With the birth of the digital age, the idea of privacy seems cozy but mostly mythical. From smartphones to voice speakers, practically every gadget we employ gathers data silently in the background. Even if we switch off location services or surf with "incognito" mode, our activities create a footprint. The bitter truth? Digital privacy is as perceived by humankind a myth. Each app, site, and platform we use paints a picture of us—monitoring clicks, buys, likes, and even moods. Tech firms claim the information drives more effective services, but it's used for manipulation, targeting, and profit as well. Governments tap this web too, using surveillance technologies for national security or public safety ends. More significant still, it happens all the time without effective consent. Terms of service are lengthy, abstruse, and never read. Information is stored forever after collected, distributed between companies, or sellable to others. And once leaked, it remains so. So w...
When software was new, "open source" meant transparency, cooperation, and freedom. Coders had access to source code, could modify it, and redistribute it. But with the age of AI, the term open source is becoming hazy—and contentious. The AI models, and especially large language models (LLMs), are highly advanced and computationally costly to train. Others claim they are open source but offer only pieces of their models—e.g., weights without training data or code without documentation. This put a demand on terms such as "open-weight" or "partially open" that don't fully represent the whole essence of the original open source. Efforts like Meta's LLaMA and Mistral have brought the debate further by publishing powerful models with less restriction, but even those stop short of complete openness. Licenses enter into this picture very much—most so-called "open" models contain usage limitations that ban commercial deployment or derivative work....