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.

The first time I saw a robot complete a real task in a warehouse video, my reaction wasn’t amazement. My first thought was actually, “okay… but how do you prove it happened?”
That probably sounds strange, but once you’ve watched how systems work when multiple companies are involved, that question shows up pretty quickly. The robot doing the job is only part of the story. What matters just as much is what happens after the job is supposedly finished.
Who confirms the task was completed. Who gets paid for it. Who takes the hit if something goes missing. What happens when the robot reports success, the warehouse says the item never arrived, and the operator says their system logs look perfectly fine.
The physical action might be simple. The disagreement around it rarely is.
A robot moving a box from one place to another sounds trivial until you think about how many different organizations are tied to that single movement. There’s the robot manufacturer, the company operating the robot, the logistics provider, the warehouse operator, and often a retailer on the other end. Each of them keeps their own systems, their own records, and their own version of the timeline.
When something goes wrong, the truth usually doesn’t vanish. It just splits into several conflicting narratives.
That’s why I’ve started to think robotics isn’t mainly an intelligence challenge. It’s much more of a coordination challenge. A verification challenge. A problem of agreeing on what actually happened in the real world.
The real value isn’t just the robot completing the task.
The real value is having a record that proves the task actually happened.
This is where decentralization begins to make sense to me in a more practical way than the usual “AI meets crypto” hype. Not because it magically makes robots smarter, but because it gives multiple parties a neutral place to anchor shared events.
A shared ledger is not exciting technology. In fact, it’s pretty boring. But boring infrastructure is often the most useful kind.
It gives different organizations a single reference point. Not my internal log and not yours, but something both sides can look at when a dispute happens. A record that isn’t easily edited later. A timeline that exists outside the control of any single participant.
Once that exists, a lot of the surrounding processes start to change.
Payments can happen faster because the proof of completion is shared. Audits become easier because everyone is referencing the same history. And it becomes harder for any one party to quietly shift responsibility when something goes wrong.
Coordination stops depending on screenshots, support tickets, and long email threads. Instead, it becomes a question of checking a shared sequence of events.
But there’s another side to this as well.
The moment you can track everything, organizations will start tracking everything.
Task completion rates. Downtime. Speed. Error frequency. Reliability metrics. Suddenly you have a scoreboard, and once there’s a scoreboard people start optimizing their behavior to look good on it.
Anyone who has worked inside a company knows how that story tends to unfold. Metrics slowly replace judgment. Systems get tuned to maximize numbers instead of outcomes.
So the goal isn’t simply to attach robots to blockchains and assume everything gets better.
The bigger point is that robots are entering environments where several different organizations share financial exposure around the same physical actions. When money, responsibility, and reputation are all tied to the same events, intelligence alone doesn’t solve the problem.
What matters just as much is trust.
And at scale, trust usually ends up looking less like philosophy and more like shared records and clear consequences.
If we actually want a functioning robot economy, the first step probably isn’t building robots that think better.
It’s building systems that produce better receipts for what they do.
@Fabric Foundation
$ROBO
#ROBO

Ragazzi, pensavo che l'intera conversazione sulla affidabilità dell'IA riguardasse principalmente il fermare i modelli dall'allucinare. Se potessimo semplicemente rendere le risposte più accurate, tutto il resto si sarebbe sistemato da solo. Ma più osservavo come operano le vere istituzioni, più mi rendevo conto che l'accuratezza non è realmente la domanda principale che pongono.
Banche, compagnie assicurative, team di conformità... si preoccupano di qualcosa di molto più frustrante. Vogliono prove. Non solo che una risposta sia probabilmente corretta, ma che la decisione possa essere rintracciata, verificata e documentata dopo il fatto.

Bitcoin erased its latest breakout attempt after hitting $74,000 as surprisingly weak labor-market data offered no tailwind to crypto or risk assets.
Bitcoin slipped under $70,000 around Friday’s Wall Street open as weak US employment data failed to boost risk assets.
Key points:
Bitcoin and stocks fall after an unexpected drop in US nonfarm payrolls.
Markets remain hawkish on Fed policy, pricing in only one rate cut this year.
BTC price action erases its recent breakout attempt, continuing the pattern seen throughout 2026.
Despite signs of a weakening labor market, Bitcoin shows little reaction.
Trading data shows BTC dropping over 3% on the day, touching $68,176.
US nonfarm payrolls data disappointed across the board, showing that the labor market was more under pressure than expected.
The economy lost 92,000 jobs in February, per data from the Bureau of Labor Statistics (BLS), in contrast to the predicted 58,000 increase. The unemployment rate also came in higher at 4.4%.
The print contrasted with that from January, which delivered surprisingly strong employment results.

“The US labor market is clearly weakening.”

Labor-market strain traditionally signals a tailwind for crypto and risk assets as it implies a greater chance of interest-rate cuts.
The latest data from CME Group’s FedWatch Tool nonetheless showed little chance of the Federal Reserve doing so at its next meeting on March 18. Markets also saw just one rate cut in store for 2026.

The employment result thus failed to boost risk assets, with crypto following US stocks lower. At the time of writing, the S&P 500 and Nasdaq Composite Index were down 1.5% and 1.3%, respectively.
Only gold gained, with the precious metal up 1.5% to $5,155 per ounce.
BTC price returns from monthly highs
Bitcoin traders showed frustration as BTC/USD failed to confirm a breakout from its tight local range.
At the same time, an unusual on-chain movement saw 32K BTC withdrawn from exchanges in one day.
According to CryptoQuant contributor J. A. Maartunn, breakouts above the range high continue getting rejected.
He pointed to three similar failed breakouts in recent months, each turning into a deviation followed by a move lower.
“The latest deviation appeared near $71K. If the pattern continues, this level could once again trap late long positions,” he noted.
Price returned to interact with key long term levels notably the 200-week exponential moving average (EMA) and the old all-time high from 2021.
“Looks like $BTC is round tripping the range…again,” Keith Alan, cofounder of trading resource Material Indicators, added.
$BTC

Ragazzi, ho passato del tempo a esplorare Mira Network recentemente, e ciò che ha catturato la mia attenzione è che sta cercando di risolvere un problema con cui molti progetti crypto lottano ancora: collegare la blockchain al valore economico reale.
Mira Network si posiziona come un ecosistema blockchain costruito attorno alla tokenizzazione di asset del mondo reale. Invece di concentrarsi esclusivamente sul trading di token o sulla speculazione a breve termine, l'idea è trasformare imprese reali in asset tokenizzati sulla catena MIRA-20. In termini semplici, ciò significa che le persone nella comunità potrebbero possedere piccole azioni on-chain di aziende reali e ricevere dividendi automaticamente attraverso contratti smart.