Bit_boy

Kvantu datoru attīstība varētu iznīcināt Bitcoin tīklu
Es izpētīju visus datus un uzzināju visu par to.
/➮ Nesen BlackRock mūs brīdināja par potenciāliem riskiem Bitcoin tīklam
🕷 Viss pateicoties straujai progresam kvantu datoru jomā.
🕷 Es pievienošu viņu ziņojumu beigās - bet pagaidām aplūkosim, ko tas patiešām nozīmē.
/➮ Bitcoin drošība balstās uz kriptogrāfiskajiem algoritmiem, galvenokārt ECDSA
🕷 Tas aizsargā privātās atslēgas un nodrošina darījumu integritāti

Svečturu modeļi ir spēcīgs tehniskās analīzes instruments, kas sniedz ieskatu tirgus noskaņojumā un iespējamās cenu svārstībās. Atzīstot un interpretējot šos modeļus, tirgotāji var pieņemt apzinātus lēmumus un palielināt savas izredzes gūt panākumus. Šajā rakstā mēs izpētīsim 20 būtiskus svečturu modeļus, sniedzot visaptverošu ceļvedi, kas palīdzēs uzlabot jūsu tirdzniecības stratēģiju un potenciāli nopelnīt USD 1000 mēnesī.
Izpratne par svečturu rakstiem
Pirms iedziļināties modeļos, ir svarīgi saprast svečturu diagrammu pamatus. Katra svece apzīmē noteiktu laika posmu, parādot atvērtās, augstākās, zemākās un slēgšanas cenas. Sveces korpuss parāda cenu kustību, bet daktis norāda uz augstām un zemām cenām.

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

Guys,I used to think the entire conversation around AI reliability was mostly about stopping models from hallucinating. If we could just make the answers more accurate, everything else would sort itself out. But the more I watched how real institutions operate, the more I realized accuracy isn’t actually the main question they ask.
Banks, insurance companies, compliance teams… they care about something much more frustrating. They want proof. Not just that an answer is probably correct, but that the decision can be traced, verified, and documented after the fact.
In regulated environments, being right isn’t enough. You can generate the correct answer and still run into serious trouble if you can’t show how that answer was produced and verified. Auditors don’t care about average model performance. They want the records. Courts don’t care that a system works 99% of the time. They care about the one decision connected to the one case that went wrong.
That’s when Mira started to make more sense to me.
The project isn’t really trying to win the race for the smartest AI model. What it’s trying to do is turn AI outputs into something institutions can actually defend. Instead of asking people to simply trust the system, it creates a record showing what was checked, when it was checked, who verified it, and how consensus was reached.
That changes the entire framing.
The way I started thinking about it is similar to quality control in manufacturing. Factories don’t just say their machines are accurate most of the time and ship everything out the door. They inspect units, log what passes inspection, record failures, and keep a clear trail of who approved what.
Mira seems to be trying to apply that same logic to AI outputs.
Rather than treating an AI answer as one big block of information, the idea is to verify each output individually. The claim isn’t that the model is generally reliable. The claim is that this specific output was inspected and approved before being used.
And the important piece isn’t just a dashboard telling you it happened. It’s the certificate attached to that verification.
That certificate becomes the artifact that actually matters in a real-world audit. It ties the output to a specific verification round. It shows which validators participated, what thresholds were met, and which exact output hash was being certified at that moment.
Without something like that, the word “verified” doesn’t mean much. It’s just a label on an interface.
With it, you have a record someone can actually examine.
This also explains why a lot of existing AI governance tools feel incomplete when the stakes get higher. Things like model cards, bias evaluations, and explainability reports are useful, but they mostly show that the model was evaluated at some point. They don’t prove that a particular output was checked before it was used.
There’s a big difference between saying “we tested the model” and saying “this exact decision was verified.”
Mira seems to be built around that gap.
Even some of the architectural choices reflect that goal. Recording verification results on Base, which is an Ethereum layer-2 network, signals that the project cares not only about throughput but also about permanence. If verification records are going to matter in disputes, audits, or investigations, they can’t feel temporary or editable. Anchoring them to a system with strong finality makes those records harder to quietly rewrite later.
A lot of the design decisions also look like they’re shaped by real enterprise constraints. There’s a focus on standardizing inputs so context doesn’t drift between validators. Work is distributed through random sharding to help with privacy and load balancing. And verification uses a supermajority style aggregation rather than a simple noisy vote.
Those are the kinds of design choices you make when you expect adversarial conditions and scrutiny, not just when you’re building something for demos.
Privacy is another piece that can’t really be optional here. Companies dealing with sensitive data can’t just send raw prompts or proprietary information across a network of validators and hope nothing leaks. Approaches like zero-knowledge style query verification start to make the model more practical, because they allow correctness to be proven without exposing the underlying data.
Then there’s the incentive layer, which is where the Web3 approach shows up more clearly.
Mira uses staking and rewards to turn verification into an economic system. Validators put capital at risk, earn rewards when they perform accurate verification, and face penalties when they’re negligent or dishonest. That structure turns reliability into something enforced by incentives rather than just policy guidelines.
Of course, this kind of system isn’t free.
Running consensus rounds takes time. Verification adds cost. Every organization using something like this has to decide when that extra assurance is actually worth the trade-off in speed.
And there’s still a major open question around responsibility. Even if an output is verified, things can still go wrong. When that happens, who is ultimately accountable? The company using the system, the network providing verification, or the validators who participated in the consensus?
Traceability doesn’t automatically answer the liability question.
But even with those uncertainties, the direction feels meaningful to me.
Because the biggest barrier to AI adoption inside institutions isn’t just whether the model is smart enough. It’s whether the organization can defend the decisions that come out of it.
Being able to say a system produced an answer is one thing.
Being able to show that the answer was checked, recorded, and tied to a verifiable trail is something very different.
And that’s what Mira seems to be trying to build.
Not just smarter AI, but a layer of evidence that sits on top of it, one verified output at a time.
@Mira - Trust Layer of AI
$MIRA
#Mira

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

Čaļi, es esmu pavadījis nedaudz laika, pētot Mira Network nesen, un tas, kas piesaistīja manu uzmanību, ir tas, ka tā mēģina atrisināt problēmu, ar kuru daudzas kriptovalūtu projekti joprojām cīnās: blokķēdes savienošana ar reālo ekonomisko vērtību.
Mira Network pozicionē sevi kā blokķēdes ekosistēmu, kas izveidota ap reālo aktīvu tokenizāciju. Tā vietā, lai koncentrētos tikai uz tokenu tirdzniecību vai īstermiņa spekulācijām, ideja ir pārvērst faktiskos uzņēmumus tokenizētos aktīvos MIRA-20 ķēdē. Vienkāršiem vārdiem sakot, tas nozīmē, ka kopienā esošie cilvēki varētu piederēt mazām on-chain daļām reāliem uzņēmumiem un automātiski saņemt dividendes caur viedajiem līgumiem.