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Satoshi9000 analog BTC key generator (mechanical)

Hacker News

Satoshi9000 analog BTC key generator (mechanical)

I built this machine so I could generate Bitcoin keys that I could trust. Air-gapped and simple to use and understand (mechanical). The Satoshi 9000 demo: https://youtu.be/bJiOia5PoGE The key value proposition of the machine is that it generates analog randomness in the physical world and converts it into digital (1’s and 0’s) randomness. Seamlessly. But it occurs to me that it may have other uses beyond crypto keys for your own use, such as: * Randomized clinical trials. Clinical trials need a high degree of transparency for ethical reasons; also, for legal reasons should it come to light after the trial has ended that patient selection and treatment selection was not random or in some way biased (say, by the researchers themselves). The machine described herein can provide that transparency to young and old patients, technical and non- technical. * Non-technical management. Many network engineers in need of security keys have bosses that are non-technical. Such managers might prefer security keys (and their generation) which are easier for them to understand. * Estate planning. Suppose members of a family were to inherit digital assets (such as Bitcoin, for example). Not all members of the family are technical and understand Bitcoin. However, each will still need to generate a secure Bitcoin key to receive their share of the inheritance. The machine described herein might help in that task because its source of randomness is more easily understood by laypeople and each can generate their own private key in private (in isolation with the machine). * Anywhere where the users have to have an intuitive understanding of how the randomness is being created; whether they are 5 years old, or 95 years old, and all ages in between. I'm curious to know if any of the folks over at HN can think of other use cases?

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
70%70% 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.
AppSumoStrong fit for a featured deal · Strong signals: intuitive, users · Missing: plus, platform, reviews
61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, user, physical · Missing: agents, macos, agent
36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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