The real use case for Jev in algo trading is in using it to quickly pathfind toward useful signals while in research mode
It is not a replacement for making individual trading decisions in realtime. Structured fits over lots of historical data, with well-crafted signals, in an optimized event loop, are much better-suited for this problem, both for precision, latency, and reproducibility
What makes a strong bounty project? @MatthewFoyle drops the alpha:
The best projects solve a real problem, have clear utility, and feel genuinely good to use.
Solve something real. Make the experience great. Use the tools where they make sense.
Check out @dynamic_xyz bounty here:
https://t.co/SvgNviP2IG
CFTC Chair Mike Selig Says Agency Is Preparing for Onchain Finance, Mass Tokenization, Agentic Finance and 24/7 Markets
this is exactly why the world needs Monad
We sat down with @itsbhaji, CEO and co-founder of @centrifuge, to chat about RWAs and tokenization
Timestamps 👇
0:00 Meet Bhaji Illuminati and Centrifuge
1:03 Tokenization explained simply
1:54 From go-to-market to CEO
3:08 Three steps to institutional adoption
5:49 What RWA infrastructure needs from a blockchain
6:53 RWA funds vs deRWA
8:35 The friction behind institutional tokenization
10:24 Collateral, disclosure and secondary liquidity
11:53 Composable RWAs in DeFi
12:57 The next phase of RWA growth
14:07 Treasury management with tokenized assets
15:22 Consensus Miami and closing thoughts
AI is changing how security work gets done.
Defenders can review more code, test more hypotheses, and explore more edge cases than ever before.
But the same tools are available to attackers.
So the job is now twofold: use AI to find bugs faster, and make sure the AI tools themselves run with limited access, limited permissions, and safe failure modes.