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9 May 2026, 21:56

Crypto Market Crash Played 60% Part of Trump Media & Technology $406M Net Loss YTD: Report

Trump Media & Technology Group reported a $405.9 million net loss for the first quarter of 2026, as unrealized losses on cryptocurrency holdings and other investments weighed heavily on the parent company of Truth Social. The company generated $871,200 in revenue during the three months ended March 31, up 6% from $821,200 in the same period last year. The revenue increase was small compared with the scale of the reported loss, which widened sharply from $31.7 million a year earlier. According to company filings , the largest part of the quarterly loss came from non-cash markdowns tied to digital assets, digital assets pledged as collateral and equity securities. Reports showed that unrealized losses on cryptocurrency holdings totaled about $244 million, equal to roughly 60% of the company’s $405.9 million net loss. Trump Media also recorded a $108.2 million investment loss tied mainly to equity securities. In a company statement, Trump Media cited $368 million in non-cash losses across digital assets, pledged digital assets and equity securities, along with $11.5 million in accreted interest and $11.8 million in stock-based compensation. Bitcoin and CRO Holdings Drive Paper Losses Trump Media built a large cryptocurrency position in 2025 after announcing a Bitcoin treasury strategy. The company raised $2.5 billion for the plan and later disclosed a Bitcoin stack valued at $2 billion at the time. At the end of March, Trump Media held 9,542.16 Bitcoin. The company reported a cost basis of $1.13 billion for the position and a fair value of $647.1 million at quarter-end. Based on later market prices cited in reports, the Bitcoin holdings were worth about $770 million. Part of the company’s Bitcoin position is pledged as collateral. Trump Media said 4,260.73 Bitcoin, worth $289 million at the end of March, supported convertible notes. The company also held covered call options on 4,000 Bitcoin, with 2,000 Bitcoin required to remain with a counterparty as collateral. Trump Media also held 756.1 million Cronos tokens, known as CRO. Those tokens had a cost basis of $113.9 million and a fair value of $53 million at the end of the quarter. The company bought $105 million in CRO last year as part of a Crypto.com-linked agreement connected to Truth Social and Truth+ rewards. Revenue Remains Limited Despite Political Visibility Trump Media’s operating business remains much smaller than its balance sheet exposure to digital assets. Media revenue totaled $810,100 during the quarter, while Truth.Fi generated $61,100 in management fees tied to exchange-traded fund offerings. Truth Social remains the company’s main platform and one of President Donald Trump’s main communication channels. The platform was created after Trump was removed from major social networks in 2021 following the attack on the U.S. Capitol. Although Truth Social has broad political visibility, its revenue remains below $1 million per quarter. The company’s first-quarter results show that investment activity and digital asset valuations currently have a much larger effect on reported earnings than advertising or platform revenue. Trump Media reported $17.9 million in operating cash flow for the quarter. The company said the figure was supported by the sale of previously purchased put options tied to pledged Bitcoin and Bitcoin-related securities. Merger Plans and Balance Sheet Strategy Continue Trump Media ended the quarter with $2.2 billion in total assets, including about $2.1 billion in financial assets such as cash and digital assets. Interim Chief Executive Kevin McGurn said the company is using its balance sheet and operating cash flow to support business growth and platform infrastructure. McGurn, appointed interim chief executive on April 21, also said Trump Media is continuing work on its proposed merger with TAE Technologies. The planned transaction, valued at about $6 billion, would combine Trump Media with a California-based nuclear fusion company focused partly on future power supply for artificial intelligence data centers. The company is also exploring a possible spin-off of Truth Social into a separate publicly traded entity. It has introduced a digital token initiative for DJT shareholders and continues to expand financial products under Truth.Fi.

9 May 2026, 21:55

AI Terms Everyone Nods Along To: A Practical Glossary

BitcoinWorld AI Terms Everyone Nods Along To: A Practical Glossary Artificial intelligence is reshaping industries, but it has also generated a dense new vocabulary that can leave even seasoned technologists struggling to keep up. Terms like LLM, RAG, RLHF, and diffusion appear constantly in headlines, product announcements, and boardroom discussions — yet their precise meanings often remain unclear. This glossary, curated and updated regularly by our editorial team, aims to provide clear, factual definitions for the most important AI terms. It is designed as a living reference, evolving alongside the technology it describes. Core AI Concepts: From AGI to Inference AGI (Artificial General Intelligence) remains one of the most debated terms in the field. While definitions vary, it generally refers to AI systems that match or exceed human capabilities across a broad range of tasks. OpenAI’s charter describes it as “highly autonomous systems that outperform humans at most economically valuable work,” while Google DeepMind frames it as “AI that’s at least as capable as humans at most cognitive tasks.” The lack of a single agreed-upon definition underscores how speculative and aspirational the concept remains, even among leading researchers. Inference is the process of running a trained AI model to generate predictions or outputs. It is distinct from training, which is the computationally intensive phase where a model learns patterns from data. Inference can occur on a wide range of hardware, from smartphone processors to cloud-based GPU clusters, but the speed and cost of inference vary dramatically depending on model size and infrastructure. Tokens are the fundamental units of communication between humans and large language models (LLMs). They represent discrete chunks of text — often parts of words — that the model processes. Tokenization bridges the gap between natural language and the numerical operations that AI systems perform. In enterprise settings, token count also determines cost, as most AI companies charge on a per-token basis. How AI Models Learn and Improve Training involves feeding vast amounts of data to a machine learning model so it can identify patterns and improve its outputs. This process is expensive and resource-intensive, requiring specialized hardware and large datasets. Fine-tuning takes a pre-trained model and further trains it on a narrower, task-specific dataset, allowing companies to adapt general-purpose models for specialized applications without starting from scratch. Reinforcement learning is a training paradigm where a model learns by trial and error, receiving rewards for correct actions. This approach has proven especially effective for improving reasoning in LLMs, particularly through techniques like reinforcement learning from human feedback (RLHF), which aligns model outputs with human preferences for helpfulness and safety. Distillation is a technique where a smaller “student” model is trained to mimic the behavior of a larger “teacher” model. This can produce more efficient, faster models with minimal loss in performance. OpenAI likely used distillation to create GPT-4 Turbo, a faster version of GPT-4. However, using distillation on a competitor’s model typically violates terms of service. Key Architectural and Infrastructure Terms Neural networks are the multi-layered algorithmic structures that underpin deep learning. Inspired by the interconnected pathways of the human brain, these networks have become vastly more powerful with the advent of modern GPUs, which can perform thousands of calculations in parallel. Parallelization — doing many calculations simultaneously — is fundamental to both training and inference, and is a major reason GPUs became the hardware backbone of the AI industry. Compute is a shorthand term for the computational power required to train and run AI models. It encompasses the hardware — GPUs, CPUs, TPUs — and the infrastructure that powers the industry. The term often appears in discussions about cost, scalability, and the environmental impact of AI. Memory cache (specifically KV caching in transformer models) is an optimization technique that boosts inference efficiency by storing previously computed calculations, reducing the need to recompute them for every new query. This speeds up response times and lowers operational costs. Emerging and Specialized Terms AI agents represent a shift from simple chatbots to autonomous systems that can perform multi-step tasks on a user’s behalf, such as booking travel, filing expenses, or writing code. Coding agents are a specialized subset that can write, test, and debug code autonomously, handling iterative development work with minimal human oversight. The infrastructure for agents is still being built, and definitions vary across the industry. Diffusion is the technology behind many image, music, and text generation models. Inspired by physics, diffusion systems learn to reverse a process of adding noise to data, enabling them to generate new, realistic outputs from random noise. GANs (Generative Adversarial Networks) use a different approach, pitting two neural networks against each other — a generator and a discriminator — to produce increasingly realistic outputs, particularly in deepfakes and synthetic media. RAMageddon is an informal term describing the acute shortage of RAM chips driven by the AI industry’s insatiable demand for memory in data centers. This shortage has driven up prices across consumer electronics, gaming consoles, and enterprise computing, with no immediate relief in sight. Why This Glossary Matters Understanding these terms is no longer optional for professionals in technology, business, and policy. As AI becomes embedded in products, services, and decision-making, a shared vocabulary enables clearer communication, more informed debate, and better strategic decisions. This glossary will be updated regularly as the field evolves, reflecting new developments and refinements in how the industry describes its own work. FAQs Q1: What is the difference between training and inference? Training is the process of feeding data to a model so it learns patterns, which is computationally intensive and expensive. Inference is the process of running the trained model to generate outputs or predictions, which can happen on a wider range of hardware and is typically faster and cheaper. Q2: What does ‘open source’ mean in the context of AI models? Open source AI models, like Meta’s Llama family, have their underlying code and sometimes weights made publicly available for inspection, modification, and reuse. Closed source models, like OpenAI’s GPT series, keep the code private. This distinction is central to debates about transparency, safety, and access in AI development. Q3: Why is ‘hallucination’ a problem in AI? Hallucination refers to AI models generating incorrect or fabricated information. It arises from gaps in training data and can lead to misleading or dangerous outputs, especially in high-stakes domains like healthcare or finance. It is driving interest in more specialized, domain-specific AI models that are less prone to knowledge gaps. This post AI Terms Everyone Nods Along To: A Practical Glossary first appeared on BitcoinWorld .

9 May 2026, 08:02

Eddie Griffin Discusses XRP’s Major Potential

Concerns about the speed and transparency of global financial transactions continue to shape conversations around digital assets. Supporters of blockchain-based systems often argue that existing banking structures remain slow, expensive, and overly dependent on intermediaries. That debate resurfaced after crypto enthusiast XRP Bags shared a video featuring American comedian and longtime crypto supporter Eddie Griffin discussing XRP and its potential role in modern finance. In the video attached to the post on X, Griffin questioned why large bank transactions can take several days to process. He argued that delays in traditional banking systems exist because financial institutions attempt to profit from transactions before settlement is completed. Griffin directly criticized the banking sector while presenting XRP as an alternative for faster transfers without relying on intermediaries. Griffin stated that XRP “cuts out the middleman,” adding that users could complete direct transfers involving hundreds of millions of dollars. He also pointed to the transparency of blockchain technology, saying that anyone can publicly view transactions recorded on an open ledger. According to Griffin, this openness contrasts sharply with what he described as “closed ledgers” used by traditional banks. Eddie Griffin: "$XRP cuts out the middleman" pic.twitter.com/ARwK5GPiOv — XRP Bags BagMan (@XRPBags) May 5, 2026 Open Ledger Transparency Remains a Key XRP Talking Point During the video, Griffin repeatedly referenced transparency as a major advantage of blockchain-based financial systems. He questioned why banking institutions maintain private internal ledgers while decentralized systems operate with publicly visible transaction records. Supporters of XRP have frequently promoted the XRP Ledger for its speed , lower transaction costs, and public transaction history. Griffin’s comments reflected many of the arguments often made by members of the XRP community, particularly those who believe blockchain technology can reduce the need for intermediaries in international payments. The actor also criticized the idea of financial institutions depending on government intervention during economic crises. Near the end of the clip, Griffin dismissed the possibility of future bailouts, saying financial firms should not expect government assistance. Griffin Continues Public Support for XRP Griffin has become increasingly associated with the XRP community in recent years due to his outspoken support for the digital asset. While widely recognized for his comedy specials and film appearances, Griffin has also publicly discussed his involvement in cryptocurrency and technology investments over the years. According to his professional background, Griffin has maintained an interest in crypto and tech investments for more than two decades. His public remarks about XRP have appeared both on social media and in segments incorporated into his comedy performances. The latest video shared by XRP Bags added to the ongoing online conversation surrounding XRP’s use case in global payments. Many XRP supporters continue to argue that blockchain-based settlement systems can improve transaction efficiency compared to conventional banking infrastructure. Griffin’s comments reinforced that position by focusing heavily on direct transfers, transparency, and reduced reliance on traditional financial intermediaries. Disclaimer : This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses. Follow us on X , Facebook , Telegram , and Google News The post Eddie Griffin Discusses XRP’s Major Potential appeared first on Times Tabloid .

9 May 2026, 07:55

Strike CEO: Wall Street Can’t Break Bitcoin — And That’s the Point

BitcoinWorld Strike CEO: Wall Street Can’t Break Bitcoin — And That’s the Point Jack Mallers, the founder and CEO of the Bitcoin payment app Strike, has dismissed concerns that Wall Street’s growing involvement in the Bitcoin market poses a threat to the cryptocurrency’s foundational principles. In a recent podcast interview, Mallers argued that if Bitcoin could be undermined by institutional participation, it was never truly designed to succeed in the first place. Bitcoin as ‘Money for Everyone’ Mallers characterized Bitcoin not merely as a speculative asset, but as a global monetary competitor. He described it as a form of money built for universal access — a system that should be resilient enough to withstand any level of market participation, including from the world’s largest financial institutions. According to Mallers, the entry of Wall Street players is not a deviation from Bitcoin’s original vision but rather a test of its structural integrity. His comments come at a time when institutional interest in Bitcoin has reached new heights. Major asset managers, including BlackRock and Fidelity, have launched spot Bitcoin exchange-traded funds (ETFs) in the United States, drawing billions of dollars in inflows. Critics have warned that such developments could centralize Bitcoin holdings and dilute its decentralized ethos. Mallers, however, views this as a natural and necessary evolution. The Demonetization of Traditional Assets Mallers also offered a broader macroeconomic prediction: traditional stores of value — including real estate, fine art, and government bonds — will gradually lose their monetary premium as Bitcoin becomes more widely adopted. He described this process as a slow but inevitable demonetization of legacy assets, driven by Bitcoin’s fixed supply, global liquidity, and resistance to censorship. This perspective aligns with a growing school of thought among Bitcoin proponents who argue that the asset’s primary use case is not as a payment system for everyday transactions but as a long-term savings technology and a hedge against monetary debasement. Mallers’ Strike app, which facilitates Bitcoin purchases and Lightning Network payments, is positioned at the intersection of these two use cases. Why This Matters for Investors For readers, Mallers’ remarks offer a counterpoint to the narrative that institutional adoption represents a betrayal of Bitcoin’s cypherpunk origins. Instead, his argument suggests that Bitcoin’s design — its proof-of-work consensus, decentralized node network, and fixed supply cap — is robust enough to absorb even the largest players without compromising its core properties. This is particularly relevant as regulators worldwide continue to grapple with how to classify and oversee digital assets. If Mallers’ thesis holds, Bitcoin’s resilience could serve as a model for other decentralized networks seeking mainstream acceptance without sacrificing their foundational principles. Conclusion Jack Mallers’ defense of Bitcoin against concerns over Wall Street influence underscores a key ideological divide within the cryptocurrency space. While some see institutional capital as a corrupting force, Mallers views it as a validation of Bitcoin’s durability. Whether Bitcoin will indeed demonetize traditional assets remains to be seen, but the debate itself signals how far the asset has come — from an obscure internet experiment to a topic of serious discussion among global financial elites. FAQs Q1: Does Wall Street owning Bitcoin threaten its decentralization? According to Strike CEO Jack Mallers, no. He argues that Bitcoin’s design is resilient enough to withstand large-scale institutional participation without compromising its decentralized nature. Q2: What does ‘demonetization of traditional assets’ mean? It refers to the gradual loss of monetary premium in assets like real estate, art, and government bonds as Bitcoin becomes more widely adopted as a store of value. Q3: How does Strike fit into this picture? Strike is a Bitcoin payment app that allows users to buy, sell, and transact in Bitcoin using the Lightning Network, positioning itself as a bridge between Bitcoin’s use as money and its role as a savings asset. This post Strike CEO: Wall Street Can’t Break Bitcoin — And That’s the Point first appeared on BitcoinWorld .

9 May 2026, 05:40

Circle Opens Applications for Developer Grant Program on Arc Chain

BitcoinWorld Circle Opens Applications for Developer Grant Program on Arc Chain Circle, the company behind the USDC stablecoin, has announced that its Layer 1 blockchain, Arc, is now accepting applications for the Circle Developer Grant Program. The initiative is designed to provide financial support to teams building real-world applications on the USDC and Circle developer platforms. Grant Program Details and Focus Areas The developer fund is open to projects across several key sectors, including payments, financial management, foreign exchange (FX), and the emerging agentic economy. This strategic focus suggests Circle is prioritizing practical, user-facing services that can drive mainstream adoption of blockchain-based financial tools. By targeting these areas, the program aims to accelerate the development of infrastructure that bridges traditional finance with decentralized technology. Implications for the Blockchain Ecosystem This grant program represents a significant step for Circle as it seeks to expand the utility of USDC beyond simple transactions. By incentivizing developers to build on Arc, Circle is fostering a more robust ecosystem of applications that could enhance the stablecoin’s use cases in everyday finance and automated economic activities. The inclusion of the agentic economy—a sector focused on autonomous AI-driven agents—signals a forward-looking approach to integrating blockchain with artificial intelligence. Why This Matters for Developers and the Industry For developers, the grant offers a clear pathway to secure funding while building on a well-established platform. For the broader industry, Circle’s investment in developer grants could stimulate innovation in areas like cross-border payments and automated financial services, potentially increasing competition with traditional financial systems. The program’s success will likely be measured by the quality and real-world impact of the projects it supports. Conclusion Circle’s decision to open the Developer Grant Program on Arc reinforces its commitment to expanding the USDC ecosystem through targeted developer support. As applications open, the industry will be watching to see which projects emerge and how they might shape the future of digital payments and decentralized finance. FAQs Q1: Who is eligible to apply for the Circle Developer Grant Program? Applications are open to development teams building real-world services on the USDC and Circle developer platforms, with a focus on payments, financial management, foreign exchange, and the agentic economy. Q2: What is the Arc blockchain? Arc is Circle’s Layer 1 blockchain designed to support the USDC stablecoin and related developer platforms, providing a foundation for building decentralized financial applications. Q3: How does this grant program benefit the broader crypto ecosystem? By funding practical applications in key financial sectors, the program aims to drive mainstream adoption of USDC and blockchain technology, potentially creating more efficient and accessible financial services. This post Circle Opens Applications for Developer Grant Program on Arc Chain first appeared on BitcoinWorld .

8 May 2026, 20:50

Canada Unemployment Rate Forecast to Hold at 6.7% in April as Labor Market Stabilizes

BitcoinWorld Canada Unemployment Rate Forecast to Hold at 6.7% in April as Labor Market Stabilizes Canada’s unemployment rate is expected to remain unchanged at 6.7% in April, according to consensus forecasts, signaling a period of stabilization in the country’s labor market after months of modest fluctuations. The figure, set to be released by Statistics Canada in its monthly Labour Force Survey, would mark the third consecutive month at this level, suggesting that hiring and job losses are roughly balanced across key sectors. What the Data Indicates Economists surveyed by major financial institutions widely anticipate that the Canadian economy added between 15,000 and 25,000 net new jobs in April, enough to keep the unemployment rate steady given a growing labor force. The projected stability follows a volatile winter period where the rate edged up from 6.5% in January to 6.7% in February and March. The services sector, particularly in healthcare, education, and retail, is expected to have driven most of the gains, while manufacturing and resource extraction continue to face headwinds from global trade uncertainties and lower commodity prices. Broader Economic Context The steady unemployment rate comes against a backdrop of cautious monetary policy by the Bank of Canada, which has held its key interest rate at 3.25% since January. Inflation has eased to around 2.4%, but the central bank remains vigilant about wage pressures and productivity growth. A stable labor market provides policymakers with room to assess the impact of previous rate adjustments without immediate pressure to cut or raise rates. For workers, the flat rate means continued competition for available positions, particularly in white-collar industries such as technology and finance, where hiring has slowed. Regional Variations and Sectoral Shifts While the national headline figure suggests calm, regional disparities persist. Alberta and Saskatchewan are expected to show slightly lower unemployment due to steady energy sector activity, while Ontario and British Columbia may see marginal increases as housing costs and slower population growth weigh on local demand. The construction sector remains a bright spot in several provinces, supported by government infrastructure spending and housing development initiatives. Why This Matters for Readers For Canadian job seekers and businesses, the April data provides a snapshot of an economy that is neither overheating nor contracting sharply. A stable unemployment rate at 6.7% is historically moderate — above the record lows of 2022 but below the peaks seen during the 2020 pandemic. It suggests that the labor market is absorbing new entrants and adjusting to higher interest rates without widespread layoffs. However, the persistence of part-time and gig work as a share of total employment remains a concern for income stability and benefits coverage. Conclusion The expected April unemployment rate of 6.7% reflects a Canadian labor market in a holding pattern — resilient enough to avoid sharp deterioration but not strong enough to signal a rapid recovery. The coming months will be crucial to determine whether this stability is a prelude to improvement or a plateau before further softening. Investors, policymakers, and workers alike will be watching the details of the Labour Force Survey for signs of underlying trends in wages, hours worked, and sectoral employment shifts. FAQs Q1: What does a 6.7% unemployment rate mean for the average Canadian? A: It indicates a moderate labor market where job availability is stable but competitive. Most workers are employed, but finding a new position may take longer than in a very tight market. It also suggests that the economy is not in recession, though growth is subdued. Q2: How does Canada’s unemployment rate compare to other countries? A: Canada’s rate of 6.7% is higher than the United States (around 3.9% as of early 2026) but lower than many European nations. Differences in labor force participation and measurement methodologies partly explain the gap. Q3: Will the Bank of Canada change interest rates based on this data? A: A steady unemployment rate alone is unlikely to trigger an immediate rate change. The Bank of Canada considers a broader set of indicators, including inflation, GDP growth, and wage data, before adjusting monetary policy. The April jobs report will be one piece of that puzzle. This post Canada Unemployment Rate Forecast to Hold at 6.7% in April as Labor Market Stabilizes first appeared on BitcoinWorld .