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Website Feature Engineering at Scale: PySpark, Python & Snowflake

The article discusses the challenge of identifying top candidates for a business proposal based on website quality scores, aiming to automate the process with machine learning pipelines.
It covers technical implementations like fetching HTML content in Python using Snowflake dataset or CSV, and assigning a quality score using PySpark for feature extraction and processing.
Legal and ethical considerations in web scraping are highlighted, emphasizing responsible practices, retention policies, and potential permissions from site owners.
The article provides instructions on getting started with the project, including the folder structure, Snowflake data preparation, and usage of scripts for fetching website content.
Advantages of using a comprehensive fetching script over a basic approach are outlined, showcasing benefits like asynchronous requests, rotating User-Agents, and efficient batching.
Storing raw HTML in databases like Snowflake is recommended for scalability, as large-scale scraping and feature engineering are more reliable when done in a suitable data warehouse.
The process of extracting features from HTML content using PySpark via Snowpark is detailed, including creating UDFs, applying feature extraction functions, and generating quality scores.
Country-specific configurations are utilized for defining keywords and patterns that signal good merchant sites, making the feature extraction adaptable across different regions.
The article concludes by emphasizing the website quality score as a key input for predictive models, showcasing its significance in ranking and recommending partners effectively for better business outcomes.
A GitHub repository link is provided for the implementation details and a disclaimer clarifies that the data and scripts used are examples and not from real business scenarios.

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Medium

319

Image Credit: Medium

The Importance of Cybersecurity in the Digital Age

Cybersecurity is essential in protecting systems, networks, and data from threats like malware, ransomware, phishing attacks, and data breaches.
Common cyber threats include ransomware attacks, phishing scams, data breaches, and IoT vulnerabilities, posing risks to individuals and organizations.
To enhance cybersecurity, best practices include regular software updates, strong password policies, multi-factor authentication, employee training, regular backups, and implementing firewalls and antivirus software.
Emerging technologies like artificial intelligence and machine learning are being leveraged to advance cybersecurity efforts and address evolving digital vulnerabilities.

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Medium

289

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Introduction to CORS and Its Impact on Hybrid Mobile Apps.

CORS (Cross-Origin Resource Sharing) is a crucial mechanism that helps prevent malicious activities and uphold web standards in the world of web development.
CORS defines different sources based on the scheme, fully qualified hostname, and port, making sure that web browsers do not allow any type of cross-origin request.
CORS helps mitigate various risks associated with cross-origin requests, enabling developers to have more flexibility in building SPAs and rich web content while maintaining security.
Understanding CORS is essential for developers, as it plays a significant role in ensuring secure cross-origin interactions and preventing potential vulnerabilities in web applications.

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Medium

362

Image Credit: Medium

Deep vs Shallow Copy Behavior in JavaScript

When working with primitive types like numbers or strings, assigning a value results in a full copy being made in memory.
In contrast, when dealing with objects and arrays in JavaScript, they are treated as references rather than copied entirely.
Assigning an object or array to another variable copies the pointer to the data location, not the content itself.
Shallow copies only copy the top layer, meaning inner objects remain shared if they are objects themselves.
Tools like Object.assign and the spread syntax can be used to create shallow copies in JavaScript.
Deep copies duplicate every nested object or array inside, ensuring no shared references are left.
JSON.stringify and JSON.parse together can be used for simple deep copies, though not suitable for all data types.
Issues may arise with shallow copies when internal objects are inadvertently shared, leading to unexpected behavior.
JSON-based deep copying may lose data for functions, Date objects, Map, Set, BigInt, Symbol-keyed properties, or undefined values.
Handling cyclic structures in copying requires special consideration to avoid infinite loops and data loss.

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Hackernoon

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The Case for Factory Functions: Reducing Bugs and Technical Debt in Angular + Microservices Projects

Managing data consistency and reliability in Angular and microservices-based applications can be challenging due to backend API changes, partial data responses, and unpredictable user input.
Manual object construction in frontend development can lead to runtime errors, broken UI states, and backend validation failures, compounding issues as applications and teams grow.
Factory functions with Partial types provide a superior approach by eliminating undefined fields, enhancing maintainability, testability, and team velocity in Angular projects working with multiple microservices.
Centralizing type definitions and factory functions ensures consistent object shapes across applications, simplifies maintenance during backend contract changes, and reduces bugs by enforcing sensible defaults.
Using factory functions preserves type safety while allowing flexible object creation, improving overall application integrity.
Issues with manual object creation include missing properties, fragile and duplicated code, challenges in testing and debugging, and unreliable data sent to backend services.
Manual object creation can lead to runtime errors, broken UI elements, inconsistencies across components, and testing difficulties, risking data integrity and backend validation issues.
By adopting factory functions and Partial types, developers can mitigate technical debt, improve data consistency, and streamline object creation in Angular and microservices environments.
Factory functions with Partial types offer a proactive strategy to handle changing backend contracts, reduce bugs, enhance testing processes, and ensure reliable data transmission to backend services.
In conclusion, embracing factory functions with Partial types over manual object construction can lead to a more robust, maintainable, and scalable codebase for Angular and microservices projects.

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PlanetPython

266

PyCon: Asking the Key Questions: Q&A with the PyCon US 2025 keynote speakers

The PyCon US 2025 keynote speakers shared insights on their tech/Python journey, including experiences with mentors and inspirations from childhood, such as Cory Doctorow's early exposure to computer science through his father and a cardboard computer.
Lynn Root, influenced by a CS course, founded PyLadies SF to create a supportive programming community, highlighting the importance of mentorship.
Geoff Hing and Tom Meagher emphasized the impact of community collaboration on their learning, with Geoff fostering open source practices in newsrooms and Tom focusing on investigative reporting and data analysis tools.
Dr. Kari L. Jordan prioritizes making coding accessible to underrepresented groups, while the keynote speakers reflect on their significant achievements, from facilitating diversity in tech to investigative reporting that raises societal awareness.
For conference attendees, the speakers offer advice like engaging in networking, taking breaks, and exploring new tools and techniques, forming connections beyond the talks which are recorded.
They also recommend focusing on personal connections, extracting value from workshops, and exploring open source projects like The Carpentries, icecream, dedupe, and user-friendly tools such as VisiData for data analysis.
Cory Doctorow highlighted the urgency of updating older CC licenses to CC 4.0 to prevent copyleft trolling, illustrating the importance of fixing attribution errors and supporting initiatives like the license upgrade project on platforms like Flickr.
The PyCon US first-time attendees, including Dr. Kari L. Jordan, are looking forward to engaging with the Python community, sharing knowledge, and discovering new opportunities for collaboration and learning.

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Javacodegeeks

436

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Swing Meets AI: Integrating GPT-4 Vision for Accessibility

In a world where inclusive design is crucial, integrating AI, such as GPT-4 Vision, with older frameworks like Java Swing is essential for accessibility standards.
Swing applications can benefit from AI enhancements to improve accessibility, generate UI descriptions for screen readers, and enable voice-controlled interactions.
Accessibility in Swing matters for millions of users relying on assistive technologies, despite its minimalistic accessibility API requiring manual tagging.
AI, particularly GPT-4 Vision, can scan UI layouts to auto-generate accessibility layers, enhancing operational efficiency and meeting legal and moral obligations.
GPT-4 Vision interprets Swing UI components visually, generating descriptions like field labels, grouping of controls, and navigation hints.
Auto-generating UI descriptions involves taking screenshots, passing images to GPT-4 Vision, receiving structured descriptions, and applying them to Swing components.
Proof-of-Concept examples demonstrate voice-controlled interfaces for Swing components using GPT-4 Vision and speech recognition APIs.
Benefits of AI-augmented accessibility include rapid retrofitting, consistency in descriptions, voice enablement, and compliance with accessibility standards.
Challenges like security, accuracy, runtime integration, and cost considerations are highlighted when integrating AI-enhanced accessibility.
Various tools and resources exist, such as OpenAI GPT-4 Vision API, Java Accessibility Guide, and Whisper Speech Recognition, for developing AI-powered accessible applications in Java Swing.

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26 Likes

Medium

445

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Mastering TypeScript: The Ultimate Guide for Beginners

TypeScript is a superset of JavaScript that adds static typing to the language, helping catch errors during development.
In TypeScript, data types are explicitly defined like string, number, Boolean, unknown, void, null, undefined, and never.
Type Inference in TypeScript allows the language to figure out types even if not explicitly mentioned.
Other concepts in TypeScript include Intersection Types, Type Alias, Literal Type, Enum, Type Assertion, and Type Narrowing.

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26 Likes

Dev

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How to Fix TS1259 Error in Next.js with Express and TypeScript?

Developing a Next.js application with an Express server in TypeScript may lead to encountering the TS1259 error regarding default imports and the 'esModuleInterop' flag.
The TS1259 error arises due to default import issues with CommonJS modules, which can be resolved by enabling the 'esModuleInterop' flag in your tsconfig.json.
Ensure your tsconfig.json file contains 'esModuleInterop': true and validate import statements like 'import express, { Request, Response } from "express"'.
To fix the TS1259 error, restart TypeScript compiling, validate imports, compile your application, and understand the importance of 'esModuleInterop' for better module interoperability.

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8 Likes

Dev

408

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Unlocking the Power of toString() in Java: Are You Using It Right?

The toString() method in Java is essential for debugging, logging, and code readability.
A well-implemented toString() helps transform objects into human-readable strings for better understanding.
Balancing detail, security, and performance is crucial when deciding what to include in toString().
Share your approaches, experiences, and best practices regarding toString() usage in Java projects.

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24 Likes

Andrew Chen

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Image Credit: Andrew Chen

Updates: 7 years at a16z, NYC tour, Speedrun deadline, etc

Andrew Chen's recent post on marketing channels being challenging has become one of the top 5 most widely-read essays.
a16z speedrun program deadline is this Saturday, May 10, for early startups to apply for an investment of up to a million.
Andrew Chen plans to be in NYC from June 2-6 for New York Tech Week events and panels organized by a16z.
Andrew Chen reflects on his 7-year journey at a16z, highlighting different phases and experiences in tech, entertainment, and gaming.

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17 Likes

Infoq

Image Credit: Infoq

Java News Roundup: OpenJDK JEP Updates, Spring AI, Quarkus, LangChain4j, JReleaser, WildFly

This Java roundup features updates on several JEPs targeting JDK 25, including Compact Source Files and Instance Main Methods, Module Import Declarations, Flexible Constructor Bodies, and Structured Concurrency.
The eighth milestone release of Spring AI 1.0.0 introduces significant changes leading up to the upcoming RC1 release, focusing on providing deprecated API and corresponding replacement APIs.
Quarkus 3.22.0 introduces new features like Compose Dev Services for Quarkus applications and an improved test class loading infrastructure.
The first release candidate of LangChain4j 1.0.0 offers five modules under release candidate status, with breaking changes such as interface renaming and reshuffling.
JReleaser 1.18.0 is released with new features like support for Forgejo, FLAT_BINARY distributions, and deployment to Sonatype Nexus 3 repository manager.
WildFly recently joined the Commonhaus Foundation, a non-profit organization aiming to support the sustainability of open source projects including WildFly, Infinispan, Debezium, and others.
JDK 25's build 21 early-access build includes various bug fixes, and developers are encouraged to report bugs via the Java Bug Database for further improvements.
Spring Cloud 2025.0.0's first release candidate, Northfields, includes bug fixes and updates to sub-projects like Spring Cloud Kubernetes and Spring Cloud Stream.
JEP 517 elevates HTTP/3 support for the HTTP Client API to Candidate status, proposing compatibility with the HTTP/3 protocol for easier interaction with HTTP/3 servers.
JEP 515 elevates Ahead-of-Time Method Profiling to Candidate status, aiming to enhance application warmup time by making method-execution profiles instantly available to the HotSpot JVM upon startup.
JEP 470's PEM Encodings of Cryptographic Objects moves to Candidate status, previewing an API for encoding cryptographic objects into the PEM transport format, allowing seamless conversions between PEM text and cryptographic objects.

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Siliconangle

245

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Amazon announces Q Developer preview in GitHub for AI-powered code generation and review

Amazon Web Services Inc. announced a preview for its Q Developer AI assistant for GitHub.
Q Developer offers AI-powered features like code generation and review directly within GitHub.
Developers can assign tasks to Q Developer for automating code generation based on provided descriptions.
The AI agent also performs automatic code checks, security scans, and provides suggestions for code enhancements.

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Dzone

Image Credit: Dzone

Java Virtual Threads and Scaling

Java Virtual Threads were introduced in JDK 19 as a preview feature as part of Project Loom, fully supported starting from JDK 21.
They simplify concurrent programming, translating blocking operations into non-blocking operations.
In traditional threading model, each Java thread maps to an OS thread, causing context switching overhead.
Java Virtual Threading uses carrier threads backed by OS threads, reducing context switching at the OS level.
When CPU-bound workloads are processed, virtual threads fully utilize CPU cores, enhancing performance.
Context switching overhead is reduced, providing predictable performance, better CPU utilization, and scalability.
Comparisons between thread pool and virtual thread execution times show faster processing with virtual threads in various scenarios.
Virtual threads simplify development by offering a straightforward, synchronous style with improved observability and performance.

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Johndcook

358

Cycle order in period doubling region

The period doubling region in the logistic map has specific cycle orders within each branching region.
The cycle order in each region remains constant and follows a specific pattern, such as [0, 2, 1, 3] for one of the regions.
The cycle orders for subsequent regions follow a similar pattern of increasing numbers, such as [0, 4, 6, 2, 1, 5, 3, 7] and [0, 8, 12, 4, 6, 14, 10, 2, 1, 9, 13, 5, 3, 11, 7, 15].
There is ongoing exploration into whether the cycle order remains constant within a region of constant period and what general conclusions can be drawn from these patterns.

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