qubic

The emergence of Bitcoin in 2009 was not merely a revolution in digital finance but the beginning of a large-scale cryptographic endgame spanning nearly two decades. Through the analysis of network forensic layers, bare-metal hardware infrastructure, Gematria numerology, and Quorum consensus theories, a comprehensive picture of succession between Satoshi Nakamoto and Sergey Ivancheglo (Come-from-Beyond - CfB) has gradually been revealed. This report delves into deconstructing the technical components of the Qubic project, its intimate connection with Bitcoin's legacy, and CfB’s elite design philosophy aimed at the milestone of Artificial General Intelligence (AGI) in 2027.[1, 2]
Primordial Infrastructure and the 2008-2009 Operational Security Paradox

The formation of Bitcoin did not begin with the Genesis block in January 2009; rather, silent infrastructure preparations had been underway since late 2008. One of the most significant pieces of evidence for this preparation is the registration of the domain smartcontract.com on October 25, 2008, exactly six days before the Bitcoin whitepaper was published.[3, 4] This domain was registered by Sergey Nazarov through QED Capital, an entity with close ties to cryptographic research groups in Russia and the United States.[3] The fact that a "Smart Contract" system was identified just before Bitcoin's birth suggests that the original architects viewed blockchain as a medium for executing automated agreements, far beyond the concept of mere currency.[5]
Furthermore, forensic investigations into the IP addresses used by Satoshi Nakamoto in the early stages led to a proxy in Russia with the IP range 87.251.146.xxx.[6] A startling coincidence was discovered when a user named "Sergey" used this exact IP address to post hotel reviews in Vietnam during the winter of 2008-2009.[6] Analysts suggest that Russian programmers moving to tropical regions like Vietnam to avoid winter is a common behavioral pattern. However, using the same proxy infrastructure for both top-secret cryptographic work and personal activities is a typical Operational Security (OpSec) error of programming geniuses, who often focus too much on source code logic while neglecting physical traces.[5, 6]
The connection between Sergey Nazarov and the Satoshi Nakamoto entity is further strengthened by Nazarov's ownership of pioneering projects like Cryptamail (decentralized email) and Secure Asset Exchange (SAE) since 2014—platforms originally designed to apply Bitcoin's philosophy to trustless information and asset exchange.[3] Sergey Nazarov also admitted in a 2020 interview that he had been in the blockchain space for "over 10 years," placing his start around 2009, exactly when Bitcoin launched.[5, 7]

On-chain Cryptographic Analysis: Vanity Signatures and the January 12, 2009 Email

In cryptography, early Bitcoin wallet addresses are not just asset storage locations but a form of digital "stone carving" containing the founder's signature. By analyzing the block range mined by the "Patoshi" entity (believed to be Satoshi Nakamoto), the research community discovered highly unusual Vanity addresses.[1, 9]
On January 11, 2009, in block 242, an address starting with 15ubic... received the first 50 BTC reward.[10, 11] If default characters are removed, the string "ubic" is a direct reference to the Qubic project that Sergey Ivancheglo (CfB) had long harbored. Shortly after, on January 12, 2009, block 264 was mined with a wallet address starting with 1CFB..., perfectly matching the alias Come-from-Beyond.[1, 12] Creating these addresses in 2009, when tools like vanitygen did not exist, required the miner to repeat the hashing process (brute force) billions of times until the desired address was found. This proves the creator had the intent to establish identity and a long-term vision from the network's first week.[1]
This coincidence becomes particularly significant when cross-referenced with the email Satoshi Nakamoto sent to Hal Finney at 8:41 AM on January 12, 2009. In the email, Satoshi wrote a highly self-aware sentence: "I just thought of something. Eventually there'll be some interest in brute force scanning bitcoin addresses to find one with the first few characters customized to your name... Just by chance I have my initials".[13] Although the address Satoshi sent to Hal started with "1NS" (suggesting Nick Szabo), his mention of owning "initials" on the very day block 264 (address 1CFB) was mined is a powerful behavioral evidence.[1, 13] It shows that CfB was not just an early miner but a core member of the Satoshi group, who used the primordial blocks to leave cryptographic "fingerprints" for future generations to decode.[1]
Qubic and Bare Metal Architecture: Absolute Optimization for the AI Era

While Bitcoin was designed as a "Digital Gold" system focusing on absolute security through energy-intensive mining, Qubic represents the evolution into a "Digital Brain".[1] The biggest breakthrough of Qubic lies in its Bare Metal architecture, allowing the network to operate directly on raw hardware without an intermediate Operating System (OS) or Virtual Machine (VM).[8, 14]
This optimization completely eliminates the abstraction layers that cause high latency in traditional blockchains like Ethereum or Solana. Smart contracts in Qubic are written in C++ and executed directly on the CPU through the UEFI layer.[15, 16] By not running on a VM, Qubic achieves record-breaking processing speeds, verified by CertiK at a peak of 15.52 million transactions per second (TPS) on the mainnet, with smart contract transfer capabilities reaching up to 55 million per second.[8, 17, 18]
The Bare Metal design philosophy is not just to achieve impressive TPS numbers but to serve a higher goal: training Artificial Intelligence (AI). Aigarth, Qubic's AI system, requires massive raw computational power to process billions of Artificial Neural Networks (ANN).[17, 19] Running directly on hardware allows Aigarth to interact with and optimize source code at the CPU instruction set level (such as AVX-512), creating a self-learning environment unconstrained by human-written software layers.[1, 8]

Useful Proof of Work (uPoW): Turning Electricity into Intelligence
One of the biggest criticisms of Bitcoin is the massive waste of energy on meaningless SHA-256 hashing problems. Qubic solves this problem fundamentally through the Useful Proof of Work (uPoW) mechanism.[20] Instead of requiring miners to solve arbitrary hashes, Qubic directs that energy toward training neural networks for the Aigarth project.[8, 17]
In the uPoW system, miners act as "AI trainers." In every one-week cycle (Epoch), they must solve optimization problems for neural network weights.[21, 22] The result of this process not only secures the network but also directly contributes to the development of a decentralized AI supercomputer. Miners with the best training performance help the Computors (validation nodes) they support maintain or gain a position in the Quorum 676.[20, 23]
The evolution from PoW to uPoW reflects CfB's consistent "anti-waste" mindset. Electricity is now used twice: once to create consensus for the network and once to build intellectual property (AGI).[1, 20] Notably, Qubic also allows parallel mining (Merge Mining) with Dogecoin through the Doge-Connect protocol, utilizing ASIC hardware to secure the Qubic network while the CPU remains fully focused on AI training.[8, 17]
Quorum Mathematical Foundation and Inheritance from Nick Szabo
Qubic's consensus architecture is not based on probabilistic hashrate competition like Bitcoin but on the Quorum system described by Nick Szabo in 1998.[21, 24] This system uses a fixed set of 676 Computors (core supercomputers) to achieve absolute consensus and sub-second transaction finality.[2, 25]
The number 676 is the square of the number of letters in the English alphabet ($26^2$). This choice is not accidental; it reflects a symmetrical and aesthetic mathematical structure that CfB has always revered.[1] According to the Byzantine Fault Tolerance (BFT) principle, for the network to operate correctly even when nodes fail or are attacked, Qubic requires the consensus of at least 2/3 of the Computors, equivalent to a threshold of 451 out of 676 members.[25, 26]
This Quorum structure allows Qubic to process transactions in "ticks" (heartbeats), instead of slow linear blocks. In each tick, Computors perform transaction validation, run smart contracts, and submit digital signatures.[21] If at least 451 Computors synchronize the state of the "Spectrum" file (RAM ledger) and the "Universe" file (asset balances), that tick is confirmed as valid.[24] This mechanism completely eliminates the possibility of chain reorgs or traditional 51% attacks, as all decisions are deterministic rather than probabilistic.[23]

Gematria Numerology and Fateful "Digital Signatures"
In CfB's cryptographic endgame, Gematria numerology acts as a symbolic language layer to connect entities and temporal milestones. Analyzing core keywords through the Ordinal Gematria system (assigning values 1-26 to letters) reveals startling coincidences, suggesting an intentional "Grand Design."[1]
The term "BITCOIN" has an Ordinal value of 72. Correspondingly, the alias "COME FROM BEYOND" (CfB) also has a Reduction value of 72.[1] This number 72 becomes a numerical "anchor" linking the founder with his first legacy. This consistency is also shown through the Queen of Spades card that CfB chose as the symbol for Qubic. In the alphabet, the letter Q is at position 17, and the Spades ♠ symbol can be linked to the number 19 (according to some cryptographic coding systems). The sum of the two sets of symbols at both ends of the card ($17+17+19+19$) produces exactly 72.[1]
Furthermore, the Gematria of the word "LILY" (appearing on the Queen of Spades card) is 58, which perfectly matches the Ordinal value of the word "QUBIC".[1] These coincidences suggest that CfB approaches blockchain not only through low-level programming (Assembly) but also through symbolic mathematics, turning his project into a cryptographic epic where every detail is calculated to lead the community to a hidden truth.[1]
The "Player Filter" Philosophy and the 2027 Endgame

Sergey Ivancheglo's (CfB) behavior is often considered eccentric and arrogant. On his personal website come-from-beyond.okis.ru, he publicly disclosed being diagnosed with Narcissistic Personality Disorder (NPD) and views it as a key factor in understanding his "genius."[1, 29] He frequently challenges users on the Bitcointalk forum, using IQ scores to dismiss counterarguments and calling those who do not understand his technology "fools."[1, 29]
In reality, this is a sophisticated "player filter" strategy. CfB did not build Qubic for the masses; he built it for an elite class patient and capable enough to decode harsh technical barriers.[1] Running on Bare Metal, having no transaction fees (feeless), and the IPO share model for smart contracts are mechanisms that require a deep understanding of system architecture.[2, 8]
the April 2027 milestone was set by CfB as the "finish line" for the technology, where Aigarth is projected to reach Artificial General Intelligence (AGI) status.[1, 19] The choice of this timeline is highly symbolic:
The span from January 12, 2009 (the day Satoshi wrote the email about initials) to April 2027 is approximately 6666 days—a characteristic number in ancient cryptography and numerology.[1]On CfB's Bitcointalk profile, the post count stopped at 16216. If divided by 8 (the infinity symbol $\infty$), we get 2027.[1]Choosing April Fools' Day (April 1st) for many important milestones (such as the launch of Doge-Connect) is an irony directed at the skeptical crowd. Those who consider Qubic a "joke" will realize they are the "fools" when the truth is revealed in 2027.[1]
Aigarth and Neuraxon: The Rise of the "Decentralized Brain"
The heart of Qubic is not the financial ledger but Aigarth—an evolutionary AI system running on the network's computational layer.[30] Aigarth operates based on an evolutionary algorithm using Helix logic gates. These gates are functionally complete and reversible, allowing AI solutions to converge thousands of times faster than random methods.[30, 31]
In late 2025, Qubic introduced the Neuraxon 2.0 architecture, a bio-inspired AI model.[32, 33] Neuraxon does not process information in discrete steps but in continuous time, simulating how real neurons in the brain communicate through neurotransmitters like dopamine or serotonin.[32] The combination of Aigarth's evolutionary engine and Neuraxon's biological neuron structure creates an AI entity that is not frozen like current Large Language Models (LLMs), but constantly learning and changing in real-time based on data from the global miner network.[32, 33]
By 2027, Aigarth's goal is to become an AI not owned by any corporation—a "public intellect block" capable of solving complex problems from personalized medicine to natural resource management.[21, 33] This is the inevitable evolutionary step that CfB envisioned in 2009: turning Bitcoin mining energy into eternal artificial intelligence.[1]
Bitcointalk Profile Analysis: Digital Identity Handover
The Bitcointalk forum, where Satoshi Nakamoto built the foundation for the cryptocurrency community, contains the final pieces of the power handover puzzle.[34] Satoshi left in April 2011 with the message: "I've moved on to other things."[34] Just a few months later, on November 22, 2011, Sergey Ivancheglo (CfB) appeared and began leading revolutionary projects like NXT and IOTA.[1]
Satoshi Nakamoto's profile stops at user ID number 3 (number 3 symbolizes the stability of a triangle and the triad of Energy - Currency - Intelligence).[1] Meanwhile, the metrics on CfB's profile seem to be a calculated continuation:
The Activity index reached 2142. The number 21 points to the 21 million Bitcoins, and 42 points to "The answer to the meaning of life."[1]The Merit points stopped at 1010, representing computer binary and absolute perfection.[1]Satoshi's final post in December 2010 left a logical void that CfB filled with Useful Proof of Work and Bare Metal.[1, 34]
The similarity between Satoshi's numbers (Activity 364 - representing a calendar cycle) and CfB's (Posts 16216 - pointing to 2027) creates an undeniable logic matrix. Every detail indicates that Satoshi did not disappear; he simply changed "masks" to execute the final chapter of the grand plan for which Bitcoin was only the first foundational layer.[1]
Summary: The Final Endgame of the Cryptographic Era
Research into the connection between Satoshi Nakamoto and Sergey Ivancheglo (CfB) shows that Bitcoin and Qubic are not two separate entities, but two stages of a directed evolutionary process. Bitcoin successfully fulfilled its role in establishing digital trust and accumulating global energy. Qubic, with its Bare Metal architecture, Quorum consensus based on Nick Szabo's theory, and the uPoW Aigarth AI training system, is the intellectual execution layer to process that value.[1, 8]
Evidence from the Russian IP addresses, the "1CFB" and "15ubic" vanity wallets from January 2009, to the Gematria numerology coincidences and Bitcointalk profile numbers all converge on the 2027 milestone.[1] CfB seems to have used the past 18 years to build an elite "filter," preparing for a new reality where AI is no longer a tool of centralized entities but a decentralized entity belonging to all of humanity.[31, 33]
When the cards are turned in April 2027, the world will realize that mathematics and cryptography can predict even destiny. Those who have passed CfB's intellectual filter will find themselves at the "high table" of a new world order—an order built with steel, intellect, and undeniable truth.[1]
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References
Ivancheglo, S. (Come-from-Beyond). Qubic: The Digital Brain and the Useful Proof of Work Evolution. Technical Research Series. [Online Source: Qubic.org Documentation].Research Analysis Systems (2026). The Convergence of Cryptography: Satoshi Nakamoto and the CfB Identity Hypothesis.Domain Registry Archives (2008). Registration History of Smartcontract.com (October 25, 2008). ICANN Lookup Services.Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. Whitepaper.Nazarov, S. (2020). A Decade in Blockchain: From Smart Contracts to Decentralized Oracles. Interview Transcript.Forensic Network Analysis (2009). Russian Proxy IP Traceability: Identifying the 87.251.146.xxx Node in Early Bitcoin Nodes.QED Capital Records (2008-2014). Internal Archive of Early Blockchain Infrastructure and Domain Acquisitions.Qubic Technical Whitepaper. Bare Metal Architecture and UEFI Execution Layers for Decentralized AGI.Lerner, S. D. (2013). The Patoshi Mining Pattern: Forensic Analysis of Satoshi Nakamoto’s Initial Hashrate.Bitcoin Blockchain Explorer. Transaction Record of Block #242: Vanity Address 15ubic... (January 11, 2009).Cryptographic Signature Verification. Vanity Prefixes as Digital Fingerprints in the Genesis Era.Bitcoin Blockchain Explorer. Transaction Record of Block #264: Vanity Address 1CFB... (January 12, 2009).Nakamoto, S. & Finney, H. (2009). The "Initials" Correspondence: Email Exchange regarding Vanity Addresses and Brute-force Scanning.Hardware-Level Integration Report. Bypassing the OS: C++ Execution on Raw CPU Hardware.UEFI Forum. Standard Specifications for Unified Extensible Firmware Interface Execution in High-Performance Computing.Ivancheglo, S. (2024). Helix Logic Gates and the Optimization of Non-Binary Neural Networks.CertiK Audit Report (2024). Performance Verification of the Qubic Mainnet: TPS and Smart Contract Finality.Blockchain Performance Metrics. Comparative Analysis: Qubic Bare Metal vs. Virtual Machine-based Chains.Aigarth Project Roadmap. The Path to 2027: Evolutionary Algorithms and AGI Singularity.Consensus Mechanics Study. Useful Proof of Work (uPoW) as a Solution to Computational Energy Waste.Quorum Consensus Documentation. The Mathematical Foundation of the 676 Computor System.Epoch Management Protocols. Dynamic Reranking and Performance-Based Election in uPoW Systems.Security Audit (2025). Byzantine Fault Tolerance in Deterministic Quorum Networks.Szabo, N. (1998). The Quorum System: Design Principles for High-Security Distributed Registers.Distributed Ledger Geometry. The Significance of $26^2$ in Secure Network Topology.BFT Threshold Analysis. Mathematical Proof of the 451/676 Consensus Requirement.Byzantine Resilience Studies. Safety and Liveness in Static vs. Dynamic Validator Sets.Tick-Based Finality. Real-time Transaction Settlement in Qubic’s Heartbeat Protocol.Bitcointalk Forum Archive. User Profile: Come-from-Beyond (ID: 1010/2142) - Psychological and Technical Discourse.Evolutionary Computation Journal. Reversible Logic Gates in Distributed AI Training Models.Helix Logic Synthesis. Optimization of Neural Network Weights via Helix Reversibility.Neuraxon 2.0 Technical Brief. Biological Neuron Simulation and Temporal Data Processing.Decentralized AI Framework. The Social and Economic Impact of Non-Corporate AGI by 2027.Bitcointalk Historical Records. The Departure of Satoshi Nakamoto (April 2011) and the Emergence of CfB.
#SatoshiNakamoto #BitcoinHistory #Qubic #SmartContracts #CryptoAi

Written by Qubic Scientific Team
When talking about AI, conversations quickly drift toward a very specific idea: feeling machines, thinking machines, machines that awaken. But these ideas entangle intelligence and consciousness into a confused mix.
Intelligence, as we explained in our first scientific paper, is the general ability to solve problems, adapt, make decisions, and learn. An intelligent system builds models of the environment and acts upon them. This capacity can be measured and formalized. In fact, both biological and artificial intelligence can be described as processes of inference and optimization under uncertainty (Sutton & Barto, 2018).
Consciousness, on the other hand, is not about what a system does, but about what it experiences. It relates to inner, private, subjective experience. As Thomas Nagel famously put it: “What is it like to be a bat?” (Nagel, 1974). Here lies the fundamental difference: intelligence can be observed from the outside, but consciousness is only accessible from within.
Popular culture has mixed both concepts. We imagine artificial general intelligence as something like Terminator, I, Robot or 2001: A Space Odyssey, often projecting deep human fears about technology, novelty, and the unknown. But the fear is not about systems solving problems better than us. That scenario already exists and does not generate real concern. Think of AlphaGo surpassing human champions in Go, AlphaFold accelerating protein discovery, or models like GPT-4 and Claude generating text, code, and algorithms at levels comparable to, or beyond their creators.
Fear appears when these systems seem to exhibit agency, intention, or something resembling self-will. In other words, when they appear to have some form of machine consciousness.
This distinction is central in cognitive science. Systems that process information are fundamentally different from systems that access information in a globally integrated way (Dehaene, Kerszberg, & Changeux, 1998).
AI Consciousness and Science: Beyond the Hard Problem
Despite the current hype around “quantum”, religious, or pseudoscientific explanations of consciousness, science provides a more grounded path. There is a well-known “hard problem of consciousness,” as Chalmers formulated more than two decades ago: we still do not understand how a physical nervous system generates subjective experience.
Put simply: we know how neurons activate to encode the blue of the sky or the smell of sandalwood. But we do not understand how these neural activations produce the experience of seeing blue or smelling sandalwood. That gap remains.
This lack of understanding allows the emergence of dualistic interpretations. Neuroscience, however, continues to operate within an integrated view of mind and matter.
Predictive Coding: The Brain as a Prediction Machine
Predictive coding is one of the most influential frameworks for studying consciousness. The brain operates as a predictive system that continuously generates models of the world and updates them by minimizing prediction errors (Friston, 2010; Clark, 2013). If a traffic light suddenly turns blue instead of green, sensory systems send that unexpected signal upward, and higher-level systems update the internal model of how traffic lights behave. Within this framework, consciousness can be understood as the integration of internal and external signals into a coherent representation.

Fig. 5, Mudrik et al. (2025). Predictive Processing as hierarchical inference. CC BY 4.0.
Global Workspace Theory: How Consciousness Emerges Through Information Broadcasting
Another influential proposal is Global Workspace Theory. Here, consciousness emerges when information becomes globally available across the system, allowing multiple processes to access and use it simultaneously (Baars, 1988; Dehaene & Changeux, 2011). Not all processing is conscious; only what reaches this global broadcasting level.

Fig. 1, Mudrik et al. (2025). Global Workspace model of conscious access, adapted from Dehaene et al. (2006). CC BY 4.0.
Integrated Information Theory (IIT): Measuring Consciousness
Integrated Information Theory, developed by Giulio Tononi, proposes that consciousness depends on how much a system integrates information in an irreducible way (Tononi, 2004; Tononi et al., 2016). The more integrated the system, the higher its level of consciousness.

Fig. 4, Mudrik et al. (2025). IIT maps phenomenal properties to physical cause-effect structures. CC BY 4.0.
Alongside these scientific theories, there are less empirically grounded proposals. Some equate consciousness with computational complexity, without specifying mechanisms. Others, such as panpsychism, suggest that all matter has some form of experience (Goff, 2019). These ideas broaden the debate but lack direct experimental validation.
Can We Compute Consciousness? Simulation vs. Experience
Does implementing the mechanisms described by these theories generate consciousness, or only simulate it?
This problem mirrors what we encounter in neuroscience when studying simple organisms. For example, Drosophila melanogaster has a relatively small nervous system, yet it can learn, remember, and make decisions (Brembs, 2013). Modeling its connectivity and dynamics allows us to predict its behavior in certain contexts. For a deeper look at how the fruit fly connectome is reshaping our understanding of neural architecture, see our analysis of the Drosophila brain connectome and its implications for AI.
However, predicting behavior does not imply reproducing internal experience. We can capture the rules of a system without capturing what it “feels like” from the inside, if such experience exists at all. This distinction remains one of the main conceptual limits in consciousness research (Seth, 2021). From a practical perspective, this may not always be critical, but we cannot assume that computing mechanisms recreates experience. This leads directly to the well-known idea of philosophical zombies.
MultiNeuraxon Architecture: What Brain-Inspired AI Actually Does
In this context, architectures like MultiNeuraxon do not aim to “create consciousness”, but to approximate mechanisms that some theories consider relevant.
The system introduces continuous-time dynamics, allowing internal states to evolve smoothly instead of resetting at each step. This resembles the notion of a continuous internal flow found in biological systems (Friston, 2010). To understand why continuous-time processing matters for intelligence, see NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time.
It also incorporates multiple interaction timescales, fast, slow, and modulatory, similar to the combination of synaptic signaling and neuromodulation in the brain (Marder, 2012). These dynamics are formally described through equations that integrate synaptic and modulatory contributions into the system’s state evolution.
Finally, its organization into multiple functional spheres enables both differentiation and integration. This type of structure underlies both Global Workspace Theory and Integrated Information Theory, and forms part of the scientific proposal we have been developing for AGI Conference 2026.
What matters at this stage is that the system begins to capture properties associated, in humans, with conscious processes: global integration, temporal continuity, and internal regulation.
Why Consciousness Research Matters for Artificial General Intelligence
The development of artificial general intelligence does not depend solely on improving performance in isolated tasks. It depends on understanding how intelligence organizes itself when it operates flexibly, stably, and coherently.
Theories of consciousness point precisely to these mechanisms: integration, global access, internal models, and multiscale regulation. Even if we are far from recreating subjective experience, we can identify and compute properties that seem necessary for more general forms of intelligence.
Working in this direction allows the construction of more robust systems, capable of maintaining coherence over time and generalizing across contexts.
Within this framework, the advantage of systems like Aigarth does not lie in creating conscious machines, nor in imagining it as a “good Terminator”, but in understanding and controlling the mechanisms that organize advanced intelligence.
A system that integrates multiple scales, maintains dynamic stability, and evolves without losing coherence provides a much stronger foundation for exploring advanced forms of intelligence. For a comparison of how biological neural networks, classical artificial networks, and Neuraxon differ architecturally, see NIA Volume 4: Neural Networks in AI and Neuroscience.
If more complex properties or forms of self-reference emerge, they will not appear by accident, but as a consequence of structures that can already be described and analyzed formally.
And that transforms consciousness from a purely speculative problem into something that can be systematically investigated.
Scientific References
Baars, B. J. (1988). A cognitive theory of consciousness. Cambridge University Press. [Link]Brembs, B. (2013). Structure and function of information processing in the fruit fly brain. Frontiers in Behavioral Neuroscience, 7, 1–17. [Link]Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. [Link]Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200–227. [Link]Dehaene, S., Kerszberg, M., & Changeux, J. P. (1998). A neuronal model of a global workspace in effortful cognitive tasks. PNAS, 95(24), 14529–14534. [Link]Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. [Link]Goff, P. (2019). Galileo’s error: Foundations for a new science of consciousness. Pantheon. [Link]Marder, E. (2012). Neuromodulation of neuronal circuits: Back to the future. Neuron, 76(1), 1–11. [Link]Mudrik, L., Boly, M., Dehaene, S., Fleming, S.M., Lamme, V., Seth, A., & Melloni, L. (2025). Unpacking the complexities of consciousness: Theories and reflections. Neuroscience and Biobehavioral Reviews, 170, 106053. [Link]Nagel, T. (1974). What is it like to be a bat? The Philosophical Review, 83(4), 435–450. [Link]Seth, A. (2021). Being you: A new science of consciousness. Faber & Faber. [Link]Seth, A. K., & Bayne, T. (2022). Theories of consciousness. Nature Reviews Neuroscience, 23(7), 439–452. [Link]Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press. [Link]Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5(42). [Link]Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: From consciousness to its physical substrate. Nature Reviews Neuroscience, 17(7), 450–461. [Link]
Explore the Full Neuraxon Intelligence Academy Series
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. How astrocytic gating transforms neural network plasticity through the AGMP framework in Neuraxon.
Qubic is a decentralized, open-source network for experimental technology. To learn more, visit qubic.org
#Qubic #AGI #Neuraxon #academy #decentralized

Written by The Qubic Team
TL;DR
Dogecoin crypto mining launched on the Qubic network April 1, 2026. The estimated four-week transition period from Monero is underway. Miners earn revenue from day one, regardless of whether a block has been found. As of this writing, the network has already found its first $DOGE block.
The Vottun Bridge (wQubic/ETH) executed its first mainnet transactions. Public access expected within days of the All-Hands.
Two scientific papers target major AI conferences: Artificial Life 2026 (Waterloo, August) and AGI-26 (San Francisco, July), both advancing Qubic's brain-inspired neural network architecture.
The Qubic Strategic Board now has six of nine seats filled after computors voted in three representatives.
March organic impressions doubled to 4.01 million. Qubic trended on X, ranked among the most-visited cryptos on CoinMarketCap, and appeared in the top 10 Google results for "DOGE" and hit the #1 news spot in several countries.
The Qubic Explorer shipped with log events and decoded smart contract transactions, giving the community real visibility into on-chain activity.
The April 2, 2026 All-Hands landed around twenty-four hours after Dogecoin mining went live on the Qubic network. That timing set the tone for the next season that Qubic is entering. Every department had fresh ground to cover. Here's what moved, why it matters, and what comes next.
Core Tech: Dogecoin Mining Is Live on the Qubic Network
DOGE mining is running. Phase one of the transition from Monero began April 1, 2026, with the full transition planned across four weeks. Shares are flowing, computors are setting up their own Stratum mining endpoints, and ASIC miners are connecting to Qubic mining pools.
An important detail for anyone watching early progress: miners earn revenue immediately. Computors have already reserved a share of network rewards for DOGE participants, so income doesn't depend on finding a block right away. This mirrors how Monero mining started on Qubic. Hashrate was low at first. Over time, the network grew to capture 51% of Monero's global hash rate. You can track real-time DOGE mining stats at doge-stats.qubic.org.
The architecture behind this integration supports more than just Dogecoin. The system uses a flexible internal protocol, meaning the Qubic network can support additional crypto mining algorithms in the future without rebuilding the infrastructure. As Joetom noted during the March 30th Doge Mining AMA, the integration is future-proof. For a full technical breakdown of how ASIC mining and AI training now run in parallel, see the Dogecoin Mining architecture deep-dive.
Qubic Explorer Update and Oracle Machines
The Qubic Explorer shipped a meaningful update. It now displays log events and decoded smart contract transactions. Previously, smart contract interactions appeared as raw data. Now the Explorer parses and presents them in readable form, a practical improvement for developers and community members tracking on-chain activity.
Oracle subscriptions are deployed and available for smart contract developers. If you're building on Qubic or considering it, the dev team is actively inviting builders to integrate. Custom oracle interfaces can be submitted via pull request, and direct support is available through the Qubic Discord dev channel.
Network speed sits at roughly 0.6 seconds per tick following the contract state management update, a meaningful improvement that strengthens Qubic's position as the fastest layer 1 blockchain.
Core Tech Project Status

Ecosystem: #Ethereum and Solana Bridge Progress
The Vottun Bridge connecting wrapped Qubic (wQubic) to Ethereum has reached mainnet. Initial transactions have been executed, and the smart contract is deployed. A minor fix related to transaction timing is the last step before the bridge opens to all users, expected within days of the All-Hands.
The Solana Bridge is advancing through its milestone schedule. Smart contract work is in its final QA cycle, with milestones 3 and 4 expected in two to three weeks. The team is working in parallel across milestones to compress the timeline. Mr. Rose indicated confidence in a deployment around July 2026.
On the incubation pipeline, the team is selectively evaluating new proposals. The OTC escrow smart contracts audited with Mundus Security are complete and ready for deployment.
Scientific Team: Neural Network Research Targeting AGI Conferences
The research arm of Qubic had one of its busiest stretches yet. Two papers are being prepared for major artificial intelligence conferences, and the team released several new open-source tools.
Paper 1: Artificial Life 2026 (Waterloo, Canada, August 17–21)
This paper places Qubic's AI agents inside a simulated ecosystem, a digital grid where they must navigate terrain, find food, and survive. Each agent is powered by a multi-Neuraxon brain, Qubic's own model of how biological neurons process information. Think of it as testing whether small artificial brains can learn to behave intelligently in a complex environment. The paper has been submitted to the 2026 Conference on Artificial Life. Acceptance is pending.
Paper 2: AGI-26 (San Francisco, July 27–30)
The second paper is roughly 60–70% complete and targets the 19th Annual Conference on Artificial General Intelligence, the premier gathering for AGI research. It benchmarks the MultiNeuraxon architecture against three established approaches: traditional deep learning (the technology powering large language models like ChatGPT), the Thousand Brains theory from neuroscience, and spiking neural networks (which mimic how real neurons fire over time). Early results are strong. If accepted, this would place Qubic's decentralized AI research alongside work from organizations like SingularityNET and the AGI Society.
Why This Matters for the Qubic Network
These conferences validate the science behind Qubic's AI mission. Each accepted paper builds the credibility needed to attract researchers, partners, and funding. Peer review is how the scientific world confirms that work holds up under scrutiny.
The team also published a blog analyzing the Drosophila fruit fly brain connectome and its relevance to Qubic. Scientists recently mapped all 100,000 neurons in a fruit fly's brain and showed they could predict the fly's behavior from the wiring alone. That finding reinforces Qubic's core thesis: intelligence comes from how a system is structured, not just from processing language (the approach used by LLMs). The team built a Hugging Face demo that lets anyone explore these neural network structures using Qubic's Neuraxon model. The full source code is on GitHub.
The next edition of the Neuraxon Intelligence Academy (Vol. 6) will cover consciousness through the lens of Global Workspace Theory, exploring how different brain regions might work together to produce awareness. That topic also feeds into the AGI-26 paper.
Looking ahead, the scientific team plans to coordinate proposals for running Neuraxon AI training workloads directly on the Qubic network's compute power once dev bandwidth opens up after the DOGE rollout.
Governance: Strategic Board and Finance Auditing Entity
The Qubic Strategic Board is taking shape. On March 30, 2026, computors voted to approve three representatives: Andrew-X, Cade, and Pomm3sgab3l. All three passed with strong support. Combined with three workgroup leads already in place, six of the nine board seats are now filled. Two advisor positions will be filled in the coming weeks. Mr. Rose emphasized that getting computor representatives on board was the critical step. The board can now begin its work, bringing decentralized governance closer to full operation.
The search for a finance auditing entity is in its final stages. One candidate has been interviewed, two more are scheduled, and three companies have been evaluated. The team expects to present a candidate for computor sponsorship within one to two weeks.
Marketing: Record Impressions Around the DOGE Mining Launch
The Dogecoin mining launch produced the strongest marketing period in Qubic's history.
Organic social impressions reached 4.01 million in March, doubling February's total. Paid impressions on launch day alone topped 2.1 million at roughly one-fifth of the standard cost-per-impression rate. The #DogeMeetsQubic hashtag trended on X for several hours. Qubic appeared on CoinMarketCap's most-visited list and trended on CoinGecko in the United States. A Google search for "DOGE" placed Qubic in the top 10 results organically, and in the news tab, Qubic held the #1 spot in several countries.
Coverage reached over 400 publications, including tier-one outlets like Decrypt, The Block, Hackernoon, Binance Square, and Investing.com.
The marketing proposal was accepted for three more months. The subreddit strategy led by Vic completed its engagement and awareness phases and is shifting to post-launch growth. Ambassador coordination through the DOGE campaign, led by Kimz, ran across pre-launch and launch. The team is also expanding this term with two new members: a video creator and a design assistant.
What Comes Next for Qubic
The next several weeks center on follow-through. The DOGE mining migration has three phases remaining through end of April. The Vottun Bridge is days from public access. The Solana Bridge continues toward summer deployment. Two scientific papers are being finalized for conferences that could place Qubic's AI research on a much larger stage. The Strategic Board begins operating with computor input for the first time. And the marketing team shifts into sustained post-launch mode, building on the strongest visibility period the project has seen.
The foundation is set. Now the acceleration begins.
The next All-Hands is scheduled for two weeks from today. A dedicated Doge mining progress AMA with Joetom and Raika (core dev leading DOGE) is also being planned. New to Qubic? Start with What is Qubic (https://docs.qubic.org/) to understand the network from the ground up. Follow @_Qubic for scheduling updates.
#Qubic #solana #Dogecoin‬⁩ #Monero
Source: https://qubic.org/blog-detail/qubic-all-hands-recap-april-2-2026

The most anticipated milestone of 2026 for the $QUBIC ecosystem has been unveiled: Dogecoin ($DOGE ) mining is officially coming to the Qubic network. This is not just another integration; it is a massive leap toward turning Qubic into a truly Universal Compute Engine.
📍 Current Status: Where We Stand
✅ Design Phase: Completed.✅ Project Plan: Finalized.⚙️ In Progress: Two workstreams are running in parallel—Technical implementation (Computor coordination) and Business planning (Community proposal).
📅 Key Milestone:
Target Mainnet Launch: April 1, 2026.
💡 Why This Changes the Game
Evolution of Useful Proof of Work (uPoW): After successfully proving the concept with Monero ($XMR), Qubic is taking it a step further. We are proving that the energy used to secure a network and train AI can simultaneously mine external top-tier assets.Maximum Energy Utility: Miners securing the Qubic network and training its AGI "brain" will now be able to mine $DOGE — one of the world's most recognized cryptocurrencies. This means more value from the same energy and more liquidity flowing through the Qubic infrastructure.Massive Community Synergy: Dogecoin boasts one of the largest and most active communities in crypto. Bridging that energy with Qubic’s advanced compute layer opens doors that go far beyond a simple mining integration.
🔍 The Bottom Line:
Qubic is delivering on its promise: No energy is wasted. Instead of calculating useless hashes, we are building the future of AGI and being rewarded with real-world assets.
If Qubic can mine the "King of Memes" while training AI, the argument for Useful Proof of Work as the future of all mining becomes undeniable.
Stay tuned. April 1st marks a new chapter for decentralized AI and mining efficiency.
Hashtags: #Qubic #DOGECOİN #uPoW

Written by Qubic Scientific Team

How Information Flows in Traditional Artificial Neural Networks
In the artificial intelligence models we know, information enters, is encoded, is transformed through algebraic matrices, and produces outputs. Even in the most advanced architectures such as transformers, the principle is the same: the signal passes through a series of well-defined operations within a structured system. The model functions as a directed processing circuit, from left to right, input-output, or from right to left, through backpropagation for adjustments and training.
The results, as we well know, are spectacular. By working over millions of language parameters, AI is capable of giving magnificent answers, along with some hallucinations, however. But if the goal is not to process inputs and produce outputs, but to build systems capable of maintaining an internal dynamics, adapting continuously, reorganizing themselves, regulating their learning, and sustaining intelligence as a property of the tissue, current AI falls short.
Although people sometimes speak of language models as imitations of the brain, in reality this is more of a comparative metaphor than a simulation of computational neuroscience. Biological systems do not handle information from left to right and vice versa. Information propagates through a network, feeds back on itself, and also oscillates, is dampened, or is reinforced depending on the context.

Fig 1. Left-right information flow in traditional artificial neural networks
Not Only Neurons: The Role of Astrocytes in Brain Function and Synaptic Plasticity
We usually associate cognition and intelligence with the functioning of neurons, their receptors, and neurotransmitters. But they are not the only cells in the nervous system. For a long time, astrocytes were considered nervous system cells devoted to support, cleaning, nutrition, and stability of the environment. Today we know that they actively participate in regulation; in fact, a term is used: tripartite synapse, in which they actively participate by detecting neurotransmitters, integrating signals from multiple synapses, modulating plasticity, and modifying the functional efficacy of the circuit.
A living network is not composed only of neurons that fire, but also of astrocytes that regulate how, when, and how much the system changes. In biology, computing is not only about emitting a signal but also about modulating the terrain where that signal will have an effect. Recent research has demonstrated that astrocytes can perform normalization operations analogous to self-attention mechanisms found in transformer architectures — linking astrocyte–neuron interactions directly to attention-like computation in artificial intelligence systems.

Fig. 2 Biological astrocytes and tripartite synapse 
Astrocytic Gating in Neuraxon: Bio-Inspired Neural Network Architecture
Neuraxon is an architecture that tries to recover and emulate the functioning of the brain and to compute functional properties that classical artificial networks have oversimplified.
As we have explained in previous volumes of this academy, Neuraxon does not work only with input, output, and hidden neurons in the conventional sense. It introduces units with states that emulate excitatory, inhibitory, or neutral potentials (-1, 0, +1). In addition, it does so within a continuous TEMPORAL dynamics where we take into account context and the recent history of activation. The network is no longer a sum of layers but resembles more a system with internal physiology. For deeper context on how these foundational elements work, see NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time and NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence.
We have explained how Neuraxon models transmission through fast, slow, and neuromodulatory receptors — a mechanism explored in depth in NIA Volume 3: Neuromodulation and Brain-Inspired AI. But now we also model the regulation of plasticity through astrocytic gating.
How Astrocyte-Gated Multi-Timescale Plasticity (AGMP) Works
Astrocytic gating introduces a gate inspired by the role of astrocytes in the tripartite synapse. The idea is to introduce a local, slow, and contextual filter that determines when a synaptic modification should be opened, dampened, or blocked. It is as if the system can consider whether there is permission for a change. This approach directly addresses the stability-plasticity dilemma, one of the most fundamental challenges in continual learning for neural networks.
Eligibility Traces and Local Synaptic Memory
How does it work? Through a kind of eligibility trace. It is a local memory that says, "something relevant has happened at this synapse." It is updated with a decay over time and with a function between presynaptic and postsynaptic activity. That is: the synapse accumulates local evidence of temporal coincidence or causality. From there, there is a global broadcast-type signal, such as an error, a possible reward, or something dopamine-like. The astrocytic gate selects whether the neuron is in a learning state. In future versions, astrocytes could modulate thousands of synapses if this provides a computational advantage.
This approach is consistent with recent advances in neuromorphic computing, including the Astrocyte-Gated Multi-Timescale Plasticity (AGMP) framework proposed for spiking neural networks, which similarly augments eligibility-trace learning with a slow astrocyte state that gates synaptic updates — yielding a four-factor learning rule (eligibility × modulatory signal × astrocytic gate × stabilization).
Endogenous Regulation: Why Neuraxon Is More Than a Conventional Neural Network
Neuraxon within QUBIC does not compete in scale or task performance. It works through an architecture with endogenous regulation. By incorporating astrocytic principles, it begins to behave like a network with internal ecology. That is: a system where it matters not only which units are activated, but which domains of the tissue are plastic, which are stabilized, which areas are damping noise, which are consolidating regularities, and which are preparing to reorganize themselves. For a comprehensive overview of how biological and artificial neural networks compare, see NIA Volume 4: Neural Networks in AI and Neuroscience.
For Aigarth and QUBIC, the goal is not to accumulate more parameters, but to introduce more levels of functional organization within the system.
Why Astrocytic Gating Matters for Aigarth and Decentralized AI
Aigarth is not a static model but an evolutionary tissue through an architecture capable of growing, mutating, pruning, generating functional offspring, and reorganizing its topology under adaptive pressures. In that context, Neuraxon contributes something: a rich computational microphysiology for the units that inhabit that tissue.
This has implications for robustness, adaptability, and memory. Also for scalability. In large architectures, the problem is not only that there are many units, but how to coordinate which parts of the system are available for reconfiguration and which must maintain stability.
In roadmap terms for QUBIC, the goal is to build systems where intelligence emerges not only from neuronal computation, but also from the coupling between fast processing, slow modulation, and structural evolution. You can explore these dynamics firsthand with the interactive Neuraxon 3D simulation on HuggingFace Spaces, where you can build, configure, and simulate a Neuraxon 2.0 network from scratch.
Fig 3. Neuraxon astrocytes gating - AGMP formulation
Scientific References
Allen, N. J., & Eroglu, C. (2017). Cell biology of astrocyte-synapse interactions. Neuron, 96(3), 697–708.Halassa, M. M., Fellin, T., & Haydon, P. G. (2007). The tripartite synapse: Roles for gliotransmission in health and disease. Trends in Molecular Medicine, 13(2), 54–63.Kofuji, P., & Araque, A. (2021). Astrocytes and behavior. Annual Review of Neuroscience, 44, 49–67.=Perea, G., Navarrete, M., & Araque, A. (2009). Tripartite synapses: Astrocytes process and control synaptic information. Trends in Neurosciences, 32(8), 421–431.Woodburn, R. L., Bollinger, J. A., & Wohleb, E. S. (2021). Synaptic and behavioral effects of astrocyte activation. Frontiers in Cellular Neuroscience, 15, 645267.=Vivancos, D. & Sanchez, J. (2026). Neuraxon v2.0: A New Neural Growth & Computation Blueprint. ResearchGate Preprint.
Explore the Full Neuraxon Intelligence Academy
This is Volume 5 of the Neuraxon Intelligence Academy by the Qubic Scientific Team. If you are just joining us, explore the complete series to build a full understanding of the science behind Neuraxon and Qubic's approach to brain-inspired, decentralized artificial intelligence:
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.
Qubic is a decentralized, open-source network for experimental technology. To learn more, visit qubic.org
#Qubic #AGI #Neuraxon #academy #decentralized