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Team Travel 12: exploring with grainorder, immutable graintime,Glow G2

Hacker News

Team Travel 12: exploring with grainorder, immutable graintime,Glow G2

hey there! ever wondered how to organize files chronologically with just 6 characters? or encode astronomical data into git branches? what is this? team travel 12 is a personal workspace exploring three interconnected systems: grainorder - permutation-based file naming (1.2m unique codes from 13 consonants). smallest alphabet = newest files. GitHub's ascending sort automatically displays chronological order! graintime - git branches that know their cosmic context. not just "oct 28, 11:30am" but "moon in uttara ashadha, aries rising, sun in 12th house." every branch encodes when AND where you were in the celestial cycle. glow g2 - patient teacher voice throughout. i ask questions, check understanding, explain WHY not just WHAT. inspired by socratic method. why does this matter? we're treating time as lived experience, not just timestamps. 4 comprehensive whitepapers inside: grainui (gpu-accelerated gui), grainorder patent, graindb (immutable database), graintime patent. built with pure rust+steel stack (steel = scheme lisp on rust). ready for questions - what part intrigues you most?

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, context, code · Missing: mac, agents, macos
77%77% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, 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.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
42%42% 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
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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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