Raven Protocol 🐦‍⬛

🏆Raven wins the @mozillabuilders Award at the Fix-the-Internet showcase. Recognition by the top open source organization in the world that Raven Protocol is a core infrastructure project. We demonstrated best traction and commitment to fix the internet! medium.com/ravenprotocol/…

TPU 8t connects 9,600 chips in a single superpod with 2 petabytes shared memory. Training massive models needs datacenter-scale unified memory and ICI bandwidth. Ravnest trains across distributed consumer hardware without requiring centralized superpods. cloud.google.com/blog/products/…

@jukan05 Hardware scarcity is exactly why distributed compute across heterogeneous hardware matters: GPUs, ASICs, CPUs, edge nodes. No single supply chain bottleneck.

2D NAND shortage spirals after Samsung, Micron, and rivals exit market Major international NAND manufacturers are gradually exiting the mature-process 2D NAND market, triggering panic buying and a sharp spike in prices. Industry sources suggest the supply shortage may be difficult to resolve. While vendors are seeking alternative solutions, product specifications are expected to become increasingly polarized as scarcity persists. Supply chain sources said multi-level cell (MLC) NAND has become extremely difficult to secure, with prices rising at least 20–30% in April 2026 alone. Spot market prices have climbed even faster. For example, MLC 64Gb 8GBx8 products are currently trading at US$19–20, with some offers reaching US$28, compared with only around US$6–7 at the end of 2025 — representing an increase of roughly 200–300%. Single-level cell (SLC) NAND prices have also surged sharply. Spot prices for SLC 2Gb products recently reached around US$4–4.5, up roughly 120–150% from late 2025 levels. Industry sources said that although other players are interested in entering the 2D NAND manufacturing market, second-hand equipment phased out by major NAND makers has recently become highly sought after. However, companies including Samsung, SK Hynix, Kioxia, and Micron Technology currently have no plans to license MLC technology externally, making it more difficult for new entrants to break into 2D NAND manufacturing. The challenge is particularly acute for niche applications that remain dependent on 2D NAND, where transitioning to alternative products is difficult, and qualification cycles are typically lengthy. Although some Chinese manufacturers are interested in entering the market, their production capacity remains limited. End customers are also reluctant to take on higher supply risks, especially for critical applications. The global NAND market is estimated to have reached around US$73 billion in 2025, with the six major NAND suppliers focusing heavily on 3D NAND development. Yet only around 1% of the overall NAND market remains tied to 2D NAND products. Despite the small share, price increases for both MLC and SLC products over the past six months have significantly outpaced those of 3D NAND. Macronix and Winbond gain pricing power in shrinking 2D NAND market Macronix and Winbond are expected to remain the leading suppliers of MLC and SLC NAND, respectively. Based on an estimated 2025 market value of around US$600 million, the two Taiwanese companies already account for roughly 59% of the 2D NAND market, with their combined share expected to expand further in 2026. Macronix returned to profitability in the first quarter of 2026, with rising prices continuing to drive operational growth. April revenue reached NT$5.91 billion (US$188 million), up 33.7% sequentially and 153.7% from a year earlier, surpassing the previous monthly record of NT$5.68 billion set in September 2021. Revenue for the first four months of the year totaled NT$16.38 billion, up 93.5% year over year. Macronix president Miin Wu said the company plans to gradually raise the proportion of NOR and NAND products, including eMMC, toward a 1:1 ratio through capacity expansion. He noted that current 4GB–32GB eMMC applications align closely with Macronix's product offerings. Wu added that there remains a clear supply gap in MLC eMMC products, with shortages expected to persist for the foreseeable future. He estimated that meaningful new competition is unlikely to emerge for at least another three years. Winbond recently said it will focus on maintaining a stable supply of SLC NAND and NOR Flash products and does not plan to enter the MLC NAND market. The company expects SLC NAND bit growth to exceed 80% over the next year. In particular, its new 24nm process accounted for only 1% of Flash-related business in the first quarter of 2026, but the contribution of 24nm SLC NAND is expected to increase from the second quarter onward, gradually becoming a meaningful revenue driver. Winbond also expects SLC NAND gross margins to surpass those of NOR Flash as it pushes toward becoming a leading SLC NAND supplier. Winbond president Chen Pei-ming said the current Flash shortage appears difficult to resolve and that the market is showing two clear trends. The first involves products originally requiring only 16GB or 32GB storage being upgraded toward higher-capacity UFS solutions such as 36GB and 48GB. The second involves customers previously using 4GB eMMC solutions compressing or optimizing code to reduce storage requirements from above 1GB down to around 1GB, enabling the adoption of 8Gb SLC NAND products and creating new business opportunities.

@Melt_Dem Distributed compute turns that supercycle into a network effect, every new node strengthens the whole.

compute supercycle confirmed CPU names are on a generational run

@haider1 Access gatekeeping by design is a different kind of centralization. Distributed compute doesn't have that problem.

Dario Amodei says Mythos is not limited by compute Anthropic can scale it 3x or 10x without creating a conflict between government and private-sector access The harder problem is who gets it "because giving access to too many organizations could create serious cyber risks"

@Dr_Singularity Engines need infrastructure that scales with them. Distributed compute is already running.

AI is the new engine of civilization. The smartest capital on Earth is moving toward AI. The future is going to be richer, faster, stranger, and more abundant than almost anyone believes.

@_RichardTeng @binance Crime detection running 24/7 at that scale needs compute that never queues. Distributed infrastructure was built for always-on workloads.

75% of financial institutions plan to increase AI use for crime detection. @Binance's AI-powered detection systems helped protect our users from US$10.53B in potential losses from 2025 through Q1 2026.

@haider1 Sharper models, longer reasoning loops, more tokens per task, compute demand per session keeps climbing. Distributed infrastructure scales with it.

gpt-5.5 is pretty solid so far gives sharp answers, pushes for accuracy, refines ideas well, and is useful for brainstorming it also handles language nuance better, makes fewer mistakes, and works well for agentic coding in codex > set reasoning to high in codex > set reasoning to extended in chatgpt

Did you know? Fine-tuning costs scale with model size. 7B models run $1,000-$12,000. Larger models hit $35,000+. Ravnest distributes training across heterogeneous hardware. Workload adapts to available capacity instead of requiring uniform expensive clusters. $RAVEN

AI storage market growing $300B 2026 to $985B by 2034. Seagate revenue up 44%, earnings up 116% driven by storage demand. Hyperscalers racing to secure supply. Ravnest distributes training across nodes, no centralized storage required. fool.com/investing/2026…

@PeterDiamandis Or coordinate the idle compute that already exists globally. Distributed infrastructure doesn't need a parallel electrical civilization.

The global AI data center industry will consume as much power as residential electricity across 2/3 of the United States by 2030 (950 TWh). We're literally building a parallel electrical civilization — except this one thinks faster and never sleeps.

@_RichardTeng Neutral infrastructure is the foundation. Distributed compute doesn't pick sides, it just runs.

AI, like any technology, is inherently neutral. While bad actors may use it to exploit others, it is also a powerful tool we can use to protect our ecosystem and community, alongside industry coordination and user education.

@haider1 220,000 GPUs added in a month shows how fast compute decisions compound. Distributed networks scale the same way, node by node.

anthropic seems to have solved its inference shortage for now and that puts them in a better position to launch mythos publicly within the next month or so yesterday, they signed a SpaceX deal for Colossus 1, adding 300+ MW of capacity and access to 220,000+ nvidia GPUs within the month

Sovereign AI initiatives accelerating, countries requiring infrastructure under local jurisdiction for compliance. Ravnest enables sovereign compute without national datacenter. Nodes coordinate within geographic boundaries. Train sovereign models on distributed local hardware.

AI datacenter power demand surging. Texas approved 7.7 GW natural gas project, NextEra 10 GW combined in Texas & Pennsylvania. Planned natural gas capacity jumped 71% 2025-26. Ravnest coordinates existing distributed hardware, no power buildout required. americanactionforum.org/insight/ai-dat…

@jukan05 Margin expansion through token volume is the hyperscaler thesis. Distributed compute offers the same economics without the centralized lock-in.

I read Goldman Sachs’ AI report, and I was genuinely impressed. The core insight is as follows: Agentic AI could turn AI from a capex-heavy cost burden into a business where usage growth drives margin expansion. As token costs fall, more complex agents become economically viable. These agents then consume far more tokens through longer context windows, repeated reasoning loops, validation, tool use, and always-on background monitoring. This increase in token usage improves infrastructure utilization, strengthens unit economics, and gives hyperscalers and model providers more room to reinvest in model quality, distribution, and capacity. In other words, the bull case for AI capex is not simply that usage will grow. It is that this usage growth can increasingly flow through at attractive incremental margins. Goldman Sachs argues that this margin inflection is beginning to appear from 2026 onward.

@milesdeutscher Workflows like this run 24/7. Distributed compute handles the always-on infrastructure without the overhead.

This is one of the most powerful AI workflows I've ever built. It literally saves me 10+ hours/week and has replaced a full-time employee on my team. If you make content on X, you NEED to replicate this. I just automated 90% of my entire content research process using the new upgraded X API. This dashboard analyses hundreds of my viral tweets, extracts my exact tone/voice, and generates hundreds of new content ideas per day. It also includes a built-in heatmap that highlights when I'm outperforming. If you're looking to build a distribution moat with content/creative, this is a must.

@rohanpaul_ai Even frontier labs are learning the lesson, single-provider dependency is a liability. Distributed compute was built on that principle from day one.

Anthropic just committed $ 200B to Google Cloud and Google’s TPU chips - per The information, Reuters Turning Claude’s growth into one of the largest cloud infrastructure bets in AI. Anthropic is not just buying servers, but reserving future compute, the scarce mix of chips, power, networking, and data-center space needed to train and serve frontier models. The reported 5-year commitment may represent more than 40% of Google’s recently disclosed revenue backlog, which means a single AI lab could be a major piece of Google Cloud’s future contracted sales. The deeper story is dependency, because Anthropic now spreads Claude across Google TPUs, Amazon Trainium, and Nvidia GPUs to avoid being trapped by one chip supplier or one cloud. --- reuters .com/business/anthropic-commits-spending-200-billion-googles-cloud-chips-information-reports-2026-05-05/

gm to those who realized the grid bottleneck makes distributed compute inevitable, not optional.

AI infrastructure driving memory chip prices up, raising costs for laptops, smartphones, automobiles. AI boom inflating consumer costs. Ravnest trains across existing consumer hardware, no chip shortage exposure. fortune.com/2026/05/04/is-…

Large models require splitting across multiple GPUs—Kimi K2.5 needs eight H100s just to load 560GB into memory. Ravnest distributes layers across heterogeneous consumer hardware globally. No single-node memory requirements. No GPU clusters. $RAVEN