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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

Mark Zuckerberg explains the 405B teacher-model flywheel that could make one giant AI the wrong end state "People are gonna wanna do inference directly on the 405 because it's, you know, by our estimates, it's gonna be about 50% cheaper, I think, than GPT-4o to do that directly." "Because it's open weights, the ability to take the model and distill it down to whatever size that you want, to use it for synthetic data generation, to use it as a teacher model." "Our vision is that there should be lots of different models. I think every startup out there, every enterprise, governments, they all kind of wanna have their own custom models." "Right now, as open source basically closes the gap, I think you're just gonna see this wide proliferation of models where people now have the incentive to basically customize and build and train exactly the right size model for what they're doing, train their data into it." "They're gonna have the tools to do it because of a lot of the partner integrations that the companies like Amazon are doing with AWS or Databricks or different folks like that who are building these whole suites of services for distilling and fine-tuning open models." The counterintuitive edge is that the 405B model may be most valuable as raw material, not an endpoint. The open model compresses into the right size, absorbs proprietary data, and turns one frontier release into thousands of company-specific systems. Distribution of intelligence beats centralization. - Mark Zuckerberg (@finkd), CEO of Meta, with @rowancheung

Beautiful paper from Google DeepMind. Explains the pathways from AGI to ASI, and why that jump could happen through several routes. The authors frame the AGI-to-ASI transition around 4 technical pathways: - continued scaling of compute, model size, data, and test-time inference; - algorithmic paradigm shifts beyond today’s transformer-based foundation-model stack; - recursive self-improvement, where AI accelerates AI R&D and improves future systems; and - multi-agent collective intelligence, where large populations of specialized agents coordinate into a superhuman group agent. Scaling may work for a while, but it could hit limits in data, compute, energy, or weaker returns from making systems larger. Recursive improvement is the most uncertain path, because AI could speed up AI research, but that loop may also slow if hard research problems need real-world testing, scarce hardware, or new ideas. Multi-agent collectives may be the most underappreciated path, because a society of competent digital workers could outperform a brilliant individual model through specialization, speed, and coordination. The big point is that ASI may not arrive as 1 sudden event, but as a chain of faster changes as AI helps create better AI and stronger scientific tools. ---- – arxiv. org/abs/2606.12683 Title: "From AGI to ASI"

Very welcome recent news from Signal: they are working on letting you register an account without a phone number. That said, an important counterpoint about what this would and would not accomplish. The good #1#: reducing dependence on phone numbers. Even aside from privacy benefits, reducing dependency on a highly oligopolistic system of chokepoints is good in itself. The good #2#: phone numbers are for many people not a good "root" of identity from an access control perspective. Phone numbers get sim swapped all the time. The good #3#: allowing phone-number-free accounts will make it harder for them in the future to discriminate against people by country - and so make it harder for governments to pressure them to block their own citizens. Now, on privacy. Significantly better than status quo, so yes it is good #4#, but... In practice, in 2026, I believe that pseudonymity (a long-term persistent account that is not tied to your primary identity) is a dead concept. There are just too many channels by which we accidentally slowly leak data about who we are - timing of messages, the pattern of who we send messages to with what frequency, size, etc. And too many highly effective AI-based means (both using LLMs per-user, and LLMs helping every person and agency under the sun use math that we had all along) to uncover and piece together those hints. As a trivial example, whatever server you interact with learns your IP address, but even if you hide *that* with a VPN or Tor, there are many other identity leakage vectors. And so the only defensible form of privacy is *message-by-message unlinkability* - no one except sender and receiver knows the (sender, receiver) pair, ideally even not knowing who the sender or the receiver are. A natural taxonomy of privacy is the following 2x2: * Sitting duck: adversary knows "X did Y" * Confidentiality: adversary knows "X did ???" [E2E encryption provides this] * Anonymity: adversary knows "??? did Y" [aka message-by-message unlinkability] * Ideal: adversary knows "??? did ???" Signal has already had confidentiality for a long time. (Note: in other contexts, "confidentiality" sometimes means "someone knows X did Y, and we trust that someone to not reveal it", ie. not true privacy. Here, by confidentiality we mean hiding contents from third parties) This adds pseudonymity: in the above schema, adversary knows "0x8b512c... did Y", where they don't initially know who 0x8b512c... is, but may figure that out over time. The ideal is getting to message-by-message unlinkability. Actually accomplishing that gets into territory that is currently being explored by mixnet projects as well as newer messengers, eg. @session_app and @SimpleXChat. Once we get deeper into this territory, I suspect the primary frontier will be spam and DoS protection. Right now, much of the internet blocks all Tor exit nodes - not because they personally hate privacy, but because that's where DoS attacks come from. So we need ways for people to prove their non-spammer status while maintaining message-by-message unlinkability. See here for one direction (which complements nicely). So I hope that we appreciate the victory that is mainstreaming of end-to-end encryption, that we actually get Signal accounts without phone number dependency (it's a great thing even if it had zero privacy consequences), and then that we keep moving forward and pushing the frontier of data leakage minimization.

𝗧𝗛𝗜𝗦 𝗜𝗦 𝗠𝗨𝗖𝗛 𝗕𝗜𝗚𝗚𝗘𝗥 𝗧𝗛𝗔𝗡 “𝗧𝗛𝗘 𝗣𝗘𝗡𝗧𝗔𝗚𝗢𝗡 𝗠𝗔𝗬 𝗨𝗦𝗘 𝗔𝗜.” President Donald Trump’s new Executive Order signals a fundamental change in defense acquisition: contractors will increasingly be expected to prove where critical materials, components, equipment, and software originate—through subcontractors and toward the raw-material source. The Pentagon wants to see the defense supply chain, identify where an adversary could interrupt it, and make prohibited foreign sourcing harder to excuse. The order directs the Department of Defense to: • Map designated critical supply chains. • Obtain hierarchical bills of materials showing what is inside a defense product, who supplied it, and where it originated. • Extend visibility beyond prime contractors into lower-tier suppliers. • Identify foreign dependencies, bottlenecks, sole-source suppliers, concentration risks, and single points of failure. • Use AI to analyze acquisition data and expose connections conventional contract reviews may miss. • Accelerate qualification of alternative sources and materials. Beginning January 1, 2027, certain waivers involving covered materials from prohibited sources will become substantially harder to obtain. Contractors seeking waivers may need approved mitigation plans showing how the dependency will be reduced or removed. Fraud, deliberate misrepresentation, or willful failure to implement an approved plan could result in contractual remedies and possible referral to the Attorney General. Restrictions under 10 U.S.C. §4872 concern designated sensitive materials from China, Russia, North Korea, and Iran, including certain specialty metals, rare-earth elements, permanent magnets, and tungsten-related materials. But one distinction matters: 𝗔𝗜 𝗜𝗦 𝗡𝗢𝗧 𝗕𝗘𝗜𝗡𝗚 𝗔𝗨𝗧𝗛𝗢𝗥𝗜𝗭𝗘𝗗 𝗧𝗢 𝗗𝗘𝗖𝗜𝗗𝗘 𝗜𝗡𝗗𝗘𝗣𝗘𝗡𝗗𝗘𝗡𝗧𝗟𝗬 𝗪𝗛𝗜𝗖𝗛 𝗖𝗢𝗠𝗣𝗔𝗡𝗜𝗘𝗦 𝗔𝗥𝗘 𝗧𝗥𝗨𝗦𝗧𝗪𝗢𝗥𝗧𝗛𝗬. AI will help map and analyze supply-chain vulnerabilities. Human officials retain responsibility for waivers, mitigation plans, contracting actions, and enforcement. President Trump and Secretary of Defense Pete Hegseth deserve recognition for this determination to expose hidden dependencies and strengthen the industrial foundation behind the American warfighter. Now comes the decisive test: implementation. AI cannot map what contractors cannot identify, subcontractors will not disclose, or suppliers report inaccurately. 𝗦𝗨𝗣𝗣𝗟𝗬-𝗖𝗛𝗔𝗜𝗡 𝗠𝗔𝗣𝗣𝗜𝗡𝗚 𝗜𝗦 𝗡𝗢𝗧 𝗔𝗗𝗠𝗜𝗡𝗜𝗦𝗧𝗥𝗔𝗧𝗜𝗩𝗘 𝗖𝗢𝗠𝗣𝗟𝗜𝗔𝗡𝗖𝗘. 𝗜𝗧 𝗜𝗦 𝗔 𝗧𝗘𝗦𝗧 𝗢𝗙 𝗪𝗛𝗘𝗧𝗛𝗘𝗥 𝗧𝗛𝗘 𝗦𝗬𝗦𝗧𝗘𝗠 𝗕𝗘𝗛𝗜𝗡𝗗 𝗧𝗛𝗘 𝗪𝗔𝗥𝗙𝗜𝗚𝗛𝗧𝗘𝗥 𝗖𝗔𝗡 𝗦𝗨𝗥𝗩𝗜𝗩𝗘 𝗖𝗢𝗡𝗧𝗔𝗖𝗧 𝗪𝗜𝗧𝗛 𝗪𝗔𝗥. “Don’t sell me hype and call it readiness. Prove it before you put a soldier’s life behind it.” — Linda Restrepo Editor-in-Chief, N360™ — Sovereign Intelligence & National Security Technologies #DefenseSupplyChain# #ArtificialIntelligence# #DefenseIndustrialBase# #NationalSecurity# #MilitaryReadiness#

SAMSUNG’S $200B BROADCOM DEAL IS BIGGER THAN A MEMORY SUPPLY CONTRACT. 📊 Samsung Electronics $005930 and Broadcom $AVGO have agreed to pursue more than $200B of semiconductor cooperation through 2030, covering HBM, advanced foundry manufacturing and packaging. Reuters separately confirmed the $200B Samsung–Broadcom portion of the wider Korea–US semiconductor agreements. The headline number is huge, but the strategic shift matters more. Samsung is trying to move from being viewed as a component supplier to becoming a full-stack AI semiconductor partner: Memory → Foundry → Advanced Packaging The reported scope includes HBM4 and HBM4E for Broadcom’s AI accelerators, sub-2nm foundry processes for key chip products, and integrated support from design optimization through packaging and mass production. That matters because Broadcom is one of the central players in custom AI accelerators and high-speed data-center networking. If this MOU converts into real production orders, Samsung would capture more value from each AI chip—not only memory revenue, but also manufacturing and packaging economics. For the Korean market, Samsung is the clearest direct beneficiary. Equipment, materials and packaging names may follow, but only after capacity expansion, supplier participation and actual purchase volumes are confirmed. I would not treat the entire $200B headline as booked revenue yet. This is still an MOU. The real revaluation depends on: → Final contract volumes → Product and process allocation → Production timing → Pricing and margins → Whether orders translate into utilization and earnings The headline creates expectations. Execution is what creates the rerating. $005930 $AVGO For reference only. This is not investment advice. Avoid excessive leverage, do not chase sharp moves and maintain strict risk management.