Using casino-grade dice, I can confirm that the seed phrases generated by SeedSigner (v0.8.0) and ColdCardQ (1.3.0Q) match!
Rolling 3 dice at a time, gives me these values:
```csv
roll,die_1,die_2,die_3
01,3,4,5
02,4,4,3
03,6,3,3
04,5,3,6
05,2,6,1
06,2,6,2
07,2,1,2
08,3,4,1
09,3,5,2
10,6,1,6
11,2,3,5
12,1,1,5
13,2,3,1
14,3,1,6
15,2,1,6
16,3,5,2
17,2,5,1
18,4,4,1
19,2,6,6
20,6,4,6
21,5,4,4
22,6,6,4
23,4,5,1
24,6,2,1
25,2,1,2
26,2,1,4
27,4,4,5
28,1,2,1
29,1,2,3
30,1,2,6
31,1,6,4
32,2,5,1
33,2,3,3
```
Which generate the following seed phrase:
```csv
index,word
01,TALEnt
02,ROUGH
03,SEA
04,WOLF
05,ORANge
06,CORN
07,MOTOr
08,RIOT
09,WRONg
10,OFFIce
11,THEY
12,RIOT
13,ZEBRa
14,VIRTual
15,PEOPle
16,CAPItal
17,DEPUty
18,PHONe
19,SCENe
20,CASTle
21,COOK
22,ISSUe
23,CRICKet
24,TERM
```
with a fingerprint of `#421e05ba`.
Sovereign Individual vibes
nostr: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 nostr:nprofile1qyxhwumn8ghj7mn0wvhxcmmvqyxhwumn8ghj7mn0wvhxcmmvqyg8wumn8ghj7mn0wd68ytnvv9hxgqgswaehxw309ahx7um5wghxcctwvsq3qamnwvaz7tmwdaehgu3wwa5kuegpzpmhxue69uhkummnw3ezuamfdejsz9rhwden5te0wfjkccte9ejxzmt4wvhxjmcpz3mhxue69uhhyetvv9ujuerpd46hxtnfduqzqjjktunxr9j2auxra6rmn8y62uc64rwc4qpjd7t2gm7h7gr7scf6g3y4wt have tests like these been done before, please?
not sure ...
```
def generate_mnemonic_from_dice(roll_data: str, wordlist_language_code: str = SettingsConstants.WORDLIST_LANGUAGE__ENGLISH) -> list[str]:
"""
Takes a string of 50 or 99 dice rolls and returns a 12- or 24-word mnemonic.
Uses the iancoleman.io/bip39 and bitcoiner.guide/seed "Base 10" or "Hex" mode approach:
* dice rolls are treated as string data.
* hashed via SHA256.
Important note: This method is NOT compatible with iancoleman's "Dice" mode.
"""
entropy_bytes = hashlib.sha256(roll_data.encode()).digest()
if len(roll_data) == DICE__NUM_ROLLS__12WORD:
# 12-word mnemonic; only use 128bits / 16 bytes
entropy_bytes = entropy_bytes[:16]
# Return as a list
return bip39.mnemonic_from_bytes(entropy_bytes, wordlist=Seed.get_wordlist(wordlist_language_code)).split()
```
Thanks for replying 🙏
Sure .. grifters gonna grift ... and if you're playing rug-pull games with over-hyped investments expect to get, unfortunately, rug ... pulled. Defo not a clown-world defender and agree that an aspect like that is a horrible hoax with undeserving victims.
But ... What are the other soultions before LLMs? ... If you program, what tools could I use in a method that takes (text, required_key_value_extracted_pairs) and returns a JSON {"key_1": "value_1", ...} object? Advanced LLMs, with guidance, really excel in that functionality. After all, they're just heavily trained neural networks ..
In particular, I have a "chat extract" method in my FOSS API, https://ajrlewis.com/api/docs, that uses a HuggingFace interference model (probs LLama, can't remember lol!) to do this task ... Would love to know a different approach if you can suggest one 😊 Defo save on the overheads + complexity if I didn't want to use a 3rd party (HuggingFace) in future ...
Beautiful country. Going to relocate there soon 😎
The rocks are everywhere on that beach and surrounding towns/villages ... Apparently in the rainy season they roll down the rivers en mass ..
The real gem in that image is the speckled rock from Bitcoin Beach in El Salvador!
The art work is mosiac.rocks ... found it on Twitter a while ago, but not privy to that platform anymore to confirm ... this looks like artist though:
Agreed; heavy computation requires heavy energy use. And wouldn't dare argue that we're awash with stealth agendas out there ....
But! Disagree on it not being a good modern tool for the right problem. Horses for courses.
For example, object extraction from a large body of text using a sophisticated LLM performs pretty well, from experience. And not sure I'd even know how to achieve similar results using other technologies? Even if I did, I'm not sure using many inferior tools to do the same thing would result in much less energy used to generate similar results?
Not that I personally care how much energy it uses if it's deemed useful by the person using that energy ... and their energy production techniques don't affect my, or others, way of life.
🤙
COLDCARD™ Q looks like a great product.
A completely air-gapped solution with an easy QR interface for transferring PSBT data to a dedicated linux box running Sparrow ..
Perfect addition to my SeedSigner + Trezor combo to create a 2-of-3, 24-word multi-sig mashup.
Nice work, ser!


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Happy New Year!

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@SeedSigner

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⚡️I will zap all 100k Bitcoin memes in replies to this post⚡️
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