.@heyibinance should have been the Binance CEO from day 1 (8 years ago). 😆
Great journey ahead with two strong leaders (with complimentary skills) leading @Binance.
Congrats! 👏
Samsara is built on a floor that cannot break.
Not in theory. In math.
We’re confident enough in that design to invite the world to stress it harder than any real market ever could.
Accumulate the tokens.
Create the black-swan moment.
The result is always the same:
The floor holds, the value rises, and the navToken cannot underperform its underlying asset.
This is the innovation: a derivative whose minimum value only moves upward, enforced by a mathematical structure.
Watch the architecture reveal itself.
Join our Discord
→https://t.co/Lrkn6k0FGQ
Join the campaign
→https://t.co/nqe0HF2H6n
This guy used to be a pro poker player until he quit for prediction markets and made $2.44M+ doing it
Yesterday, @60Minutes aired an interview with one of Polymarket’s top traders - Domer
His profile - https://t.co/XZhVhWzJDB
“I don't think of myself as a gambler,” Domer said. “I'm taking very, very well-researched views on things.”
One of his cleanest hits: He turned a $4k bet on JD Vance becoming Trump’s running mate into $250,000
Domer made this prediction after researching Trump's choice in 2016 and noticing that Trump likes one-syllable names
“It works from a marketing perspective,” Domer said
He also won $100k betting that Cardinal Robert Prevost would become the next pope, after digging through interviews and speeches of the cardinals
In total, he has wagered over $400M on @Polymarket
And i think it clearly changed his entire life
This is what deep research and understanding how narratives and marketing work can do for you on prediction markets
Whale 0x755b keeps buying $MON.
Since launch, he has spent 2.31M $USDC to buy 70.64M $MON at an average price of $0.03267 — now sitting on over $250K in unrealized losses.
https://t.co/rEr88EKTuz
gSwarm
@gensynai launched Verde
this is the first-ever cryptographically verifiable system for ML on untrusted nodes running not in testnet but in a real network
what this means: every training result can now be independently verified without re-running the entire model through refereed delegation - the first disputed operator is found and only that one is verified
Reproducible Runtime guarantees bitwise reproducibility of results on any hardware - from data centers to Macbooks through deterministic CUDA kernels
combined with CheckFree this means the network can recover training after failures without rollbacks while cryptographically proving all computations were executed honestly
while the market discusses "AI agents" Gensyn ships to production the only working verification for decentralized ML with academic papers and real adoption
Verde in production - this is the shift from "promising to decentralize AI" to "already decentralized and can prove it mathematically"