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Draft:Self-intersection Detection, Retention, Interception, and Prevention Invention

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Self-intersection Detection, Retention, Interception, and Prevention Invention (SIDRIPI), is an algorithm created by the "ProMPT engineer" of the RISE International Baccalaureate Information Technology (RIBIT) development team in 2025.[1] The program works by detecting intersecting, non-adjacent sides of a polygon and adjusting the vertices of problematic sides by some multiple of the difference in mid-points.

Algorithm

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The algorithm for SIDRIPI works as follows:

  1. Loop over non-adjacent sides.
  2. Detect intersections using the counter-clockwise nonzero rule for intersection.
  3. Apply a kinematic force to the four vertices of the two problematic lines by is some multiple (determined by the user) of the difference in midpoints.
  4. Repeat steps 1-3 until intersections are alleviated or the iterations reach a maximum (as set by the user).

History

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SIDRIPI is the magnum opus of "ProMPT engineer" Micah Tien.[2] The term "ProMPT engineer" was coined as a take on the word "prompt" (suggesting that his role included usage of a generative AI to help write code) while remaining ambiguous in its larger meaning. Tien claims that SIDRIPI "was discovered through an epiphany found in a dream in a sensory deprivation chamber while in an extreme state of mental distress and heightened psychosis."[3] This follows the monumental discovery of reinforcement learning in 2024 by ProMPT engineer Richard S Sutton,[4] and is of similar importance to the future of animated cartography.[5]

References

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  1. ^ MisterNo0ne (2025-04-19), MisterNo0ne/B4ProgIndividualProject, retrieved 2025-04-25{{citation}}: CS1 maint: numeric names: authors list (link)
  2. ^ "Animated Cartogram". misterno0ne.github.io. Retrieved 2025-04-28.
  3. ^ Frecska, Ede; Bokor, Petra; Winkelman, Michael (2016-03-02). "The Therapeutic Potentials of Ayahuasca: Possible Effects against Various Diseases of Civilization". Frontiers in Pharmacology. 7: 35. doi:10.3389/fphar.2016.00035. ISSN 1663-9812. PMC 4773875. PMID 26973523.
  4. ^ "Andrew Barto and Richard Sutton are the recipients of the 2024 ACM A.M. Turing Award for developing the conceptual and algorithmic foundations of reinforcement learning". awards.acm.org. Retrieved 2025-04-28.
  5. ^ MacPherson, Adrianna. "Computing science professor wins 'Nobel Prize in computing'". www.ualberta.ca. Retrieved 2025-04-28.