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YashasEdu

@YashasEduΒ·9.5K followersView on X

v2 | Writing uncomfortable truths about money, systems and what we pretend we believe

First Venice mention: 2mo agoΒ·Last: 18d ago
πŸ“Posts
15
πŸ‘οΈViews
12.3K
❀️Likes
237
πŸ”Retweets
15
πŸ”–Bookmarks
37
πŸ†Best post
5.6Kviews
VVVVenice activity over time
VeniceStats
πŸ“ŠBuzz Score
30 days
456#61 / 2,699
90 daysβ˜… MAIN
1,219#82 / 7,629
6 months
1,219#98 / 10,174
1 year
1,219#101 / 11,506
All time
1,219#121 / 15,253
πŸ“ˆ
Climbing
Steady climb β€” #61 this month, #82 over 90d, #98 over 6mo.
YashasEduUnverified
@YashasEdu
Just in case if you're wondering why $VVV is still pumping. This is for youπŸ‘‡ @AskVenice' moat isn't the AI models. Models commoditize every 6 months. The moat is being the only inference layer where the prompt never exists on a server. βž₯ 80-90% of AI compute is now inference not training βž₯ Agentic AI multiplies that demand 24x by 2030 βž₯ 39.7% of all AI interactions already touch sensitive corporate data Regulators are shifting from policing training data to policing runtime decisions. Every
May 7
91275.6K22
YashasEduUnverified
@YashasEdu
Ngl @valueverse_ai' 30D Holder P/FCF screener is quietly one of the sharpest lenses in DeFi right now. Lowest multiples (tightest cash flow to holders)πŸ‘‡ β€’ @pendle_fi: 4.2x β€’ @Pumpfun: 6.3x β€’ @AerodromeFi: 7.3x β€’ @AskVenice : 7.8x These protocols don’t just generate revenue. Their mechanics route a high % straight to locked/buyback holders with almost no leakage. ve models especially are showing why they were built this way. Higher up the board you get staking heavy names and scale monsters l
Jun 23
4523.2K11
YashasEdu
@YashasEdu
For those who compare $POD and $VVV remember they’re closely related but operate at completely different layers of the stack. Venice: End user experience layer focused on privacy, ease of use, and creative tools POD: Infra layer focused on scalable, permissionless inference compute and open model development They are synergistic partners rather than direct competitors. See I f you break the AI value chain in layersπŸ‘‡ 1. Frontier training (hyperscaler-dominated) 2. Model weights & fine tunes
Jun 30
2412.1K4
YashasEdu
@YashasEdu
@nikshepsvn Still feel $NEAR $VVV and $AERO will outrun alot other projects in the coming days
Jun 4
5450
YashasEdu
@YashasEdu
@thelearningpill @arndxt_xo Also near:native $AKT $VVV are good choices
May 13
4386
YashasEdu
@YashasEdu
@Defi_Rocketeer @valueverse_ai @pendle_fi @Pumpfun @AerodromeFi @AskVenice Pendle is underrated
Jun 23
179
YashasEdu
@YashasEdu
@vasily_sumanov @valueverse_ai @pendle_fi @Pumpfun @AerodromeFi @AskVenice Just entered 🀝
Jun 23
176
YashasEdu
@YashasEdu
@sakshimiishra @valueverse_ai @pendle_fi @Pumpfun @AerodromeFi @AskVenice Exactly
Jun 23
276
YashasEdu
@YashasEdu
@Karamata2_2 @valueverse_ai @pendle_fi @Pumpfun @AerodromeFi @AskVenice Pendle
Jun 23
170
YashasEdu
@YashasEdu
@Tom_Degen68 @valueverse_ai @pendle_fi @Pumpfun @AerodromeFi @AskVenice 🀝🀝
Jun 23
169

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