Bit_boy

Die Entwicklung von Quantencomputing könnte das Bitcoin-Netzwerk gefährden
Ich habe alle Daten recherchiert und alles darüber gelernt.
/➮ Kürzlich hat BlackRock uns vor potenziellen Risiken für das Bitcoin-Netzwerk gewarnt
🕷 Alles aufgrund des schnellen Fortschritts im Bereich der Quantencomputing.
🕷 Ich werde ihren Bericht am Ende hinzufügen - aber lassen Sie uns zunächst aufschlüsseln, was das tatsächlich bedeutet.
/➮ Die Sicherheit von Bitcoin basiert auf kryptographischen Algorithmen, hauptsächlich ECDSA
🕷 Es schützt private Schlüssel und gewährleistet die Integrität von Transaktionen

Candlestick-Muster sind ein leistungsstarkes Werkzeug der technischen Analyse und bieten Einblicke in die Marktstimmung und mögliche Preisbewegungen. Durch das Erkennen und Interpretieren dieser Muster können Händler fundierte Entscheidungen treffen und ihre Erfolgschancen erhöhen. In diesem Artikel untersuchen wir 20 wichtige Candlestick-Muster und bieten einen umfassenden Leitfaden, der Ihnen dabei hilft, Ihre Handelsstrategie zu verbessern und möglicherweise 1.000 US-Dollar pro Monat zu verdienen.
Candlestick-Muster verstehen
Bevor Sie sich mit den Mustern befassen, müssen Sie die Grundlagen von Candlestick-Charts verstehen. Jede Kerze stellt einen bestimmten Zeitrahmen dar und zeigt die Eröffnungs-, Höchst-, Tiefst- und Schlusskurse an. Der Körper der Kerze zeigt die Preisbewegung, während die Dochte die Höchst- und Tiefstkurse anzeigen.

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

After sliding 35% between Jan. 14 and Feb. 5, Bitcoin has moved sideways, holding between $60,000 and $70,000 for the past three weeks. Meanwhile, key adoption indicators across ETFs, whales, miners, and corporate treasuries are trending in different directions.
These mixed signals suggest quiet but persistent capital accumulation beneath the calm price range, offering a broader view of underlying market strength.
Bitcoin ETF flows remain negative
The 90-day rolling average of US spot Bitcoin ETF net flows has dropped to -$2.18 billion. Over the past two years, the metric has turned negative only twice: from March to May 2025, and in the current stretch that began on December 11, 2025. In both instances, Bitcoin followed with a corrective phase.
When the rolling average turns negative, it means more money is leaving ETFs than coming in over a longer period. That reduces buying pressure, weakens overall demand, and can make it harder for prices to move higher.
A move back above zero, followed by steady inflows, may mark the return of institutional participation. Sustained positive readings tend to align with stronger price action from BTC, alongside improving liquidity conditions.
BTC whale accumulation versus dominant trend
CryptoQuant data tracks the one-year change in total whale holdings and its 365-day moving average. Addresses holding 1,000 to 10,000 BTC added more than 200,000 BTC from June to November 2023, while the price ranged from $25,000 to $30,000.
When the raw one-year change crosses above its 365-day average, whales are accumulating faster than their longer-term trend. That crossover in 2023 coincided with supply absorption during sideways trade, which eventually led to BTC’s bullish rally.
Thus, a bullish trend may unfold for BTC once the one-year change sustainably moves above its moving average (365-SMA), signaling renewed large-scale absorption.
Hash rate and infrastructure signal
Bitcoin’s 30-day mean hash rate stands near 0.99 ZH/s after peaking at 1.10 ZH/s in November 2025. Both hash rate and price have moved lower in recent weeks.
Hash rate measures the computational power securing the network and reflects miner investment in hardware and energy capacity. Rising hash rate during price consolidation points to infrastructure expansion independent of short-term price gains.
If the hash rate trends higher while the price trades sideways, it points to a stronger long-term commitment from miners. A sustained divergence, where hash rate rises ahead of price, can signal growing confidence within the mining sector.
Likewise, miner economics must also improve. Stabilizing the hash price and lower miner sell pressure confirms that rising computational power is backed by healthier revenue conditions rather than tightening margins.
Corporate Bitcoin treasury growth slows
According to a recent bitcointreasuries.net report, companies added roughly 43,200 BTC in January, with Strategy contributing about 40,150 BTC.
Looking at the bigger picture, Strategy’s corporate accumulation has decelerated noticeably since late 2024. Monthly additions hit highs of around 148,000 BTC in November 2024 and 87,000 BTC in July 2025.
Recent monthly increases are much smaller, and the past 30 days added only a slight uptick to the 1.13 million BTC currently held by public firms.
The latest monthly net increase equates to roughly 0.1% growth relative to total public company holdings. That pace signals stability rather than acceleration in treasury expansion.
For BTC price, broader and accelerating treasury inflows help absorb available supply more effectively. Slower increases, by contrast, signal companies are largely maintaining positions rather than driving new demand.
$BTC

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

Die meisten Menschen, mit denen ich über Fogo Official spreche, konzentrieren sich sofort auf eine Sache: Geschwindigkeit. Schnellere Blöcke, geringere Latenz, reibungslosere Ausführung. Und ja, dieser Teil ist beeindruckend. Aber je mehr Zeit ich damit verbringe, seine Dokumente zu lesen und über das Design nachzudenken, desto mehr habe ich das Gefühl, dass Geschwindigkeit nicht wirklich das Ziel ist.
Was mir wirklich auffällt, ist, dass FOGO wie ein Experiment mit etwas Tieferem wirkt — fast wie ein anderes politisches Modell dafür, wie Blockchains funktionieren sollten.
Als ich die Whitepapers und Spezifikationen durchging, kam ich immer wieder zu einer unangenehmen Frage, die die meisten Chains zu vermeiden scheinen: Wo endet die Verantwortung des Protokolls und wo beginnt meine als Nutzer? FOGO umgeht das nicht. Es beantwortet es direkt, manchmal unverblümt.

Key takeaways:
Bitcoin buyers still need roughly a 9% move higher to gain control ahead of Friday’s $10.5B options expiry.
Bitcoin continues to trade in step with the Nasdaq 100, meaning tech market sentiment remains a key driver of confidence.
Bitcoin climbed to an eight-day peak on Wednesday, carving out a clear double bottom around $62,500. Even with the rebound, price is still down 21% over the past month, leaving bulls at a disadvantage into the large monthly options expiry. A late surge could shift momentum, but the outcome remains uncertain.
Deribit remains the dominant leader with a 76% market share, totaling $4.5 billion in call (buy) options and $3.4 billion in put (sell) instruments. OKX follows in second place with $610 million in calls and $385 million in puts, representing 10% of the aggregate total. CME rounded out the top three with $255 million in calls and $287 million in puts, accounting for a 5% market share.
Put options are better positioned despite having less open interest
At first glance, the aggregate put options open interest appears 25% lower than equivalent call options. However, a more granular view reveals that neutral-to-bullish strategies were caught off guard by Bitcoin’s sharp decline below $75,000 in early February. 88% of call options on Deribit will expire worthless if the Bitcoin price remains below $70,000 on Friday.
Even after excluding call options aimed at $105,000 and above — often tied to multi-leg setups with cheaper premiums — just 37% of the remaining positions are placed below $75,000. This leaves the effective call open interest on Deribit near $780 million. Under these conditions, it raises the question of whether bearish traders may have pushed their bets too far.
$1.44 billion in put options open interest on Deribit targets Bitcoin prices below $60,000, although it is unlikely that bets at $40,000 and $45,000 effectively aimed for those specific levels. Calendar strategies and ratio spreads are typically associated with extreme price targets, as they do not require a price crash to achieve profitability.
Put options at $72,000 and above total $1.15 billion in open interest on Deribit, which is more than enough to offset existing call options. Although Bitcoin’s decline toward $60,000 was likely not tied to macroeconomic trend, the relevance of Nvidia’s (NVDA US) earnings outcome after the US market close on Wednesday should not be understated.
The success of the artificial intelligence sector, particularly the sustainable operational margins of the world’s largest companies, remains decisive for every risk market. History suggests that Bitcoin’s correlation with the stock market seldom lasts long, but the fate of Friday’s $10.5 billion options expiry could be decided by stock market performance.
The current 90% correlation between Bitcoin and the Nasdaq 100 Index is clear evidence that the tech play is the leading driver of trader confidence, but as long as Bitcoin price remains below $75,000, the advantage continues to favor put options.
Below are three probable outcomes for Friday’s BTC options expiry at Deribit based on current price trends:
From $65,000 to $69,000: The net result favors the put (sell) instruments by $1.15 billion.From $69,001 to $71,000: The net result favors the put (sell) instruments by $845 million.From $71,001 to $74,000: The net result favors the put (sell) instruments by $470 million.
Ultimately, Bitcoin bulls need a 9% rally from the present $68,800 level to flip the tables on the February options expiry.
$BTC

Wenn ich mir Fogo anschaue, sehe ich nicht eine weitere Kette, die versucht, alles für jeden zu sein. Ich habe nicht das Gefühl, dass es NFTs, Gaming, soziale Apps und jeden neuen Trend gleichzeitig verfolgt. Was mir stattdessen auffällt, ist, wie eng der Fokus ist.
Und ehrlich gesagt, respektiere ich das ein bisschen.
Für mich fühlt es sich so an, als würde Fogo sagen: „Lass uns einfach eine Sache wirklich gut machen.“ Diese eine Sache sind Märkte.
Ich komme immer wieder zu der Idee zurück, dass in Handelsumgebungen das Timing nicht etwas ist, das man haben kann. Es ist das gesamte Spiel. Ein paar Millisekunden können darüber entscheiden, ob du eine Ausführung erhältst oder abgelehnt wirst, ob du sicher bist oder liquidiert wirst. Wenn also eine Blockchain sagt, sie sei für Finanzen gebaut, denke ich weniger an TPS-Diagramme und mehr an Konsistenz. Verhält sie sich gleich, wenn die Dinge chaotisch werden?