3 days to go π
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@luannq1990@uchecrypt@CryptoDefiLord@marjidofficial Judging from this table, the total capital invested for the year was $900 ($100 monthly * 9 months)
The total number of shares bought was around 700 * the price of the stock on December which was $2.16.
So the annual return should have been
700 * 2.16 ~ $1512 (+68%)
just discovered @giniedev and my brain is going crazy
im building a structured prediction market on @CantonNetwork
private. atomic. institutional-grade settlement
and i don't have to write a single line of DAML manually
we are so early π
yo @Kiwi_Nod
i build trading bots for a living. got one that tracks oracle lag between Chainlink and Polymarket in real time using dual WebSocket feeds, not theory, actual running code.
When i saw how Pharos handles state contention during parallel execution, it clicked. most L1s serialize at the wrong layer and that's exactly where they choke under real load.
Pharos moves that bottleneck upstream. That's not me reading the docs. that's me recognizing the same problem i debug weekly.
The part that clicked was how Pharos uses speculative parallel execution with conflict detection instead of just optimistic concurrency.
in my Polymarket bot, the biggest lag isn't the WebSocket feed, it's state resolution. when two signals hit at the same millisecond, whichever one commits first wins, and the loser has to retry from scratch.
most L1s have that same problem at the contract level. high contention = failed txns = retry spam = network congestion loop.
Pharos breaks that by detecting conflicts before committing, not after. means valid txns don't get punished for sharing state with a bad one.
for oracle-dependent contracts specifically, price feeds, prediction markets, RWA settlement, that's the difference between a system that works under load and one that falls apart exactly when you need it most.
I noticed this because i've watched my bot choke on the same pattern on other chains.
@Kiwi_Nod
0x1006C2F6AEf9832658925843e00bc128FFd62798