Bit_boy

Development of quantum computing might kill the Bitcoin network
I researched all the data and learn everything about it.
/➮ Recently, BlackRock warned us about potential risks to the Bitcoin network
🕷 All due to the rapid progress in the field of quantum computing.
🕷 I’ll add their report at the end - but for now, let’s break down what this actually means.
/➮ Bitcoin's security relies on cryptographic algorithms, mainly ECDSA
🕷 It safeguards private keys and ensures transaction integrity
🕷 Quantum computers, leveraging algorithms like Shor's algorithm, could potentially break ECDSA
/➮ How? By efficiently solving complex mathematical problems that are currently infeasible for classical computers
🕷 This will would allow malicious actors to derive private keys from public keys
Compromising wallet security and transaction authenticity
/➮ So BlackRock warns that such a development might enable attackers to compromise wallets and transactions
🕷 Which would lead to potential losses for investors
🕷 But when will this happen and how can we protect ourselves?
/➮ Quantum computers capable of breaking Bitcoin's cryptography are not yet operational
🕷 Experts estimate that such capabilities could emerge within 5-7 yeards
🕷 Currently, 25% of BTC is stored in addresses that are vulnerable to quantum attacks
/➮ But it's not all bad - the Bitcoin community and the broader cryptocurrency ecosystem are already exploring several strategies:
- Post-Quantum Cryptography
- Wallet Security Enhancements
- Network Upgrades
/➮ However, if a solution is not found in time, it could seriously undermine trust in digital assets
🕷 Which in turn could reduce demand for BTC and crypto in general
🕷 And the current outlook isn't too optimistic - here's why:
/➮ Google has stated that breaking RSA encryption (tech also used to secure crypto wallets)
🕷 Would require 20x fewer quantum resources than previously expected
🕷 That means we may simply not have enough time to solve the problem before it becomes critical
/➮ For now, I believe the most effective step is encouraging users to transfer funds to addresses with enhanced security,
🕷 Such as Pay-to-Public-Key-Hash (P2PKH) addresses, which do not expose public keys until a transaction is made
🕷 Don’t rush to sell all your BTC or move it off wallets - there is still time
🕷 But it's important to keep an eye on this issue and the progress on solutions
Report: sec.gov/Archives/edgar…
➮ Give some love and support
🕷 Follow for even more excitement!
🕷 Remember to like, retweet, and drop a comment.
#TrumpMediaBitcoinTreasury #Bitcoin2025 $BTC

Candlestick patterns are a powerful tool in technical analysis, offering insights into market sentiment and potential price movements. By recognizing and interpreting these patterns, traders can make informed decisions and increase their chances of success. In this article, we'll explore 20 essential candlestick patterns, providing a comprehensive guide to help you enhance your trading strategy and potentially earn $1000 a month.
Understanding Candlestick Patterns
Before diving into the patterns, it's essential to understand the basics of candlestick charts. Each candle represents a specific time frame, displaying the open, high, low, and close prices. The body of the candle shows the price movement, while the wicks indicate the high and low prices.
The 20 Candlestick Patterns
1. Doji: A candle with a small body and long wicks, indicating indecision and potential reversal.
2. Hammer: A bullish reversal pattern with a small body at the top and a long lower wick.
3. Hanging Man: A bearish reversal pattern with a small body at the bottom and a long upper wick.
4. Engulfing Pattern: A two-candle pattern where the second candle engulfs the first, indicating a potential reversal.
5. Piercing Line: A bullish reversal pattern where the second candle opens below the first and closes above its midpoint.
6. Dark Cloud Cover: A bearish reversal pattern where the second candle opens above the first and closes below its midpoint.
7. Morning Star: A three-candle pattern indicating a bullish reversal.
8. Evening Star: A three-candle pattern indicating a bearish reversal.
9. Shooting Star: A bearish reversal pattern with a small body at the bottom and a long upper wick.
10. Inverted Hammer: A bullish reversal pattern with a small body at the top and a long lower wick.
11. Bullish Harami: A two-candle pattern indicating a potential bullish reversal.
12. Bearish Harami: A two-candle pattern indicating a potential bearish reversal.
13. Tweezer Top: A two-candle pattern indicating a potential bearish reversal.
14. Tweezer Bottom: A two-candle pattern indicating a potential bullish reversal.
15. Three White Soldiers: A bullish reversal pattern with three consecutive long-bodied candles.
16. Three Black Crows: A bearish reversal pattern with three consecutive long-bodied candles.
17. Rising Three Methods: A continuation pattern indicating a bullish trend.
18. Falling Three Methods: A continuation pattern indicating a bearish trend.
19. Marubozu: A candle with no wicks and a full-bodied appearance, indicating strong market momentum.
20. Belt Hold Line: A single candle pattern indicating a potential reversal or continuation.
Applying Candlestick Patterns in Trading
To effectively use these patterns, it's essential to:
- Understand the context in which they appear
- Combine them with other technical analysis tools
- Practice and backtest to develop a deep understanding
By mastering these 20 candlestick patterns, you'll be well on your way to enhancing your trading strategy and potentially earning $1000 a month. Remember to stay disciplined, patient, and informed to achieve success in the markets.
#CandleStickPatterns
#tradingStrategy
#TechnicalAnalysis
#DayTradingTips
#tradingforbeginners

For years, blockchain applications have focused on one question: Can a transaction execute?
A different question is becoming increasingly important:
Should it execute?
That distinction sits at the center of Newton Protocol's "Policies in Motion" approach.
The Gap Between Rules and Execution
Traditional finance relies heavily on policies. Investment mandates, compliance requirements, exposure limits, and operational controls are carefully documented before capital is deployed.
On-chain systems often separate those rules from execution.
A protocol may define risk limits in governance documents or frontend interfaces, but once a transaction reaches a smart contract, execution typically depends on contract logic rather than the broader policy framework.
This creates a gap between governance and execution.
Moving Policy Into the Transaction Flow
Newton Protocol proposes treating policy as part of the authorization process rather than treating it as external guidance.
Instead of asking whether a transaction is technically valid, the system can evaluate whether it also satisfies predefined policy requirements before execution.
Those requirements might include:
Exposure limits
Approved markets
Identity verification
Sanctions screening
Collateral health
Risk thresholds
Wallet reputation
Only after the required conditions are satisfied can the protected action proceed.
Why This Matters
Markets move quickly.
Risk conditions change.
Compliance requirements evolve.
Protocols need ways to adapt operational rules without rebuilding every application from scratch.
Separating policy logic from application logic allows governance to update certain rules while leaving the underlying smart contracts unchanged.
That flexibility could become increasingly valuable as DeFi expands into institutional products, tokenized assets, stablecoins, and AI-driven finance.
Challenges Remain
No authorization framework removes every source of risk.
Policy governance still matters.
External data providers must remain reliable.
Users need transparency into how policies change over time.
A cryptographic approval can demonstrate that a transaction followed the configured policy, but it cannot determine whether the policy itself was well designed.
Looking Ahead
The success of this model won't depend on a launch announcement.
It will depend on adoption.
Do protocols continue enforcing these policies months after deployment?
Do users begin expecting verifiable authorization before assets move?
Do institutions view programmable policy as a necessary layer of infrastructure?
If the answer becomes yes, "Policies in Motion" may represent more than a feature.
It could become a new way of thinking about how blockchain systems authorize the movement of value.
@NewtonProtocol $NEWT #Newt #newt

I've been thinking about what response time can reveal.
Most people treat latency as a performance problem. Newton's documentation highlights another angle: for cryptographic operations, timing can also become a security concern.
That's why Newton relies on audited constant-time cryptographic implementations for algorithms like secp256k1, Ed25519, X25519, and HPKE. The goal is straightforward: reduce timing differences that could be linked to secret keys, making it much harder for attackers to extract sensitive information through repeated measurements.
At first, that sounded like a small implementation detail.
The more I looked into it, the more important it became.
If the time taken by a cryptographic operation varies depending on secret material, an attacker may be able to collect enough measurements to infer information that should never be exposed. Constant-time implementations are designed to break that connection.
For a protocol like Newton, which relies on cryptography for signatures, authorization, and encrypted communication, that's a meaningful security boundary.
But there's an important distinction.
Constant-time cryptography does not mean the entire network or every request runs in constant time.
Newton's own documentation explains that overall latency is largely driven by policy evaluation, network communication, and data retrieval. The cryptographic work itself typically completes in microseconds to low milliseconds on standard hardware, while everything around it can vary depending on the request.
So two authorization requests taking different amounts of time doesn't automatically suggest that secret keys are leaking.
It usually reflects differences in the work being performed.
Some requests naturally require more policy checks, additional data lookups, or extra network coordination than others. Those variations are part of application behavior, not evidence that the underlying cryptography is exposing secrets.
That's the distinction I keep coming back to.
Constant-time cryptography protects sensitive key operations from timing-based attacks. It doesn't promise that every API call, policy evaluation, or end-to-end authorization flow will have identical response times.
Those are two very different security questions.
One asks whether cryptographic operations leak secret material through timing.
The other asks what an observer might infer from the broader behavior of an application.
Newton explicitly addresses the first with audited constant-time cryptographic implementations. It does not claim that every end to end request should take exactly the same amount of time.
The real question for builders is this: if the cryptography is already protected, should applications also think about what their overall latency patterns might reveal?
#NEWT #Newt @NewtonProtocol $NEWT #newt

The more I read Newton's integration guide, the more I realized that adding an authorization layer isn't really the hard part.
The critical part is everything that happens around the upgrade itself.
One thing I appreciate is that Newton doesn't force developers to rebuild an application from scratch. An existing upgradeable contract can inherit NewtonPolicyClient through a proxy upgrade, keeping its existing storage and business logic intact. Once upgraded, the owner can initialize the Newton client and gradually introduce attestation checks only where they're actually needed.
That kind of flexibility is valuable.
For applications that already manage assets or long-lived state, redeploying an entirely new contract often isn't realistic. Being able to add policy enforcement later makes adoption much more practical.
But the migration process is surprisingly strict.
The storage layout has to remain untouched. Any new variables must be appended, not inserted. Newton also recommends a dedicated initialization flag so the setup function can only run once, along with thorough testing on a fork and using a timelock or multisig for the initialization transaction.
Those recommendations made me pause.
The real security focus isn't just the authorization logic it's the upgrade and initialization process surrounding it.
Before initialization, the upgraded contract may already contain Newton's code, but it still isn't connected to the intended TaskManager or configured with the correct policy-client owner. If either address is wrong, attestation validation may fail or policy management could end up under the wrong control.
That's why the one time initialization flag matters.
It protects against running the setup twice, but it doesn't guarantee the first execution was correct. If incorrect addresses are provided during that initial call, preventing reinitialization won't fix the original mistake.
What's interesting is that initialization doesn't permanently lock everything.
The policy-client owner can still update policy settings, change the policy contract, or transfer ownership later through the functions exposed by NewtonPolicyClient. So while initialization is critical, it's only the beginning of the contract's authorization lifecycle.
Another detail that stood out is storage safety.
Newton allows developers to extend existing contracts instead of replacing them, but proxy upgrades still rely on preserving the exact storage layout. A misplaced storage variable can silently corrupt existing contract state, even if the authorization layer itself appears to be working perfectly.
There's another subtle point as well.
Simply adding a new Newton-protected function doesn't automatically secure older execution paths that perform the same action. Every sensitive path must explicitly call validateAttestation or validateAttestationDirect before business logic executes. Authorization only works where validation is actually enforced.
Overall, I think Newton's modular design is one of its biggest strengths.
It lets developers adopt policy enforcement gradually instead of forcing a complete architectural rewrite. Existing applications can keep most of their logic while selectively protecting higher-risk operations.
What I'm still thinking about is the trade-off.
Does this modular approach actually reduce upgrade risk, or does it concentrate a huge amount of trust into just a few critical moments—the proxy upgrade, the storage migration, and that very first initialization transaction?
Newton clearly makes authorization easier to introduce.
The bigger question is whether those few setup steps become the most important security decisions in the entire integration.
#Newt @NewtonProtocol $NEWT #NEWT

Every cycle has a new AI narrative.
Some projects promise smarter trading, others promise faster execution. But the question I've been thinking about isn't whether AI can outperform humans.
It's much simpler:
How much freedom should an AI have when it's managing real money?
That's what made me pay attention to NEWT.
In crypto, automation can be incredibly powerful, but it can also amplify mistakes. A bot doesn't hesitate. It simply follows instructions, even when those instructions are wrong.
The faster everything becomes, the more important guardrails become.
That's the part of Newton Protocol I find interesting. Instead of focusing only on making AI more capable, it also focuses on defining what AI should and shouldn't be allowed to do.
To me, that's a healthier direction.
Trust in DeFi isn't built by making systems faster. It's built by making them predictable, transparent, and safe enough that users aren't relying on blind faith.
Of course, NEWT is still an early-stage token. A good idea doesn't guarantee adoption, and markets often move on sentiment long before fundamentals catch up.
That's why I'm watching the protocol more than the price.
If autonomous agents become a meaningful part of onchain finance, they'll need clear rules, permissions, and limits not unlimited authority.
Maybe that's where Newton fits.
The projects that matter in the long run may not be the ones that build the smartest AI.
They may be the ones that make AI accountable.
That's why NEWT is on my watchlist.
#NEWT @NewtonProtocol $NEWT #newt