depin

Podczas gdy większość traderów z niepokojem obserwuje wygasające dziś opcje o wartości ponad 14 miliardów dolarów, my szukamy fundamentów, które nie boją się rynkowych burz. Czy w świecie zdominowanym przez strach, realna infrastruktura (DePIN) wygra z czystą spekulacją? 🕵️‍♂️📊
​Dlaczego to test charakteru dla Twojego portfela?
​DePIN – Fundamenty Silniejsze niż Wykresy: Mimo rynkowej paniki, sektor DePIN (zdecentralizowana infrastruktura fizyczna) rozrósł się do ponad 650 aktywnych projektów. Sieci takie jak Grass pokazują zdumiewającą siłę, odrabiając straty dzięki realnemu generowaniu przychodów z wynajmu mocy obliczeniowej dla AI. Gdy "papierowe" zyski znikają, zostaje to, co faktycznie działa. 🏗️⚡​Ethereum i "Smart Money" ($ETH ): Ethereum handlowane jest rano w okolicach 2 065 USD. Mimo presji spadkowej, widzimy, że poziom 2 000 USD staje się "linią na piasku" dla długoterminowych inwestorów. Czy dzisiejsze wygaśnięcie opcji będzie katalizatorem do odbicia, czy pogłębi marcową korektę? Kliknij w oznaczone tokeny, by śledzić płynność w czasie rzeczywistym! 🏛️⚓​Szok Podażowy vs. Adopcja ($ONDO ): Marzec to miesiąc rekordowych odblokowań (ponad 6 mld USD). Dziś oczy na WhiteBIT ($WBT), który przechodzi przez jeden z największych procesów uwolnienia podaży w tym roku. Z drugiej strony, projekty takie jak Ondo Finance ($ONDO ) notują wzrosty dzięki uruchomieniu nowych, całodobowych funduszy ETF z Franklin Templeton. To dowód na to, że RWA (Real World Assets) to narracja odporna na chwilowy strach. 🏦💎
​Strategia na dziś: Zamiast gonić za zielonymi świecami, skup się na Yield Farmingu na L2/L3 oraz projektach DePIN, które mają realnych klientów poza światem krypto. W 2026 roku wygrywają ci, którzy budują portfel na fundamentach, a nie na nadziejach. 🛡️💼
​A Ty? Jak przetrwasz dzisiejszy "Dzień Rozliczenia"? Czy wykorzystujesz poziom strachu 13 do akumulacji liderów DePIN, czy czekasz na stabilizację po wygaśnięciu opcji? Napisz w komentarzu swoją strategię na weekend! 👇

​#DePIN #Ondo #WhiteBIT #SmartInvesting #BinanceSquare

Rynek kryptowalut nie rośnie w sposób jednostajny. Rotuje płynność. A ta różnica całkowicie zmienia odczyt ruchu.
To, co wielu postrzega jako prosty wzrost Bitcoina, jest w rzeczywistości połączeniem trzech sił, które rzadko występują jednocześnie: poprawa w tonie makro z oczekiwaniami obniżek stóp, wcześniejsze opróżnienie dźwigni niedźwiedziej oraz zderzenie narracji, które faworyzują kategorie o większej gęstości kapitału, takie jak RWA, IA on-chain i niektóre nisze DePIN. Kiedy te elementy pasują, rynek nie posuwa się naprzód w linii prostej: eksploduje. #RWA

Written by Qubic Scientific Team

Imagine a building with thirty people. Knowing how many there are adds little. What really explains what is happening is who depends on whom, who is a son, father, wife, husband, who coordinates the building, who is the president of the community, who is the doorman, the delivery person, the owner or the tenant. The dynamics of the group are not in the number, but in the structure of relationships. It is the essence of the social brain that we are.
In the brain, the connectome (https://en.wikipedia.org/wiki/Connectome) is similar to the previous example: a complete description of that dynamic structure. The key is not the map, but understanding what kind of dynamics can emerge from it when it is activated. In the building, what happens when the son of a family moves to another city, when a couple separates and apartments become available, when the president changes, when new neighbors arrive. To understand this biologically, scientists map the connectome of organisms simpler than Homo sapiens. In this recent paper, they analyze the connectome of the fruit fly: Drosophila melanogaster (https://www.nature.com/articles/s41586-024-07558-y).
The underlying idea is profound: in biological systems, part of intelligence is not learned; it is already contained in the architecture. This concept, known as strong architectural priors (https://www.nature.com/articles/s41467-019-11786-6), challenges the prevailing paradigm of AI that relies solely on learning from data.
The Complete Fruit Fly Brain Connectome: A Landmark in Neural Circuit Mapping
The complete connectome of the fly brain, more than 125,000 neurons and around 50 million synapses, is not only a technical achievement, but a new computational unit of analysis (Shiu et al., 2024). For the first time, we can study a complete nervous system as an almost closed functional graph. The FlyWire project, a Princeton-led consortium of over 200 researchers across 127 institutions, made this whole-brain connectome possible through a combination of AI-assisted segmentation, citizen science, and expert proofreading.

Spiking Neural Network Model: How Connectivity Drives Sensorimotor Computation
On top of that graph, the authors build a very simple model. They construct a network of neurons (leaky integrate-and-fire type: https://neuronaldynamics.epfl.ch/online/Ch1.S3.html) where activity propagates according to synaptic connectivity and the type of neurotransmitter (Gerstner et al., 2014; Shiu et al., 2024). No training is needed. The spiking neural network does not “learn” in the classical sense, but executes what its structure allows. Similar to the building example, where the functions and connections between members of the community guide and preconfigure their behaviors.

The model created by the researchers is capable of predicting complete sensorimotor transformations. If they activate gustatory neurons, it allows them to anticipate which motor neurons will be activated, and these predictions are experimentally validated using a technique known as optogenetics (Shiu et al., 2024). That is, function emerges directly from architecture. That is, by manipulating how the fly collects and constructs stimuli related to taste, they can know how it will react. Connectivity is not only a support; it is also computation (Bargmann & Marder, 2013).
Architectural Priors: Intelligence Encoded Before Learning Begins
In biology, brains do not start empty. An organism is born with organized circuits that allow functional behaviors from the beginning. In simple systems such as C. elegans or other insects, much of the functional dynamics is directly conditioned by connectivity (Winding et al., 2023; Scheffer & Meinertzhagen, 2021). When a complete connectome is reconstructed, recurrent patterns appear. These are feedback loops, competitive inhibitory circuits, highly directed sensorimotor pathways. These patterns are not due to real-time learning, but to evolutionary processes that have, so to speak, “encoded” solutions into their own structure.
In deep learning, however, networks start with arbitrarily initialized parameters and intelligence, or rather its appearance, emerges through optimization with large volumes of data (LeCun, Bengio, & Hinton, 2015). Architecture introduces biases, but through training they are gradually smoothed out to some extent, purely through computational scalability.
The fruit fly connectome suggests another possibility: part of intelligence may reside in the structure even before learning. This opens an alternative paradigm for brain-inspired artificial intelligence, since architectures that already contain useful computational properties enhance the role of learning. This approach has been formulated as the use of strong architectural priors or connectome-based approaches (Zador, 2019).
Energy Efficiency in Neural Computation: Why Brain Architecture Matters
There is also a physical argument that reinforces this idea: efficiency. The brain of a fly performs complex tasks with very low energy consumption. This suggests that efficiency does not depend on the number of parameters, but on how neural circuits are organized (Laughlin & Sejnowski, 2003). Connectomes allow us to study precisely that organization explicitly. This principle is at the heart of the growing field of neuromorphic computing, which seeks to build hardware and algorithms that mirror the brain’s remarkable energy efficiency.
Limitations of the Drosophila Connectome: Why a Brain Wiring Diagram Is Not Enough
The paper has gained some recent visibility, but it is important to ground it properly.
The connectome of the fly does not allow complete prediction of behavior. It allows fairly accurate prediction of some local sensorimotor transformations, such as which neurons are activated or which nodes are necessary for a response, but it does not constitute a complete theory of behavior. The work itself recognizes clear limitations, since the model does not adequately incorporate neuromodulation, internal states, extrasynaptic signaling or sustained basal activity, and is based on highly simplified assumptions such as a null basal firing rate, that is, without spontaneous activity, very different from real biological behavior where the brain is active at all times (Shiu et al., 2024). Here the connectome rather describes a structure of possibilities, but not the complete dynamics of the system. The same network can produce different behaviors depending on the internal state, prior history or context. This idea is well established: connectivity constrains dynamics, but does not completely determine it (Marder & Bucher, 2007; Bargmann, 2012). In your residential community, relationships mark a high probability of functions and behaviors, but do not fix them. If an unexpected event occurs, such as a party, a meeting, or a power outage, people will act according to the context, not only based on their structural connectome. The paper has emphasized that “a connectome is not enough” to understand a brain (Scheffer & Meinertzhagen, 2021).
The Human Brain: Beyond Structural Connectivity
This limitation becomes even clearer if we consider the human case. Even if we had a complete human connectome, something that does not exist today and whose availability is uncertain, it would not be sufficient to fully understand behavior. It would serve to delimit structural constraints, understand organizational principles and improve dynamic models, but human behavior also depends on development, plasticity, the body, endocrinology, language, culture and social context.
Current studies that attempt to predict behavior from brain connectivity show clear limitations, where effect sizes are modest and strongly dependent on sample size (Marek et al., 2022). Therefore, the idea that a human connectome would allow us to completely “read” behavior would be an overinterpretation.
From Connectome to Neuraxon: QUBIC’s Brain-Inspired AI Approach
In Neuraxon, we know that architecture contains computation, that it supports emergent intelligence and induces probable behaviors. But we also know that it is not sufficient, which is why we add rich internal dynamics, neuromodulation and state. Neuraxon aims to position itself in that space. It introduces endogenous activity, neuromodulators, multiple temporal scales and plasticity, trying to simulate several functions of the human brain, not only structural ones. As explored in our deep dive on neural networks in AI and neuroscience, the gap between biological and artificial neural networks is precisely what Neuraxon bridges.
Aigarth takes this approach one step further. The connectome of the fly is a closed system. Aigarth proposes systems where structure can evolve, dynamics are continuous and function emerges without explicit training. Here, intelligence is not only the result of optimization, but a property of organized dynamical systems (Friston, 2010).
From Optimization to Organization: The Future of Artificial Intelligence
Overall, the connectome of Drosophila does not solve the problem of behavior, but it shows us the importance of the starting point and the initial structure. It shows us that a significant part of intelligence lies in architecture. But between architecture and behavior there are still dynamics, state, history and context.
We must move from optimization (LLMs) to organization (Aigarth). We strongly believe this is one of the most relevant shifts in the future of artificial intelligence. Even a fly helps us defend these ideas.
Explore the Full Neuraxon Intelligence Academy
The fruit fly proved that intelligence begins with architecture. Neuraxon is building on that principle. Explore how brain-inspired AI is taking shape on QUBIC, start with the Neuraxon Intelligence Academy.
NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time— Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence — Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.NIA Volume 3: Neuromodulation and Brain-Inspired AI — Covers neuromodulation and how the brain's chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon's architecture.NIA Volume 4: Neural Networks in AI and Neuroscience — A deep comparison of biological neural networks, artificial neural networks, and Neuraxon's third-path approach.NIA Volume 5: Astrocytes and Brain-Inspired AI — Explores how astrocytes regulate synaptic plasticity through the tripartite synapse, and how Neuraxon incorporates astrocytic gating to address the stability-plasticity dilemma, enabling the network to locally control when, where, and how much learning occurs.
Qubic is a decentralized, open-source network for experimental technology. To learn more, visit qubic.org. Join the discussion on X, Discord, and Telegram.
References
Bargmann, C. I. (2012). Beyond the connectome: How neuromodulators shape neural circuits. BioEssays, 34(6), 458–465.Bargmann, C. I., & Marder, E. (2013). From the connectome to brain function. Nature Methods, 10(6), 483–490.Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal dynamics. Cambridge University Press.Laughlin, S. B., & Sejnowski, T. J. (2003). Communication in neuronal networks. Science, 301(5641), 1870–1874.LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.Marek, S., et al. (2022). Reproducible brain-wide association studies require thousands of individuals. Nature, 603, 654–660.Marder, E., & Bucher, D. (2007). Understanding circuit dynamics. Annual Review of Physiology, 69, 291–316.Scheffer, L. K., & Meinertzhagen, I. A. (2021). A connectome is not enough. Journal of Experimental Biology, 224.Shiu, P. K., Sterne, G. R., Spiller, N., et al. (2024). A Drosophila computational brain model reveals sensorimotor processing. Nature.Winding, M., et al. (2023). The connectome of an insect brain. Science, 379.Zador, A. M. (2019). A critique of pure learning. Nature Communications, 10, 3770.
Source: https://qubic.org/blog-detail/fruit-fly-connectome-drosophila-brain-architecture-ai-qubic
#Neuraxon #Qubic
#artificialintelligence
#AGI
#DePIN

$FLUX W ciągu ostatnich ośmiu lat Flux był budowany cicho i stopniowo, bez typowej książki dla startupów. Nie było wsparcia kapitału venture, żadnych agresywnych rund zbierania funduszy i żadnych dużych wydatków na marketing. Zamiast tego projekt rozwijał się organicznie — napędzany przez mały, ale zaangażowany zespół i społeczność, która wierzyła w ideę zdecentralizowanej infrastruktury.
W tym czasie zespół nie tylko nakreślił wizję; dostarczył rzeczywiste produkty. Zdecentralizowana infrastruktura chmurowa, globalnie rozproszone węzły obsługiwane przez członków społeczności, FluxOS do wdrożeń, portfele i usługi wsparcia — wszystko to budowane było krok po kroku. Ekosystem dzisiaj odzwierciedla lata ciągłego wysiłku inżynieryjnego, a nie krótkoterminowego szumu. To fundament zaprojektowany z myślą o długoterminowej użyteczności: zdecentralizowane obliczenia, obciążenia AI oraz aplikacje Web3 działające na infrastrukturze, która jest własnością i obsługiwana przez samą społeczność.

一、23:00开盘前，我先查了XT的底
消息刚出，我没急着分析项目，先查交易所。VTGO选在XT首发，这不是偶然，是信号。
XT是什么？2018年成立的老牌二线所，800+币种，125倍杠杆，无政府监管。CoinListing显示其上市费约3万美元，在二线所里算中等偏上。但关键数据是Trustpilot评分——被移除，原因"违反指南"。Reddit和Trustpilot上充斥着"盈利后无法提现"、"账户被锁"的投诉。
我的判断：XT是一个"高风险高波动"的流动性池。它适合首发小市值DePIN项目，因为用户基数大、上币门槛低、散户FOMO情绪浓。但这也意味着，VTGO的开盘波动将极其剧烈，且项目方与交易所的利益绑定程度存疑。
我曾在2024年参与过XT上另一个DePIN项目的开盘，经验是：前30分钟决定生死，要么翻倍，要么腰斩。
二、VTGO的叙事拆解：充电宝+零售收入=？
官方描述：VTGO是"面向智能充电设备的数字货币，将零售收入与代币挂钩，核心产品为移动充电宝，构建Web 3.0智能充电生态"。
翻译成人话： 扫码借充电宝，支付的是法币，但部分收入"映射"到链上代币。用户持有VTGO，相当于持有充电宝业务的"股权收益权"。
这听起来像DePIN（去中心化物理基础设施网络），但我看到的第一个红旗是：收入上链的"摩擦成本"。
真实世界的充电宝业务，现金流是人民币/美元，要变成链上代币，需要：
法币→USDT的兑换通道（监管风险）收益分配的预言机喂价（中心化风险）代币流动性的维持（做市成本）
这三层摩擦，每一步都在吞噬"Web3化"的收益。我追踪过类似的"实体收益代币化"项目，90%死在第2步——预言机要么太贵，要么太容易被操纵。
三、链上侦查：我找不到VTGO的预习资料
这是最令人不安的部分。距离上线还有7小时，我在Etherscan、BscScan、Arbiscan上搜索VTGO合约地址，零结果。
可能的情况：
合约尚未部署（最危险，意味着开盘时合约新鲜出炉，未经审计）部署在非EVM链（Solana？Move系？但XT主要支持EVM生态）项目方刻意保持低调（但DePIN项目需要透明性）
我的操作：开盘前绝不预授权任何合约。如果开盘时合约地址突然放出，我会先查部署者地址的历史记录——是否与已知诈骗地址有交互？是否使用开源模板？创建时间是否过于新鲜？
四、XT上市模式的链上规律
我分析了XT近6个月的10个首发项目，发现一条规律：XT首发项目的链上资金流向高度一致。
模式是：
开盘前30分钟，大额USDT从XT热钱包流入项目方地址（疑似"市值管理"资金）开盘后15分钟内，出现密集的小额买单（<500 USDT），制造交易活跃度前1小时内，必有至少一次"插针"行情（价格瞬间拉升30%+后回落），清洗早期多头
这意味着什么？XT的流动性是"人造"的。项目方和交易所之间存在某种默契，开盘初期的价格波动不完全由市场决定。
我的策略：绝不参与前15分钟。等第一波插针完成，观察链上大额转账方向，再决定是否入场。
五、DePIN赛道的真实痛点：我实地看过
2024年我考察过深圳一家"区块链充电宝"项目，实地看了他们的设备。结论是：硬件成本吞噬代币激励。
一个带4G模块、GPS定位、扫码支付的智能充电宝，硬件成本约150元。要覆盖这个成本，需要用户租借约300次（按单次0.5元利润算）。但代币激励的发放，往往在设备回本前就开始，导致早期参与者赚的是"补贴"，不是"收益"。
VTGO如果遵循同样模式，代币价值将完全取决于补贴持续时间，而非真实业务收入。这是一个庞氏结构，不是商业模式。
我查不到VTGO的设备投放数据、合作商户数量、真实租借流水，这意味着无法做基本面估值。在这种情况下，我只能按"叙事投机"处理——设定严格的止损，绝不恋战。
六、我的开盘操作清单
23:00前：
确认合约地址已公开，且通过至少一家审计公司审计检查部署者地址历史，标记任何与已知诈骗地址的关联准备小额资金（不超过总仓位2%），分3笔入场
开盘后0-15分钟：
观察链上大额转账（>10万USDT），判断是流入还是流出监控XT的USDT热钱包余额变化，识别"市值管理"迹象绝不追涨，等待插针后的回调
开盘后15-60分钟：
如果出现连续小额买单（疑似机器人刷量），视为负面信号如果链上出现单笔大额卖单（>5万USDT），立即跟随减仓设定硬性止损：亏损15%立即离场
七、结语：充电宝是刚需，但VTGO可能是伪需求
移动充电宝是真实需求，但用代币重构充电宝经济，可能是伪需求。
用户要的是"随时能借到电"，不是"持有代币分享收益"。商户要的是"稳定的租金收入"，不是"代币波动的风险"。VTGO试图在两者之间插入一个代币层，但没有解决任何真实痛点，只创造了新的摩擦。
我参与这个开盘，不是为了长期持有，是为了验证一个假设：在DePIN叙事退潮的2026年，市场是否还愿意为"实体资产代币化"的故事买单。
如果VTGO开盘即破发，意味着DePIN赛道的投机情绪已耗尽。如果开盘暴涨，意味着叙事仍然有效，但窗口期正在关闭。
23:00见。我会带着链上监控工具，而不是信仰。
本文基于公开信息独立分析，所有链上数据均可验证。不构成投资建议，开盘波动极大，请谨慎决策。
#VTGO #VoltGo #XT #DePIN #链上侦查