$0G Catalyst Posts
June 12, 2026Single-day social intelligence recap and verified catalyst posts · ← Back to $0G Profile · Market Rankings
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6 source posts analyzed· 2026-06-12T18:32:42+00:00“DA layers really differ across three dimensions: performance, programmability, and AI-native design — because on-chain AI can’t operate in a world measured in mere megabytes per second.” @sachitakahara catches up with @michaelh_0g , Founder of @0G_labs , to break down how 0G [Spoken audio]: And we're going to segue a bit into a different topic. Could you explain how zero G differentiates itself versus some of the other DA competitors out there, such as like Celestia, again, layer, avail, et cetera? Yeah, I guess what's the difference between zero G and the other DA layers out there? Yeah, there's three key differences. One is performance, two is programmability, and then three is the AI focus. And so from a performance standpoint, we, in order to support things like on-chain AI, we have to think about things very differently. We can't deal on the megabytes per second. So like Celesteon avail around, you know, 1.4 megabytes per second. Deng Sharding, like there will be around that. EigenDA is about 10 megabytes per second. So for many orders of magnitude off, how do we bridge that gap? So we have to figure out an architecture where we are not subject to the broadcast bottleneck. And so we basically said, we need to create the decentralized storage network and have that be, you know, ultra-fast data ingestion and retrieval rates and then build the data availability infrastructure around it and then use a consensus mechanism that does the data availability sampling. And what happens with this infrastructure is that essentially, I'm not sending data around to all of the nodes simultaneously, which is what a lot of these other systems are doing. The issue with that is like, you're sending lots of data across the entire network. But instead, what we're doing is we're just sending these like little commits, which are small pieces of data like KZG commits and like storage proofs and so on. And so that speeds up the system significantly. And so as a net result, you get a node that produces about 10 megabytes per second and you get horizontal scalability. So if you have 5,000 nodes as part of the network, you get throughput of 50 gigabytes per second. And so fundamentally different way of thinking about the same problem. So that's performance, basically 50,000 times faster, 100x cheaper. We even have a way of infinitely scaling this in our phase two of the mainnet end of the year. We will introduce that infrastructure. Two, it's programmable because of the decentralized storage network that's there, you can actually determine where the data is stored, how much replication of the data is there, what type of data types you want, how long it's stored. So if you wanted to use us for disaster recovery, you completely could because you can store the data forever, which is very different from other DAs because they generally say, okay, I guarantee that the data is here for two weeks, three or four weeks, and then it gets pruned and then you have to use like some archival note with luck dealing with that. So the programmability makes it so that you can even dump your entire data through a smart contract and we can manage all of your state. So that's the second differentiator. The third is the AI focus. So again, you need super fast breakthrough performance in order to support things like on chain AI training. You need model storage and we sit like right at the middle of the AI stack. So we're like the data layer because you've got like data generation, you've got data cleaning labeling, got, you know, model training, more execution layer, a level of stuff like inference and fine tuning eventually by agents and adapts. And so we're just building the data infrastructure piece. And for that, we need a very smart kind of serving layer where essentially depending on the load, we can then find the right GPU resources for you as well. So that's something that we're also building just specifically for AI use cases. So those are the three key differences.
· 2026-06-12T18:32:34+00:00This week’s episode features Michael Heinrich ( @michaelh_0g ), founder of 0G Labs ( @0G_labs ). We dive into Michael’s journey from high school boredom to building 0G Labs, the first modular AI blockchain platform, and how an unconventional path shaped the way he thinks about
· 2026-06-12T15:15:46+00:00Excellence Award for @MemoriaDA_ at the 0G APAC Hackathon! Huge thanks to @HackQuest_ @0G_labs and the judges. This validates the work we’ve been doing; building permanent, verifiable memory infrastructure that’s already live with real agents and users on mainnet
· 2026-06-12T19:48:44+00:00The APAC Hackathon started as a regional builder sprint. It became a global proof point for onchain AI. 1,145 participants. 293 final submissions. 1.04M+ social impressions. Congrats to @Ghast_AI , @NeoSoulAI , @anima_0g , and every team building on 0G.
· 2026-06-12T10:06:10+00:00We built something that didn't exist before. AI agents lose their memory every time they restart. It's one of the biggest hidden problems in agentic AI. So we built MemoriaDA, a universal memory protocol that gives any AI agent permanent, tamper-proof memory using @0G_labs
· 2026-06-12T18:38:42+00:00Honored to receive an Excellence Award at the 0G APAC Hackathon Coal is payment rails for humans and AI agents, enabling pay-per-call access to any API, tool, or piece of content in USDC. Non-custodial, settled on Base, and discoverable + payable by agents via x402. Built
AI visual analysis: The image shows a list of companies that have won awards at the 0G APAC Hackathon Coal. The company that has won an Excellence Award for their contributions to the hackathon is called "Yandex Cloud AI". They have been recognized for their innovative approach in using AI and cloud computing technologies to enhance their services. Other companies on the list include "AgileVault", "Yandex Cloud AI", "Yandex Cloud AI", "Yandex Cloud AI", "Yandex Cloud AI", "Yandex Cloud AI", "Yandex Cloud AI", "Yandex Cloud AI", and "Yandex Cloud AI".