accdoo cipher – like xkcd/936 but with dial-pad only

Hacker News

Hi, this is an intro for a cipher App I've built, instead of making another markdown-based SSR blog system from scratch to write the hello world post, I will just use show HN as my cold opening. The algorithm is easy to perform and can be practiced on any calculator (small input), making it friendly to non-tech-savvy users and without vendor lock-in. Implementation-wise, to its very core just one-liner of JavaScript: arr.map(BigInt).reduce((a, b) => (a * b) ** 2n) By using BigInt gives arbitrary precision, soft limits are set at 10n ^ 100_000n for Web App, 10n ^ 1_000_000n for CLI, configurable from API. It comes with battery-included, API (npm packed with source code), CLI (via node, deno, bun, qjs), and a responsive-design Web page, with offline-ready PWA that is optimized for small screens (iPhone SE). Aside from the spirit of xkcd/936, it is widely applicable (does not require English-speaking knowledge nor an ANSI keyboard) and can be used for things like door locks, vaults, pins, passcodes, entropy seeding, etc. The default UI is tailored for regular daily activities (90% on phones), and an advanced mode with features such as keyboard typing, copy/paste, and QR code for air gapping is planned. About the naming, it's just a meaningless throwaway code name, one could grep by exact keyword matching and scrap everything before 2024, I hope it's not too much of asking for search engines nowadays. I am composing this post on HN as its primary discussion spot, if it somehow gains ENOUGH hype, I will create a GitHub Discussions to gather long-term conversations. To test its strength, I'll end with a ciphertext it produced. Once someone manages to crack it, I will reveal the story behind the making of this App. 00062973002248148460

Visit product View on Hacker NewsJan 24, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, code · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: friendly, users · Missing: plus, platform, intuitive

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

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

53%53% 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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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

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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