Monday, 12th September 2022
In a previous iteration of the machine learning paradigm, researchers were obsessed with cleaning their datasets and ensuring that every data point seen by their models is pristine, gold-standard, and does not disturb the fragile learning process of billions of parameters finding their home in model space. Many began to realize that data scale trumps most other priorities in the deep learning world; utilizing general methods that allow models to scale in tandem with the complexity of the data is a superior approach. Now, in the era of LLMs, researchers tend to dump whole mountains of barely filtered, mostly unedited scrapes of the internet into the eager maw of a hungry model.
— roon
Ladybird: A new cross-platform browser project (via) Conventional wisdom is that building a new browser engine from scratch is impossible without enormous capital outlay and many people working together for many years. Andreas Kling has been disproving that for a while now with his SerenityOS from-scratch operating system project, which includes a brand new browser implemented in C++. Now Andreas is announcing his plans to extract that browser as Ladybird and make it run across multiple platforms. Andreas is a former WebKit engineer (at Nokia and then Apple) and really knows his stuff: Ladybird already passes the Acid3 test!