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1️⃣ Branding: This year’s visual brand identity was a close collaboration between our team and AI. We started by giving Gemini previous brand guidelines and five years of I/O recaps. We then generated new imagery and iteratively fed outputs back into Nano Banana with feedback. We also used Nano Banana to explore icon styles. Here’s a prompt we used to explore icon styles with Nano Banana in the @GeminiApp: You are an expert image editor. You will be given two images. **Image 1** provides the **texture and material**. **Image 2** provides the **pose, shape, and lighting**. Your task is to create a new image by applying the detailed texture and pattern from the icon in **Image 1** onto the surface of the white icon in **Image 2**. **Crucial Constraint:** The output image must perfectly preserve the exact pose, camera angle, scale, and lighting of the icon in **Image 2**. Do not change its orientation or position in any way. The final result should be the icon from Image 2, but with the texture of Image 1.

Today on MCG: @ColbySaysHi | @prism_lp | $PRISM $PRISM is the first token where holding is providing liquidity. Each whole $PRISM you hold auto-mints one Prism NFT (a 1/5000 share of the same Uniswap v4 LP position) INSANE TEK Highlights from our convo: 03:18 - Uniswap v4 launched with a hooks whitelist, hooks didn't take off until UniPet (a viral unicorn NFT mint), then a Prism dev took it further 04:50 - How Prism works 05:38 - Full-range concentrated liquidity means fees accrue regardless of price/market cap 06:25 - Token unit economics 08:00 - Spectrum index tokens 14:32 - Colby's own product 15:00 - 10% of all Spectrum index fees 19:24 - Article release: "Retail is right to hate crypto" 22:18 - Why burn vs distribute 27:30 - PMF 30:00 - Spectrum V2 35:00 - @Uniswap team has reached out to learn how hooks are being used in the wild

It's been a rough year, but I am still here! I was laid off from my job over a year ago. They moved my operation center to India and I haven't been able to find a job since. In my last attempt, I went through 3 rounds of interviews for a Systems Engineer position. The recruiter informed me I was the best candidate by far. 2 weeks and some days after the third interview I was told I didn't get the position. And that was just this week. At this moment is when I have decided that I need to pursue something I am passionate about. I want to create my own animation studio. I have written several short stories that I want to bring to life and some music videos I'd need to purchase rights to, to use the music for, but I need help! I promise my ideas are worth the investment, especially if you enjoy anime! Please donate to my fundraiser below. Please share if you can. By supporting my fundraiser, you’re helping bring my short stories to life and launching me into a new career I’ve dreamed about since I was young, while still providing for my family. Thank you for believing in me and being part of this journey.

$AMD| The FOMO to buy @AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: @WSJ yesterday came out with an article that @OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from @AnthropicAI has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited @AnthropicAI is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why @OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

NEW: Michael Saylor says confidence in Ethereum has collapsed as competition from Solana, BNB, Hyperliquid, and L2s has weakened the monetary value of crypto tokens, while Bitcoin has strengthened its position as digital capital

🛠️ Heads up: TermMax leaderboard will be down for maintenance on June 12, 8:00 AM (UTC+8), for up to an hour. The app is unaffected: lending, borrowing, and all positions work as normal. Thanks for your patience! 🐬

As we cross into the middle of June, the distinction between legacy blockchains and next generation decentralized infrastructure has never been sharper. The deliberate extension of Season 3 by @NomismaNetwork and the @XOOBNetwork ecosystem is proving to be a masterclass in structural refinement rather than a simple mainnet delay. By operating a fully decentralized application stack natively on dedicated @Chromia subchains, they are actively gathering granular, verifiable data on real user behavior and high frequency profit and loss competitions. This AI ready infrastructure relies on relational database architecture to completely eradicate gas fees and state bloat, allowing users to execute complex decentralized finance strategies without any capital degradation. The market is finally waking up to the fact that building robust, MEV resistant systems requires intensive live environment stress testing, not just rushed timeline promises. This extended testing window is exactly where smart capital is aggressively positioning itself before mainnet finality locks everything in. Because the upcoming token generation event guarantees a ten percent total supply airdrop directly tied to your verifiable onchain footprint, every single transaction you make right now represents a massive, open upside. Securing your Nomizen ID remains the absolute highest priority, as it instantly triggers a three times point multiplier and secures your daily NPoints compounding rate. Every liquidity provision, gasless swap, and daily check in is continuously tracked by the network's relational architecture to validate your authentic cost per action utility. The opportunity to accumulate these massive ecosystem rewards against a token price that does not yet exist is an unprecedented structural advantage, so secure your ecosystem identity today and let your onchain execution dictate your ultimate leaderboard tier.

How to get going: - Turn on perps in your near​.com account. - Fund your account from any supported chain. - Place your trade. - Set your SL and TP. - Monitor your position. Refer a trader and you both earn on every Hyperliquid trade.

Il faut avoir l'honnêteté de reconnaître le coup de génie de la gauche, parce que c'en est un. Le plus grand hold-up rhétorique du siècle tient en un seul mot : raciste. Voici le mécanisme. Après 1945, après les droits civiques, l'Occident a fait du racisme le mal absolu. À juste titre : c'est une de ses plus grandes conquêtes morales. « Raciste » est devenu le mot le plus radioactif de la langue, l'excommunication moderne, la mort sociale instantanée. Le coup de génie a été de détourner ce capital moral. Pas pour protéger des personnes : pour protéger une idéologie. L'égalitarisme des résultats ne gagne jamais un débat sur les faits. Il produit l'inverse de ce qu'il promet, partout, à chaque fois. Alors plutôt que de gagner le débat, on a rendu le débat impayable. Tu questionnes les résultats de l'immigration sans assimilation ? Raciste. Tu défends le mérite ? Raciste. Les maths avancées ? Racistes. Les frontières ? Racistes. Le mot a cessé de décrire un comportement pour décrire une position sur l'échiquier. Et regardez la beauté technique du dispositif. Pas besoin d'arguments : l'accusation suffit. Pas besoin de procès : la dénégation aggrave le cas (votre défensivité prouve votre culpabilité). Pas besoin de police : la peur fait le travail, chacun se surveille lui-même et surveille son voisin gratuitement. Il suffit d'exécuter publiquement quelques exemples par an pour tenir des millions de gens. Une idéologie irréfutable, protégée par un mot imprononçable. Les deux pare-feux du même système : la French Theory avait aboli la vérité, l'accusation a aboli le débat. Est-ce qu'un comité s'est réuni pour concevoir ça ? Pas besoin. Les idées subissent une sélection darwinienne : celles qui survivent sont celles qui se défendent le mieux. Marcuse avait quand même déposé le brevet dès 1965, noir sur blanc : tolérance pour les mouvements de gauche, intolérance pour ceux de droite. Le reste a évolué tout seul. Il faut l'avouer : c'était génial. Mais ce dispositif génial avait un coût, et le coût a un bilan. À Rotherham, le rapport officiel Jay a établi que des fonctionnaires britanniques ont laissé plus de 1 400 gamines se faire exploiter pendant seize ans, en partie par peur d'être traités de racistes s'ils nommaient les faits. Relisez cette phrase. Des enfants ont été sacrifiées à un mot. Voilà ce que veut dire idéologie mortifère : pas une métaphore, un bilan. Et maintenant, regardez ce qui s'effondre sous nos yeux. Une insulte ne fonctionne que si elle fait peur, et une monnaie ne fonctionne que si elle est rare. Ils ont imprimé le mot comme Weimar imprimait le mark. Quand tout est raciste, plus rien ne l'est. Résultat : des tweets qui commencent par « traitez-moi de raciste si vous voulez » récoltent des dizaines de milliers de likes et l'approbation de l'homme le plus riche du monde. Il y a dix ans, cette phrase était un suicide professionnel. Aujourd'hui, c'est un haussement d'épaules. L'hyperinflation a tué la monnaie. Et voilà la vraie tragédie, que les faussaires devront porter : en imprimant le mot sans limite, ils l'ont brûlé pour tout le monde. Y compris pour nommer le vrai racisme quand il existe, car il existe. Les faux-monnayeurs ne détruisent pas que leur arme. Ils détruisent le mot dont une société honnête a besoin. Privée de son mot magique, l'idéologie va maintenant devoir faire ce qu'elle n'a jamais su faire : gagner un débat sur les faits. Elle ne le gagnera pas. Au travail.

There has been a lot of hand wringing on the appropriate valuation of SpaceX. Some large institutions believe SpaceX can only be valued at half what the market seems to be willing to pay for it. Others are claiming it has 15X appreciation ahead of it. Almost all of this difference of opinion comes down to how comfortable you are modeling beyond 2030 and what valuation method you use. 2030 valuation using a traditional Gordan DCF produces a very different result than a 2040 EV/EBITDA Multiple. Both have pros and cons. Most analysts don’t really discuss this and lead with a headline number. We are very comfortable modeling out to 2040, as large portions of what SpaceX is proposing is real world infrastructure, which provides modelable physics constraints to anchor against. The analysis we released today explores this in-depth, its open to the public all the way through IPO. I highly encourage you check it out prior to then. We’ve run 5,000 monte carlo runs across 500 variables (real number, even though it sounds fake) and three valuation methods. This video is of a 3D cloud chart showing every simulation outcome expected in valuation output across two of the most impactful variables to the model when using an EV/EBITDA multiple from 2026 to 2040. The horizontal axis is the steepness of the orbital data center demand S-curve. The vertical axis is the rate at which chip compute efficiency becomes cheaper. Each of the 5,000 dots is one simulated future; green dots are the ones where SpaceX's 2040 value clears the $1.77T IPO line, over time. Under EV/EBITDA valuation through 2040, 96% of our simulated futures clear the expected IPO price once the bell rings Friday. We aren’t publishing this publicly to tell investors what the stock is worth, we’re publishing this to help investors understand the world of outcomes, what the fundamentals suggest through 2040, and what frankly most analysis simply won’t share. SpaceX is a generational company working on long term infrastructure harnessing a domain no one has been able to tap in so far: space. It deserves doing the work as an investor. because this in not financial advice. The cleanest way to hold SpaceX is a bond stapled to a call option (AI-Compute); Starlink is the bond, the near term SatCom annuity that funds the next flywheel. Understand the world of outcomes and take your position accordingly. Comparables and P/E won't take you far enough.