Quiet month? Not on Base
Here’s 25 things the Base ecosystem launched and announced in August
1) @bitso made MXNB available through Base. Users can deposit Mexican pesos on Bitso, withdraw MXNB on Base, and use the Juno vault on @morpho
2) @bitwise launched Automated Token Portfolios on Base with @glider__, using Coinbase Tokenized Stocks. Eligible users can hold 1:1 share-backed equity portfolios in self-custody and use them as collateral in DeFi
3) @awscloud made AgentCore Payments generally available with @coinbase and @stripe using x402. Agents can pay for APIs, content, data feeds, and more
4) @travalacom integrated Amazon Bedrock AgentCore payments from @awscloud into the Travala Travel MCP. Agents can book 2.2M+ hotels with USDC using Base
5) @o1_exchange launched a Stock launchpad, built natively on the B20 Stock token standard on Base. Issuers can launch stock tokens on Base through that pad
6) @avantisfi launched V2 in private beta on Base. Existing Avantis traders can trade 100+ assets, RWAs, and Upside Perps
7) @youdotcom is adding x402 support to its Web Search API on Base. AI agents can pay per query for live web data without API keys, subscriptions, or a human checkout
8) @bankrbot partnered with @aerodromefi on an Aerodrome Stock LP skill. Users can become LPs for tokenized stocks on Base with a Bankr agent opening and managing the position 24/7
9) @baibai_cx went public on Base as a PropAMM + aggregator built by @spire_labs. Traders can swap against onchain market-maker quotes and aggregated DEX liquidity
10) @overseepay went live on Base. Users can move dollars onchain for remittances to Pakistan
11) @credifi opened unsecured loans of up to $3,000 for @ethos_network users with a Credibility Score of 1800+. Eligible borrowers can take credit with no collateral and no liquidation
12) @send launched Send Cards. Users can fund a card with USDC on Base in the Send app and spend that balance on everyday purchases
13) @joincero introduced their product. Cero card will convert everyday spend into a 0-1000 score toward rewards and credit when early access starts
14) @longshotxyz launched on Base. Eligible non-US regions get free and paid prediction contests plus customizable combo markets
15) @syntetika opened deposits with BTC Basis+, a delta-neutral Bitcoin basis strategy managed by @hilbertcapital using cbBTC on Base
16) @peerxyz launched on iOS and Android. Venmo, Cash App, Revolut, Wise, PayPal and similar apps can fund USDC on Base from the phone
17) @zothdotio announced zVaults on Base opening soon. One deposit will hold a basket of tokenized US stocks in a self-custodial wallet
18) @forevermoney_ai brought the Bittensor ecosystem to Base via Chainlink CCIP. Bittensor’s AI network can now plug into Base apps for compute, coding, vision, and prediction
19) @zaruniversal launched ZARU on Base. A 1:1 rand-backed stablecoin settles payments onchain in seconds instead of one to three business days
20) @anoma launched AnomaPay on Base. Base assets can be deposited and sent confidentially with payment links
21) @rena_labs launched Insider Scan on Base. Users can exchange liquidity, spreads, depth, and volume sit in one view, with plain-language questions
22) @veildotcash activated NVDAc in its Multi-Asset Pool on Base. Tokenized Nvidia can be deposited and sent privately through Veil
23) @dinariglobal launched regulated dShares of the full S&P 500 for US investors and businesses, live on Base. US holders get 24/7 onchain access with voting, dividends, and a claim to the backing security
24) @b3labs introduced B3IQ. Teams can own a hosted Nvidia GPU over time, run private inference on that hardware, and rent idle capacity, with payouts in USDC on Base
25) @pixiechess is live on a mobile browser. Chess for real money can be played from a phone browser on Base
reply @n1ckler @robin_linus So my point is, the most recent CPU improvements targeted at specific crypto algorithms have skipped over SHA-256 and improved other crypto algorithms. ⤵️
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@n1ckler @robin_linus Armv8.2-A crypto in 2016, added improvements for SHA-3, SHA-512, SM3, and SM4, but not SHA-256. SVE2 in 2019 improved AES, SHA-3, and SM4, but not SHA-256. Intel Lunar Lake in 2024 improved SHA-512, SM3, and SM4, but not SHA-256: zooko.github.io/cpu-crypto-sup… ⤵️
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reply @n1ckler @robin_linus Armv8.2-A crypto in 2016, added improvements for SHA-3, SHA-512, SM3, and SM4, but not SHA-256. SVE2 in 2019 improved AES, SHA-3, and SM4, but not SHA-256. Intel Lunar Lake in 2024 improved SHA-512, SM3, and SM4, but not SHA-256: https://zooko.github.io/cpu-crypto-support-history/htmlversion.html
Nvidia just sent a strong signal to AMD and Intel investors
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Nvidia just sent a strong signal to AMD and Intel investors. Nvidia's Vera CPU push targets a market AMD and Intel have long controlled. Here's what the $20 billion server CPU forecast means for chip investors in 2026.
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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reply I’m not quite sure what they mean by an improvement in notebook CPU supply at this point. Could they be referring to improved yields on Intel 18A? @Alex_Intel_
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SK hynix sharply raises share of leading edge DRAM… 1c to become main process early next year
SK hynix is rapidly expanding the production share of its sixth generation 10nm class (1c) DRAM. With 1c DRAM set to serve as the core die stacked into next generation high bandwidth memory (HBM4E), the company appears to be accelerating its process transition. As demand for high value added memory for servers and artificial intelligence (AI) grows, the race with Samsung Electronics and Micron to migrate to finer process nodes is also heating up.
"1c process share expected to exceed 34% within the year"
According to industry sources on the 7th, SK hynix's 1c DRAM share rose from around 10% in the first quarter of this year to the 13% range in the second quarter. It is projected to reach the 24% range in the third quarter and the 34% range in the fourth quarter. In the first quarter of next year, the 1c share is expected to climb to the 35% range, overtaking 1b (33% range) for the first time and becoming the company's main process.
The share of 1b (fifth generation 10nm class) DRAM was found to have peaked at the 43% range in the second quarter of this year and turned downward. As the production shift to 1c gets into full swing, the share of older generation processes is shrinking in sequence. Industry estimates show that, as of the end of the second quarter, Samsung Electronics' 1c share stood in the 16% range and Micron's in the 19% range, somewhat ahead of SK hynix (13% range). However, with SK hynix stepping up the pace of its transition in the second half of this year, it is expected to overtake Samsung Electronics (31% range) on a fourth quarter basis (34% range).
On its second quarter earnings conference call last month, SK hynix said that supply of DRAM built on the sixth generation 10nm class (1c) process had begun in earnest in the second quarter. The company projected that bit growth (the rate of increase in production volume) in the second half of this year would exceed the first half, driven by expanding HBM4 (sixth generation HBM) volumes and rising shipments of 1c based commodity DRAM.
Process transition in preparation for HBM4E performance gains
The battle for HBM4 leadership between Samsung Electronics and SK hynix is also intertwined with the pace of the 1c transition. Samsung Electronics is applying 1c DRAM from the HBM4 stage onward and is touting top tier performance with operating speeds of around 11.7Gbps. SK hynix, by contrast, chose a strategy that prioritizes mass production stability, relying on its proven 1b DRAM and advanced MR-MUF packaging technology, and is applying 1c DRAM as the core HBM die for the first time starting with next generation HBM4E (seventh generation HBM).
Industry observers say this difference in strategy is being reflected in the two companies' market shares. Major research firms including Counterpoint Research project this year's HBM4 market share, on a combined basis across NVIDIA, Google, AMD and others, at the mid 50% range for SK hynix, the high 20% range for Samsung Electronics, and the high 10% range for Micron. The picture is one in which SK hynix holds its volume advantage on the strength of mass production stability, while Samsung Electronics seeks to expand share through a technical spec advantage.
Against this backdrop, analysts say SK hynix's push to speed up the 1c transition will translate into tangible benefits in cost and productivity, beyond simply shrinking the node. Since bit output per wafer rises compared with the previous generation, more bits are produced from the same wafer input, improving cost competitiveness, which is cited as a factor that will help defend DRAM segment profitability from the second half onward. With the 1c transition proceeding alongside a growing mix of high value added products for servers and HBM, analysts say productivity gains are highly likely to feed directly into margin improvement.
An official in the semiconductor industry said, "The pace of the 1c transition itself is encouraging, but it only becomes meaningful if actual production yields and customer qualification schedules back it up," adding, "Starting with HBM4E, the fine process competition between Samsung Electronics and SK hynix will intensify further."
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AMDSTOCK/USDT OI 5min Up 5.51% $1.54M rose to $1.63M, Price -0.04%, New Shorts Entering
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AMD/USDT OI 5min Down 7.89% $114K dropped to $105K