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Systema Robotica, a treatise on the order and evolution of robotkind

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Systema Robotica, a treatise on the order and evolution of robotkind

Systema Robotica is a multi-year endeavor to define, classify and demystify robots. The treatise has been published as a book on Amazon and also as an open access public work on LessWrong and TechRxiv. The treatise defines what a robot is, classifies all past, present and future robots, and proposes a novel definition and test for sentience. I welcome everyone's comments, inputs, feedback and suggestions. This is the first edition of Systema Robotica, and I intend to continue to build upon this early framework.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% 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
18%18% predicted probability of success on BetaList, based on ML models trained on real launch data.

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