
baised
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𝐃𝐚𝐲 𝟐 @SeismicSys 𝐂𝐚𝐫𝐝 𝐏𝐢𝐱𝐞𝐥 𝐄𝐝𝐢𝐭𝐢𝐨𝐧 The second pixel art card for a community member ✅ Traits : 1. Number 0002 2. Year of joining Seismic - 2025 3. @IlGrebenuk card, currently my bro working on pfps for community 4. Mag 7.0 Here we go, who next? gmic



Your prediction markets view lives in your head. Your positions live on the exchange. Your AI chat knows neither. @ElasticsAI puts all three in one window. AI trading terminal that knows your book. RT and I'll DM you access.


Still thinking about 32… Now @get_optimum drops 6700. My bet? Maybe it’s: • 6,700 nodes • a network capacity milestone • another key metric behind Optimum’s architecture We’ll find out soon enough





» My Seismic Card 📊 Already at 90 posts and 13,439 messages in the @SeismicSys community. ➥ Simple card, but it represents countless hours of activity. Want your own personalized Seismic Card? 📩 Send your PFP in the replies, and I'll create one for you.



Optimizing Exchange Latency At HFTs I built low-latency trading systems for traditional and cloud-native exchanges including Nasdaq, NYSE Arca, CME, ICE, SGX, JPX, Coinbase/GDAX, and others. A summary/discussion of the current split in low-latency network infrastructure Colocation: Traditional exchanges are hosted in physical datacenters. Colocation involves renting rack space with cross-connects to the exchange matching engine. While it is expensive and time-consuming to set up, physical colocation guarantees an equalized path to the exchange. By contrast, it is fast and inexpensive to set up cloud servers but difficult to guarantee the lowest latency. To be colocated with an exchange running in AWS, firms first need to understand not only what region the exchange is deployed in (e.g. us-east-1) but also the availability zone (e.g. use1-az4). Even within a single availability zone, there is variance in how close a randomly placed EC2 instance lands to the matching engine. Non-determinism: Networking setups for traditional exchanges offer deterministic access to matching engines by running the same cable lengths to all client boxes. Public clouds have a significant degree of non-determinism due to routing within a cloud region. Two different EC2 instances in us-east-1/use1-az4 could have different latencies to the same matching engine in the same zone. Firms mitigate this issue by spinning up multiple instances and choosing the one with lowest ping times, as well as using cluster placement groups and AWS technology such as ENA Express. Unicast instead of multicast: Traditional exchanges disseminate market data over UDP multicast, which puts a single copy of the book on the wire and delivers it to every participant simultaneously. Public clouds don't offer multicast in any form usable for market data: VPCs have no native support, and Transit Gateway multicast is built for enterprise applications rather than microsecond-sensitive feeds. Cloud-native exchanges therefore publish over TCP unicast, which involves a separate copy for every subscriber and a fan-out order that no longer treats them equally. HFTs recover what they can by bypassing the kernel for their networking stack. They use a poll-mode driver such as DPDK over ENA that lets the NIC DMA frames directly into userspace ring buffers, with a userspace TCP stack handling the protocol above it. Inter-region latency: Expensive microwave, millimeter-wave, and subsea fiber links carry the lowest-latency paths between physical datacenters, and none of them terminate inside a cloud region. To move data between cloud regions and traditional venues, firms combine the long-haul routes of established market data vendors with specialized cloud on- and off-ramps, paying a penalty at each transition. Since price discovery still originates largely from traditionally colocated exchanges, trading on public cloud venues adds to a firm's market data infrastructure footprint rather than replacing it. Partnerships between CME and Google and between Nasdaq and Amazon aim to deliver infrastructure that combines the flexibility and global reach of public clouds with the deterministic latency of purpose-built exchange datacenters. If that convergence arrives, most of the practices above stop paying. The advantage in trading on cloud-native exchanges may shift back toward the firms with decades of colocated experience rather than those who have built expertise in cloud-latency engineering.











validators work hard to capture every bit of MEV but sometimes the best bid arrives just a few milliseconds after the block is already selected and that's it the opportunity to get the best bid is gone. @get_optimum ran research across 22,000+ Ethereum slots and found bids coming in right after selection that were 68x more valuable than what actually got accepted not bcz validators did anything wrong just bcz of the data didn't arrive in time and faster propagation fixes this, that's the whole point of mump2p btw this music is highly addictive, isn't it? ♾️








One thing I realized while comparing RLNC and Reed-Solomon They aren't alternatives. They're designed to solve different problems. Reed-Solomon is built for data recovery. If some pieces are lost, the original data can still be reconstructed. RLNC is built for data propagation. Instead of simply forwarding packets, nodes can continuously re-encode and relay information as it moves through the network. That's why @get_optimum is building around RLNC. Faster propagation means validators receive block data sooner, reduce redundant traffic, use bandwidth more efficiently, and have more time to verify blocks before the next slot. It's not about replacing Reed-Solomon. It's about solving a different bottleneck in decentralized networks.




