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Qubitor $QBT is a Base-launched post-quantum security ecosystem built around the next migration in crypto security. CA: 0x24D621B1f5f1CF295313C88dC331012140e52D54 The first focus is the account and operations layer: wallets, smart accounts, bridge controls, admin keys, sequencer/operator keys, governance, and upgrade authority. Qubitor Network is developing a post-quantum execution layer track with PQ-native accounts, ML-DSA verification, and no-default-EOA architecture. Quanta Wallet is the official wallet/account layer owned and maintained by Qubitor, built for smart accounts, safer key management, hybrid signing paths, recovery, and future post-quantum upgrades. Quantum resistance is not a single switch. It is a full-stack migration. Liquidity Lock: - Liquidity is locked for 365 days and will be extended 3 months before unlock. Qubitor Foundation: - 3.33% supply locked for 3 months. This allocation is reserved for Tier 1 CEX listing preparation. - 3.33% supply locked for 6 months. This allocation is reserved for ecosystem expansion after launch. Qubitor's direction goes beyond the first market phase, with focus on account-layer security, stronger wallet infrastructure, safer signer models, recovery systems, key rotation, and future post-quantum readiness. This work needs room to grow properly. It involves development, testing, integrations, audits, infrastructure support, and long-term planning. - 3.33% supply locked for 1 year. This is Qubitor's long-term strategic reserve. It is reserved for bigger growth phases such as major partnerships, exchange expansion, market-making support, ecosystem incentives, audits, product development, wallet infrastructure, marketing, grants, and future integrations. The lock keeps this allocation out of early circulation while giving Qubitor room to support larger opportunities when the ecosystem is ready.

$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!

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.

Everyone is talking about getting teams to use more AI. But how much of your AI spend is going toward work that's already been done? In PE diligence, the same VDR documents often get reprocessed across users, workstreams, and sessions. That means firms are paying for the same analysis again and again. As AI vendors move toward usage-based pricing, those inefficiencies start showing up fast. Our latest ToltIQ Insights article from Co-Founder and CIO @RikerTrek looks at why the Silicon Valley obsession with maximizing tokens misses the point in PE, and how the architecture underneath an AI platform affects cost, efficiency, and analytical depth throughout a deal. Read more:

✨ Good news in design! The Sichuan Hall of Historic Figure and Douban Museum, both originally designed by CSCEC, became the Platinum Winner at the 2026 MUSE Design Awards. Inspired by Sichuan’s landscapes and culinary culture, CSCEC design continues to connect architecture with place, culture and people.

🛖The charm of ancient Chinese architecture — the more you appreciate it, the more stunning it becomes! @salahzhang @consulat_de @zhang_heqing @pan_xuesong @xuejianosaka @YDunhai @CG_WangBaodong #architecture# #culture# #building# #AmazingChina# #reels#

Morgan Stanley says SemiAnalysis' report on Nvidia's planned large-scale shipments of 800VDC power architecture being pushed back to 2028 is contrary to their own supply chain checks at Computex. "Nvidia at GTC Taipei Indicated 800 VDC developments are ongoing with 800 VDC power rack being ready for mass production in 3Q26. +-400 VDC development has been redirected to 800 VDC focus across various hyperscalers."

🚨 BMW HAS SOLVED ONE OF HYDROGEN’S BIGGEST PACKAGING PROBLEMS. The company has developed a new “Hydrogen Flat Storage” system for the iX5 that uses seven slim hydrogen tanks instead of two large ones. This flat design fits into the same space as the high-voltage battery pack used in the electric iX5. This is significant because it allows BMW to build the hydrogen-powered iX5 on the same production line as petrol, diesel, plug-in hybrid, and fully electric versions without major changes to the factory or vehicle architecture. The system stores 7kg of hydrogen at 700 bar and gives the iX5 an estimated range of 385 miles. BMW plans to start series production of the iX5 Hydrogen in 2028, using a fuel cell developed in partnership with Toyota. Why this matters: • One of the biggest barriers to hydrogen vehicles has been packaging the tanks without sacrificing interior space or requiring completely separate production lines • This modular “flat storage” approach makes hydrogen powertrains much more practical to manufacture at scale • It gives BMW flexibility to produce multiple powertrains on one platform depending on demand and regional infrastructure The deeper implication: While battery electric vehicles currently dominate, BMW is continuing to develop hydrogen as a parallel technology, particularly for larger vehicles and longer-range applications. Being able to build both BEVs and FCEVs on the same line is a pragmatic engineering step that could make hydrogen vehicles more commercially viable in the future if the refuelling infrastructure catches up. Follow for more frontier automotive and energy technology.

It is with a heavy heart that we announce we are winding down the Botanix network. This decision is the hardest one we have made in four years, and we want to share the reasoning openly because the people who backed us, built with us, and used what we shipped deserve more than a quiet shutdown notice. First off, an immediate practical consideration for the Botanix community: please withdraw your Bitcoin and other assets before July 9th, 2026. When we started in 2022, the pitch was simple enough to say in a sentence: bring real utility to Bitcoin. What that actually meant in practice, and what we have spent nearly four years building toward, was more ambitious than that sentence made it sound. We were trying to build a Bitcoin-based blockchain that could find genuine product-market fit as a platform for Bitcoin applications, without using token incentives to drive growth, manufacture users, or simulate utility. Almost every chain that has launched in the last cycle has reached for the same playbook (issue a token without PMF, engineer the incentive surface, point at the resulting metrics), and we did not believe this route is a viable strategy in the long term. We wanted to know whether a Bitcoin chain could earn its users on the strength of what was built on top of it, the value it brings in the market with Bitcoin itself as the only meaningful economic primitive in the system. And we built it. The Spiderchain went live and stayed live, a year of mainnet operation with one hundred percent uptime and zero security incidents on a genuinely novel cryptographic architecture. We built Dynafed, a dynamic federation that turned the Spiderchain from a static multisig set into a rotating, decentralized one, the technical milestone that most people in this space said could not be built on Bitcoin without compromising trust assumptions. Twenty-five million transactions, two hundred thousand wallets, and tens of millions of dollars in assets moved across the chain, every single number of that earned organically without a token, without airdrops, without points programs, or any of the manufactured-demand machinery. Chainlink, Morpho, GMX, Dolomite, Fireblocks, Alchemy, Galaxy, OKX Wallet, all integrated. We shipped a Bitcoin neobank with BINK on iOS and Android, with self-custodial email login for Bitcoin (something that had never existed before), native Bitcoin yield, and the lowest borrowing rates against Bitcoin anywhere in the world, all of it downstream of owning the infrastructure. The point of saying this is not to argue with our own conclusion. The protocol works, the product works, and our team and ecosystem worked in concert to do exceptional work. We have run this experiment in earnest, with a working protocol, real applications, and a serious team, for over a year on mainnet and nearly four years in total. The honest answer we have arrived at, after living inside it every day, is that it did not work, at least not in this market and not on this timeline. We want to share what we think we learned, with the caveat that some of this is conviction and some of this is still suspicion, and we would rather be transparent about the difference than pretend to have clarity we do not have. The first thing I've had to sit with is timing. Bitcoin utility, making Bitcoin programmable, productive, and integrated into real financial activity, isn't where the real world users sit right now. The conversation is still on Bitcoin as a reserve asset, on its monetary and political positioning, on base-layer conservatism. Those questions are upstream of the ones a Bitcoin L2 needs people to be asking. I still believe Bitcoin gets there, but belief in the destination is not the same as being able to predict when, and nobody can. It's also possible the destination never materialises at all, and that Bitcoin's role as a reserve asset is simply where it settles. If that's true, there will never be a market for what we were building, and no amount of time or capital would change that. The second is the token question. We intended to eventually launch a token. We saw it, and still see it, as a genuinely new form of equity, something closer to an IPO than an airdrop, to be done when you reach product market fit and the moment is right. That moment never came. What became clear over the last year is that the market largely stopped rewarding even the more considered versions of that playbook. Token launches across the board have broadly underperformed, and those that did go to market with tokens haven't seen the outcomes or PMF that the model is supposed to produce. The third lesson is about where DeFi demand on Bitcoin actually lives. For most use cases that exist today, lending, yield, leveraged exposure, WBTC on a mature general-purpose L2 is genuinely sufficient. Users have voted with their behaviour, and the verdict is that the trust assumptions of a wrapped representation on Ethereum are acceptable to almost everyone who wants Bitcoin-denominated DeFi. Decentralisation matters to people in principle and in conversation; in practice, when something cheaper and easier is in front of them, they use it. The security case for a dedicated Bitcoin L2 is real, but it only matters for a narrower band of applications than our thesis required, one of the clearer lessons this market has taught us. The fourth lesson is structural. The on-chain economy is consolidating around venues that own the user relationship: Hyperliquid, Robinhood, the major CEXes, and now TradFi participants absorbing an ever-larger share of attention, flow, and revenue. Convenience and institutional credibility win, every time, as soon as they're available. As retail participation thins, that concentration only deepens. We were, and still are, believers in decentralisation, but the current direction of on-chain growth is running through distribution, and any team building base-layer infrastructure today is rowing upstream against that current. We were no exception. The fifth lesson is the most concrete. Both of the above played out directly in our economics. The users we attracted were primarily using Bitcoin as a store of value for yield, a legitimate use case, but not the high-frequency transaction volume that drives fee revenue on a network like ours. BINK was our answer to that: a Bitcoin neobank designed to bring daily usage of BTC and stablecoins on-chain, driving the transaction volume the network needed. It was the right strategic instinct, and one we never got the chance to fully test. BINK only landed on both app stores in the last few weeks, a product that by its nature could only be built once the underlying infrastructure was proven and live. When users choose the convenient option and economic gravity pulls toward distribution, what's left on a decentralised infrastructure layer is a user base that costs more to serve than it generates. Infrastructure costs are what they are, and the fee income never came close to covering them. If you would like to see how we were imagining a Bitcoin future and what we have been working on since September, feel free to download BINK and give it a spin: it’s a full-fledged self-custodial Bitcoin Neobank with email login, one click borrowing, a Lightning integration and more. App store: Play store: This UX is where we think Bitcoin is ultimately heading towards although it feels too early. You can use invite code 1SD31R, but remember to remove your funds by July 9th. We could keep going. We have chosen not to, however, because continuing past the point where additional time stops producing additional learning is not conviction, it is something that looks like conviction from the outside while corroding into something else on the inside. We would rather stop now, with integrity intact and resources available to take care of the people who took a chance on us, than push the experiment past the point where it still has something to teach us. Reminder: Please withdraw all your assets by July 9th. After this, the federation will sweep the remaining Bitcoin. Any other assets or tokens on the network from then onwards will unfortunately be unrecoverable. After this, the federation will sweep the remaining Bitcoin. Any other assets or tokens on the network from then onwards will unfortunately be unrecoverable. To our investors, who backed a thesis that was harder to defend than it should have been, to our partners who built alongside us and bet pieces of their own roadmaps on ours, to the developers who deployed on Spiderchain, to our users and the BINK community who showed up for something experimental and stayed, and most of all to the Botanix team who shipped a genuinely novel system with rigour and care and who made every hard day worth the difficulty: Thank you, more than the words available here can carry.

🌍 GLOBE HUB We're allocating Genesis Hub access to the strongest signals inside the network. Study GLOBE. Understand the model. Synthesize the signal. How to participate: 1. Publish a Twitter thread explaining GLOBE and its network architecture. 2. Join Telegram: ➔ 3. Submit your thread together with your ETH wallet inside Telegram. ⏳ Submissions close in 48 hours. Selection is based on: • Depth of analysis • Original insight • Community engagement Write in any language. The network remembers signal.