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    A GitHub repository by user rohitg00 called 'ai-engineering-from-scratch' is trending, described with the tagline 'Learn it. Build it. Ship it for others.' It is a Python-based educational resource aimed at people who want to learn AI engineering from first principles and move toward shipping real applications. The repository has collected hundreds of stars today. The snippets do not reveal exactly what sparked the surge in attention, beyond apparent interest in self-taught AI engineering material.

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    University of Washington robot controls its jump height precisely▼Tiny robot developed at the UW can precisely control its jump height✉newsTechnologyRobotics33 min ago

    Engineers at the University of Washington have developed a tiny jumping robot whose jump height can be precisely controlled, a notable advance in miniature robotics. The development is drawing attention for its potential uses in navigating uneven terrain, search-and-rescue work and other tasks where small machines need controlled, repeatable movement.

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    Machine learning boosts production of licorice-derived drug molecule●Machine Learning and Molecular Scaffolds Supercharge Production of a Licorice-Derived Drug Molecule✉newsScienceBiology28 min ago

    Researchers report using machine learning together with molecular scaffold engineering to dramatically increase production of a drug molecule derived from licorice. The approach is described as a way to make plant-derived pharmaceutical compounds more efficiently and at higher yields than traditional extraction or biosynthesis methods allow, drawing attention in the biotechnology community.

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    Scientists Decode Moth Sex Pheromones via Insect Smell Receptors●Scientists Reverse-Engineer Insect Smell Receptors to Identify Moth Sex Pheromones✉newsScienceBiology28 min ago

    Researchers have reverse-engineered insect smell receptors to identify the sex pheromones of moths. By understanding how the receptors detect these chemical signals, scientists hope to open new avenues for pest control, potentially allowing farmers to disrupt moth mating without heavy insecticide use. The work highlights growing interest in decoding insect olfaction for agricultural applications.