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Three AI tools predict XRP price on August 1st

"Three AI tools have made price predictions for XRP as of August 1st. Despite Bitcoin's struggles in July, XRP is currently trading at around $1.1, with a market cap of $68.9 billion and a 24-hour trading volume exceeding $575.7 million, indicating investor interest. To navigate the uncertain price dynamics, analysis was sought from three different AI agents using complex mathematical models and machine learning: 1. **CoinCodex's predictive algorithm** offered the most optimistic forecast. It predicts the average price of XRP in August 2026 will rise to $1.52, with a minimum not falling below $1.21, anticipating a strong influx of liquidity and a bullish sentiment. 2. **Changelly's AI agent** presented a more conservative and realistic outlook based on technical analysis. It forecasts an average August price of $1.18, with a monthly peak unlikely to exceed $1.21, suggesting consolidation without aggressive upward breaks. 3. **DigitalCoinPrice's neural network**, analyzing macroeconomic indicators and trading volumes, showed maximum caution. It predicts very low volatility, with the average price stabilizing at $1.11 and a maximum price also at $1.11. In summary, the AI tools present divergent views: one predicts confident growth above $1.50, another forecasts a slight increase, and the third anticipates a sideways trend (stagnation). This underscores a key rule of the cryptocurrency market: no algorithm offers a 100% guarantee. Investors should always conduct their own research and be aware of the risks."

cryptonews.ruHace 2 días 16:42

Three AI tools predict XRP price on August 1st

cryptonews.ruHace 2 días 16:42

Tsinghua AI Mathematician Emerges: From Intuition to Theorem, Contributing to an 84-Page Quantum Algorithm Paper

Tsinghua University’s Intelligent Industry Research Institute (AIR) has developed an AI mathematician agent named AIM, designed not just to solve math problems but to actively participate in early-stage research. In a recent study, researchers collaborated with AIM to develop "Sign Embedding Quantum Algorithms," resulting in an 84-page paper on quantum algorithms for matrix equations and functions. The research began with a human researcher's intuition: can rational approximation serve as a design principle for quantum algorithms? AIM helped expand this idea into multiple candidate research directions. Human researchers then filtered and focused on the most promising path. AIM assisted in organizing theorems, generating proof drafts, and performing complexity analysis, while humans maintained oversight, auditing assumptions and refining derivations. This case illustrates a human-AI collaborative workflow: AI rapidly explores and expands research avenues, generates draft materials, and aids in checking derivations; human researchers provide critical judgment on direction, value, and validity. The process emphasizes "high-throughput candidate generation + human value gating + AI-assisted audit and repair + human final integration." The resulting quantum algorithm framework offers a unified approach to several matrix problems, advancing quantum linear algebra under more general conditions. This work suggests AI's role in theoretical research is evolving from task-specific assistance to supporting the entire research lifecycle—enhancing exploration and efficiency while keeping human expertise central to guiding inquiry and ensuring rigor. *Paper & System Links:* - AIM application report: https://arxiv.org/abs/2606.24899 - Quantum algorithm paper: https://arxiv.org/abs/2604.25333 - AIM repository: https://github.com/TheoryFoundry/AIMv2

marsbit07/10 02:53

Tsinghua AI Mathematician Emerges: From Intuition to Theorem, Contributing to an 84-Page Quantum Algorithm Paper

marsbit07/10 02:53

IOSG: Q-Day Countdown, Will Quantum Computing End Cryptocurrency?

IOSG: The Q-Day Countdown – Will Quantum Computing End Cryptocurrency? This analysis explores the looming threat quantum computing poses to blockchain technology. Quantum computers, leveraging Shor's algorithm, could theoretically break the elliptic curve cryptography (ECC) underpinning cryptocurrencies like Bitcoin and Ethereum. The article outlines a hypothetical "Q-Day" scenario where exposed public keys from dormant assets are compromised, leading to fund theft and a deep governance crisis. The core risk is not the complete erasure of blockchains but a systemic reset of public-key cryptography. Bitcoin faces significant challenges due to its "code-is-law" ethos and the immense social consensus required for migration. Its primary vulnerability lies in legacy UTXOs with publicly exposed keys. Ethereum's path involves a more complex, full-stack cryptographic agility upgrade across execution, consensus, and data layers. The industry has a limited "engineering comfort window" of 5-8 years to coordinate a migration to post-quantum cryptography (PQC), such as lattice-based or hash-based signatures. While the existential threat is often overstated, the real bottleneck is the immense coordination required across protocol developers, node operators, wallet providers, exchanges, and custodians. Market repricing of crypto assets may occur well before an actual Q-Day if quantum hardware roadmaps accelerate or regulatory pressure mounts. The article concludes that quantum computing is not a doomsday weapon but a severe stress test for blockchain's foundational security model and governance structures.

marsbit07/07 08:40

IOSG: Q-Day Countdown, Will Quantum Computing End Cryptocurrency?

marsbit07/07 08:40

Q-Day Countdown: Will Quantum Computing End Cryptocurrencies?

Quantum Computing's Threat to Cryptocurrency: A Countdown to Q-Day Quantum computing, specifically Shor's algorithm, poses a fundamental threat to the public-key cryptography (e.g., ECDSA, RSA) that secures blockchain networks like Bitcoin and Ethereum. This critical juncture, known as Q-Day, is estimated to occur potentially within the next 5-15 years. The core vulnerability stems from the public and immutable nature of blockchains. Assets in addresses where the public key is already exposed on-chain (e.g., spent outputs) are at direct risk, as a sufficiently powerful quantum computer could derive the private key. This threatens the very trust model of cryptocurrencies. The response lies in Post-Quantum Cryptography (PQC)—algorithms like lattice-based ML-DSA and hash-based SLH-DSA, which are resistant to quantum attacks. NIST has standardized key PQC algorithms (FIPS 203, 204, 205), providing a migration path. However, the primary challenge is not technical but socio-economic and involves complex governance: * **Bitcoin's** path is constrained by its conservative ethos. Migrating requires a soft-fork to new address types, facing hurdles like significantly larger signature sizes and, most critically, the divisive governance question of how to handle at-risk legacy UTXOs without violating core principles. * **Ethereum** is pursuing a "cryptographic agility" strategy, with a multi-layered roadmap. It leverages account abstraction for user accounts and is developing compressed hash-based signatures (e.g., leanXMSS) for its consensus layer, aiming for a full-stack upgrade over time. In conclusion, quantum computing does not spell an instant end for cryptocurrency but initiates a critical countdown. The industry has a limited "engineering comfort window" to orchestrate a coordinated, ecosystem-wide migration to PQC. The ultimate bottlenecks are the immense coordination efforts and governance decisions required for this foundational transition.

marsbit07/06 15:12

Q-Day Countdown: Will Quantum Computing End Cryptocurrencies?

marsbit07/06 15:12

Planck Retracted? The Father of Quantum Tripped by an Algorithm

The recent discovery that two articles (published in 1940 and 1942) by Max Planck, the Nobel laureate and founder of quantum theory, are marked as "retracted" on Springer's digital platform highlights a curious clash between historical publishing practices and modern automated systems. An investigation suggests these retractions are algorithmic errors, not due to fraud or misconduct. The papers, philosophical reflections on science published in *Die Naturwissenschaften*, were likely flagged by the platform's systems. One article, a republished lecture, may have been mistaken for duplicate publication. Another, sharing a title with a prior article by a different author (a common practice for continuing debates at the time), may have triggered a similar automated check. The digital versions have even been replaced with blank pages, contrary to normal practice of preserving retracted texts. This incident underscores how contemporary digital infrastructure, built around concepts like "self-plagiarism" and strict copyright, can misclassify and obscure legitimate historical scholarly communication. It serves as a warning that digital archives are not neutral mirrors of the past but are filtered by platform rules, potentially distorting the scientific record. As AI systems increasingly rely on such databases, such erroneous metadata could propagate, affecting how future tools interpret and access historical knowledge.

marsbit06/30 12:44

Planck Retracted? The Father of Quantum Tripped by an Algorithm

marsbit06/30 12:44

12.9 Million Candidates: The First Summer of Fate in the Hands of AI

The 2026 Chinese college entrance exam, or Gaokao, saw a novel phenomenon: AI aggressively entering the college application advice arena before results were even released. Major tech companies like Alibaba, Tencent, Baidu, and others launched free AI-powered "agents" and tools designed to generate personalized university and major recommendations for over 12.9 million candidates. For years, a lucrative industry thrived on the "information gap" in college applications, with personalized consulting services costing families thousands of dollars. AI is now disrupting this by providing similar, data-driven analysis for free. These tools process standardized data—scores, rankings, historical admission trends—to create tailored application strategies, offering a form of information parity previously unavailable, especially to students from rural or less-resourced backgrounds. This shift represents more than just a marketing trend; it signifies AI's first large-scale entry into a critical, high-stakes life decision for millions of Chinese families. The Gaokao application, with its clear inputs and outputs, is an ideal scenario for AI. Its involvement begins to level the informational playing field, potentially reducing the advantage held by families with greater social capital or access to expensive consultants. However, the article raises a profound question: while AI can optimize choices for employability and financial return based on cold data, it risks promoting a homogenized, utilitarian path. It might steer a passionate student away from a less lucrative field like literature or archaeology toward supposedly "safer" options like computer science. The core dilemma remains: as AI flattens information disparities, does it also flatten the diversity of life choices and the freedom to make—and learn from—mistakes? Ultimately, 2026 may be remembered not for exam questions, but as the year AI began formally influencing the life trajectories of ordinary Chinese people. The real test lies not in the algorithm's recommendations, but in whether individuals will retain the courage to make their own choices and bear the consequences in an increasingly algorithmic age.

marsbit06/11 00:49

12.9 Million Candidates: The First Summer of Fate in the Hands of AI

marsbit06/11 00:49

The Quantum Threat Is Coming—Stellar Reveals Its Defense Strategy

The Stellar Development Foundation has released a Quantum Preparedness Plan, aiming to migrate the entire Stellar network to quantum-safe cryptography by the end of 2027. The urgency stems from quantum computers potentially using Shor's algorithm to crack the elliptic curve cryptography currently securing blockchains. A significant challenge identified is handling dormant accounts, where quantum attacks could derive private keys from public ones, risking account takeover. Stellar's structural advantage is that its account addresses are separate from signing keys. This allows users to add quantum-safe signers via existing operations without changing their address or moving assets, unlike most other blockchains. The migration will occur in phases: post-quantum signature verification for smart contracts in 2026, native support for classic accounts in 2027, and a future deprecation of the old Ed25519 standard. However, the plan notes that zero-knowledge proof systems on the network remain vulnerable and require further research.

bitcoinist06/10 18:02

The Quantum Threat Is Coming—Stellar Reveals Its Defense Strategy

bitcoinist06/10 18:02

Turing Award Laureate Sutton's New Work: Using a Formula from 1967 to Solve a Major Flaw in Streaming Reinforcement Learning

New research titled "Intentional Updates for Streaming Reinforcement Learning" (arXiv:2604.19033v1), involving Turing Award laureate Richard Sutton, addresses a core challenge in deep reinforcement learning (RL): the "stream barrier." Current deep RL methods typically rely on replay buffers and batch training for stability, failing catastrophically when learning online from single data points (streaming). The authors propose a fundamental shift: instead of prescribing how far to move parameters (a fixed step size), their "Intentional Updates" method specifies the desired change in the function's output (e.g., a 5% reduction in value prediction error). It then calculates the step size needed to achieve that intent. This idea is inspired by the Normalized Least Mean Squares (NLMS) algorithm from 1967. Applied to value and policy learning, this yields algorithms like Intentional TD(λ) and Intentional AC. The method inherently stabilizes learning by adapting the step size based on the local gradient landscape, preventing overshooting/undershooting. In experiments on MuJoCo continuous control and Atari discrete tasks, Intentional AC achieved performance rivaling batch-based algorithms like SAC in a streaming setting (batch size=1, no replay buffer), while being ~140x more computationally efficient per update. The work demonstrates significant robustness, reducing reliance on numerous stabilization tricks. A remaining challenge is bias in policy updates due to action-dependent step sizes. Overall, this approach advances efficient, online, "learn-as-you-go" RL, enabling adaptive systems without massive data buffers or compute clusters.

marsbit05/10 06:28

Turing Award Laureate Sutton's New Work: Using a Formula from 1967 to Solve a Major Flaw in Streaming Reinforcement Learning

marsbit05/10 06:28

From Theory to Countdown: Google Sounds the Blockchain Quantum Resistance Alarm with Zero-Knowledge Proofs

An article discusses the significant threat quantum computing poses to blockchain and classical encryption systems, triggered by Google's recent research. By optimizing Shor's algorithm, Google reduced the logical qubits required to break 256-bit elliptic curve encryption from around 6,000 to just 1,200—slashing computational costs by 20 times. This advancement sets a potential countdown, with Google estimating 2029 as the deadline for upgrading to quantum-resistant cryptography. Both Bitcoin and Ethereum face severe risks. About 25-35% of Bitcoin addresses have exposed public keys, making them vulnerable to attacks, especially during transaction processing. Ethereum’s design exposes public keys upon first use, jeopardizing its entire network if signatures aren’t updated. Historical blockchain data remains permanently available for future quantum attacks. The solution lies in adopting post-quantum cryptography (PQC). Ethereum is already implementing account abstraction and PQC-based signatures, leveraging its upgradeable architecture. Bitcoin is considering BIP-360 to introduce quantum-resistant algorithms like FALCON or CRYSTALS-Dilithium, though consensus may delay action. Notably, Google used zero-knowledge proofs to disclose this threat responsibly, aiming to prevent panic. Collaboration with Ethereum Foundation researchers suggests抗量子 (quantum resistance) could become a major narrative, aligning with crypto’s cryptographic roots.

marsbit04/16 06:38

From Theory to Countdown: Google Sounds the Blockchain Quantum Resistance Alarm with Zero-Knowledge Proofs

marsbit04/16 06:38

What To Expect For The Solana Price In April As Metrics Line Up Again

Based on historical trends and algorithmic predictions, the Solana (SOL) price could see a significant rebound in April 2026 after a prolonged period of decline that pushed it below the $100 mark. The prediction algorithm from CoinCodex forecasts a potential 30% rally for the month, targeting a price of $103.76. Furthermore, a 63% increase to $130 is projected over a three-month timeframe, suggesting a bullish third quarter. Historical data from CryptoRank supports this outlook, showing that April has typically been a positive month for SOL. Over the past five years, the cryptocurrency ended April in the green three times, with gains as high as 60.8%. While there have been negative Aprils, the average return for the month is a strong +18.7%. Despite this positive seasonal trend, the overall performance for the second quarter remains uncertain.

bitcoinist04/07 14:32

What To Expect For The Solana Price In April As Metrics Line Up Again

bitcoinist04/07 14:32

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