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The 3 players behind every interrupt: vector table, hardware, and you

The confusion around interrupt service routines (ISR) in embedded systems can lead to bad code and unpredictable bugs.
The three key players behind every interrupt are the interrupt vector table, hardware, and the programmer.
The interrupt vector table is a static array of addresses that points the CPU to the address of the ISR when an interrupt occurs.
Hardware steps in instantly when an interrupt occurs and saves essential context like registers automatically.
The CPU jumps to the address fetched from the vector table to begin executing the ISR code.
The compiler handles most of the saving and restoring in ISRs, including additional register saves.
Programmers are responsible for writing logic in ISRs, such as clearing interrupt sources or toggling flags, without touching the program counter or status register directly.
The process remains the same even for interrupts triggered by software instructions like SVC.
Understanding the roles of the interrupt vector table, hardware, and programmer separately helps in writing efficient and precise interrupt handlers.
Having clarity on who does what in handling interrupts is crucial for embedded engineers targeting companies like Qualcomm, Bosch, or Texas Instruments.
This knowledge distinguishes professionals and enables them to build better relationships with processors in embedded systems.
Separating these roles leads to leaner code, quicker debugging, and improved efficiency in embedded systems.
Understanding the ISR process is essential for success in embedded systems development.
Knowing the roles of the vector table, hardware, and the programmer is crucial for efficient and effective ISR programming in embedded systems.

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Medium

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Juris Domain-Based Non-Reactive Global State: Why AI-Era Frameworks Need Transparent Architectures

Many modern frameworks rely on proxy-based reactivity for state management but come with hidden costs in terms of performance and scalability.
Juris introduces a path-based state access with optional reactivity tracking, offering O(1) hash map lookups without proxy overhead.
The domain-based state architecture of Juris enables clean serialization, server-side rendering compatibility, and improved organization of state by domain.
Juris's state architecture is designed for AI-assisted development, providing complete visibility for AI assistants, improved debugging capabilities, and efficient analysis of application state.
Load testing results show Juris's performance advantage, achieving high requests per second with server-side rendering and universal async support.
Juris offers transparency, explicit reactivity control, and domain-based state organization, representing a shift towards collaborative and transparent state management in the AI era.

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Infoq

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GPULlama3.java Brings GPU-Accelerated LLM Inference to Pure Java

The University of Manchester's Beehive Lab has released GPULlama3.java, the first Java-native implementation of Llama3 with automatic GPU acceleration.
GPULlama3.java leverages TornadoVM to enable GPU-accelerated large language model inference in Java without requiring developers to write CUDA or native code.
TornadoVM, at the core of GPULlama3.java, extends OpenJDK and GraalVM to automatically accelerate Java programs on GPUs, FPGAs, and multi-core CPUs.
TornadoVM works by extending the Graal JIT compiler with specialized backends that translate Java bytecode to GPU-compatible code at runtime when marked for acceleration.
The project supports NVIDIA GPUs, Intel GPUs, and Apple Silicon through various backends for diverse hardware execution.
GPULlama3.java leverages modern Java features like Vector API, Foreign Memory API support, GGUF format for model deployment, and quantization support.
The project builds upon Mukel's original Llama3.java, integrating GPU acceleration capabilities through TornadoVM.
GPULlama3.java is part of the expanding Java ecosystem for AI/ML, allowing developers to build LLM-powered applications without leaving the Java platform.
TornadoVM aims to make heterogeneous computing accessible to Java developers and has been evolving since 2013 with new backend support and optimizations.
GPULlama3.java is currently in beta, focusing on performance optimization and benchmark collection, especially for Apple Silicon support.
The project signifies a significant advancement in bringing GPU-accelerated LLM inference to Java, showcasing the potential for Java-based AI applications in enterprise settings.
Developers interested in exploring GPU-accelerated LLM inference in Java can access the open-source project on GitHub with comprehensive documentation and examples.

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Javacodegeeks

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Compile Java Code with Java Compiler API

Java 6 introduced the Java Compiler API (JSR 199) for compiling Java source code within Java applications, providing a dynamic and programmable way to compile code.
The Java Compiler API allows compiling Java source code without the need for external tools, beneficial for tools like IDEs and online coding platforms.
Key components of the Java Compiler API include JavaCompiler, StandardJavaFileManager, DiagnosticCollector, and JavaFileObject.
These components work together to offer flexible and embeddable Java code compilation capabilities.
By implementing a compilation check using the Java Compiler API, developers can compile Java source files programmatically.
The code example demonstrates compiling a Java file, capturing compilation diagnostics, and handling the compilation process.
The Java Compiler API is powerful for integrating dynamic code compilation into Java applications and requires handling input sanitization and security measures.
Developers can leverage the Java Compiler API for building online compilers, tools, and platforms that evaluate Java code at runtime.
The API provides fine-grained control over compilation tasks and file operations for efficient code compilation.
Compilation success or failure is indicated with detailed diagnostic messages to assist developers in locating and resolving errors.

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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

177

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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Logrocket

334

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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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Medium

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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

128

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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

359

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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

111

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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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