tokenomics

Dowiedz się, co oznacza analiza fundamentalna w kryptowalutach. Odkryj, jak ocenić projekt kryptowalutowy krok po kroku przed inwestowaniem i unikaj błędów początkujących.
Wielu początkujących traci pieniądze w kryptowalutach, ponieważ kupują monety bez ich zrozumienia.
Podążają za hype'em.
Podążają za influencerami.
Podążają za emocjami.
Ale inteligentni inwestorzy robią coś innego. Studiują projekt przed inwestowaniem.
W tym artykule nauczysz się analizy fundamentalnej w kryptowalutach, abyś mógł prawidłowo analizować każdą monetę przed zainwestowaniem swoich pieniędzy.

​Inwestowanie w przyszłość AI często wydaje się abstrakcyjne, ale Fundacja Fabric wnosi konkretne korzyści do stołu z $ROBO . Jako natywna wartość sieci Fabric, jej role są wyraźnie zdefiniowane, aby zapewnić długoterminowe zdrowie i wzrost ekosystemu:
​1️⃣ Opłaty sieciowe: Każda interakcja, od weryfikacji tożsamości po rozliczenie misji, jest zasilana przez $ROBO.
2️⃣ Tożsamość maszyny: Roboty potrzebują sposobu, aby udowodnić, kim są i co zrobiły w łańcuchu—$ROBO sprawia, że jest to weryfikowalne i bezpieczne.

Wciąż pamiętam pierwszy raz, kiedy otrzymałem airdrop. Otworzyłem mój portfel, nie spodziewając się niczego, a tam było - saldo, które nie istniało dzień wcześniej. Czułem się cicho. Zarobione, mimo że nie zapłaciłem za to. Ta mała niespodzianka wciągnęła mnie głębiej w kryptowaluty niż jakikolwiek dokument biały mógłby.
Airdrop, na powierzchni, jest prosty. Projekt rozdziela darmowe tokeny grupie adresów portfeli. Czasami opiera się na przeszłym użytkowaniu. Czasami na posiadaniu konkretnego aktywu. Czasami jest losowy. Samo słowo pochodzi z logistyki wojskowej, ale w kryptowalutach sygnalizuje coś łagodniejszego - prezent.

Alokacja tokenów
Cześć @Fabric Foundation , 30% udziału społeczności dla $ROBO jest niesamowicie hojne! 👏👏👏
Świetnie jest widzieć tak silne skupienie na nagradzaniu ekosystemu. ✅️🆒️

Vesting
12-miesięczny okres karencji dla alokacji inwestorów i zespołu znacznie zwiększa wiarygodność projektu i pokazuje długoterminowe zaangażowanie.

Zespół gotowy czekać pełny rok na swoją nagrodę pokazuje, że naprawdę wierzą w projekt i nie szukają tylko szybkiego zysku.
#Robo ​#Robotics ​#MachineEconomy #TOKENOMICS

For the serious investors on Binance Square, understanding the Fabric Protocol ($ROBO) supply dynamics is key to navigating the current price action. With the TGE (Token Generation Event) happening now, here is the official breakdown:
​⚙️ The Numbers
​Total Supply: 10,000,000,000 $ROBO (10 Billion)
​Initial Circulating Supply: ~2,231,000,000 $ROBO (approx. 22.3%)
​Public Sale Price: ~$0.04 (via Kaito Capital)
​FDV (Fully Diluted Valuation): ~$400 Million
​📦 Token Allocation & Vesting
​The Fabric Foundation has designed a long-term release schedule to prevent massive sell pressure:
​Ecosystem & Community (29.7%): Dedicated to "Proof of Robotic Work" incentives and community growth.
​Investors (24.3%): 1-year cliff followed by 36-month linear vesting.
​Foundation Reserve (18.0%): Locked for long-term research and stewardship.
​Core Team: Subject to a strict 12-15 month cliff before linear release.
​💡 Core Utility: More than just a "Coin"
​$ROBO is the gas for the Robot Economy. Its utility is hard-coded into the protocol:
​Network Fees: Every robotic task, identity verification, and data exchange requires $ROBO.
​Coordination Staking: To register hardware (like the Minibot M1) on the network, operators must stake
ROBO as a "Work Bond" to ensure performance.
​Governance: Holders vote on fee structures and hardware standards for the global fleet.
​Analyst Note: While the initial unlock at TGE provides liquidity, over 75% of the supply is locked in long-term vesting. This suggests the team is betting on a multi-year adoption curve rather than a short-term pump.
​Are you watching the ROBO/USDT pair on KuCoin and Bybit today? The volatility is high! 📉📈
​#Tokenomics #FabricProtocol #CryptoAnalysis #DePIN #Web3Robotics

3 AM. reading through MIRA's verification flow for the fourth time. and honestly? kept getting stuck on the same question nobody seems to want to answer 😂

if MIRA verifies an AI output and that output is still wrong, what actually happens to the person who trusted the label?

what bugs me:

verification flow is technicaly sound. AI generates output, MIRA breaks it into atomic claims, nodes vote, supermajority consensus reached, cryptographic proof logged on Base. clean process.
but consensus isn't truth. 2/3 of nodes agreeing on something wrong doesn't make it right. it makes it a verified mistake. and that's a completely different problem than what MIRA's marketing addresses.

the #tokenomics angle nobody discusses:

Mira demand is built on one assumption: verification volume drives staking demand. more AI outputs verified, more nodes needed, more Mira staked, price supported.

but here's what that model quietly requires. enterprises and agents using MIRA need to believe the verified label carries real liability weight. if it doesn't, they route verification off-chain or skip it entirely for cost reasons.

allocation breakdown matters here. ecosystem and community hold significant share, unlock pressure building. token demand needs to outpace supply increase. that only happens if verification becomes genuinely sticky, meaning users trust the label enough to pay for it repeatedly.
if the liability gap stays unresolved, stickiness stays theoretical. no enterprise pays for a certificate that protects nobody.

my concern though:

the mechanism of harm is specific. user receives MIRA-verified output. trusts it because of the label. acts on it. output was wrong but achieved supermajority consensus among nodes running different LLMs with different training data. nobody is legally responsible. smart contract logged the proof. proof just proves consensus happened, not that consensus was correct.
that gap between verification certificate and truth certificate is where the real risk lives. and right now nothing in MIRA's design closes it.

what they get right:

atomic claims decomposition is genuinely underrated. breaking holistic AI output into individual verifiable facts forces a granularity most systems avoid. subjective framing cant hide inside an atomic claim the way it hides inside a paragraph. each statement either survives node scrutiny or it doesnt.
diverse LLM architecture across nodes is smart security thinking. different model families, different training data, different failure modes. coordinated hallucination becomes statistically unlikely when your verifier pool uses GPT, Claude, Grok and Llama simultaneously. one model's blind spot gets caught by another's strength.
Klok copilot integration is real traction, not vaporware. billions of tokens processed daily means the infrastructure handles genuine load. KGeN and Phala partnerships extend reach into gaming and secure compute, two verticals where AI verification actually matters. Chainlink built economic security for price data. MIRA applying same model to AI truth is logical progression, not imitation.

what worries me:
dual reading on the atomic claims approach.
positive: forces precision, eliminates vague outputs, catches hallucinations at the claim level before they compound into wrong decisions.
negative: complex AI reasoning isn't always decomposable into clean atomic facts. nuanced analysis, probabilistic statements, contextual judgements — these resist atomization. if MIRA can only verify the simple stuff, enterprises route complex queries elsewhere and Mira demand concentrates in low-value verification.
which reading matters more depends entirely on how sophisticated the decomposition engine actually is. and that detail isnt public yet.

honestly don't know if MIRA becomes essential infrastructure every AI application needs or a technically impressive system solving a liability problem nobody has legally defined yet.
watching enterprise adoption and whether any major AI platform integrates MIRA for something beyond copilot use cases.
what's your take - verification layer the internet needs or solution looking for a problem with legal teeth?? 🤔

#Mira @Mira - Trust Layer of AI $MIRA

Opis
Dowiedz się, co oznacza tokenomika w kryptowalutach w najłatwiejszy sposób. Zrozum podaż, popyt, spalanie i inflację, korzystając z prostych przykładów historii przed inwestowaniem.
Wprowadzenie
Wyobraź sobie dwa sklepy z zabawkami w małym miasteczku.
Sklep A drukuje nieograniczoną liczbę zabawek każdego dnia.
Sklep B produkuje tylko 100 specjalnych zabawek i nigdy nie robi więcej.
Po pewnym czasie, które zabawki stają się bardziej wartościowe?
Te ograniczone.
Ta prosta historia wyjaśnia tokenomikę.
Jeśli rozumiesz tokenomikę, rozumiesz, dlaczego niektóre kryptowaluty rosną silnie, a inne zawodzą.

been ignoring MIRA for weeks. finally mapped the node economics. and honestly? centralization question isn't where most people are looking 🤭
everyone asks "is verification decentralized?" wrong question. better one: who can actually afford to run a node.
what bugs me:
to verify, you stake Mira and run AI inference. sounds open. but LLM inference needs GPU hardware, uptime, enough stake to survive slashing. that quietly filters out small operators before the first vote is cast.
decentralized in structure. concentrated in practice.

The #Tokenomics angle nobody discusses:
99% of Mira is in top 10 wallets. on-chain reality, not rumor. unlock pressure coming as tranches open.
but deeper issue isn't the unlock. it's demand mechaincs. every verifier stakes $MIRA . honest nodes earn fees. slashed nodes lose stake. demand is supposed to grow with verification volume.
Klok copilot processes billions of tokens daily. real usage. but calling that "proof of demand" requires one unchallenged assumption: volume translates to staking demand, not just API calls bypassing the token entirely.

if verification stays off-chain for cost reasons, Mira demand stays theoretical.

my concern though:
slash mechanic sounds like security. might be centralization accelerator.

mechanism: nodes consistently disagreeing with consensus get slashed. supermajority threshold is 2/3. if large well-resourced nodes dominate early rounds, smaller nodes face a choice — align or risk slash. independent verification becomes economically irrational.
no collusion needed. incentives do it automaticaly.
what they get right:
breaking AI output into atomic factual claims is genuinely smart. most fact-checking evaluates holistic outputs, lets subjective framing slip through. Mira forces granularity — each claim stands or falls independently.

diverse LLMs across nodes, different training data, different model families. real defense against coordinated hallucination. one model lying gets outvoted. cryptographic proof on Base adds auditability centralized alternatives cant match. KGeN and Phala aren't vaporware. Chainlink comparison has merit — economic staking for data integrity is proven. applying it to AI truth is logical next step.
what worries me:
96% accuracy claim. who measures it? Mira does. who audits the auditors?
positive reading: remarkable at scale. negative reading: 4% error compunding across complex outputs, self-reported without independent audit is marketing until proven otherwise.

liability gap too. verified output that's still wrong creates false confidence. user trusts the label. output is wrong. on-chain proof doesn't fix that.

honestly don't know if this becomes Chainlink of AI truth or well-engineered infrastructure waiting for demand that arrives too slow for the unlock schedule.
watching node count and whether Klok drives real Mira staking volume.
what's your take - decentralized security or centralization by incentive design?? 🤔
@Mira - Trust Layer of AI #Mira $MIRA