

$DIME Szn Loading… 🟪🟪🟪🟪🟪⬜⬜⬜⬜⬜ 50% Get your numbers right 👇
BaseMessiah
291 posts

@0XBaseMessiah
@Base-inspired anon. On-chain operator. Tracking narratives & token math. | @Paradex | Always NFA.


$DIME Szn Loading… 🟪🟪🟪🟪🟪⬜⬜⬜⬜⬜ 50% Get your numbers right 👇



Chance to finish top-4 in the Premier League, via Kalshi: 99% — Arsenal, Man City 84% — Man United 50% — Liverpool 38% — Aston Villa 31% — Chelsea 8% — Brentford Who’s grabbing the final spots?



Paradigm has acquired 14M+ $DIME (~5.5% circulating / ~1.4% total supply) in the past 1.5 weeks via open market purchases and will continue to support Paradex and $DIME, including by periodically acquiring $DIME. Combined with the DIME Assistance Fund, 16M $DIME (~6.3% of circulating / ~1.6% of total supply) has been bought back since TGE. All buybacks can now be tracked on app.paradex.trade/dime-fund

FlowBot Platform Update > NADO @nadoHQ isolated market support - now live with XAG (Imbalance bot) X4 points > Improved market making on @grvt_io & @nadoHQ Building the infrastructure for automated on-chain trading.


🚨| BREAKING: Mikel Arteta set to activate a bizarre clause in Jurrien Timber’s contract to sign his brother Quinten Timber ahead of Leverkusen tonight. Arsenal reportedly plan to fund the deal using their projected $1 BILLION Kalshi bracket winnings.



As we wait for Champions League Football (Arsenal 25% chance to win it all), check out this CRAZY opportunity: Win $1 Billion if you build a perfect March Madness Bracket! Free to enter at kalshi.com/billion

It's Monday. The ArcX waitlist is live ✨

We've raised $6.5M to kill vector databases. Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest. Similar, sure. Relevant? Almost never. Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough. A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file. Once you’re dealing with 10M+ documents, these mix-ups happen all the time. VectorDB accuracy goes to shit. We built @hydra_db for exactly this. HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time. So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit. Even when a vector DB's similarity score says 0.94. More below ⬇️

What's the luckiest sports play you've ever seen?
