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The project states that: > LLM & AI-Native: Its structured, tokenizable nature makes it powerful primitive for time-based reasoning, prompt engineering, and lo
by rfoo 1y ago
The project states that:
> LLM & AI-Native: Its structured, tokenizable nature makes it powerful primitive for time-based reasoning, prompt engineering, and log analysis in AI systems.
Being significantly OOD usually does not help in this case, did I miss something here?
- firasd 1y agoThe OOD thing ironically helps a bit because sometimes AI can skim over regular timestamps and say two different times are the same etc. LLMs don't actually parse and calculate the timestamps after all. With AlphaDec it's very clear that two timestamps are different Also I use a little preamble... here is what I send Anthropic's Claude Sonnet when it asks for the current AlphaDec over an MCP tool // AlphaDec units (approx): Period = UTC yr (different length leap yr vs common yr) / 26 ≈ 14.04 days | Arc ≈ Period / 10 ≈ 33.7 hours | Bar ≈ Arc / 26 ≈ 77.75 minutes | Beat ≈ Bar / 10 ≈ 7.78 minutes. The final part of canonical AlphaDec is milliseconds offset within the beat. Period F, Period M, Period S, and Period Z always contain equinoxes/solstices. Truncating significant digits creates natural time groupings, eg 2025_M2 contains 'every alphadec in this arc' [ { "timezone": "UTC", "iso": "2025-07-25T00:47:09.219Z" }, { "timezone": "AlphaDec", "alphadec": "2025_O6B3_087680", "readable": "O6:B3" } ]