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Bitcoin has been trading in a tight range between $83,000 and $86,000 for the past five days, showing signs of indecision in both price action and momentum indicators. While the number of whale wallets has begun to decline, on-chain data reflects high interest from large holders. Technically, BTC remains in a price stagnation phase with weak EMA signals and mixed Ichimoku readings. Bitcoin Whales Pull Back: Is Confidence Declining? Bitcoin whales—wallets holding between 1,000 and 10,000 BTC—have declined slightly in recent days, falling from 2,015 on April 14 to 2,010 as of April 16. This pullback comes just after the metric reached its highest level since May 2024, signaling a potential shift in sentiment among large holders. The decline may seem small, but whale behavioral movements often precede broader market trends, so even small changes are worth monitoring. Whale activity is an important on-chain signal as these large holders can significantly influence market liquidity and price direction. An increase in whale wallets usually reflects accumulation and long-term confidence, while a decrease could indicate strategic profit-taking or risk-averse behavior. The recent decline from the local peak could indicate that some whales are reducing their exposure as market uncertainty increases. If the number continues to fall, it could signal weakening institutional confidence and potentially put short-term pressure on Bitcoin’s price. Bitcoin Stalls at Ichimoku Turning Point The Ichimoku Cloud chart for Bitcoin is showing a period of price stagnation, with price currently trading near the flat Tenkan-sen (blue line) and Kijun-sen (red line). This alignment suggests a lack of short-term momentum as both lines are moving sideways, suggesting balance between buyers and sellers. The Kumo (cloud) ahead is in an uptrend, with Senkou Span A (green cloud border) above Senkou Span B (red cloud border), but the distance between them is relatively narrow. This suggests weak bullish momentum at the moment. The price is just above the cloud, which is a positive sign, but without a clear break above the Tenkan-sen and recent highs, the trend remains undecided. The Chikou Span (lag line) overlaps with the recent candles and reinforces the sideways movement. Overall, Bitcoin is trading in neutral to slightly bullish territory, but a stronger push is needed to confirm a clear trend direction. Bitcoin Seeks Direction at Critical Levels Bitcoin’s EMA lines are currently horizontal, indicating a weak and uncertain trend. Price action is hesitant, with bulls and bears indecisive. If the support at $83,583 is tested and fails to hold, the market could enter a sharper correction and target the next support at $81,177. A break below this level could push Bitcoin below the psychological $80,000 level, with $79,890 being the next possible downside target. However, if the bulls regain control, Bitcoin could be headed for a recovery. The first significant resistance is found at $86,092—a break of this level would suggest renewed upward momentum. From here, the next upside targets would be $88,804 and, if the trend strengthens, $92,817. Reaching this level would mark the first time it has broken above $90,000 since March 7, which could spark renewed interest from both retail and institutional investors.

Today, the European Central Bank (ECB) implemented its sixth consecutive interest rate cut, reducing key rates by 25 basis points to address the rising economic challenges in the eurozone. This includes lowering the deposit rate to 2.25%, main refinancing operations rate to 2.4%, and marginal lending facility rate to 2.65%. The ECB’s rate cut occurs against a backdrop of escalating global trade tensions, particularly following US President Donald Trump’s announcement of new tariffs on imported goods, including a proposed 20% tariff on European Union products. This was paused for 90 days, but the risk it could still go into effect worries European leaders. ECB President Christine Lagarde highlighted that these developments pose a big risk to Eurozone growth, with the potential to dampen exports, investment, and consumption. She emphasized the “cloud of uncertainty” in the global economic outlook due to these trade barriers. It was reported that the EU officials proposed a ‘zero for zero’ deal to Trump, eliminating industrial goods tariffs, including cars, but he rejected it as insufficient. These talks are crucial as the U.S. is Europe’s top trade partner; about $5 billion in goods/services are exchanged daily. ECB is one of the biggest central banks globally, with its total assets approximated at 7 trillion. Despite inflation easing to 2.2% in March, which is close to the ECB’s 2% target, economic growth remains sluggish, with only a 0.2% expansion in the last quarter of 2024. As such, with the rate cuts, the ECB is likely trying to make borrowing cheaper and encourage spending and investment. This is an attempt to offset the negative impact of trade barriers on the already fragile economic recovery. While the ECB has not committed to a specific rate path, it maintains a data-dependent approach, closely monitoring economic indicators to determine future policy actions. Analysts anticipate that if current trends persist, further rate adjustments may be necessary to support the eurozone economy. In case the current situation continues or even escalates, it won’t be a surprise if a seventh consecutive interest rate cut happens in the future. Disclaimer: The information presented in this article is for informational and educational purposes only. The article does not constitute financial advice or advice of any kind. Coin Edition is not responsible for any losses incurred as a result of the utilization of content, products, or services mentioned. Readers are advised to exercise caution before taking any action related to the company.

$FET USDTAnalysis: Price Movement: The chart shows a downtrend from April 14 to April 16 with prices dropping from around 0.540 to a low of 0.424. A consolidation phase follows from April 16 to April 18,with prices ranging between 0.424 and 0.450. On April 19, a sharp upward move begins, breaking above the consolidation zone,reaching 0.500 by April 20. ShockaLad Indicator: The ShockaLad indicator is represented by the shaded area (likely a cloud or band) and a trendline. The price breaks above the ShockaLad trendline around April 19,which aligns with the "LONG" signal at 0.4495 (with a 1.5% move). This breakout suggests a potential trend reversal from bearish to bullish,as the indicator likely identifies momentum shifts. Trade Statistics (from the chart): Total trades:216 Win rate: 98.61% (213 winning trades out of 216) Total profit/loss: 2999.42% Interpretation: The breakout above the ShockaLad trendline, combined with the LONG signal,indicates a strong bullish momentum for $FET USDT. The high win rate and significant profit percentage suggest the ShockaLad indicator has been historically reliable for this trading group. The current price around 0.500 shows a continuation of the upward trend potentially targeting higher levels if momentum persists.

As AI becomes more embedded in daily life, concerns about data privacy grow. Fully Homomorphic Encryption ($FHE ) is emerging as a key enabler of **Confidential AI**—a new paradigm where AI models can be trained and run on encrypted data, preserving privacy without sacrificing functionality. What is Confidential AI?** - AI that operates on sensitive data while ensuring that neither the data nor the model parameters are exposed. - Confidential AI is powered by privacy-enhancing technologies like FHE, secure enclaves, and differential privacy. How FHE Enables Confidential AI** - **Inference on Encrypted Data**: A user can send encrypted input to an AI model, which returns encrypted predictions—without ever seeing the raw input. - **Training on Encrypted Data**: Sensitive datasets (e.g., medical or financial) can be used to train models securely. - **Secure Model Sharing**: AI models can be deployed without leaking proprietary or sensitive intellectual property. --- ### **Example Use Cases** - **Healthcare AI**: Predicting disease outcomes using encrypted patient data. - **Finance AI**: Risk scoring and credit analysis on encrypted customer information. - **Smart Devices**: Encrypted voice commands processed securely in the cloud. - **Federated Learning**: Multiple entities collaborate to train AI models without exposing their data. --- Tech Landscape & Tools** - **Zama’s Concrete ML**: An open-source framework for running neural networks on encrypted data. - **IBM HELayers**: Designed to accelerate encrypted AI workloads. - **Microsoft SEAL**: A popular $FHE library supporting encrypted machine learning applications. - Growing integration with PyTorch and TensorFlow for FHE-ready AI. --- ### **Challenges** - **Performance**: $FHE operations are still slower than plaintext equivalents. - **Model Accuracy**: Some encryption schemes limit precision, affecting AI model outputs. - **Tool Maturity**: Development tools are still emerging and often complex to use. Why the Buzz?** - **Trust in AI**: With $FHE , companies and governments can adopt AI without fearing data leaks. - **Privacy Regulations**: Confidential AI helps comply with laws like GDPR, HIPAA, and CCPA. Business Advantage**: Enables cross-institutional collaborations without sharing raw data. Conclusion** FHE is unlocking a future where AI and privacy are no longer at odds. While challenges remain, the ability to run encrypted AI workflows is a massive leap toward responsible, confidential computing.

$FHE LIMITATIONS AND CHALLENGES Great choice—FHE is promising, but it definitely has some big hurdles. Here are the major limitations and challenges: 1. Performance Overhead (Speed) FHE is slow—like, thousands to millions of times slower than computing on plaintext. Even basic operations like addition and multiplication can be extremely resource-intensive. This is especially true for schemes that require bootstrapping (refreshing ciphertexts to keep them valid for more operations). 2. Complexity of Implementation FHE schemes (like BFV, BGV, CKKS) are non-trivial to implement and require deep expertise in both math and cryptography. Optimizing them for real-world applications involves tons of trade-offs—between accuracy, noise, performance, and security. 3. Large Ciphertext Sizes Encrypted data under FHE is massive compared to plaintext. This leads to increased bandwidth and storage costs, which is a major issue for scalable systems like cloud or edge computing. 4. Limited Functionality in Some Schemes Some schemes (like CKKS) only support approximate arithmetic, which is fine for things like ML inference but not for precise calculations (like in finance). Others are better for integer operations, but may not suit other data types well. 5. Bootstrapping Bottleneck Bootstrapping resets the noise in a ciphertext so more computations can be done—but it's computationally expensive. Some recent schemes reduce or avoid frequent bootstrapping, but it’s still a critical bottleneck for many applications. 6. Lack of Standardization There’s no widely accepted "gold standard" FHE library yet (though libraries like Microsoft SEAL, HElib, Zama’s Concrete are popular). This makes integration and compatibility harder across different systems. 7. Security vs. Efficiency Trade-offs Stronger encryption (more secure parameters) usually means even worse performance. Tuning parameters for the right balance of security, accuracy, and performance is a delicate art.