Cash markets do not share a clock and RWA perps don’t wait for them to agree. August was another record month against the previous report’s published July baseline. The tracked RWA perp market generated $751.9 billion in RWA perp volume, versus $708.3 billion in July, a 6.2% increase. The market also handled a wider range of situations. Memory equities reversed after a long run higher. Seoul paused program trading during a sharp selloff. Moderna doubled after a clinical readout. Different markets, different schedules, one place to keep trading. The month was larger AND broader: more assets, more types of event, and more reasons to trade around the clock. Yet, still a lot of market left to build. Memory Equities Traded in Both Directions July was a one-way memory trade. August brought the other side of it. The tracked memory complex — SNDK, SKHYNIX, SKHY, MU, SNXX, DRAM, and SAMSUNG — generated $327.4 billion, or 43.5% of August volume. Four of the ten most-traded assets came from the group: SNDK ranked first, SKHYNIX third, MU sixth, and SKHY tenth.
On August 4, the memory trade moved higher. SanDisk rose 8%, Micron 6%, and SK hynix 4% after the companies advanced the first Open Compute Project HBF specification. On August 18, it moved lower: Micron fell 5%, SanDisk 6%, Western Digital 7%, and SK hynix 6% as Treasury yields moved higher and investors repriced the trade. The same group drove both the rally and the selloff and kept trading through both. Korea Sold Off; Perps Kept Trading Korean exposure was not a side story. SKHYNIX ranked third, KORU ninth, and SKHY tenth. Together, they generated $117.1 billion, or 15.6% of August volume. On August 19, the KOSPI fell nearly 6% and a Korea selloff triggered a five-minute sell-side sidecar for program trading. After the close, SK hynix announced a 40 trillion won buyback plan, and the KOSPI recovered almost all of the previous session’s loss the next day. MRNA Perps Listed in Hours. Depth Did Not. On August 19, Moderna and Merck reported positive Phase 3 results for their personalised mRNA cancer vaccine in melanoma. Moderna’s stock rose 176.97% in the session. Very few venues had an MRNA market live before the result; TrueCurrent was one. A few more markets followed shortly after the news broke, including TradeXYZ. From its August 19 listing through month-end, MRNA generated $571.6 million and ranked 69th by volume. Its first two sessions produced $213.5 million combined, representing close to 40% of its total monthly volume. The point is access to the event, not the total volume. Traders already have venues for recurring events around the MAG7 and large technology and AI companies, especially earnings. MRNA showed how that can extend to a smaller public company when a one-off clinical result drives attention. With the right risk, and market-data systems, an exchange can make the event tradable quickly, while the news is still relevant and driving volatility. The 24/7 Reference-Price Problem August brought a market-structure question into focus: who produces a usable price when traditional market infrastructure is closed, paused, or has not opened yet? Douro Labs and the Hyperliquid Policy Center brought that question into the SEC’s market-structure process, arguing that the SEC should recognize qualifying independent reference prices for onchain markets where the SIP-derived NBBO is unavailable or does not reflect onchain conditions. The standard they describe rests on direct contributors, a published methodology, transparent publishers, and checks against traditional market data. A separate SEC comment from the Hyperliquid Policy Center and trade[XYZ] used IPOPs — cash-settled pre-IPO perpetuals with no shares, voting rights, or claim on the issuer — as an example of price discovery before a public listing. The CFTC comment process raises a related question for 24/7 futures and perpetuals in energy markets, where the underlying can keep moving after U.S. futures close. All point to the same shift: perps are bringing questions of data provenance, instrument classification and market access into policy discussions. That is directly relevant to RWA markets. These issues are directly relevant to Pyth, whose data infrastructure is used across much of the tracked volume. August in Numbers August closed at $751.9 billion in tracked volume, a 6.2% increase from the previous month.
Asset Class Ranking The market is very much still equity-led with $487.3 billion or 64.8% of the total volume. Commodities followed at $152.3 billion (20.3%), then indices at $103.9 billion (13.8%) and FX at $8.3 billion (1.1%).
Venue Ranking August showcased a reshuffle behind Binance which is head and shoulders above the rest and still growing ($385.6 billion to $437.4 billion). OKX took the 2nd spot as it held its volume above $100 billion and moved from third to second, while Hyperliquid dropped sharply from July’s second-place position to $84.6 billion in August. The RWA perp volume remained top-five concentrated with over 95% of it being traded on Binance, OKX, Hyperliquid, Bitget, and Bybit.
Market-Data Provider Ranking On the data provider and infrastructure front, Pyth remained the undisputed leader with over $715 billion in RWA perp volume secured, representing 96.27% of the total tracked RWA perp volume. One extra percentage point compared to July further solidifying Pyth Pro and Indices as the products powering 24/7 tradfi markets.
Methodology and Sources All volume, listing and provider figures are drawn from Refraction Research and the RWA Markets dashboard, built by @zinnresearch. Volume is notional traded volume across tracked perpetual venues. Volume priced per market data provider attributes each venue-symbol pair to its stated pricing source, weighted by volume. Pairs without a confirmed source are recorded as unverified.
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news9/8news
August Yielded a Record Month in RWA Perp Volume
Cash markets do not share a clock and RWA perps don’t wait for them to agree.
August was another record month against the previous report’s published July baseline. The tracked RWA perp market generated $751.9 billion in RWA perp volume, versus $708.3 billion in July, a 6.2% increase.
The market also handled a wider range of situations. Memory equities reversed after a long run higher. Seoul paused program trading during a sharp selloff. Moderna doubled after a clinical readout. Different markets, different schedules, one place to keep trading.
The month was larger AND broader: more assets, more types of event, and more reasons to trade around the clock. Yet, still a lot of market left to build.
Memory Equities Traded in Both Directions
July was a one-way memory trade. August brought the other side of it.
The tracked memory complex — SNDK, SKHYNIX, SKHY, MU, SNXX, DRAM, and SAMSUNG — generated $327.4 billion, or 43.5% of August volume. Four of the ten most-traded assets came from the group: SNDK ranked first, SKHYNIX third, MU sixth, and SKHY tenth.
On August 4, the memory trade moved higher. SanDisk rose 8%, Micron 6%, and SK hynix 4% after the companies advanced the first Open Compute Project HBF specification. On August 18, it moved lower: Micron fell 5%, SanDisk 6%, Western Digital 7%, and SK hynix 6% as Treasury yields moved higher and investors repriced the trade.
The same group drove both the rally and the selloff and kept trading through both.
Korea Sold Off; Perps Kept Trading
Korean exposure was not a side story. SKHYNIX ranked third, KORU ninth, and SKHY tenth. Together, they generated $117.1 billion, or 15.6% of August volume.
On August 19, the KOSPI fell nearly 6% and a Korea selloff triggered a five-minute sell-side sidecar for program trading. After the close, SK hynix announced a 40 trillion won buyback plan, and the KOSPI recovered almost all of the previous session’s loss the next day.
MRNA Perps Listed in Hours. Depth Did Not.
On August 19, Moderna and Merck reported positive Phase 3 results for their personalised mRNA cancer vaccine in melanoma. Moderna’s stock rose 176.97% in the session.
Very few venues had an MRNA market live before the result; TrueCurrent was one. A few more markets followed shortly after the news broke, including TradeXYZ.
From its August 19 listing through month-end, MRNA generated $571.6 million and ranked 69th by volume. Its first two sessions produced $213.5 million combined, representing close to 40% of its total monthly volume.
The point is access to the event, not the total volume. Traders already have venues for recurring events around the MAG7 and large technology and AI companies, especially earnings. MRNA showed how that can extend to a smaller public company when a one-off clinical result drives attention. With the right risk, and market-data systems, an exchange can make the event tradable quickly, while the news is still relevant and driving volatility.
The 24/7 Reference-Price Problem
August brought a market-structure question into focus: who produces a usable price when traditional market infrastructure is closed, paused, or has not opened yet?
Douro Labs and the Hyperliquid Policy Center brought that question into the SEC’s market-structure process, arguing that the SEC should recognize qualifying independent reference prices for onchain markets where the SIP-derived NBBO is unavailable or does not reflect onchain conditions. The standard they describe rests on direct contributors, a published methodology, transparent publishers, and checks against traditional market data.
A separate SEC comment from the Hyperliquid Policy Center and trade[XYZ] used IPOPs — cash-settled pre-IPO perpetuals with no shares, voting rights, or claim on the issuer — as an example of price discovery before a public listing. The CFTC comment process raises a related question for 24/7 futures and perpetuals in energy markets, where the underlying can keep moving after U.S. futures close.
All point to the same shift: perps are bringing questions of data provenance, instrument classification and market access into policy discussions. That is directly relevant to RWA markets. These issues are directly relevant to Pyth, whose data infrastructure is used across much of the tracked volume.
August in Numbers
August closed at $751.9 billion in tracked volume, a 6.2% increase from the previous month.
Asset Class Ranking
The market is very much still equity-led with $487.3 billion or 64.8% of the total volume. Commodities followed at $152.3 billion (20.3%), then indices at $103.9 billion (13.8%) and FX at $8.3 billion (1.1%).
Venue Ranking
August showcased a reshuffle behind Binance which is head and shoulders above the rest and still growing ($385.6 billion to $437.4 billion). OKX took the 2nd spot as it held its volume above $100 billion and moved from third to second, while Hyperliquid dropped sharply from July’s second-place position to $84.6 billion in August. The RWA perp volume remained top-five concentrated with over 95% of it being traded on Binance, OKX, Hyperliquid, Bitget, and Bybit.
Market-Data Provider Ranking
On the data provider and infrastructure front, Pyth remained the undisputed leader with over $715 billion in RWA perp volume secured, representing 96.27% of the total tracked RWA perp volume. One extra percentage point compared to July further solidifying Pyth Pro and Indices as the products powering 24/7 tradfi markets.
Methodology and Sources
All volume, listing and provider figures are drawn from Refraction Research and the RWA Markets dashboard, built by @zinnresearch.
Volume is notional traded volume across tracked perpetual venues.
Volume priced per market data provider attributes each venue-symbol pair to its stated pricing source, weighted by volume. Pairs without a confirmed source are recorded as unverified.
75·ALong
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news9/7news
Self-Sovereign GraphQL in Every Browser
Arweave holds the data and the index; PermawebOS gives every user a node that can answer and prove its own queries.
The third principle of Arweave is “Guarantee the right to listen”. In cyberspace, you can only hear if you can actually discover the data. On the permaweb, that’s made possible with GraphQL.
GraphQL provides a critical link in the composability architecture of the permaweb: allowing all apps to build on top of the same, shared content lake, joined by a single global index. Arweave transactions are posted with tags, discoverable by wallet address, or the block they were mined into, but GraphQL is the layer that lets users and applications see all data matching these queries. Without it, there’d be no way to find data matching criteria, or build data protocol-based apps.
It’s one of the permaweb’s core utilities, but until now it has been confined to the realm of enterprise-grade hardware. Services like that are usually incredibly difficult to decentralize because the hardware requirements are high and the incentives are low. The permaweb’s GraphQL services so far have depended on building and maintaining gargantuan off-chain indexes of more than 76 billion rows: scanning the historical chain, keeping it current and serving it quickly. In practice, this has meant that permaweb apps inherit the availability and policy of a tiny number (often two, sometimes – like today – even just one) of hosted indexers which had no incentive to adopt a decentralized model.
Offset queries (queries that seek, intersect and page by weave position) will change everything about how Arweave’s query layer is served. HyperBEAM's offset-query path makes the index compact enough to publish on Arweave, orders it by weave offset, and lets any node or client query the relevant pages directly.
(txid)
This is not another centralized GraphQL service, and it’s not just a way to make GraphQL more available to node operators. It is a method that makes GraphQL execution so lightweight that it can be run directly on users’ browsers, with Arweave nodes simply serving them data chunks.
Hyperoptimized GraphQL with Offsets
All pieces of data in Arweave already have unique positions in the weave – every byte is either ‘before’ or ‘after’ every other byte. We call these byte positions in the network offsets. In March, we showed how any transaction on the weave can be referenced by its offset as a name -- short, deterministic values like 101t.arweave.net, derived only from the data’s onweave properties.
That same offset property gives us more than a way to retrieve bytes. It also can power queries because it gives us a common ordering that can be reused across every possible match in an index.
The new match index stores rows using three extremely compact values:
a hash of the field name being matched;
a hash of the value that is present;
the weave offset of the item carrying that predicate.
Each potential match is compressed into an average of just ~9.5 bytes per row, each stored in an onweave ArLMDB database. By utilizing Arweave chunks as batches of LMDB pages, ArLMDB allows us to traverse the database to find any specific node with only a few individual Arweave node requests. By organizing rows into the compressed components 1-3 above, a query with two or three factors to match can walk those ordered sets together, advancing whichever cursor is behind until the offsets meet – a ‘leapfrog’ version of the same flow as a single lookup. Critically, finding the intersection is part of finding the results for each match criteria -- not a second step.
The same ordering solves pagination -- page fifty can seek to its starting offset instead of replaying pages one through forty-nine. Offset lookups give results a stable order without adding another ordering database.
AO Compute; Arweave the Shared Hard Drive
LMDB is normally a local database file. HyperBEAM's new arlmdb store reads that database from Arweave instead.
Try decentralized GraphQL from your browser
This is already proven at production scale with item lookup. A 622 GiB transaction contains the locations of over 70 billion rows. HyperBEAM reads the database where it sits. A cold lookup traverses it with three Arweave chunk requests; once the shared branches are cached, another lookup needs just one. The ArLMDB implementation is merged into HyperBEAM and used for ID lookups from Arweave.
With an immutable index on Arweave, anyone can read it without trusting the publisher to keep a query endpoint online. Like the Arweave schedulers powering Bazar, this is another example of AO employing Arweave as the source of truth, and using it to power the permaweb.
Instead of every query operator repeating the historical sweep and building the same large database, nodes serve chunks while the client traverses the index locally. The node's job is reduced to serving bytes quickly. It does not decide which results exist, execute the filter or ask the application to trust its view of the weave. The live tip still needs rolling indexes, but the expensive historical work no longer has to be repeated by every participant.
The client does not even need the whole database (depending on optimization, we’ve seen database sizes anywhere between 1 and 60 terabytes). The production offset index already demonstrates the access pattern: in the live chunkar browser demo, the second arbitrary lookup needs only around half a megabyte of new index data to traverse the full offset index. The match index works the same way. It traverses the pages needed for the requested predicates, intersects the ordered rows, reads the candidate items and checks that they actually carry the fields requested by the query. This is lightweight enough to make it so that every user can be their own personal no-dependency GraphQL service provider, with provable data, from the browser.
As well as the browser playground, today you can use the arlmdb.js library to integrate Arweave-stored database lookups into UIs.
From Centralized Node to Self-Sovereign Service
Today an application sends a GraphQL request to a server that already holds an index. With a published offset index, the application can instead hold a locator and a cache.
A HyperBEAM node can do that, but one way we imagine most users will access the query layer is through the browser: a user's application fetches Arweave chunks, keeps the hot index pages locally and performs the query for itself. This can be baked into the PermawebOS browser extension along with the local AO node the extension already spawns. This shift brings more and more AO services that were previously hosted (on exclusive TEE hardware) into a local-first environment. The stack is becoming so lightweight it’s able to be run cheaply, per-user, as background services in the browsers, phones and laptops everyone already has.
Previous designs for a decentralized GraphQL layer were unable to answer the question of trust without TEEs. How can you be sure that the response you get back from a GraphQL server has arrived complete and uncensored? While PermawebOS node architectures like LapEE and AndEE solve the trust question in theory, in practice the job of indexing the entire blockweave is too big for small workers.
Offset queries over onweave LMDB data -- made lightweight enough to traverse and prove by any browser -- change the equation entirely.
The browser never asks a server to decide the answer. It asks nodes for the exact index chunks it needs and walks the authenticated LMDB pages itself, intersecting the offset-ordered sets locally.
The new schema provides chunks and Merkle paths for each page accessed; the recipient can repeat the exact same work and see that they get the same complete set, with no missing results. The hashpath signs the request + response pair, and the validator can trivially repeat the work, whether on a node or in a browser. The resulting item is then bound back to its ANS-104 ID. A node can withhold bytes and make itself unavailable, but it cannot alter a row, skip a qualifying result or invent one without breaking the proof.
The Personal Permaweb Stack
Decentralizing GraphQL does not necessarily require a decentralized fleet of GraphQL servers. The permaweb is unique in that it combines a decentralized permanent storage layer with everyday web semantics. HTTP, lightweight proofs, and browser-based nodes handle the workload when the logic is optimized enough to sidestep expensive hardware.
The network holds the whole index. Each user keeps only the path to their answer.
With PermawebOS putting an AO node in every browser and LapEE turning abundant consumer hardware into secure bundlers, schedulers and tunnels, local GraphQL pushes the permaweb towards a cyberspace everyone can own.
Read this on the Permaweb:
https://ao.arweave.net/#/blog/self-sovereign-graphql-in-every-browser
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A renewed push to end the war in Ukraine is underway as U.S. envoys Steve Witkoff and Jared Kushner return from back-to-back talks with Vladimir Putin and Volodymyr Zelenskyy saying they are "feeling good" about the prospects for peace.
Kushner says President Trump has tasked the pair with creating a peace package and that they will do everything they can to end the war for good.
However, major hurdles remain. Russia and Ukraine continue exchanging strikes, while Zelenskyy says any agreement must end the war on fair terms.
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Permissionless Launch on @flapdotsh is now live on BNB Chain!
With support for custom quote tokens, you can now launch your pair using RWAs, blue-chip crypto, trending memes and more.
Note: This post is for informational purposes only and not financial advice. DYOR.
Meritz Securities (Korean sell side): GPT-6 Astra launch and implications for the memory stock rebound
On September 3, OpenAI unveiled GPT-6 Astra. As interpretations of this model became a hot topic, semiconductor stocks rebounded on September 4 even as the broader US market fell on rising rates following a jobs surprise, with the DRAM ETF up 6.6% versus the prior day.
Astra's implication is unlikely to be simply whether AGI has been achieved. What drew the most attention was the score of 99.9% on ARC-AGI-3, a benchmark used to judge AGI, a huge improvement over the previous model (Sol at 7.8%). This benchmark is not a knowledge test; it evaluates a model's ability to learn on its own in an abstract environment it has never seen before, and this is where the improvement over the previous model was large. General intelligence, as measured by AAII or Humanity's Last Exam, did not improve much.
The differentiated strength improved in Astra is "the ability to acquire skills that humans learn in an unfamiliar environment as efficiently as a human does." Where AI until now found the answer by pressing this and that 100 times, Astra has started to behave more like a human: observing the phenomenon, inferring the rules, and executing right away.
The key point is "an expanded scope for replacing human intelligence and human work." If existing AI was an AI that told you what to do, Astra is closer to an AI that, given only a goal, uses the computer directly and produces the result all the way to the end. In other words, an easy to use OpenAI model has begun to handle on its own part of the agent orchestration layer that had been the domain of less accessible tools such as OpenClaw. For users, the barrier to entry for AI agents has been lowered, meaning more work can be handed over.
Expansion of AI workloads
Astra naturally also comes with efficiency gains that lower the token cost per task versus the previous model. This is a trend across the AI industry as a whole, and if AI workloads were fixed, demand for AI data centers would have to plunge.
The reason Jevons paradox continues to operate even after token price declines became a trend following the rise of Chinese models is that AI technological progress also expands the workload. What Astra's technological progress means is that where humans used to hand five minute, ten minute, and twenty minute tasks to AI, as AI performance improves and token prices get cheaper there is more to hand over, such as one hour and 24 hour tasks.
Just as news flow about rising GPU rental prices has spread since Astra's arrival, it must be understood that falling AI token prices do not necessarily shrink or slow the AI hardware TAM. Rather, one should recognize that the emergence of a model like Astra can create another inflection point for the AI industry and structurally grow AI demand.
Our understanding is that since early July, as the pace of GPU rental price increases slowed and token prices fell, a long IGV (software) / short SOX (semiconductors) pair trade has persisted in the US. This is because falling token prices were interpreted as positive for software, where tokens are a cost, but negative for infrastructure.
If progress in models like Astra structurally spreads AI workloads and GPU rental prices begin to respond again, the perception that falling token prices are bad news for AI infrastructure companies could weaken (on 9/4 the DRAM ETF rebounded while IGV fell).
As we have argued consistently, the issues accumulating in the AI industry since June (the proliferation of open models, this GPT-6 Astra release, and so on) are, in our interpretation, positive catalysts that generate new demand for AI infrastructure and hardware that did not exist before. We think that in a phase where rates are rising overall and liquidity is becoming scarce, these accumulated positives are not being reflected.
The stock market in September is still uncomfortable with high rates, and within the Korean market there remain hurdles to get through, including digesting a round of earnings estimate cuts driven by the sharp won appreciation before the 3Q26 preview season. There is still discomfort standing in the way of the accumulated positives being reflected in a sustained trend. Overall, we continue to view the market conservatively.
Even so, as emphasized in our September strategy, we believe one should not substantially empty out positions in core AI infrastructure stocks centered on memory. Positive catalysts not reflected in share prices are accumulating. While our baseline is conservative through mid October, one should keep the upside risk open that the trend, led by AI and semiconductor leaders, could turn at any time, even before October.
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LaunchLab now supports any token pair on Raydium.
The upgrade brings flexible pairing directly to Solana, with deeper liquidity, lower fees, and stronger meme-native trading.
@LaunchOnSF is the first integration partner to bring the model live on LaunchLab.
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PAIR/USDT 180s Up 5.09% $0.0299 rose to $0.0314
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quote: home of the hatted https://x.com/blknoiz06/status/2096455045810147651 | solana is better when you pass the baton to the wif guys instead of the new pair abusers