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How to Speed Up Your API Response Time (Performance Optimization Tips)

Performance optimization is crucial for API response time to provide a good user experience.
Inefficient database queries are often the main cause of slow APIs, so query optimization is important.
Sending unnecessary data in API responses can waste bandwidth and increase latency.
Improving code quality and removing unnecessary operations can also contribute to faster API response time.

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Medium

114

Getting Students Ready for a Future Driven by AI

AI-powered solutions can facilitate access to education for students with multiple languages.
Incorporating AI education into the curriculum prepares students for the AI-driven workforce.
Teaching students to interact with AI systems, evaluate data, and write code prepares them for the future.
Overcoming obstacles in the implementation of AI education requires awareness, training, and partnerships.

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Medium

335

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Late Chunking in LLM Pipelines: A Deep Dive into Optimized Text Retrieval

Late chunking is a query-driven segmentation technique that allows more flexible and dynamic document segmentation at retrieval time based on the query.
Late chunking provides distinct advantages over traditional early chunking methods, including better contextual awareness, reduced indexing overhead, better query adaptability, and improved performance of language and learning models (LLMs).
Optimizations to enhance the efficiency of late chunking include efficient embedding retrieval, adaptive windowing, vector pruning, parallelized late chunking, and re-ranking with LLMs.
Late chunking is particularly effective in domains such as enterprise knowledge management, legal document search, medical Q&A systems, technical support chatbots, and scientific research assistants.

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Alvinashcraft

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Dew Drop – April 2, 2025 (#4395)

The Dew Drop for April 2, 2025, #4395 highlighted various notable articles and links from the tech industry.
It covered topics including advancements in AI on Windows, contributions to the Uno Platform, new Azure AI Foundry innovations, and Docker Desktop 4.40 updates.
Web and cloud development articles discussed .NET on Heroku, Docker image layers, Blazor authentication, AWS vs. Azure, htmx usage for building dynamic apps, and more.
Articles related to WinUI, .NET MAUI, and XAML featured WinUI3Localizer for localizing apps, a guide on adding a WinForms rich text editor, and linear support vector regression using C#.
AI-focused topics covered Rook AI introduction, GitHub Copilot insights, OpenAI's GPT-4o improvements, and the integration of AI in healthcare with Model Context Protocol.
Design, methodology, and testing articles discussed MSIX flat bundle application packages, SNI updates for Azure DevOps, microservices with Spring Boot, and battling code duplication.
Additionally, there were links to various screencasts, podcasts, events, database updates, PowerShell commands, and miscellaneous tech news.

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Logrocket

264

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What does keeping the lights on (KTLO) mean?

KTLO, which stands for “keeping the lights on,” is about maintaining a business's core services and operations.
KTLO in product management involves ongoing efforts to support a digital product's functionality.
It covers aspects like bugs, maintenance, and technical debt to ensure users benefit from the product.
KTLO activities include customer support, system maintenance, data management, and bug fixing.
KTLO is crucial in various contexts like project management and IT operations to maintain infrastructure.
Balancing KTLO with innovation is a challenge for product managers, who need to focus on both product maintenance and new opportunities.
Strategies to balance KTLO include dividing tasks, dedicating team members, allocating specific time, and minimizing tech debt.
Reducing KTLO work involves practices like solid test coverage, managing tech debt, and encouraging code improvements.
Maintaining a separate KTLO budget can aid in resource allocation and strategic planning.
Product managers should focus on objectives rather than prescriptive feature roadmaps to minimize KTLO and achieve strategic goals.

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Dev

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Understanding concepts in Event Driven Architectures (EDA)

Event Driven Architecture (EDA) has become a popular choice for developing highly scalable distributed systems.
In EDA, different parts of the system communicate by sending and reacting to events, instead of making direct requests.
Key components include event producer/publisher, message broker, event consumer/subscriber, and the event itself.
Advantages of EDA include scalability, flexibility, real-time processing, resilience, and loose coupling, while disadvantages include eventual consistency, complex debugging, increased latency, event duplications, event ordering issues, and a learning curve.

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Medium

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The Things About AI That I Know as a Corporate Employee

Since the release of ChatGPT in November 2022, discussions about AI have surged, impacting various sectors and prompting curiosity from individuals, organizations, and governments.
The author, a corporate employee in Indonesia, shares insights on leveraging AI to enhance productivity in a state-owned electrical corporation.
Supervised learning aids in categorizing data and making predictions, employing algorithms like decision trees, neural networks, and support vector machines.
The author utilizes supervised learning with tools like random forest classifiers to automate data categorization tasks based on field officers' inputs.
Unsupervised learning helps reveal hidden patterns and relationships in operational data, offering insights into outage trends and network issue segmentation.
Generative AI is highlighted as a transformative technology, enabling the author to create original content, automate processes, and find solutions efficiently.
AI tools like ChatGPT assist in tasks such as matching IDs to names and generating scripts for functionalities like file uploads in Google Sheets.
AI has revolutionized the author's work processes, enhancing productivity and streamlining tasks, even without being an AI expert.
The author expresses excitement about the future potential of AI and its ongoing impact on daily operations and tasks, considering it a tool that will continue to evolve and shape work processes.
Generative AI, in particular, is praised for its ability to create detailed reports quickly and automate arduous tasks, signaling AI's significant role as a game-changer in the workplace.
The author looks forward to further exploration of AI's capabilities and anticipates its continued transformative effects, emphasizing the importance of embracing AI for enhanced productivity and problem-solving.

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Javacodegeeks

186

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[FREE EBOOKS] The Ultimate Linux Shell Scripting Guide, Artificial Intelligence for Cybersecurity & Four More Best Selling Titles

The Ultimate Linux Shell Scripting Guide is available for free for a limited time, offering hands-on experience for programmers looking to automate tasks with Linux shell scripting.
Artificial Intelligence for Cybersecurity explores AI approaches to solving cybersecurity issues, providing insights on applying data analytics methods effectively.
See Your Elephant helps in overcoming personal barriers to reach one's true potential by addressing limiting beliefs and habits.
Brain Tools for the Busy offers strategies from neuroscience and Eastern wisdom to enhance resilience, energy, and productivity in a demanding lifestyle.
Digitally Curious is a guide to navigating future technologies for business leaders, focusing on AI and its impact on work and society.
The New Rules of Marketing & PR, 9th Edition, delves into using content marketing, AI, social media, and more to capture audience attention and drive business success.
The updated edition of the marketing book covers the integration of generative AI like ChatGPT and provides strategies for effective public relations and marketing communications.
The New Rules of Marketing & PR is a key resource for entrepreneurs, marketers, and PR professionals to stay ahead of the digital marketing landscape.
The offer includes free resources such as eBooks on JPA, JVM troubleshooting, JUnit, Java annotations, Java interview questions, Spring interview questions, and Android UI design.
Explore these valuable resources to enhance your skills and knowledge in various IT domains.

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Medium

352

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Real World Event Sourcing

Real World Event Sourcing by Kevin Hoffman is now released in both ebook and print.
Event sourcing treats data as a series of events, allowing for a complete system narrative.
Event sourcing requires understanding of patterns and constraints like commands, aggregates, projectors, and process managers.
The ebook can be purchased from pragprog.com/titles/khpes.

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Dev

374

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How to create a decentralized AI agent with Gaianet.

Decentralized AI is gaining importance in the evolving AI ecosystem, offering solutions to centralized AI problems.
True decentralized AI agents feature infrastructure decentralization, model ownership, data sovereignty, resilience, reduced censorship risk, and knowledge diversity.
Gaianet is a decentralized computing infrastructure enabling the creation of personalized AI agents with unique characteristics.
It provides a web-based chatbot UI, an OpenAI-compatible API, and customization options for AI models and knowledge bases.
Gaianet democratizes AI creation by allowing individuals and businesses to deploy, scale, and monetize their AI agents independently.
The process of setting up a Gaianet node involves installation, initialization, and customization through model selection and knowledge base creation.
Users can integrate Gaianet nodes with various applications in the OpenAI ecosystem for enhanced functionality.
Benefits of creating decentralized AI agents include ownership, customization, privacy, monetization, and interoperability.
Gaianet allows users to monetize their AI agents through the Gaianet Protocol by setting pricing for API access.
Creating decentralized AI agents with Gaianet opens new possibilities for personalized AI experiences, catering to developers, businesses, and knowledge workers.

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Dev

207

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Let Cursor Talk to Your APIs in Minutes! The Magic Bridge You Never Knew You Needed

The integration of Apidog MCP Server with Cursor revolutionizes the way developers work with APIs, eliminating the need for constant context-switching between coding and API documentation.
The common problem of debugging due to outdated API documentation is addressed by providing AI assistants like Cursor direct access to specific API blueprints through the MCP Server.
Setting up the Apidog MCP Server is straightforward, requiring minimal configuration steps, including obtaining an access token from Apidog and project ID identification.
By configuring Cursor with the obtained information, developers enable their AI assistants to generate code accurately aligned with the API specifications in a matter of minutes.
Examples demonstrate the capabilities of this integration, such as generating TypeScript interfaces, updating code with API changes, and creating complete API clients effortlessly.
Key benefits include faster API integration, reduced errors, synchronized documentation and code, and streamlined onboarding for new team members.
Pro tips suggest meaningful MCP server configurations, specific prompting techniques, and securing access tokens for enhanced usability.
The integration offers immediate and tangible benefits, enhancing developer productivity by saving time, minimizing errors, and making API work more enjoyable.
Implementing this integration is a valuable investment that improves efficiency, accuracy, and overall satisfaction in working with APIs, empowering developers to focus on coding rather than documentation.
Take advantage of this magic bridge between Apidog and Cursor to boost your development workflow and experience the seamless synergy of AI assistance with accurate API insights.
Embrace this tool to enhance your productivity, reduce errors, and unlock the full potential of your AI assistant in coding tasks involving API interactions.

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Dev

370

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Should the Backend Be “Beautiful”?

Beauty in backend development refers to clarity, structure, and maintainability.
A beautiful backend has clear architecture, is easy to read and navigate, and handles errors gracefully.
The backend's beauty matters for scalability, collaboration, debugging, onboarding, and pride in work.
Backend beauty comes from using async/await properly, keeping methods lean, writing tests, and avoiding 'magic' code.

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PlanetPython

405

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Django Weblog: Django security releases issued: 5.1.8 and 5.0.14

Django security releases issued: 5.1.8 and 5.0.14
The Django team has issued security releases for Django 5.1.8 and 5.0.14
The releases address a potential denial-of-service vulnerability in LoginView, LogoutView, and set_language() on Windows
Users of Django are encouraged to upgrade to the latest releases

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24 Likes

Dev

202

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How to Parse Environment Variables into DTO in .NET: AWS S3 Bucket Example

Treating configuration settings as a DTO class will make your code more readable.
To parse configuration into a DTO class, you can create a separate class to hold the configuration properties.
In .NET, you can configure the dependency injection container to store your configuration using IOptions and Configure().
If you prefer using records instead of classes, you can use the Get extension method to map configuration to a strongly typed object.

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Medium

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Into the world of HFTs and Market-Statistical arbitrage Introduction

High-Frequency Trading (HFT) leverages advanced computing to execute trades in milliseconds using mathematical models and automation.
Key components include co-location services, ultra-fast networks, and AI for analyzing market trends.
HFT strategies include latency arbitrage, market making, statistical arbitrage, and momentum trading.
Technological advancements like quantum computing, blockchain, and AI enhance HFT efficiency.
Market arbitrage exploits price differences across markets, contributing to market efficiency.
Factors influencing market arbitrage include exchange latency, liquidity variations, and regulatory differences.
Statistical arbitrage uses historical data and quantitative models to profit from price relationships.
Machine learning has transformed statistical arbitrage by analyzing vast amounts of financial data.
While HFT benefits market efficiency and liquidity, concerns about unfair advantages and market instability exist.
Regulators globally are refining policies to balance HFT innovation with market fairness.

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