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Medium

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Running A Decentralized Network From Your Phone: Meet Avail’s Light Client

  • Full nodes in blockchain networks require high resources and technical skills, leading to centralization among less than 1% of users.
  • Avail's Light Client offers full node guarantees with lower resource demands through a modular and mathematically sound approach.
  • Avail's Light Client employs Data Availability Sampling (DAS) by sampling data cells with KZG commitments, ensuring data availability.
  • Block Verification in Avail's Light Client involves creating a data matrix, sampling cells, and repeating the process for data confidence.
  • The Light Client Network uses a peer-to-peer (P2P) structure with Kademlia DHT for data sharing and block verification.
  • Developers can customize the Light Client for Execution Verification, ensuring the State Transition Function for full execution correctness.
  • Sophon Network successfully implemented Avail's Light Client for decentralized verification, allowing users to participate and earn rewards.
  • Avail's Light Client provides trustless verification, low hardware requirements, and scalable infrastructure, enabling decentralization for various applications.
  • It stands out for its validator decentralization and empowers users on any device to contribute to network verification.
  • Avail's Light Client is suited for DEXs, Validiums, L2s, DA chains, consumer apps, and more.

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RealPython

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Starting With DuckDB and Python

  • The DuckDB database offers a streamlined way to manage large datasets in Python with OLAP optimization.
  • Users can create databases, verify data imports, and execute efficient data queries using SQL and DuckDB's Python API.
  • The video course teaches users to create DuckDB databases by importing data from files like Parquet, CSV, or JSON and storing it in a table.
  • Querying a DuckDB database in Python involves utilizing standard SQL syntax through a DuckDB connection object.
  • DuckDB's Python API enables method chaining for an object-oriented approach to database queries.
  • Concurrent access in DuckDB allows multiple reads while limiting concurrent writes to uphold data integrity.
  • DuckDB seamlessly integrates with pandas and Polars by transforming query results into DataFrames using the .df() or .pl() methods.

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Medium

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Title: “How to Become Popular on Instagram in 2025: My Honest Journey”

  • 1. Posting consistently is key to gaining recognition on Instagram; using the same audio clip in Reels helped create an identifiable brand for the user.
  • 2. Developing a strong visual style using tools like Figma and Behance can make an Instagram page look professional and build trust.
  • 3. Engaging actively on Instagram Threads can lead to building real conversations and increase visibility on the platform.
  • 4. Maintaining a consistent digital identity across all platforms is essential for users and algorithms to recognize and connect with your content.
  • 5. Building your digital presence on Google through platforms like Medium and Blogger can increase discoverability and aid in becoming verified on Instagram.
  • 6. Becoming popular on Instagram requires strategy and recognition rather than just amassing a large number of followers.
  • 7. The user emphasizes the importance of being discoverable in the world of algorithms for achieving true popularity on social media.

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How to design apps with Apple Intelligence in mind

  • Apple introduced significant advancements to Apple Intelligence at the recent WWDC Conference, shifting towards an ambient entity rather than a chatbot approach.
  • Developers and designers need to adapt to these changes when designing modern web applications.
  • Apple Intelligence is integrated into Apple devices, providing context-aware assistance such as replying to emails or summarizing conversations.
  • AI developments include on-device Foundation Models Framework, ChatGPT integration, and Visual Intelligence for screen interaction.
  • Design implications focus on intent-based design, user privacy, and the shift from chatbot-focused AI to personal assistant AI.
  • Best practices highlight the importance of user control, minimizing bias, transparency, and respecting data privacy.
  • Apple Intelligence signals a future of ambient intelligence, personal expression through AI, and privacy-centric AI integration into the OS.
  • Overall, the article discusses the shift towards ambient AI, privacy considerations, and the future of interface design with Apple Intelligence.
  • The article provides insights on Apple Intelligence's impact on interface design and the role of developers and designers in creating intelligent, user-centric applications.
  • Key takeaways include a focus on intent-based interactions, privacy-first design, and embracing generative AI for personal expression and creativity.
  • Embracing Apple Intelligence marks a fundamental shift in AI design towards ambient, personalized user experiences while prioritizing privacy and user control.

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Medium

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Software Migrations Are Not Free

  • Software migrations can be complex and costly, especially when managing multiple versions simultaneously.
  • Before initiating a migration, it's crucial to involve the team in discussions about supporting multiple versions.
  • Strategic planning for migrations signals leadership readiness and ensures deliberate decision-making.
  • When rolling out a new version, gradual adoption and clear communication with clients are key.
  • Each new version commits to maintaining the old one for a period.
  • Setting a time frame for migrations helps maintain momentum and stakeholder interest.
  • Partial migrations are advised over all-or-nothing approaches to reduce risks.
  • Assigning ownership of the migration plan within the team ensures accountability and continuity.
  • Thorough documentation of migration processes and reasons is essential for future reference.
  • Migration involves both technical and organizational shifts, necessitating careful planning.
  • If stuck during migration, reassess remaining tasks, set new deadlines, and designate responsibility for completion.
  • Success in migrations requires planning, timeboxing, and careful execution to minimize challenges.
  • Migrations can be eased with proper preparation and adherence to planned timelines.
  • Managing migrations diligently can lead to smoother transitions and future project success.

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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

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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

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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

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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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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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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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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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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

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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

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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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