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Function Calling in Large Language Models: Unlocking Practical Applications with Real-World Cases…

ChatGLM's function calling feature enhances the model's reasoning and external operation capabilities.
Tools are an optional parameter in the content generation API for providing function definitions to the model.
The API generates function parameters based on user specifications without performing actual function calls.
Developers can use the model's output parameters for executing function calls in applications.
The tutorial covers building a student grade database, implementing query and data filtering functions, and using LLM function calls for database interactions.
Non-technical users can interact with the database using natural language through LLM function calls.
For technical personnel, understanding LLM function calls aids in implementing relevant functions.
A student exam score Q&A service example demonstrates generating scores for 100 students and saving data to an SQLite database.
Simple database query operations are implemented to ensure proper database functioning.
Five functions are defined and structured for LLM selection, including name, description, parameter list, and descriptions.
Function calling, obtaining return results, and using LLM for question answering are demonstrated.
The tutorial summarizes the working principle of LLM function calls for practical applications.
This content focuses on the application of function calling in ChatGLM for various operations and interactions.
It emphasizes the integration of external function libraries with ChatGLM models.
The tutorial highlights the importance of understanding LLM function calls for both technical and non-technical users.
Overall, the tutorial showcases the potential of LLM function calls in enhancing model capabilities and real-world applications.

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Dev

133

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The Importance of Leveraging AI to Learn

Artificial Intelligence (AI) is crucial for tasks requiring human intelligence like reasoning and learning.
It is essential to use AI effectively in the learning process as it is becoming increasingly prominent.
Junior developers shouldn't depend solely on AI for generating code; it's crucial to learn from the process.
AI should be used as a learning companion and guide to challenge understanding and support growth.
The quality of AI-generated responses is determined by the quality of the prompts given to it.
Six building blocks for a good prompt include clear task definition, providing relevant context, sharing exemplars, specifying persona, defining format, and deciding on tone.
Following a prompting framework can enhance communication with AI and lead to accurate and helpful responses.
AI should be viewed as a supportive tool in the educational journey, not as a replacement for learning.

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Medium

257

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Difference Between Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) is a branch of computer science that aims to create intelligent machines capable of performing tasks requiring human intelligence.
AI systems simulate human intelligence and can be rule-based or data-driven.
Examples of AI include voice assistants like Siri, self-driving cars, and chatbots.
Machine Learning (ML) is a subfield of AI that focuses on developing algorithms allowing computers to learn from data and make predictions without explicit programming.
Key characteristics of ML include learning from historical data, improving accuracy over time, and requiring large datasets for training.
Examples of ML include email spam filters, movie recommendations on Netflix, and product recommendations on Amazon.
Real-life examples of AI vs. ML include a robot vacuum planning its cleaning path (AI) and predicting prices of used cars based on data (ML).
Differences between AI and ML include the approach to problem-solving and the focus on learning from data.
Understanding the distinctions between AI and ML is crucial for further advancements and application development in modern computing.

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Technically Dev

372

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Breakdown: the cloud infrastructure market

Infrastructure and cloud refer to where software runs, involving hardware, networking, and systems enabling online activity.
In the past, software primarily operated locally on individual devices, but now most software is cloud-based.
The shift to cloud computing led to new considerations for developers, such as choosing the right cloud provider and service model.
Different types of infrastructure include IaaS, PaaS, and SaaS, offering varying levels of management and customization.
IaaS provides basic server resources, PaaS offers more managed services, and SaaS is fully managed.
The choice between these options depends on the level of control and customization required by developers.
The components of an app that need infrastructure support include frontends, backends, databases, analytics, and miscellaneous tasks.
Companies like AWS, Cloudflare, DigitalOcean, Heroku, and Vercel cater to different infrastructure requirements.
The evolving landscape of cloud infrastructure offers diverse solutions for developers based on their needs and preferences.

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Dev

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Writing Your First Automated Test Using Python (unittest Framework)

Python's unittest framework is a good starting point for testing journeys, as it comes bundled with Python and is an industry standard.
unittest is a unit testing framework in Python standard library, following the xUnit style, used for writing test cases as classes and using special methods for test context.
The article presents a step-by-step guide to building your first Python test, starting with creating a function to be tested and writing test cases using assertions.
Testing for exceptions using methods like assertRaises( ) is highlighted, emphasizing the importance of handling edge cases in real projects.
Maintaining scalability in test projects involves organizing the structure effectively, separating production code from test files for easy management.
The usage of setUp() and tearDown() methods for managing test context is explained, ensuring tests run independently and cleanly.
Running tests using verbose mode (-v flag) provides more detailed output, beneficial when dealing with numerous test cases.
The article outlines when to use unittest versus Pytest, highlighting the significance of thinking like a tester while mastering unittest syntax.
Mastering unittest is crucial for Python developers to write clean, testable, and production-ready code, instilling confidence in test automation processes.

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Logrocket

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How to optimize your Next.js app with after()

Building apps with Next.js involves optimizing server tasks post-response using the after() API of Next.js 15.
after() allows running logic after route rendering without impacting the client's experience, beneficial for tasks like analytics or logging.
It works in Server Components, Actions, Route handlers, and Middleware, with varying access to request info.
Nested after() calls execute in reverse order of their rendering, providing a clean way to handle post-response tasks.
after() runs even during errors, ensuring critical tasks like logging continue despite route failures.
Key considerations include after()'s static nature, need for manual gating of conditional logic, and limitations in dynamic reactivity.
alternatives to after() include waitUntil() for Edge Middleware and await for tasks that must complete pre-response.
Utilizing after() effectively can significantly enhance web app performance and user experience by deferring non-critical tasks.

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Logrocket

328

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A complete guide to AdTech for product managers

Monetizing products with ads through AdTech can be highly profitable for high-traffic websites.
Understanding the complexities of the advertising ecosystem and how AdTech works is crucial for maximizing ad revenue.
AdTech involves publishers, advertisers, ad slots, inventory, ad servers, SSPs, DSPs, ad exchanges, direct deals, and more.
Automation by ad servers streamlines the process of delivering and tracking ads on publisher websites.
SSPs and DSPs help automate buying and selling inventory between publishers and advertisers.
Ad exchanges function as automated online auctions, facilitating ad placements based on bids from DSPs.
Direct deals between advertisers and publishers can occur for premium ad placements.
All-in-one platforms like Google Ad Manager streamline ad serving and exchanges, reducing costs.
Various other players like ad networks, data platforms, and agency trading desks play roles in the AdTech ecosystem.
Adding ads to products involves building ad slots, utilizing ad server tags, selecting SSPs, and setting up header bidding.

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Alvinashcraft

166

Dew Drop – June 24, 2025 (#4445)

Inside Access: Join Us at VS Live! Redmond for a Week of Deep Developer Learning (Mads Kristensen)
LINQPad 9 Early Preview – LINQPad 9 is a major update unifying the Windows and macOS codebases (Joe Albahari)
Build your code-first agent with Azure AI Foundry (Microsoft)
Create a Contact Management App in .NET MAUI Using ListView and DataForm (Jayaleshwari N.)
Introducing Mu language model and its impact on Windows Settings (Vivek Pradeep)
Modernizing Win32 Apps with WinUI – Advanced Installer’s Approach (Renato Ivanescu)
Technology & Friends – Scott Hanselman on New Technology and Old Friends (David Giard)
Using SQL Server 2025 Vector Search in .NET Aspire – eShopLite Style! (Bruno Capuano)
How to create light and dark color modes with CSS (Chris Ferdinandi)
SSO Comes to Telerik and Kendo UI for a Simpler, More Secure Account Access (Dragan Grigorov)

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Medium

370

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A 5-Step Prompt To Land More Data Job Interviews

A 5-step prompt designed for Google Gemini to enhance landing more data job interviews.
The prompt involves feeding information to an AI Agent, aligning job roles with resume summaries, updating resume for stronger applications, and ranking job matches based on descriptions.
The strategy includes engaging in conversations with the AI agent for detailed resume refinement and highlighting key skills.
The approach focuses on iterating over an existing resume for impactful improvements.
The prompt aims to streamline the data recruiting process and optimize resume content.
It encourages personalized interactions with AI to enhance application success.

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Dev

291

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Steering Clear of Implementation Disasters: Your Guide to Successful Dynamics 365 Deployment

Organizations embarking on Dynamics 365 deployment face common pitfalls that can hinder success.
Thorough planning with proper business analysis and requirements gathering is crucial for success.
Stakeholder alignment is essential to prevent conflicting priorities from derailing the project.
Data migration is a challenging aspect due to data quality issues and requires comprehensive audits.
Change management is critical for successful implementation, requiring more than just training.
Creating a culture that embraces change and clear communication are key to overcoming resistance.
Integration challenges with existing applications require detailed planning and testing.
Testing and quality assurance are crucial to catch issues before deployment and ensure a smooth transition.
Comprehensive testing should include functional, performance, security, and user acceptance testing.
Parallel running periods with old and new systems aid in a safe deployment transition.
Successful implementation involves addressing planning, data quality, change management, integration complexity, and testing.
Investing time and resources in these areas increases the chances of realizing the full benefits of the new system.

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UX Design

285

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Test smart: how to escape burnout?

Burnout is a hot topic among IT individuals due to technological advancements and cognitive load.
Tips include setting boundaries to maintain personal space and assertiveness.
Prioritizing a healthy lifestyle, such as nutritious eating and regular physical activity, is crucial.
Balancing emotional well-being by sharing feelings with trusted individuals or seeking therapy is essential.
Escaping burnout requires regaining control over well-being through boundary setting, healthy living, and emotional care.
Various resources and expert advice are cited for further exploration on avoiding burnout and maintaining mental health.
Preventing burnout involves a multifaceted approach focusing on self-care, emotional support, and work-life balance.
Taking breaks, offline activities, and seeking help when needed contribute to maintaining a sustainable work environment.
The article addresses the prevalence of burnout in the IT sector and emphasizes the importance of self-care and boundaries.
It provides practical advice on avoiding burnout, such as setting boundaries, prioritizing health, and seeking emotional support.

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Medium

264

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How Remittance Kiosks Help Save Time and Reduce Fees

Remittance kiosks have revolutionized international money transfers by offering convenience, accessibility, and cost savings.
They bypass traditional banking inefficiencies like long queues, paperwork, and delays in fund access.
Operated like ATMs, remittance kiosks enable users to send money independently with user-friendly interfaces.
They ensure real-time processing, currency conversion, and regulatory compliance.
Remittance kiosks are strategically located for easy access, enhancing convenience for users.
They save time by providing round-the-clock availability and near-instant processing of transactions.
Kiosks offer cost advantages through lower fees, competitive exchange rates, and reduced intermediary bank charges.
Security features, user-friendly interfaces, and diverse payment methods make remittance kiosks appealing.
Future trends include enhanced technologies and deeper integration with digital wallets for a seamless user experience.
Remittance kiosks are reshaping the global money transfer landscape by offering efficient and affordable solutions.

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Medium

144

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Agentic AI Training in Ameerpet | Agentic AI Course Online

Agentic AI systems can perceive environments, make decisions, and take actions independently, revolutionizing data science.
This advancement allows for autonomous systems in sectors like healthcare, finance, marketing, and logistics, transforming business operations.
Agentic AI empowers machines to act as decision-making agents, enhancing productivity, accuracy, and scalability in data science.
Mastering Agentic AI concepts opens doors to advanced roles and impactful projects for data science professionals.
The course covers foundational data science and advanced Agentic AI concepts with practical, hands-on experience.
Agentic AI Online Training offers a structured path to mastery from home, featuring interactive sessions, real-time projects, and mentor support.
Skills gained in Agentic AI find applications in sectors like healthcare, finance, marketing, logistics, and more, boosting career opportunities.
Course highlights include a balance of theoretical depth and real-world implementation, making it globally accessible.
The course is suitable for individuals looking to contribute to innovative projects and explore advanced career opportunities.
Understanding Agentic AI is crucial for those aiming to lead tech transformations, with enrollment in a course offering future tech career prospects.
Enrolling in an Agentic AI course presents the opportunity to upgrade AI capabilities, boost careers, and enhance data science skills.
VisualPath offers Agentic AI Online Training designed for global access, providing in-depth knowledge and hands-on experience.
The course is ideal for professionals across various backgrounds, equipping them with the tools to excel in AI-focused job markets.
Agentic AI is reshaping and defining the future of technology, with intelligent systems leading autonomous and responsive advancements.
Learning Agentic AI now prepares individuals for impactful tech careers, emphasizing the importance of skill development for the future.
To enhance AI capabilities, boost careers, or advance in data science, enrolling in an Agentic AI course at VisualPath is recommended.

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Medium

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Mobile App Hacking | Professional Hacking Services

Mobile app hacking involves various techniques like reverse engineering, tampering, network attacks, exploiting vulnerabilities, malware, and social engineering.
Hackers decompile apps to reveal code and sensitive information like API keys, and manipulate apps to add malicious features or bypass security mechanisms.
Network attacks, including Man-in-the-Middle attacks, intercept app-server communications to manipulate data or capture sensitive information.
Common vulnerabilities in mobile apps, such as SQL injection or insecure data storage, are exploited by attackers to access sensitive data.
Malware is used to distribute malicious versions of legitimate apps through unofficial channels, leading to theft of personal information and other malicious activities.
Social engineering techniques like phishing are employed to trick users into revealing personal information or credentials.
Mobile app hacking is a significant threat to individuals and organizations, requiring vigilance and expertise to mitigate.
HackersClique is suggested as a reputable source to hire hackers for assistance with mobile app security.
URL: https://www.hackersclique.com

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Dev

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I published my first Desktop App after 6 months of work — Everything I learnt

The author developed PyUIBuilder to simplify Python GUI development, focusing on requirements like web/desktop accessibility, clean code export, and support for various GUI libraries and widgets.
The initial prototype included only a web version, showcasing a demo on Reddit to gain interest from Python developers.
Months were spent improving the web app's features before starting work on the Desktop app using Electronjs for live previews.
Github Actions helped facilitate cross-platform compilation, allowing the author to build the app for different operating systems without owning machines for each OS.
Code signing for desktop apps, especially on Mac OS and Windows, was addressed by transparently informing users about the lack of code signing for trust.
The author's journey with PyUIBuilder demonstrates the challenges and solutions encountered in developing and releasing a Python GUI builder.

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