopilot help users understand predictions and feature impacts.
Recent updates to Autopilot include support for time series forecasting tasks.
Starting with clean, well-structured data is recommended for optimal Autopilot results.
Users should set appropriate time constraints, review generated notebooks, iterate, refine, monitor resource usage, and validate models on test data.
Autopilot can create ensemble models and integrates seamlessly with other SageMaker components.
While powerful, Autopilot has limitations, but it advances automated machine learning accessibility.
The tool benefits both seasoned ML practitioners and domain experts looking to accelerate ML workflows.
By following best practices, users can maximize Autopilot's value in machine learning projects for accurate predictions.
Amazon SageMaker Autopilot automates tasks to allow data scientists to focus on higher-value activities like feature engineering and problem formulation.

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Medium

109

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AWS vs. GCP Networking at a Glance

When moving between AWS and GCP, there is a contrast in their networking approaches.
AWS focuses on regionally-scoped building blocks while GCP provides many global resources by default.
In AWS, a Virtual Private Cloud (VPC) is regional, requiring connections like VPC Peering or AWS Transit Gateway for multi-region networks.
GCP has a global VPC Network that simplifies multi-region architectures.
Subnets in AWS are zonal while in GCP, they are regional, allowing VMs in different zones to share the same subnet.
NAT Gateway in AWS is zonal while Cloud NAT in GCP is regional and cheaper for smaller deployments.
AWS offers VPC Endpoints while GCP offers various private access options.
Load balancers in AWS are regional, requiring AWS Global Accelerator for global traffic distribution.
GCP's external HTTP(S) Load Balancer is global by default, using anycast IP for traffic routing.
AWS utilizes Amazon Route 53 for DNS, domain registration, and health checking, while GCP offers Cloud DNS.
AWS prices internet egress using a tiered model, while GCP has Network Service Tiers for internet egress.
Both AWS and GCP provide hybrid connectivity solutions.
AWS uses a layered model for networking while GCP employs a single, unified VPC firewall rule system.
VPC Flow Logs in AWS capture IP traffic information, while in GCP, VPC Flow Logs sample network flows for VM instances.

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Cloudwards

283

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Safest and Riskiest U.S. States for Financial Cybercrime

Safest states for financial cybercrime are Mississippi, Texas, and Minnesota, with low victim rates and strong legislation, while Washington, D.C., Iowa, and Nevada are the riskiest.
Most states saw double-digit increases in victim count or monetary losses from 2022 to 2024, indicating a nationwide escalation in cybercrime.
Financial losses from cybercrime in the U.S. doubled from 2022 to 2024, reaching billions in damages.
Certain scam types like investment fraud and tech support scams caused disproportionately high losses, even in states with low victim counts.
Mississippi ranked as the safest state despite minimal legislative activity, while Illinois saw major losses despite strong regulation.
The report analyzed per-capita victim rates, financial losses, trends, and state-level legislation to map cybercrime risk in the U.S.
The data used for the analysis was from the FBI's 2024 IC3 annual report, providing the most recent cybercrime numbers.
Advance fee scams, lottery and inheritance fraud, business email compromise, romance scams, and non-payment/delivery fraud are common types of financial cybercrime.
Steps to protect against cybercrime include verifying claims, monitoring accounts, enabling multi-factor authentication, and avoiding unsolicited offers.
If a victim of cybercrime, report to the FBI's IC3, notify financial institutions, monitor accounts, update passwords, and consider credit freezing.
Financial cybercrime affects all states, necessitating efforts at every level to combat rising risks of online scams, fraud, and data breaches.

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Livescience

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'Reliable quantum computing is here': Novel approach to error-correction can reduce errors in future systems up to 1,000 times, Microsoft scientists say

Microsoft scientists have developed novel 4D codes for quantum error-correction, addressing fault tolerance in quantum computing.
These 4D codes aim to reduce errors in quantum systems significantly by allowing error checking without collapse of qubits.
Unlike classical computing, quantum error-correction involves entangled physical qubits to detect and correct errors.
The new approach by Microsoft exhibits a 1,000-fold reduction in error rates and requires fewer physical qubits per logical qubit.
The 4D geometric codes use a twist in torus-shaped code for efficient error detection in quantum computing systems.
Researchers successfully tested the 'twisted' 4D code on existing quantum computers and validated the effectiveness of the approach.
The 4D code family may lead to the development of universal fault-tolerant quantum computers with increased logical qubits using fewer physical qubits.
Microsoft's novel error-correction technique involves a theoretical 'twist' in the geometry to cover system space using fewer qubit entanglements.
The study also introduces a groundbreaking technique to replace lost atoms used as qubits during quantum computations.
The findings suggest that the 4D geometric codes could represent a significant breakthrough in quantum error-correction with broader applications and efficiency.

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Medium

128

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The Future of Hybrid Cloud in Financial Services: Balancing Flexibility and Control

The financial services industry is facing digital challenges, requiring a balance between innovation and regulatory requirements.
Hybrid cloud is seen as a solution that combines the benefits of public cloud scalability with private infrastructure control.
It offers a middle ground for modernization without compromising governance needs in the financial sector.
Hybrid cloud integrates private, public, and on-premises infrastructure to create a unified environment.
Financial institutions can maintain sensitive data in secure, private environments while leveraging public cloud flexibility for less sensitive tasks.
Hybrid cloud is a deliberate long-term strategy, not just a step towards full cloud migration for industries with complex needs.
Challenges of hybrid cloud adoption exist alongside benefits such as total cost optimization.
Technological advancements will shape the future of hybrid cloud in financial services.
Some major financial institutions are already leading in hybrid cloud adoption, highlighting its strategic importance.
The financial sector is transitioning towards being cloud-smart, choosing cloud models based on workload needs and regulatory compliance.
Key trends include a focus on security risk models and the combination of public cloud innovation with private infrastructure control.
Hybrid cloud enables financial services to adapt to customer expectations, technological demands, and regulatory pressures for a more competitive future.
By balancing flexibility and control, hybrid cloud is allowing financial firms to envision new possibilities and enhance competitiveness.

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Medium

1.4k

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Cloud Computing Course in Chennai — Ed11

Cloud computing is a service where companies rent computer resources over the internet rather than purchasing expensive servers.
A Cloud Computing Course in Chennai can provide individuals with skills in cloud technology, which is in high demand by companies like Amazon, Google, and Microsoft.
Chennai is a major IT hub in India, offering plenty of opportunities for skilled cloud professionals to secure high-paying jobs.
A Cloud Computing Course in Chennai covers basic to advanced topics, equipping students with the ability to design, manage, and secure cloud systems.
Anyone, regardless of technical background, can benefit from enrolling in a Cloud Computing Course in Chennai.
Various institutes in Chennai, such as Datamites, Skillfloor, and 360DigiTMG, offer expert training, practical projects, and job placement assistance.
Completion of a Cloud Computing Course in Chennai can lead to job opportunities with salaries starting at 4.8 lakhs per year at top companies.
Chennai's thriving IT industry provides a conducive environment for learning cloud computing, with affordable course options available.
Many institutes in Chennai, including those in Velachery, Anna Nagar, and T. Nagar, offer Cloud Computing Courses, with some providing online learning options.
Following recommended tips can help individuals make the most of their Cloud Computing Course in Chennai and advance their tech careers.
Enrolling in a Cloud Computing Course in Chennai can be a stepping stone to a successful career in technology with the opportunity to work with top companies.
Chennai's tech scene, experienced trainers, and cost-effective courses make it an attractive location to kickstart a career in cloud computing.
Researching institutes, reviewing their offerings, and enrolling in a Cloud Computing Course in Chennai can be the beginning of a promising tech career.

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Tech Radar

214

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New research says using AI reduces brain activity – but does that mean it's making us dumber?

A study from the MIT Media Lab suggests using AI tools reduces brain activity, particularly in tasks like writing essays.
Participants using AI showed less engagement, memory recall, and brain connectivity compared to those relying on their own brains.
The study did not undergo peer review and focused solely on essay writing, using EEG measurements to track brain activity.
The authors emphasize the importance of how AI is used, suggesting that it's more about the purpose and context than the tool itself.
AI may not necessarily make users dumber but could lead to a reliance on outsourcing thinking tasks rather than engaging in critical thought processes.
The study prompts reflection on whether AI usage enhances or hinders cognitive abilities, highlighting the importance of thoughtful and judicious application of AI technology.
Human intelligence is evolving to encompass the ability to collaborate with AI effectively, prioritizing skills such as synthesis, discernment, and emotional intelligence.
Using AI as a tool for enhancing tasks can complement human intelligence, but solely relying on AI without engaging in critical thinking may result in decreased cognitive engagement and ownership of tasks.
The study underscores the idea that tools influence thinking and calls for a balanced approach to AI usage to preserve cognitive abilities and encourage critical thinking.
The future of human-AI interaction involves individuals who can leverage AI effectively while maintaining their cognitive prowess and critical thinking skills.
In summary, the study advocates for a nuanced approach to AI utilization to ensure that it enhances human intelligence rather than diminishing it.

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Medium

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Building Generative AI Applications with Gemini and Streamlit

The course focused on using generative models to create functioning applications, combining AI model design and real-world usability.
The badge emphasized integrating Google’s Gemini multimodal models into deployable web applications using Streamlit.
Participants were guided through the process of wrapping prompt logic in app workflows and building interactive applications with Streamlit.
Streamlit enables rapid development of interactive applications without complex front-end code, reinforcing the importance of user experience with AI deployment.
The final module integrated all concepts into a full-stack application prototype, showcasing scalable and human-centric GenAI systems.
By combining Gemini and Streamlit, developers can quickly prototype tools for various applications.
The framework's flexibility allows extension to enterprise-grade tools with proper backend integration.
The course signifies a move from experimenting with GenAI to building production-ready solutions.
The focus is on designing responsible, accessible, and valuable experiences powered by large models, offering actionable steps to achieve this.
With tools like Gemini and Streamlit, individuals with Python experience can now bring AI-driven ideas to life rapidly and effectively.

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Medium

352

My Journey with Gemini & Imagen: Building Real-World AI Applications on Vertex AI

The author initially found JSON configuration and API structure overwhelming but later found it smooth and enjoyable, especially with building real-world AI applications on Vertex AI.
Turning imagination into visuals quickly was empowering, especially for someone inclined towards design and visuals, which was a favorite part of the course.
Exploring how Gemini handles complex queries and provides natural responses helped in gaining insights into AI chat tools like ChatGPT, enhancing understanding of their functioning.
The lab exercises in the course tested skills like patience, logic, and creativity, resulting in a sense of accomplishment and the feeling of building something meaningful with advanced tools.
The course enabled practical application of AI knowledge, moving beyond theoretical learning to hands-on creation, bridging the gap between learning and practical implementation.
Key takeaways include gaining confidence in exploring AI creatively, understanding that building real-world AI tools is not limited to experts, and with proper guidance and curiosity, anyone can engage in such endeavors.

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Medium

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AWS Summit Stockholm: Learning from the Leaders in AI

Companies like Booking.com have deeply integrated AI into their products and development processes.
Attendees at AWS Summit Stockholm learned from industry leaders about AI integration.
A demo of Amazon Q Developer operational investigations was presented at the summit.
Amazon Q can automatically investigate alarms and provide key observations for issue investigation.
The feature helps users understand why an alarm was triggered by highlighting key information.
Users can review services inspected, information discovered, and suggestions to fix the issue.
In some cases, runbook suggestions in Systems Manager can resolve issues automatically.
Users are advised to carefully review suggestions before execution.
As models behind detection/resolution get smarter, developers can save time investigating root causes.

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Whizlabs

348

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Is Model Monitoring & Debugging Critical for AWS MLS C01?

Model monitoring and debugging are crucial for deploying complex machine learning models in production.
AWS Machine Learning Speciality MLS-C01 certification is essential for understanding these concepts.
Model monitoring involves tracking performance metrics like accuracy, latency, data drifting in production.
AWS SageMaker and CloudWatch are commonly used tools for model monitoring.
Model debugging in MLS-C01 exam includes identifying and resolving issues affecting model performance.
AWS SageMaker Debugger and SHAP are tools used for model debugging and explainability.
Monitoring tools like SageMaker and CloudWatch are crucial for detecting model and data drift.
Common model issues in production include underfitting, overfitting, feature leakage, and data quality problems.
Amazon SageMaker Debugger is used for real-time monitoring and debugging of machine learning models.
Best practices for model monitoring and debugging in AWS include automated monitoring, tracking metrics, detailed logging, and validating models against fresh datasets.

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