According to Omkar Godbole, Bitcoin (BTC) retreated from a high of $108,000 but remains supported by bullish institutional developments, including Bill Pulte of the Federal Housing Finance Agency ordering Fannie Mae and Freddie Mac to consider cryptocurrency holdings in mortgage criteria. The Federal Reserve's plan to overhaul bank capital requirements could boost credit creation and benefit risk assets like crypto, as noted in the report. Alex Kuptsikevich from FxPro highlighted the crypto market cap at $3.31 trillion approaching a volatility threshold, while spot BTC ETFs saw $548 million in net inflows for 12 consecutive days, per Farside Investors data. Metaplanet increased its BTC holdings, and Bit Digital shifted to ETH staking, reinforcing market optimism.

According to Lennix Lai, institutional demand is making $3,000 ETH increasingly likely, with ETH outperforming BTC in derivatives markets at 45.2% volume share. Glassnode reports BTC accumulation continues despite volatility, with long-term holders realizing profits but supply growing. CryptoQuant notes stablecoin reserves hit a record $228 billion, while Presto Research highlights Tron leading with $6 billion in net inflows in May. Scott Duke Kominers argues crypto blockchains are essential for AI agent interoperability, enabling cross-system transactions. DappRadar indicates Web3 gaming's decline to $9 million in funding stems from a lack of engaging gameplay.

According to the author, blockchain valuation models remain debated and unstandardized, similar to early internet eras, which affects cryptocurrency trading by increasing volatility in assets like ETH and BNB. Traditional models like discounted cash flow are inadequate for decentralized systems, as they miss key dynamics such as token emissions and infrastructure value [source: article]. This uncertainty can lead to price swings, as seen in ETH's recent 2.442% rise. The proposed velocity and flow framework, focusing on transaction movements and capital reuse, offers a potential solution for traders to assess real-time market activity and make informed decisions.

According to the anonymous author, the crypto industry's erosion of cypherpunk values, as evidenced by Coinbase's political sponsorships and Ripple's lobbying activities cited in the article, could increase regulatory scrutiny and market volatility for cryptocurrencies like BTC and ETH, potentially undermining investor confidence and long-term liquidity in trading markets.

According to crypto valuation experts, the ongoing debate over blockchain valuation models contributes to crypto market volatility, with ETH surging 6.997% to $2399.38 and ADA rising 6.095% to $0.5779 as traders grapple with the lack of standardized metrics. The article emphasizes that traditional models like DCF are inadequate for blockchains, proposing a new velocity-based framework focusing on DeFi lending, NFT trading, and stablecoin turnover to better assess economic activity and trading opportunities.

According to market analysis, blockchain valuation models remain unstandardized, leading to investment uncertainties and market volatility. Current price surges, such as ETH up 7% to $2399, ADA up 6% to $0.58, and DOT up 8.6% to $3.42, reflect the ongoing debate over how to assess crypto assets effectively. A proposed velocity-based model could offer traders better insights into economic flows and utility, as per the analysis.

According to DeepLearning.AI, researchers have launched STORM, a groundbreaking text-video AI model that reduces video input size to just one-eighth of the standard, while still achieving state-of-the-art benchmark results. STORM integrates mamba layers between a SigLIP vision encoder and the Qwen2-VL language model, allowing efficient cross-modal information aggregation. For crypto traders, this innovation could accelerate the development of AI-driven trading bots and data analytics tools, enhancing real-time market sentiment analysis and automated trading strategies. Source: DeepLearning.AI Twitter, June 21, 2025.

According to DeepLearning.AI, Apple has refreshed its Apple Foundation Models (AFM) with new versions optimized for both on-device and server deployment, targeting enhanced performance in image understanding and multilingual reasoning tasks (source: DeepLearning.AI, June 20, 2025). The release of the Foundation Models API enables developers to integrate advanced AI capabilities into applications more easily, potentially increasing demand for AI infrastructure and related crypto projects. Traders should monitor AI-linked tokens and blockchain platforms supporting decentralized AI services, as this move by Apple could spur further adoption and innovation in the intersection of AI and cryptocurrency markets.

According to DeepLearning.AI on Twitter, Andrew Ng emphasizes that stricter H1-B and F-1 visa policies are undermining U.S. competitiveness in artificial intelligence and global innovation, which may lead to a talent drain from the U.S. tech sector (source: DeepLearning.AI, June 20, 2025). This policy shift could negatively affect the development pipeline for AI-driven crypto projects, potentially impacting sentiment and long-term investment in related cryptocurrencies. Additionally, Apple’s updates to AI models and developer APIs, as well as Hollywood’s lawsuit against Midjourney for copyright use, highlight ongoing regulatory and technological shifts that traders should monitor for their potential to influence the blockchain and crypto markets.

According to DeepLearning.AI, Meta's Llama 4 introduces a Mixture-of-Experts architecture that significantly reduces serving costs for developers, alongside advanced multimodal capabilities such as image grounding and expansive context windows able to process entire books or codebases (source: DeepLearning.AI on Twitter, June 18, 2025). These enhancements lower operational expenses and boost efficiency for AI-driven trading bots and DeFi platforms, potentially increasing the adoption of AI models in crypto markets. Traders should monitor how Llama 4's cost-effective performance and new features could accelerate innovation in blockchain analytics, automated trading, and on-chain data analysis.

According to DeepLearning.AI, independent lab Artificial Analysis reported that benchmarking OpenAI o1 on seven major chain-of-thought reasoning tests required 44 million tokens and cost $2,767 (source: DeepLearning.AI, June 18, 2025). This sharp rise in evaluation expenses is making it increasingly difficult for resource-constrained researchers to compete, which may slow the pace of open AI advancements and limit participation in crypto-related AI innovation. For crypto traders, rising AI model costs could restrict the development of decentralized AI protocols and increase barriers for new AI-powered blockchain projects, potentially impacting future token valuations and adoption.

According to DeepLearning.AI, neural networks have transformed modern AI by progressing from basic brain-inspired models in the 1950s to driving deep learning breakthroughs today. Their resurgence, powered by increased computational resources and big data, now enables advanced AI models that impact automated crypto trading, sentiment analysis, and blockchain analytics, improving trade decision-making and market efficiency (source: DeepLearning.AI Twitter, June 17, 2025). Traders should monitor AI-driven tools that leverage neural networks for real-time crypto market insights and algorithmic trading strategies.

According to DeepLearning.AI, investment analyst Mary Meeker has published her first tech market survey since 2019, 'Trends — Artificial Intelligence (May ’25).' The comprehensive 340-page report highlights the rapid adoption of AI and surging capital expenditures, signaling record-breaking opportunities for investors. For crypto traders, the report points to increasing demand for AI-powered blockchain solutions and data-driven trading strategies, with capital inflows likely to boost AI-linked tokens and projects. Verified by DeepLearning.AI on June 17, 2025.

According to DeepLearning.AI on Twitter, a programming meme originally seen on Reddit has gone viral, sparking widespread discussion within the developer and AI communities (source: DeepLearning.AI Twitter, June 16, 2025). This surge in AI-related humor reflects a strong engagement with current deep learning trends and may signal increased retail interest in AI-driven crypto tokens. Historically, heightened social media engagement around AI can precede volatility in AI-linked cryptocurrencies, such as FET and AGIX, as traders react to shifts in sentiment and narrative momentum.

According to DeepLearning.AI, Columbia University researchers demonstrated that large language model (LLM) agents can be manipulated by attackers who embed malicious links within trusted sites like Reddit. This technique involves placing harmful instructions in thematically relevant posts, potentially exposing automated AI trading bots and crypto portfolio management tools to targeted attacks. Source: DeepLearning.AI (June 15, 2025). Traders relying on AI-driven strategies should monitor for new security vulnerabilities that could impact algorithmic trading operations and market stability in the crypto ecosystem.

According to DeepLearning.AI, Germany’s Black Forest Labs has launched three advanced text-to-image AI models—FLUX.1 Kontext max, pro, and dev—that specialize in maintaining consistent characters while altering backgrounds, poses, or styles (source: DeepLearning.AI, June 13, 2025). This technical approach, combining a convolutional encoder-decoder with a transformer, is set to enhance NFT creation and digital collectibles by enabling artists and traders to generate diverse visuals with reliable character identity. As NFT and crypto projects increasingly rely on unique, high-quality generative art, these innovations could drive new trading volumes and utility in the ETH and broader Web3 markets.

According to DeepLearning.AI on Twitter, the instructors of the 'Reinforcement Fine-Tuning LLMs with GRPO' course are hosting a live AMA to discuss practical applications of reinforcement fine-tuning for large language models. This event is particularly relevant for traders and investors monitoring the intersection of AI and cryptocurrency markets, as reinforcement learning techniques are increasingly deployed in algorithmic trading strategies and blockchain analytics tools (source: DeepLearning.AI, June 13, 2025). Enhanced AI model performance could impact the efficiency and accuracy of crypto trading bots and DeFi platforms.

According to DeepLearning.AI, Andrew Ng emphasized the importance of practical GenAI engineering skills, such as prompt engineering and model evaluation, as crucial for hiring in the rapidly evolving AI sector (source: The Batch, June 12, 2025). The debut of FLUX.1 Kontext image model and STORM's video token compression are expected to accelerate AI-powered applications and data handling. Mary Meeker’s 2025 AI trends report underlines the increasing costs of testing reasoning LLMs, impacting both institutional investors and blockchain developers integrating these models into crypto platforms. The rising demand for efficient, scalable GenAI tools is likely to drive adoption of AI-related cryptocurrencies and tokens, as project teams seek to leverage new models for decentralized applications (source: DeepLearning.AI, June 12, 2025).

According to DeepLearning.AI, a new short course developed in partnership with Astronomer.io introduces traders and developers to orchestrating generative AI (GenAI) workflows using Apache Airflow. The course addresses critical challenges such as scaling, reliability, and failure recovery for GenAI applications (Source: DeepLearning.AI Twitter, June 11, 2025). For crypto traders, the adoption of robust AI orchestration tools like Apache Airflow could significantly enhance automated trading infrastructure, increase data pipeline reliability, and improve backtesting for trading bots, potentially impacting algorithmic trading strategies and increasing the efficiency of crypto market operations.

According to @GoogleDataCert, the launch of Course 5 in the Data Analytics Professional Certificate focuses on data storytelling, teaching techniques like choosing the optimal medium for data insights, designing interactive dashboards with Tableau, and aligning presentations with audience needs (source: @GoogleDataCert, 2024-06-20). For cryptocurrency traders, these enhanced analytics skills enable more effective visualization of on-chain data and market trends, supporting better-informed trading decisions and faster reaction to market shifts. The integration of Tableau for real-time dashboard creation is especially relevant for tracking crypto price movements and sentiment, providing traders with actionable insights to optimize their strategies.