Morphing Somatic Quasicrystal Neural Network

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A... n fold orthographic quasicrystal-structured neural network scan behaviour pattern routine that manifests as a new type of tri dimensional artificial intelligence scan behaviour path pattern algorithm. WHAT IS MSQNN? What is Morphing Somatic Quasicrystal Neural Network (M.S.Q N.N)? Quintessentially, quasicrystals occur midst nature as multi-dimensional, semiregular uniform n-polytope hypercubic voluminous structures. The core algorithm is dynamic, as such is able to produce n-sized polygons stemming from tesseract to octeract petrie polygon projection, so as to gracefully collapse n dimensional existences into ones of orthographic nature. Different n-sized polygon projections yield multifarious AI path grid pattern configurations. This orthographic resulting manifest's as M.S.Q N.N's base blueprint AI path network\electron diffraction pattern. It is this condensed electron diffraction pattern nodal collection that is compacted to compose projected path sequences. M.S.Q N.N - Morphing (expansive/contractive), non-abstract, non-hierarchical (somatic) n fold orthographic quasicrystal-structured neural network scan behaviour pattern algorithm manifests as a new type of tri dimensional artificial intelligence scan behaviour path pattern algorithm. https://github.com/JordanMicahBennett/MORPHING-SOMATIC-QUASICRYSTAL-NEURAL-NETWORK/

Visit product View on Hacker NewsApr 5, 2015

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Hacker NewsStrong engagement from HN community · Strong signals: exist, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

25%25% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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