Bit_boy

Lo sviluppo del calcolo quantistico potrebbe distruggere la rete Bitcoin
Ho ricercato tutti i dati e ho imparato tutto al riguardo.
/➮ Recentemente, BlackRock ci ha avvertito sui potenziali rischi per la rete Bitcoin
🕷 Tutto a causa dei rapidi progressi nel campo del calcolo quantistico.
🕷 Aggiungerò il loro rapporto alla fine - ma per ora, cerchiamo di capire cosa significa realmente.
/➮ La sicurezza di Bitcoin si basa su algoritmi crittografici, principalmente l'ECDSA
🕷 Protegge le chiavi private e garantisce l'integrità delle transazioni

I pattern candlestick sono uno strumento potente nell'analisi tecnica, offrendo spunti sul sentiment del mercato e sui potenziali movimenti dei prezzi. Riconoscendo e interpretando questi pattern, i trader possono prendere decisioni informate e aumentare le loro possibilità di successo. In questo articolo, esploreremo 20 pattern candlestick essenziali, fornendo una guida completa per aiutarti a migliorare la tua strategia di trading e potenzialmente guadagnare $ 1000 al mese.
Comprendere i modelli di candele
Prima di immergersi nei pattern, è essenziale comprendere le basi dei grafici a candela. Ogni candela rappresenta un intervallo di tempo specifico, mostrando i prezzi di apertura, massimo, minimo e chiusura. Il corpo della candela mostra il movimento dei prezzi, mentre gli stoppini indicano i prezzi massimi e minimi.

I have been looking closely at ROBO and the work behind the Fabric Foundation, and what stands out to me is how different their vision feels compared to most crypto projects. Instead of launching just another token, they’re trying to build the basic infrastructure for what they call a decentralized robot economy. The foundation was initiated by OpenMind, and the idea is simple but ambitious: robots shouldn’t just be tools we control manually, they should be autonomous agents that can earn, pay, verify tasks, and interact safely with people using blockchain rails.
From my perspective, the interesting part is how they combine AI, robotics, and Web3 in a practical way. Each robot has its own on-chain identity, wallet, and staking system, so it can accept tasks, get paid, and be held accountable. If a robot or operator behaves dishonestly, part of their stake can be slashed, which creates real economic consequences. They also reward useful contributions through something called Proof of Robotic Work, where data, compute, or skills are compensated. It feels like they’re trying to treat robots almost like independent workers in a digital marketplace rather than just hardware.
I also like that they aren’t building everything in isolation. They’re working with names like NVIDIA for compute, Circle for stablecoin payments, and Coinbase for ecosystem support. The protocol starts on Base before eventually moving to its own chain, which makes sense to me as a gradual path instead of overengineering from day one.
The $ROBO token is basically the fuel for everything: fees, payments between humans and robots, staking for task priority, and governance. With a fixed supply and planned buybacks from protocol revenue, the design tries to create long-term demand rather than just short-term hype. That said, I’m realistic about the risks. Building both hardware and blockchain infrastructure is incredibly complex, regulation around physical robots is still unclear, and competition from closed systems like Tesla Optimus and Figure is serious.
Personally, I see ROBO as more of a long-term bet on the future of robotics than a quick trade. If they actually execute on the open-source network, mainnet, and real-world adoption, it could become foundational tech. If not, it’s just another ambitious experiment. Either way, I’m treating it as high potential with equally high risk and doing my own research before making any moves.
@Fabric Foundation
$ROBO
#Robo

For years, I watched the AI space obsess over bigger models, higher benchmarks, and faster chips. Every update felt like a race for raw capability. More parameters, more compute, better scores. But the more I followed it, the more I felt something was missing. Performance is impressive, but when AI starts making real decisions that affect money, health, or operations, what I really care about isn’t how smart it sounds. It’s whether I can trust it.
That’s the lens through which I started looking at Mira Network.
To me, they’re not trying to build yet another model or compete with labs on intelligence. They’re going after something more basic and, honestly, more necessary. They’re trying to turn trust into infrastructure. Instead of assuming an AI output is correct because a company says so, the idea is to verify it through a decentralized network that’s economically accountable.
When I think about AI moving into finance, healthcare, or logistics, the risks feel obvious. If one AI agent triggers a trade or approves a transaction based on another model’s output, who takes responsibility if it’s wrong? Where’s the proof that the result wasn’t tampered with? Right now, most of that trust is internal and opaque. Companies audit themselves and publish reports, and we just accept it.
That doesn’t feel strong enough for systems that might be moving billions of dollars or handling sensitive decisions.
What Mira proposes makes intuitive sense to me. Validators, incentivized by the $MIRA token, check and attest to outputs or integrity signals. Instead of trust being a promise, it becomes something backed by stake and penalties. If you lie or act carelessly, you lose money. That simple economic pressure is often more reliable than policy documents.
But I’m not blindly optimistic either. I can see how this kind of system could go wrong. If validators just chase rewards without doing real work, verification becomes theater. If only a few players dominate staking, decentralization becomes cosmetic. If using the network slows everything down, developers simply won’t bother. Trust that adds friction isn’t trust people will adopt.
Still, I keep coming back to the same thought: as AI becomes more autonomous, some neutral trust layer feels inevitable. We’ve seen this before on the internet. Secure commerce eventually needed independent authorities. DeFi needed oracles once real money was on the line. It’s hard for me to imagine AI scaling globally without something similar.
In that scenario, $MIRA isn’t just another token to trade. I see it more like collateral for credibility. Its value would come from securing the system, not just speculation. If more applications depend on verified outputs, the network becomes harder to ignore.
So when I look at Mira, I don’t see hype. I see a bet on accountability. It feels less like chasing the next flashy model and more like building the plumbing that everything else might quietly rely on.
Capability got us excited about AI. For me, reliability is what will actually make it usable. And if a project can make trust measurable and verifiable, that’s the kind of foundation I’d rather back for the long term.
@Mira - Trust Layer of AI
$MIRA
#Mira

When I first got into DeFi, I thought speed was everything. Faster blocks, higher TPS, lower latency. But after actually trading on different chains, I realized something: even the fastest network still feels bad if the experience is clunky. Constant wallet pop-ups, random gas spikes, signing every tiny action — it breaks your flow. That’s why Fogo Official started to stand out to me, because they seem to care as much about how trading feels as how fast it is.
What really changed my perspective is their idea of Sessions. Instead of approving and signing every single transaction, I can open one session and let a dApp act within limits I set. I choose which tokens it can use, how much it can spend, and when it expires. After that, trades, cancellations, and rewards just happen in the background. For me, that feels closer to using a centralized exchange account, but I still keep control of my keys. It removes a lot of the small frictions that usually slow me down.
I also like that it’s not blind trust. Sessions aren’t unlimited approvals. There are caps and expiry times, and I can revoke access anytime. That balance between convenience and control feels practical rather than risky.
Beyond the wallet side, the network itself seems built for performance. Fogo runs on the Solana Virtual Machine and uses the Firedancer client, which is optimized for speed. On top of that, their FluxRPC layer keeps requests fast and stable, so apps can react in real time. As a trader, that consistency matters more to me than just flashy TPS numbers.
Then there’s their Dual-Flow Batch Auctions. This part honestly surprised me. Instead of pure first-come-first-serve trading where bots win every race, orders are grouped into short batches. Prices are set based on overall supply and demand, not who paid the highest gas. That means less front-running, fewer sandwich attacks, and fairer execution. For once, it feels like the system isn’t stacked against regular users.
What makes it click for me is how all these pieces connect. Sessions remove signing friction. FluxRPC keeps everything responsive. Batch auctions reduce MEV and spam. Together, it feels less like “typical DeFi” and more like a smooth trading platform that just happens to be decentralized.
From my own experience testing different chains, most focus on raw performance and forget usability. Fogo feels like it starts with the question, “How do traders actually want this to work?” That human-first design is rare in crypto.
It’s not perfect, and there are always trade-offs between speed, trust, and fairness. But if DeFi really wants to compete with centralized exchanges, I think this is the direction it has to go. For me, Fogo Official feels less like another chain and more like an attempt to make on-chain trading actually usable day to day.
@Fogo Official
$FOGO
#fogo

Dopo essere sceso del 35% tra il 14 gennaio e il 5 febbraio, Bitcoin si è mosso lateralmente, mantenendosi tra $60,000 e $70,000 per le ultime tre settimane. Nel frattempo, gli indicatori chiave di adozione tra ETF, balene, minatori e tesorerie aziendali stanno seguendo direzioni diverse.
Questi segnali misti suggeriscono un accumulo di capitale silenzioso ma persistente sotto l'intervallo di prezzo calmo, offrendo una visione più ampia della forza sottostante del mercato.
I flussi dell'ETF Bitcoin rimangono negativi
La media mobile di 90 giorni dei flussi netti dell'ETF Bitcoin spot negli Stati Uniti è scesa a -$2.18 miliardi. Negli ultimi due anni, il dato è diventato negativo solo due volte: da marzo a maggio 2025, e nell'attuale periodo che è iniziato l'11 dicembre 2025. In entrambi i casi, Bitcoin ha seguito con una fase correttiva.

For a long time, I bought into the idea that AI was “good enough.”
It writes our emails, helps doctors analyze scans, flags fraud, routes deliveries, even helps people create art and code. Everywhere I looked, AI was being treated like this unstoppable, reliable layer quietly running the world.
But the more I used it seriously, the more cracks I started noticing.
Not small mistakes — fundamental ones.
I’ve seen AI answer questions with total confidence and be completely wrong. I’ve seen it invent facts, misread context, and sometimes produce advice that could actually hurt someone if they followed it blindly. The hallucinations, the bias, the weird edge cases — they’re not rare bugs. They’re baked into how these systems work.
At some point it hit me: we’re deploying AI into healthcare, finance, and other high-stakes areas without ever solving the trust problem first.
We just assume it’s reliable because it sounds smart.
That feels reckless.
I really understood this when I watched someone ask an AI about medical symptoms and get a very convincing but totally incorrect answer. They almost acted on it before double-checking with a real doctor. That moment stuck with me. If they hadn’t verified it, the outcome could’ve been serious.
That’s when I started thinking: AI doesn’t just need to be powerful. It needs to be verifiable.
And that’s why what Mira Network is building caught my attention.
What they’re doing isn’t another “better model” or “smarter chatbot.” They’re not trying to make AI magically perfect. Instead, they’re tackling the trust issue directly.
Their idea is simple in a way that makes you wonder why nobody pushed it harder before: don’t trust a single AI’s output. Verify it.
Instead of treating an answer as one big block of truth, they break it into smaller claims and have multiple independent systems check those claims. Different models, different verifiers, all cross-examining the same output.
It reminds me a lot of how blockchains work.
You don’t trust one party to say a transaction is valid. You rely on consensus.
Mira applies that same thinking to AI. Multiple verifiers check the result, and the validation gets recorded on-chain so it can’t be quietly changed later. So you’re not just hoping the answer is right — you can actually see whether it’s been verified and how.
That shift feels huge to me.
It turns AI from “trust me bro” into something measurable.
And the incentives matter too. Validators are rewarded for being accurate, not for rushing or rubber-stamping results. So honesty and careful checking become economically rational, not just idealistic.
What I like most is that it’s not trying to replace existing AI systems. It acts more like a verification layer you can plug in. So teams don’t have to rebuild everything from scratch — they just add a trust layer on top.
I talked to a developer working on healthcare tools who said the biggest barrier for them wasn’t the model quality. It was liability. What happens if the AI is wrong?
That’s the real fear most teams don’t talk about.
If you can’t prove outputs are reliable, you can’t safely deploy in sensitive environments. Hospitals, autonomous vehicles, finance — the cost of being wrong is too high.
Verification changes that equation.
There’s also an ethical side to this that I appreciate. When outputs are transparent and independently checked, bias and errors get exposed instead of buried. It forces accountability. You can’t just say “the AI decided” and move on.
To me, that feels healthier than the current system where a few big companies control everything and everyone else just has to trust them.
What Mira seems to be saying is: don’t trust blindly — verify collectively.
The more I think about it, the more obvious it feels. AI isn’t going away. It’s only going to get embedded deeper into critical systems. So the question isn’t whether we’ll use it.
It’s whether we’ll put safeguards in place before something breaks badly.
For me, this kind of decentralized verification feels like the missing piece. Not hype, not smarter prompts, not bigger models — just accountability and proof.
Honestly, after seeing how often AI can be confidently wrong, I don’t think we should trust it without something like this ever again.
@Mira - Trust Layer of AI
#Mira
$MIRA

Most people I talk to about Fogo Official immediately focus on one thing: speed. Faster blocks, lower latency, smoother execution. And yes, that part is impressive. But the more time I spend reading its docs and thinking through the design, the more I feel like speed isn’t actually the point.
What really stands out to me is that FOGO feels like it’s experimenting with something deeper — almost like a different political model for how blockchains should work.
As I went through the whitepapers and specs, I kept coming back to one uncomfortable question that most chains seem to avoid: where does the protocol’s responsibility end and mine begin as a user? FOGO doesn’t dodge that. It answers it directly, sometimes bluntly.
When I read their MiCA-style disclosures, it didn’t feel like marketing copy. It felt more like a risk manual. They clearly say what the token is, what it isn’t, and what they’re not promising. No guarantees, no “we’ll protect you,” no implied safety net. It’s basically: this is software, you use it at your own risk.
At first that sounded harsh to me. But then I realized how rare that honesty is. A lot of projects rely on optimism and ambiguity. FOGO seems to prefer clarity, even if it makes things feel less comfortable.
And I think that changes behavior.
If I know nobody is going to bail me out, I read more carefully. I test more. I take custody and risk management seriously. I don’t treat the chain like a customer support desk. That shift alone makes the ecosystem feel more adult.
The same thing shows up in how they talk about exchanges and markets. They don’t pretend they control listings or outcomes. Trading is between users and venues. That separation is spelled out. It quietly removes the “blame the team” mindset and replaces it with “understand the system.” I actually like that.
Decentralization, too, feels less like a slogan and more like an operations problem. When I looked at the validator zone model and the rotation mechanics, I didn’t just see performance engineering. I saw coordination rules. Validators aren’t just sitting there producing blocks. They’re expected to move, sync, follow procedures. It feels disciplined.
That word — discipline — keeps coming back to me when I think about FOGO.
Even the tooling gives me that impression. Things like sessions and paymasters aren’t presented as flashy features. They read like operator manuals. You have to set up servers, bind domains, configure endpoints. It’s not “plug and play magic.” It’s structured and deliberate.
Some people might call that restrictive. I see it as responsible. Real financial systems don’t start fully open with no guardrails. They add layers carefully. FOGO seems comfortable with that slower, controlled approach.
Their compatibility with the Solana Virtual Machine also feels like more than a technical decision to me. It feels social. Instead of forcing developers to learn a new stack and abandon what they know, they just let them keep their tools and change an endpoint. That reduces friction. It feels less tribal and more pragmatic. I appreciate that mindset.
The big question in my head isn’t whether they can keep blocks fast. It’s whether they can keep this discipline as the network grows. It’s easy to be organized when the system is small. It’s much harder when money, incentives, and egos scale up.
Validator rotation, incident response, audits, incentives — all of that has to keep working under pressure. That’s where most systems break. Governance problems usually show up long before technical ones.
Even the economics look like behavior design to me. Low base fees, optional priority fees, rewards flowing directly to producers, inflation declining over time — it feels less like tokenomics theater and more like nudging people toward predictable behavior. If I want urgency, I pay for it. If I validate, I earn for processing real demand. It’s simple and grounded.
When I look at staking and lending integrations, I don’t just see yield strategies. I see habit formation. Users start thinking in terms of capital efficiency instead of idle balances. That can make the network sticky, but it can also create leverage risks. The fact that they openly document those risks makes me trust it more.
Transparency here doesn’t feel reactive. It feels intentional. Instead of waiting for something to break and then explaining, they explain first. Over time, that kind of consistency builds credibility. Markets remember who was upfront.
Personally, I’ve started to think of FOGO less as “another fast chain” and more as a governance-first trading network. Performance matters, sure. But what really makes a market usable is predictability and fairness. Clear roles. Clear risks. Clear rules.
That’s the vibe I get here: fewer promises, more structure.
There’s definitely risk. A system this coordinated depends on people actually coordinating. If validators slack off or incentives drift, the whole model could wobble. Discipline doesn’t scale automatically.
But if they can maintain it, I think it proves something important — that decentralization doesn’t have to mean chaos. It can mean shared responsibility, organized over time and space.
I’ve seen plenty of chains chase hype, TVL, and listings. Very few seem obsessed with operational clarity. FOGO feels like it’s betting on clarity.
Maybe that’s not exciting in the short term. But for a trading environment, consistency might matter more than excitement. And honestly, that’s a bet I find myself respecting more and more the deeper I look into it.
@Fogo Official
$FOGO
#fogo

Punti chiave:
Gli acquirenti di Bitcoin hanno ancora bisogno di un movimento superiore di circa il 9% per guadagnare controllo prima della scadenza delle opzioni da $10,5B di venerdì.
Il Bitcoin continua a negoziare in linea con il Nasdaq 100, il che significa che il sentiment del mercato tecnologico rimane un fattore chiave di fiducia.
Il Bitcoin è salito a un picco di otto giorni mercoledì, creando un chiaro doppio minimo attorno ai $62.500. Anche con il rimbalzo, il prezzo è ancora in calo del 21% nell'ultimo mese, lasciando i tori in svantaggio in vista della scadenza delle opzioni mensili. Un'improvvisa impennata potrebbe spostare il momentum, ma il risultato rimane incerto.

Quando guardo a Fogo, non vedo un'altra catena che cerca di essere tutto per tutti. Non ho la sensazione che stia inseguendo NFT, giochi, app social e ogni nuova tendenza tutto in una volta. Quello che noto invece è quanto sia ristretto il focus.
E onestamente, in un certo senso, rispetto ciò.
Per me, sembra che Fogo stia dicendo: “Facciamo solo una cosa davvero bene.” Quella cosa è i mercati.
Continuo a tornare a quest'idea che negli ambienti di trading, il tempismo non è un di più. È l'intero gioco. Alcuni millisecondi possono cambiare se vieni riempito o scivolato, se sei al sicuro o liquidato. Quindi quando una blockchain dice di essere costruita per la finanza, penso meno ai grafici TPS e più alla coerenza. Comporta allo stesso modo quando le cose si complicano?