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Identify and Fix Code Smells in TypeScript

Code smells indicate potential issues in a codebase, impacting maintainability, readability, and scalability.
Some common TypeScript code smells include long functions, duplicated code, and complex conditionals.
These issues can accumulate in large projects, leading to technical debt over time.
AI tools can automatically detect and fix TypeScript code smells, enabling teams to focus on feature development.
Inadequate state management in the codebase results in a lack of loading indicators and error-handling states, leading to a poor user experience.
Using 'any' type in Promise returns can reduce type safety and clarity; it's advisable to implement strongly typed return values and input validation.
Poor accessibility attributes in the form render it challenging for screen readers to provide meaningful information to users with special needs.
AI code review tools help in identifying code smells and suggesting strategies to address gaps, improving code quality.
Automated code review tools enhance development efficiency by identifying and addressing code smells in real-time.
Early identification and resolution of code smells help prevent technical debt and promote sustainable codebases.
Well-structured code with accurate typing reduces bugs and supports smooth evolution of open-source projects.

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Dev

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Building an AI-Powered Product Price Insurance Agent with LangGraph & Streamlit

Creating an AI-powered Product Price Insurance Agent using LangGraph & Streamlit to find market values for insurance claims and price comparisons.
Solution involves automation with AI to replace manual searching, copying/pasting, and inconsistent data formats.
Implemented a 3-stage workflow with LangGraph providing state management, error handling, and conditional routing.
Tech stack includes LangGraph for orchestration, Bright Data for web scraping, Google Gemini for LLM, Streamlit for chat interface, and Python for implementation.
Detailed implementation covers state definition, product search, price extraction, and report generation.
Graph assembly using LangGraph for creating and compiling the insurance price analysis graph.
Streamlit interface designed as a chat-style app for user interaction with real-time progress updates.
Implemented a testing suite to validate each node individually for reliability.
Results show successful product URL finding, accurate price extraction, and professional report generation.
Key learnings include the importance of structured outputs, power of MCP integration, simplicity of LangGraph, and real-time progress updates.
Real-world applications include insurance claims, procurement decisions, market research, and consumer shopping.

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RealPython

301

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Write Pythonic and Clean Code With namedtuple

Python's namedtuple in the collections module allows you to create immutable sequences with named fields, providing a more readable and Pythonic way to handle tuples.
The main difference between tuple and namedtuple is that namedtuple allows attribute access via named fields, enhancing readability.
Using namedtuple improves code clarity by allowing access to elements through descriptive names.
Namedtuple offers features like immutability, hash value for dictionary keys, and a helpful string representation.
Named tuples support indexing, slicing, and additional methods like ._make(), _asdict(), and ._fields.
You can use namedtuple instances where you need a tuple-like object for improved readability.
Creating namedtuple classes helps in representing data structures with named fields and dot notation access.
Named tuples are immutable and suitable for scenarios requiring descriptive data access.
Tuples or named tuples can hold mutable values, but such tuples are not hashable.
Namedtuple provides a Pythonic approach to writing cleaner and more maintainable code.

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Alvinashcraft

Dew Drop – June 16, 2025 (#4440)

INotifyPodcastChanged Episode 5: Building features with Lance McCarthy (Joseph Finney & Tamás Deme)
Best Practices for Handheld Gaming Development (Dean Hume)
Build a Flutter Expense Tracker with Advanced Transaction Features (Naveen Kesavaraj)
Controlling spacing in modern CSS layouts (Chris Ferdinandi)
Announcing comprehensive sovereign solutions empowering European organizations (Judson Althoff)
Visual Studio Code now supports Baseline (Rick Viscomi)
Learn and Integrate: Azure and AI Web Development and Navigating the New AI Landscape: A Developer’s Journey Through the Noise (Konstantinos Passadis)
The Month of MCP (Den Delimarsky)
How to use Lotties in Figma (Andrei Marius)
Apple Updates Developer Tools (Kay Ewbank)

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Dev

223

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Genkit Tool Calling: Give AI Models (LLMs) the Tools to Get Things Done

AI Models need tools like Retrieval-Augmented Generation (RAG) to tackle specific tasks beyond their training data limitations.
Function calling in Genkit enables AI Models to interact with systems and external data sources through structured methods.
Genkit's tool creation involves defining tools with descriptive names, descriptions, and implementation functions.
Using tools like GetCustomerDetails in AI Models to fetch specific customer data enhances response accuracy.
Genkit offers a developer UI for testing flows, tools, and interactions in a user-friendly manner.
Defining flows in Genkit involves setting up input-output schemas and executing functions for specific tasks.
Adding tools like GetCustomerOrders and GetProductDetails broadens an AI Model's capabilities for diverse queries.
Testing flows and tools in the Genkit Developer UI helps ensure proper functionality and troubleshooting.
Function calling in Genkit allows for tailored AI responses to user queries in various contexts.
Genkit empowers users to bridge the gap between AI hype and practical, real-world applications with AI agents.

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Dev

111

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🪂 import antigravity — A Deep Dive into Python's Most Uselessly Brilliant Module

Python's 'import antigravity' module is a playful feature in the language, not meant for defying gravity through code.
The module, existing since Python version 3.0, humorously opens the xkcd comic #353 when imported.
Import antigravity is Python's way of injecting humor and light-heartedness amidst serious programming tasks.
In a world of intense coding, this module serves as a gentle reminder to take a breather and enjoy the process.
Python includes other playful Easter eggs like 'import this' and 'import __hello__' for developers to explore.
These features, though not essential for coding tasks, add a touch of fun and creativity to the development process.
Software development can be challenging, but Python's quirky modules bring moments of joy and unnecessary brilliance.
Importing antigravity is more than just a technical action; it embodies a light-hearted spirit within the programming community.
Python developers are encouraged to embrace the unconventional and appreciate the playful side of coding.

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Dev

236

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Peak Performance Understated Power（1750079863883000）

As a junior in Computer Science, the sluggishness of network requests and high concurrency led to frustration until discovering a game-changing framework.
The article details scenarios of performance bottlenecks in projects, like a flash sale system crash, configuration complexities, and high resource consumption.
It highlights issues faced due to Node.js's limitations, framework configuration mazes, and resource-heavy server demands for low-configuration setups.
The pursuit for high performance and efficiency paved the way to Hyperlane, a fast and lightweight Rust web framework, leading to a transformative experience.
Encountering Hyperlane through a niche tech forum, the framework's asynchronous design, extreme performance, and gentle learning curve stood out.
The README's simplicity and positive user feedback on GitHub hint at Hyperlane's exceptional performance and elegant codebase.
This discovery felt like finding an oasis in a desert, offering hope for overcoming performance bottlenecks in backend development projects.

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Medium

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Quantum Computer-X: A Real-World Look at the Future of Computing

Traditional computing relying on shrinking transistors is reaching its limits as they bump into the laws of physics.
Computers are being tasked with more complex problems like designing drugs and modeling the climate, challenging traditional computing capabilities.
Quantum computing offers a different approach using qubits that can exist as both 0 and 1 simultaneously due to superposition.

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CSS-Tricks

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A Better API for the Resize Observer

ResizeObserver, Mutation Observer, and Intersection Observers are more performant APIs than their older counterparts.
To use an observer, create a new observer, do something with the observed changes via the observer function, observe a specific element, and optionally unobserve the element.
Improvements can be made to the observer APIs like simplifying the way ResizeObserver code is written.
Refactoring ResizeObserver code can involve encapsulating the observer creation and passing elements as parameters.
Passing a callback function into resizeObserver can make it operate like an event listener for element resize events.
ResizeObserver's observe method can take in an options object to determine what changes to observe.
An optional event listener pattern can be implemented for ResizeObserver by emitting a custom event.
Providing methods to unobserve and disconnect from observing elements adds flexibility to the observer usage.
The article discusses an enhanced version of ResizeObserver by Splendid Labz and its features for observing multiple elements.
The refactoring exercise aims to make code easier to use and maintain, emphasizing the importance of refactoring for improved code quality.

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Medium

122

Smart Document Parsing: Transforming PDFs into AI-Ready Knowledge

Building AI systems to understand content from thousands of PDFs is challenging due to their structure and complexity.
Traditional text extraction methods lose crucial document relationships and semantic meaning.
Document parsing methods involving regular expressions and predefined rules face challenges with inconsistent formatting and OCR errors.
An AI-first approach focuses on understanding document structure and content semantically.
Intelligent document parsing involves recognizing document architecture before chunking text.
Chunking based on semantic coherence and assigning confidence scores to different text segments is crucial.
Handling messy real-world documents requires sophisticated systems and adaptive approaches.
A hybrid approach combining AI-powered parsing with traditional methods proves effective in document parsing.
Smart optimization strategies are necessary to manage costs associated with AI-powered document parsing.
Lessons learned from implementing intelligent document parsing highlight the importance of augmenting human understanding with AI.
Intelligent document parsing lays the groundwork for advanced applications like question-answering systems and automated compliance checking.
Upcoming articles will delve into building intent detection systems for better user interaction with parsed documents.

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Medium

How AI Is Changing Frontend Development (And How to Keep Up)

AI tools such as GitHub Copilot, CodeWhisperer, and Codeium are transforming frontend development by assisting developers in various tasks like scaffolding React components and form validations.
Tools like Galileo AI, Builder.io, and Uizard can convert text prompts or mockups into editable UI code, boosting prototyping speed and enhancing collaboration between developers and designers.
AI is being used to auto-fix issues, suggest best practices, and enable features like smart product recommendations in e-commerce sites.
Examples of AI tool use cases include code generation with GitHub Copilot, design to React code conversion with Galileo AI, integrating ChatGPT for logic explanation and bug fixing, and more.
Frontend roles are evolving with AI, making developers faster, more creative, and emphasizing the importance of collaboration to enhance user experiences.
AI is not replacing frontend developers but is enhancing their capabilities to work alongside AI tools effectively in creating improved user experiences.

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Dev

413

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A Duet of Performance and Safety（1750074697194200）

The article discusses the author's experience with Hyperlane, a Rust web framework known for its performance and safety features.
Hyperlane offers extreme speed, zero platform dependency, and robust real-time communication support.
The framework utilizes Rust’s safety and concurrency features, leading to impressive benchmark results in performance tests.
Hyperlane leverages Rust's memory safety and Ownership, Borrowing, and Lifetimes system for stable operation and high resource control.
The framework’s core components are meticulously crafted in Rust, ensuring stability and efficiency from the ground up.
Tokio, a renowned Rust asynchronous runtime, handles massive concurrent connections efficiently with low overhead.
The framework's macro system reduces boilerplate code and enhances development experience, offering powerful metaprogramming capabilities.
It features a flexible middleware mechanism for handling cross-cutting concerns like authentication, logging, and data compression.
The framework demonstrates remarkable performance advantages over mainstream dynamic language frameworks, making it ideal for high-concurrency scenarios.
The article highlights the rising popularity of Rust and its ecosystem, indicating a promising future for the framework.
The author emphasizes the framework's stability, elegance, and consideration for maintainability alongside its impressive performance.

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Dev

262

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A Duet of Performance and Safety（1750074676522000）

The article discusses the experience of a third-year computer science student with Hyperlane, a web backend framework built on Rust known for its performance and safety.
Hyperlane is praised for its extreme speed, safety, and development efficiency, leveraging Rust's capabilities.
The framework showcases exceptional benchmark results surpassing other popular web frameworks.
Rust's memory safety and concurrency features eliminate common issues like null pointer dereferences and data races.
Hyperlane's extreme optimization in critical modules leads to rock-solid stability and lightning-fast execution.
The framework's use of Tokio for concurrency provides efficient handling of large numbers of concurrent requests.
Rust's macros reduce boilerplate code, enhancing development experience and code maintainability.
The middleware mechanism allows for flexible extension and logical decoupling in the framework.
The article highlights the framework's performance advantages over mainstream dynamic language frameworks.
The growth of Rust's ecosystem and the framework's focus on documentation and community building are also discussed.
The author emphasizes the importance of choosing a promising technological direction for future career development in the web backend field.

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Dev

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Introduction to Gen AI

Generative AI (GenAI) refers to AI systems that can create text, images, or media based on prompts using advanced machine learning.
GenAI impacts customer support, content creation, education, and software development by automating responses, generating content, aiding in education, and assisting in coding.
GenAI is built on neural networks, deep learning, natural language processing (NLP), and transformer architecture for understanding and generating human language.
Key terms in GenAI include specific models for particular tasks and foundation models for versatile tasks trained on vast datasets.
GenAI's power comes from extensive training on diverse datasets and transformer architecture, enabling efficient handling of complex data.
GenAI components include foundational models like LLM and LAMA, frameworks like OpenAI, and fine-tuning tools like LoRA and RAGs.
AI with autonomous decision-making capabilities and vector databases optimized for AI operations are also essential components of GenAI.

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Medium

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Thought about Anti-Debugging in Malware Development

The developer explored anti-debugging techniques in malware development using C/C++ programming language.
The Windows API function IsDebuggerPresent() was used to detect the presence of a debugger.
The IsDebuggerPresent() function returns a non-zero value if a debugger is present.
Sample code demonstrated how this function can be utilized to determine the presence of a debugger.
The code successfully detected the presence or absence of a debugger.
The effectiveness of this technique against experienced individuals remains a question.
A VirusTotal scan easily detected the malicious nature of the program created.
The project served as a valuable learning experience.
The developer acknowledged the limitations of relying solely on this strategy in a red team situation.
The news did not provide detailed information on the specific anti-debugging techniques used.
The article includes output examples showcasing detection with and without a debugger.
The final code snippet shared excluded the shellcode intentionally.
The developer remains cautious about the effectiveness of this technique in high-security environments.
The project highlighted the need for more advanced evasion strategies in red team scenarios.
The anti-debugging exploration focused on basics and may not be sufficient for sophisticated threat actors.
The technique may serve as a starting point for those interested in malware evasion tactics.

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