qubic

UPOW x DOGECOIN: Рассвет Универсального Вычислительного Движка!
Самая ожидаемая веха 2026 года для экосистемы $QUBIC была раскрыта: майнинг Dogecoin ($DOGE ) официально приходит в сеть Qubic. Это не просто еще одна интеграция; это огромный шаг к превращению Qubic в настоящий Универсальный Вычислительный Движок.
📍 Текущий статус: Где мы находимся
✅ Этап проектирования: Завершен.
✅ План проекта: Окончательный.
⚙️ В процессе: Два рабочих потока работают параллельно — Техническая реализация (Координация компьютеров) и Бизнес-планирование (Предложение сообщества).

Introduction: A Shift in Understanding
In the world of Qubic, we often hear terms like AI, ANNA, and Aigarth used interchangeably. However, according to CFB’s vision, we must look deeper. If the current AI industry is building "tools," Qubic is building a new Paradigm. As CFB famously stated: "Aigarth is not AI; it’s a project aiming to find new paradigms for the creation of AI."
1. ANNA: The Living Neural Engine
ANNA (Artificial Neural Network Assembly) is the raw, evolving intelligence within the Qubic ecosystem. It is the active force trained by the global network through uPoW (Useful Proof of Work).
The Actor: Unlike a static database, ANNA is the active intelligence that can execute, learn, and eventually act. As CFB pointed out, while AI agents can "deploy contracts," Aigarth itself serves a different purpose.
2. Aigarth: The "Book" of Universal Patterns
If ANNA is the brain, then Aigarth is the Library. * Not an Agent, but a Paradigm: Aigarth is not a chatbot or a functional AI agent. It is a repository of discovered logic and patterns. CFB describes it as a "Book"—a collection of wisdom and instructions that define how intelligence should be structured.
A Blueprint for Creation: The goal of the Aigarth project is to move away from the "black box" of modern deep learning and find a transparent, trinary-based logic for creating AI that is more efficient and truly decentralized.
3. The 15.52M TPS Infrastructure: Why It Matters
To write this "Book" of intelligence, you need a massive, high-speed recording system.
The 15.52 million TPS (verified by CertiK on 22/04/2025) isn't just for financial transactions. It provides the high-frequency "ticks" necessary for the network to synchronize complex neural updates.In this ecosystem, speed equals the resolution of the "Book." Higher throughput allows for more complex paradigms to be discovered and recorded within Aigarth.
4. The Countdown to April 13, 2027
The "launch" of AiGarth on 13/04/2027 is not the release of a product, but the completion of a foundational phase.
It is the day the "Book" becomes readable for external developers and AI agents.It marks the moment when the world can use the paradigms discovered within Aigarth to create AI that is censorship-resistant, zero-fee, and truly autonomous.
Conclusion: The Future is Decentralized Creation
We aren't just waiting for a smarter Siri. We are waiting for a new way to create intelligence. Aigarth is the vessel, ANNA is the spark, and Qubic is the furnace. On April 13, 2027, the "Book" opens, and the era of centralized AI monopolies ends.
#Qubic #AiGarth #Anna

As of January 14, 2026, contracts now pay for the computational resources they actually consume.
The update was first validated in a live testnet environment, then rolled out to mainnet, introducing organic burn directly proportional to the work a contract performs.
Why Execution Fees Matter
Every smart contract on Qubic maintains an execution fee reserve, essentially a prepaid balance that covers its compute costs.
When that reserve is depleted, the contract doesn’t disappear, but it does go dormant. It can still receive funds and respond to basic system events, however its core functions can’t be called again until the reserve is replenished.
Previously, a contract only needed a positive balance to remain active. The system verified that a reserve existed, but execution costs were not deducted based on actual computation. That has now changed. Contracts are charged proportionally to how long their procedures take to execute, aligning fees directly with real computational work.
How the System Works
The fee mechanism operates in phases, each lasting 676 ticks. Here's the process:
Execution and Measurement: When computors run your contract's procedures, they measure how long each execution takes.
Accumulation: These measurements build up over a complete 676-tick phase.
Consensus: Computors share their measured values through special transactions. The network aggregates these reports and uses the  two-thirds percentile to determine a fair, agreed-upon execution fee.
Deduction: The consensus fee gets subtracted from the contract's reserve in the following phase. This phase-based approach keeps consensus efficient while ensuring accuracy across the network.
Phase n-1          Phase n              Phase n+1
(676 ticks)        (676 ticks)          (676 ticks)
    │                  │                    │
    └── Fees computed ─┘── Fees deducted ───┘
Who Pays for What
The system follows a simple principle: whoever initiates an action pays for it. When a user calls a contract procedure, that contract's reserve covers the cost. When Contract A calls Contract B, Contract B's reserve gets checked before execution proceeds.
Some operations remain free of execution fee checks:
Operation
Fee Check
User procedure calls
Yes
Contract-to-contract procedures
Yes
Contract-to-contract functions
Yes
System callbacks (transfers, etc.)
No
Read-only functions
No
Epoch transitions
No
Functions that only read data never cost anything. They provide access to contract state without modification, so they run regardless of reserve status. For more details on how procedures and functions differ, see the QPI documentation.
What Builders Should Do
If you maintain a smart contract on Qubic, consider these steps:
Review your reserve status. Check contracts.qubic.tools to see current fee consumption for your contract based on execution patterns. You can also monitor contract activity through the Qubic Explorer.
Examine your procedures. Code that returns early, uses fewer resources. Procedures that loop excessively or repeat redundant operations will cost more.
Plan for sustainability. Contracts can replenish their reserves through the qpi.burn() function or through QUtil's BurnQubicForContract procedure. You can execute these operations using the Qubic CLI. It is recommended that you ensure your contract includes a reliable mechanism for maintaining adequate reserves throughout its lifecycle.
Handle errors gracefully. When calling other contracts, check whether those calls succeeded. If a target contract has insufficient fees, your call will fail and return an error code. Build in fallback logic where appropriate.
For developers new to building on Qubic, the smart contract development guide provides a solid starting point.
What Computors Should Know
Computors have a new configuration option: the execution fee multiplier. This setting converts raw execution time into fee amounts. The network reaches consensus using the two-thirds percentile of all computor-submitted values, preventing any single operator from dramatically shifting costs.
For more information about running a computor, refer to the computor documentation.
Refilling Reserves
Three methods exist for adding to a contract's execution fee reserve:
Internal burning: Contracts can call qpi.burn(amount) to convert collected fees into reserve balance. They can also fund other contracts using qpi.burn(amount, targetContractIndex).
External contributions: Anyone can send funds to the QUtil contract's BurnQubicForContract procedure, specifying which contract should receive the reserve boost.
Legacy method: QUtil's BurnQubic procedure adds specifically to QUtil's own reserve.
These mechanisms tie directly into Qubic's tokenomics, where burning serves as the core deflationary mechanism rather than traditional transaction fees.
Protection for Users
The system includes built-in safeguards. If you send a transaction to a contract with depleted reserves, any attached funds are automatically returned. You won’t lose money because a contract failed to maintain its balance.
Read-only queries remain available even for dormant contracts. You can check its state at any time, but state-changing procedures won’t run until the reserve is replenished.
What This Means for Qubic
This update marks a meaningful shift in how Qubic handles smart contract economics. 
Contracts that perform more work, pay more. Efficient code becomes genuinely valuable. And the network gains a sustainable mechanism for burning tokens tied to actual utility rather than arbitrary fixed amounts.
If you build on Qubic and haven't yet reviewed your contracts under this new model, now is the time. For technical details, see the full reference documentation on GitHub.
Join the Qubic Discord or Telegram community to ask questions, share ideas, and discuss implementation strategies with other builders.or Telegram community to ask questions, share ideas, and discuss implementation strategies with other builders.
#Qubic #SmartContracts

Written by Qubic Scientific Team

How does a neuron function over time?
Biological neurons do not function like a bedroom light switch being turned on. They are a continuous dynamic system. The neuronal state evolves constantly, even in the absence of external stimuli.
How does a neuron function over time?
Basically, by moving electrical charges (ions) in or out of its membrane, that is, by changing its electrical potential. Ions enter or leave (mainly sodium and potassium) through the different gates of the neuron with a certain intensity, modifying the potential. There are some gates, called leakage gates, where ions are always entering and leaving.
Time is implicit. The electrical potential changes constantly, over time.
The change in a neuron’s electrical potential over time depends on:
The external current applied + the balance between the flows of sodium ions (which increase it) and potassium ions (which decrease it) through the gates that open and close.
Don’t panic with the graph. Positive and negative electrical charges (ions) flow through the gates causing depolarization (so current moves along to the end of the neuron) or hiperpolarization (so it comes back to a neutral state). 

The potential (V) changes over time, that is mathematically, dV/dt, as a function of the sum of the input and output gates.
This is the fundamental model of computational neuroscience, which expresses that the state of the neuron depends both on current signals and on its immediate history. There is no “reset” between events, since each stimulus falls onto a system that is always running.
Now let’s move to Neuraxon, which is a bio-inspired model.

We want it to be alive, an intelligent tissue. It cannot have discrete states, but continuous ones.
In Neuraxon, instead of ion gates that open and close and move charges with a certain intensity, changing voltage, we have dynamic synaptic weights. But the model equation maintains a clear and direct similarity with the biological neuron.
What does this mean?
Instead of V, voltage in biological neuron, the state of Neuraxon, is s. And it changes over time too, therefore ds/dt is a function of the weights and activations and the previous state.
Unlike a classical AI model, where the synaptic weights of a network represent stereotyped outputs to an input, in Neuraxon the weights are not static.
Imagine, for example, an “email inbox” automatic response mechanism.
In classical AI, the rule does not adjust or change over time or context.
In Neuraxon, it is taken into account whether the “email input” comes from the same person (which could indicate urgency) or whether it arrives on a weekend (which may generate a no-response output). In other words, the rule remains, but when and how the response is given is modulated.
Do LLMs compute time?

Large language models appear to show deep understanding in many contexts, but they operate under a logic different from biological systems (Vaswany, 2017). They do not function based on an internal temporal dynamic, on a “change in potential” or on “synaptic weights” that modulate response, but rather process discrete sequences.
In LLMs, “time” does not exist, which makes it difficult for them to simulate biological behavior (such as intelligence). LLMs know how to distinguish which word comes before and which comes after, but they do not grant an experience of duration or persistence. Order replaces time.
Unlike Neuraxon, they do not possess internal rhythms that speed up or slow down, nor do they show progressive habituation to repeated stimuli, nor can they dynamically anticipate based on an internal state that changes over time.
The LLM model computation would be something like:
output = Fθ(input)
so outcomes are fixed solutions from a function (combination) of inputs.
There is no state as a function of time. These are data that form huge matrices and change their value through a specific function, which, as in the example cited, restricts the possibilities: email input → automatic response.
Wrapping up. The distance between bio-inspired models such as Neuraxon and large language models should not be explained in terms of computational power or data volume. There is a deeper difference.
The brain is, in itself, a continuous temporal system. Its functioning is defined by dynamics that unfold over time, by states that evolve, decay, and reorganize permanently, even in the absence of external stimuli (Deco et al., 2009; Northoff, 2018).
Neuraxon deliberately positions itself within that same logic. It does not attempt to imitate 1 to 1 the biophysical complexity of the brain, but it explicitly incorporates time as a computational variable. Its internal state evolves continuously, carries the past, and modulates the present, allowing adaptation without the need for a reset.
LLMs, by contrast, operate very differently. They manipulate symbols ordered in discrete sequences without their own temporal dynamics. There is no time, only order. There is no adaptation, only pre-defined responses.
As long as time does not form part of the state governing computation, LLMs may be effective, but they will hardly be autonomous in a strong sense.
Future artificial intelligence aims to operate in dynamic environments. This is the reason why Neuraxon includes time as a fundamental variable.
A living intelligence tissue…
How This Relates back to Qubic?
Qubic provides the continuously running, stateful computational environment required for time-aware intelligence.
It is the natural substrate on which models like Neuraxon - adaptive, persistent, and never “resetting” - can exist and evolve.
Addenda
Take a look at the equations. Don´t panic!
1 Biological neuron, V potencial, “sum of gates flux in & out”

2 Neuraxon model equation - clear and direct similarity with the biological neuron.
s state, wi & f(si) dynamic synaptic weights 

3 LLM model equation. Inputs (ordered in a matrix) create matrix outputs through a fixed function 
p (xn+1 | x₁, …, xn) = softmax (Fθ (x₁, …, xn) )

References
Deco, G., Jirsa, V. K., Robinson, P. A., Breakspear, M., & Friston, K. J. (2009). The dynamic brain. PLoS Computational Biology, 5(8), e1000092.Northoff, G. (2018). The spontaneous brain. MIT Press.Vaswani, A., et al. (2017). Attention is all you need. NeurIPS.Vivancos, D., & Sanchez, J. (2025). Neuraxon: A new neural growth & computation blueprint. Qubic Science.rint. Qubic Science.
#Qubic #Neuraxon

Анализ информирования глубоких нейронных сетей многомасштабными принципами
В мозге нейромодуляция — это набор механизмов, с помощью которых определённые нейромедиаторы изменяют функциональные свойства нейронов и синапсов, изменяя то, как они реагируют, как долго они интегрируют информацию и при каких условиях они изменяются с опытом.
Эти эффекты в основном вызываются нейромедиаторами, такими как дофамин, серотонин, норадреналин и ацетилхолин, которые действуют на рецепторы, известные как метаботропные рецепторы. В отличие от быстрых рецепторов, эти не генерируют электрический сигнал напрямую, а вместо этого активируют клеточные сигнальные пути, которые изменяют динамический режим нейрона и цепи.

Вот список известных блокчейн-сетей и связанных с ними токенов, отсортированных по их теоретическим максимальным возможностям транзакций в секунду (TPS), от наибольших к наименьшим:
1. Qubic - 15 520 000 TPS
2. Solayer ($LAYER ) – 1 миллион TPS обновленные значения*
3. $TON (Открытая сеть) – 1 миллион TPS (теоретический максимум)
4. $SUI ~ 297 000 TPS
5. Aptos (APT) – 160,000 TPS
6. Solana (SOL) – 65,000 TPS
7. Polygon (MATIC/POL) – 65,000 TPS
8. Arbitrum (ARB) – 40,000 TPS
9. Internet Computer (ICP) – 11,500 TPS