Atharv Gyan

Building an operating system is a complex and low-level task, and the choice of programming languages is critical.  Here are some of the programming languages commonly used for building operating systems: 1. Assembly Language : Assembly is the lowest-level programming language and is often the primary choice for OS development. It allows for direct control over the hardware and provides a level of detail required for an OS. 2. C: C is widely used in OS development due to its efficiency, low-level system access, and portability. Many parts of popular operating systems, like the Linux kernel, are written in C. 3. C++: While less common than C, C++ is used in some modern operating systems. It offers additional features like object-oriented programming and can be used for certain parts of an OS. 4. Rust: Rust has gained popularity for OS development, particularly for its memory safety features and performance. It provides low-level control without some of the pitfalls of C ...

1. What Is Neuralink’s Brain Computer Interface? Neuralink’s core technology is the N1 implant: a coin sized, wireless device embedded flush with the skull, connected to the brain via up to 96 ultra-thin polymer “threads” carrying over a thousand electrodes. These electrodes record neural spikes and can stimulate specific brain regions. A custom surgical robot places the threads with micron-scale precision, minimizing tissue damage. The implant streams neural data over Bluetooth to an external receiver and is powered wirelessly, eliminating bulky batteries. 2. Timeline of First-in-Human Trials May 2023: FDA grants an Investigational Device Exemption, greenlighting the first clinical trial after addressing safety concerns around batteries and wire migration. September 2023: Recruitment opens for the PRIME study, targeting participants with quadriplegia due to ALS or cervical spinal cord injury. January 2024: The first human patient, later revealed as 30 year old ...

Introduction In recent years, the convergence of machine learning (ML) and the Internet of Things (IoT) has given rise to Tiny Machine Learning (TinyML), a paradigm that enables on-device inference on resource-constrained microcontrollers and edge devices. TinyML shifts intelligence from centralized cloud servers to the very edge of networks, unlocking new possibilities in privacy, latency, and energy efficiency. This article provides a comprehensive, in-depth exploration of TinyML its origins, core frameworks, optimization techniques, real-world applications, challenges, and future directions designed as a standalone primer for developers, researchers, and technology enthusiasts. What Is TinyML? Historical Context and Definition TinyML is broadly defined as the practice of running ML models on microcontrollers and low-power embedded systems, typically operating in the milliwatt (mW) power range or below. Historically, ML inference required significant computational resources, rel...