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🍌 nano banana pro prompt Isometric Miniature Stock Scene Enter a company name or stock ticker to generate an exquisite, miniature isometric 3D scene integrating real-time stock data for the specified date. inspired by @keithso27 's tweet ---- Present an exquisite, miniature 3D cartoon-style scene of the company corresponding to the user-specified company name or stock ticker, clearly viewed from a 45° top-down perspective. Place the company's most iconic building or campus prominently at the center, complemented by proportionally-sized icons of its key products, charming cartoon-style figures, vehicles, and other elements illustrating everyday company activities. The scene should be detailed, finely crafted, and playful. Rendered with Cinema 4D, the modeling should be refined, smoothly rounded, and rich in texture, accurately capturing realistic PBR materials. Gentle, lifelike lighting and soft shadows should create a warm, comfortable ambiance. Creatively integrate the company's real-time stock market data for the user-specified date (or automatically retrieved current date) into the scene, maintaining a clean, minimalist layout and a solid-color background to highlight the primary content. At the top-center of the scene, prominently display the company name or stock ticker in a large font size, followed by the specified date in extra-small font, and the stock price range in a medium-sized font. Include clear, intuitive stock trend icons and charts. All texts should be displayed in the language specified or entered by the user, without any background, and may subtly overlap with the scene elements to enhance overall design integration. Very Important: Before generating, ensure accurate and up-to-date stock market data based on the user-inputted company name or stock ticker and the specified date. If such data is unavailable, notify the user immediately and stop the generation process. Parameters: * Aspect ratio: {User input, default 1:1} * Date: {User input, current date} * Company name or stock ticker: {User input} --- Company Name / Stock Ticker: Google Date: 12/3/2025

GF Overseas Electronics & Communications NVIDIA (NVDA Buy): Earnings Preview — Product Mix Offsets the Impact of Rubin Delay Reiterate Buy rating; target price modestly raised to $308: Driven by rising market expectations ahead of the upcoming earnings call and the U.S. approval on May 14 to export H200 chips to 10 Chinese companies, NVIDIA’s share price has reached a new all-time high. For the May 20 earnings call, we expect results to be in line with expectations, with guidance slightly above expectations. Given the company’s sizeable cash position and free cash flow, the announcement of a new share repurchase program during the earnings call would be a reasonable expectation. In addition, Rubin’s timeline remains a key market focus. We reiterate our view that mass production will be delayed by about one month to September, while the 2300W specification remains unchanged. Beyond GPUs, with Vera CPU and LPX, the company is expected to capture a larger share of value within the data center silicon TAM. For product re-rating, we still believe NVIDIA needs to launch an inference-focused GPU, potentially Feynman. Taking into account higher Blackwell shipments and ASPs, lower Rubin shipments, and LPX contribution, we adjust our FY27E/28E EPS forecasts by +0%/+13%, respectively, and modestly raise our target price from $292 to $308, based on 28x FY27E/28E P/E. Earnings preview — expected to be moderately positive: Driven by normal GB300 NVL72 production, approximately 10% QoQ growth in Blackwell shipments, and a small contribution from RTX6000, we now expect F1Q revenue of $80.6 billion, compared with Bloomberg consensus of $78.8 billion and buy-side expectations of $80.0 billion. As Blackwell shipments continue to increase, we expect F2Q guidance of $91.0 billion, or actual revenue of $93.0 billion, compared with Bloomberg consensus of $86.2 billion and buy-side expectations of around $90.0 billion. Our forecast does not include any Rubin contribution, as we believe it has already been delayed to mass production in September. Given the company’s sizeable cash position and free cash flow, its capital return plan is also worth watching. Blackwell, Vera, and LPX will offset the widely known impact of Rubin’s delay: According to our monthly report published on the 12th, due to earlier heatsink design changes, we expect Rubin’s timeline to be: QS in July, MP in September, and rack mass production in October. In terms of performance, we believe the redesigned version will still maintain the 2300W specification this year. On the other hand, LPX rack ramp-up is faster than expected. We still expect 16,000 LPX racks to ship from 4Q26 through 2027, contributing approximately $70 billion in revenue during this period. In addition, we believe NVIDIA may have shifted N3 wafer capacity from Rubin to Vera CPU in order to capture the Agentic AI trend. Overall, with deeper software–chip integration, the company is likely to capture a larger share of the data center silicon TAM. $NVDA

Why did xAI hand over a 220,000-GPU cluster to Anthropic? The technical backdrop to xAI's decision to hand Colossus 1 over to Anthropic in its entirety is more interesting than it appears. xAI deployed more than 220,000 NVIDIA GPUs at its Colossus 1 data center in Memphis. Of these, roughly 150,000 are estimated to be H100s, 50,000 H200s, and 20,000 GB200s. In other words, three different generations of silicon are mixed together inside a single cluster — a "heterogeneous architecture." For distributed training, however, this configuration is close to a disaster, according to engineers familiar with the setup. In distributed training, 100,000 GPUs must finish a single step simultaneously before the cluster can advance to the next one. Even if the GB200s finish their computation first, the remaining 99,999 chips have to wait for the slower H100s — or for any GPU that has hit a stack-related snag — to catch up. This is known as the straggler effect. The 11% GPU utilization rate (MFU: the share of theoretical FLOPs actually realized) at xAI recently reported by The Information can be read as the numerical fallout of this problem. It stands in stark contrast to the 40%-plus MFU figures achieved by Meta and Google. The problem runs deeper still. As discussed earlier, NVIDIA's NCCL has traditionally been optimized for a ring topology. It works beautifully at the 1,000–10,000 GPU scale, but once you push into the 100,000-unit range, the latency of data traversing the ring once around becomes punishingly long. GPUs need to churn through computations rapidly to keep MFU high, but while they sit waiting endlessly for data to arrive over the network fabric, more than half of the silicon falls into idle. Google sidestepped this bottleneck with its own custom topology (Google's OCS: Apollo/Palomar), but xAI, by my read, has not yet reached that stage. Layer Blackwell's (GB200) "power smoothing" issue on top, and the picture comes into focus. According to Zeeshan Patel, formerly in charge of multimodal pre-training at xAI, Blackwell GPUs draw power so aggressively that the chip itself includes a hardware feature for smoothing power delivery. xAI's existing software stack, however, was optimized for Hopper and does not understand the characteristics of the new hardware; when it imposes irregular loads on the chip, the silicon physically destructs — literally melts. That means the modeling stack must be rewritten from scratch, which in turn means scaling is far harder than most of us imagine. Pulling all of this together points to a single conclusion. xAI judged that training frontier models on Colossus 1 simply was not efficient enough to be worthwhile. It therefore moved its own training workloads wholesale onto Colossus 2, built as a 100% Blackwell homogeneous cluster. Colossus 1, on the other hand — whose mixed architecture is far less crippling for inference, which parallelizes more forgivingly — was leased in its entirety to an Anthropic that desperately needed inference capacity. Many observers point to what looks like a contradiction: Elon Musk poured enormous capital into building Colossus, only to hand the core asset over to a direct competitor in Anthropic. Others read it as xAI capitulating because it is a "middling frontier lab." But these are surface-level reads. Look at the numbers and a different picture emerges. xAI today holds roughly 550,000+ GPUs in total (on an H100-equivalent performance basis), and Colossus 1 (220,000 units) accounts for only about 40% of the total available capacity. Colossus 2 — built entirely on Blackwell — is already operational and continuing to expand. Elon kept the all-Blackwell homogeneous cluster (Colossus 2) for himself and leased out the older, mixed-generation Colossus 1. In other words, he handed the pain of rewriting the stack — the MFU-11% debacle — to Anthropic, while keeping his own focus on training the next generation of models. The real point, then, is this. Elon's objective appears to be positioning ahead of the SpaceXAI IPO at a $1.75 trillion valuation, currently floated for as early as June. The narrative SpaceXAI now needs is that xAI — long the "sore finger" — is not merely a research lab burning cash, but a business with a "neo-cloud" model in the mold of AWS, capable of leasing surplus assets at high yields. From a cost-of-capital perspective, an "AGI cash incinerator" is far less attractive to investors than a "data-center landlord generating cash." As noted above, the most important detail of the Colossus 1 lease is that it is for inference, not training. Unlike training, inference requires far less tightly synchronized inter-GPU communication. Even when the chips are heterogeneous, the workload parcels out cleanly across them in parallel. The straggler effect — the chief weakness of a mixed cluster — is essentially neutralized for inference workloads. Furthermore, with Anthropic occupying all 220,000 GPUs as a single tenant, the network-switch jitter (unanticipated latency) that arises under multi-tenancy disappears. The two sides' technical weaknesses end up complementing each other almost exactly. One insight follows. As a training cluster mixing H100/H200/GB200, Colossus 1 was an asset that could only deliver an MFU of 11%. The moment it was handed over to a single inference customer, however, that asset transformed into a cash-flow asset rented out at roughly $2.60 per GPU-hour (a weighted average of the lease rates across GPU types). For xAI, what was a "cluster from hell" for training has become a "golden goose" minting $5–6 billion in annual revenue when redeployed for inference. Elon's genius, I would argue, lies not in the model but in this asset-rotation structure. The weight of that $6 billion becomes clearer when set against xAI's income statement. Annualizing xAI's 1Q26 net loss yields roughly $6 billion in losses per year. The $5–6 billion in annual revenue generated by leasing Colossus 1 to Anthropic, in other words, almost perfectly hedges xAI's loss figure. This single deal effectively pulls xAI to break-even. Heading into the SpaceXAI IPO, this functions as a core line of financial defense. From a cost-of-capital standpoint, if the image shifts from "research lab burning cash" to "infrastructure tollgate stably printing $6 billion a year," the entire tone of the offering can change. (May 8, 2026, Mirae Asset Securities)

Microsoft Wants to Use “Dirty Energy” for AI Data Centers Microsoft may scale back some of its clean energy goals for data centers because AI electricity demand growth is too fast so they will find alternatives The company already met its 2025 target early by signing contracts for more than 40 GW of renewable energy, but new AI facilities are expanding faster than clean power projects, and they can’t connect it to “clean energy.” Microsoft is investing in nuclear power, such as restarting the Three Mile Island plant, while considering natural gas plants as a solution

📌 VI. Trigger Matrix (V2.0 – Observation Status Log) Observation Item Current Value Threshold Status Consecutive Days/Trend Super-Capital Concentration Risk 9.3 8.0 ESCALATION ↑ 1 day (new) AI Governance Risk 8.8 8.0 ESCALATION ↑ 1 day (new) Resilience Ratio 0.63 0.70 ESCALATION ↑ 4 days US-Iran Deal Signing Status 接近 Formal Signing WATCH — Brent Crude Oil Price 3-mo low — THRESHOLD_CROSSED 1 day New Ebola Health Zone (DRC) Confirmed spread — THRESHOLD_CROSSED 3 days EU Accession Talks Launched — THRESHOLD_CROSSED 1 day Items Near Threshold (Elevated Observation): Observation Item Current Value Threshold Current Status • Formal signing of US-Iran deal 接近 Formal Signing ALERT • SpaceX market cap stability Above $2T Drop below $2T WATCH • OpenAI probe scope expands Multi-state Federal involvement ALERT • G7 Summit statements on AI & trade 即将 held Substantive regulatory共识 WATCH • Cross-border Ebola spread Risk rising First邻国 confirmed case ALERT • Clustered cases in fan zones No reports Confirmed cluster transmission WATCH --- 📅 VII. Key Observation List for the Next 72 Hours Grade A Observations (High Impact): Observation Item Potential Impact if Triggered 1. Formal signing of US-Iran MOU Geopolitical entropy pressure declines further, but execution risk仍需 assessed. 2. SpaceX market cap stability above $2T Test of sustainability for super-capital concentration narrative. 3. OpenAI probe expands to federal level Potential further upgrade to AI governance risk level. 4. G7 Summit statements on AI & trade First collective test of institutional response capacity. Grade B Observations (Medium Impact): Observation Item 1. Expansion of Ebola outbreak zone in DRC 2. Subsequent日程 for EU accession negotiations 3. Public health data during FIFA World Cup --- 📜 VIII. CRI Calculation Summary (V1.6) Variable Weight Risk Score Weighted Contribution V_capital 20% 9.3 1.86 V_tech 18% 8.8 1.58 V_inst 18% 8.1 1.46 V_geo 15% 7.5 1.13 V_human 10% 7.6 0.76 V_expansion 8% 7.9 0.63 V_market 6% 7.2 0.43 V_energy_price 5% 6.5 0.33 Total 100% CRI = 8.2 Calibration Notes: Added V_capital variable (weight 20%) to reflect super-capital concentration as a new structural risk dimension. V_tech上调 to 8.8 (AI governance race launch). V_geo下调 to 7.5 (US-Iran deal接近, declining war risk). --- 📌 IX. Structural Conclusion On June 13, 2026, the global civilizational system's Resilience Ratio remains below the 0.70警戒线 for the fourth consecutive day. What is most worth recording today is not war – but the first time in human civilization that private wealth approaches the GDP of a中等发达国家. When a single entrepreneur owns a satellite network, rocket system, AI platform, energy network, financial capital, and global data流入口, civilization is entering a new organizational form: Transitioning from nation-state-led civilization to platform-infrastructure-led civilization. If the core question of the 20th century was "How to constrain state power?", then the core question of the latter half of the 21st century may well become "How to govern super-platform power."

$LMND is in a war most investors still don’t understand. Not just a war for customers. A war for narrative. A war for credibility. And ultimately, a war for its cost of capital. That matters because public markets do not merely observe a company’s trajectory. They can shape it. A company with a trusted narrative gets patience, liquidity, talent, strategic freedom, and cheaper capital. A company trapped inside the wrong narrative pays a tax on every ambition. Lemonade is still widely framed by many as an unproven, money-losing insurtech experiment. But the operating data has been moving in the opposite direction. IFP is growing. Revenue is accelerating. Gross profit is scaling. Loss ratios have improved materially. Cash flow is inflecting. The company is no longer asking investors to believe in a concept. It is increasingly asking them to reconcile their old model with new facts. And I have seen this movie before. First with Apple. Then with Tesla. In both cases, the market spent years debating the wrong questions while the business quietly answered the important ones. The consensus kept focusing on what the company used to be, or what incumbents wanted it to be, while the operating model kept compounding underneath. Lemonade is not Apple. Lemonade is not Tesla. But the pattern is familiar: a misunderstood company, a disruptive operating model, a hostile narrative environment, and a widening gap between perception and execution. That gap is where the opportunity lives. I have spent twenty years studying disruption as an investor. I also spent twenty years inside financial markets infrastructure, transformation, and business management. Those two tracks have rarely felt as connected as they do here. This is not about blind faith. It is about pattern recognition, operating evidence, market structure, and narrative reflexivity. The short interest is not the thesis. The business is the thesis. But when a company is executing and the market remains anchored to an outdated story, narrative becomes part of the battleground. And when that narrative affects valuation, liquidity, and cost of capital, it becomes more than noise. It becomes strategic. I am long $LMND because I believe the market is still underestimating the scale of what is being built. I could be wrong. That is always possible. And I invite the scrutiny. But I know what this setup looks like. And I know how rare it is. So strap in. Because history may not repeat itself. But it all too often rhymes.

Last week in crypto was not just about prices going down. It was more about seeing how fragile the market still is when liquidity starts to thin out. BTC traded below 60K during the week. According to CoinShares, digital asset investment products saw 1.67B in outflows. 1.438B of that came from Bitcoin products. 257M came from Ethereum products. At the same time, ETF demand clearly started to weaken. Then the stronger US jobs data reminded the market of something simple: the liquidity story is not over yet. The US added 172K jobs in May. That made rate cut expectations weaker, and pressure came back across risk assets. Not just crypto. Nasdaq fell 4.2% on Friday. S&P 500 closed the week down 2.6%. In crypto, that pressure was amplified by leverage. Around 1.6B in positions were liquidated. The lesson for me is pretty simple. When the market is going up, everyone looks at the story. When the market is going down, you have to look at the mechanics. → who was buying → are they still buying → how much leverage was sitting in the system → does the macro environment still support the move This week was a good reminder of something people often forget. A good narrative does not mean there is always a real buyer at every price level. When ETF inflows are strong, dips get absorbed more easily. When ETF flows turn negative, the same dip feels much heavier. That was the main takeaway for me. Not that crypto is broken. More that even strong narratives become fragile when liquidity is not there to support them.

So how is my upcoming meeting going to go with @BarrsDerek @FMCSA? The UCR System is illegal. Your release of PII data, including truckers' social security numbers, which the UCR System Plan published on their website in 2019, to them is illegal and therefore beyond "routine use." We asked you to make the UCR System legal through the rulemaking required by Congress under the UCR Act. You refused. So we sued you. We have asked you to decertify CA and NY CDL issuing authority because the law requires you do so whenever you make a final determination of substantial non-compliance with 49 USC Chapter 313. You issued such notices to both states. After the VA bus 5 person fatality last week, now I guess we will have to sue you, in addition to these two states. The agency has engaged in unreasonable delays on our May 2020 petition for freight broker rate transparency in violation of the APA (5 USC 555). Your promise to issue a second NPRM in May of 2026 was reneged upon. Now I guess we will have to sue you. Your agency is supposed to respond to FOIA requests within 20 days. I filed a FOIA/Privacy Act request in November of 2016 designed to obtain information about an illegal investigation FMCSA conducted on my private business without statutory authority in malicious retaliation for two lawsuits I filed against FMCSA on behalf of industry. My FOIA request is the oldest open FOIA request according to the office of @SecDuffy. It is approaching 10 years that the agency has withheld records that would show it made false claims against me in conspiracy and collusion with the states & UCR Plan System Board members, which includes establishment trade associations. I guess I will have to sue you. Did I get that right @SocciLaw? @DOTInspectorGen Are you seeing a pattern of corruption, obstruction, and negligence here @FMCSA? Do you even care? You and @FMCSA didn't care when we implored you to stop daily data breaches by a permitting service apparently engaged in criminal instrusion through corrupt FMCSA or LEO real-time data access. Instead, you issued a false audit report on FMCSA system vulnerabilities. So we went to the @FBI and media in 2021... How is that intrusion investigation & prosecution going @USDOT @USDOTRapid @USAttyPirro @DAGToddBlanche @TheJusticeDept? This is just the tip of the iceberg, folks. Time to weed out the corruptocrats, Mr. Barrs.

Former BlackRock fund manager Ed Dowd on the AI bubble "pop" "we're at maximum AI hype right now" "they're [punching] out three IPOs, SpaceX, Anthropic and OpenAI" "a lot of these companies... [are] not going to go away" "[But] OpenAI may go to zero and Anthropic may go to zero, [and] their assets will be bought up for pennies on the dollar" This clip of Dowd (@DowdEdward), a former BlackRock fund manager and co-founder of Phinance Technologies, is taken from an interview with Greg Hunter (@USAWatchdog) posted to Rumble on May 29, 2026. ----------------Partial transcription of clip--------------- "What it means is eventually all this CapEx spending stops because the credit markets and I suspect— we're at maximum AI hype right now because they're trying to punch out three IPOs, SpaceX, Anthropic and OpenAI. And these guys are not making enough money to justify the amount of CapEx they're doing. "The other thing that I think is going to potentially blow up the AI bubble is they don't have enough power to plug in all this CapEx into. "So they're announcing all this CapEx, they're pre-buying equipment and chips but they can't plug it into the power grid. We just don't have enough power to justify all these data center buildouts. The constraining factor is power. "And look, there's a disconnect. I think Wall Street is less focused on the public outrage that's going on that you can see happening all across the country. People are protesting these data centers. College, students are booing commencement speakers that talk about AI. "There seems to be a very, very large anti AI sentiment going on out there which will muck up the works and slow down the data center buildouts politically. "And if you slow down the capex build out, the valuations of all these companies go a lot lower because they rely on you know, exponential growth and when the growth doesn't show up at these valuations they'll pop... "And look a lot of these companies that are doing the AI, Microsoft, Oracle, Google, they're not going to go away. They'll just cut back their CapEx. They won't go bankrupt but their valuations and their earnings will go lower as they write off all these mal investments. So it's not like a lot of companies are going to go bankrupt. I mean, OpenAI may go to zero and Anthropic may go to zero, but their assets will be bought up for pennies on the dollar."