The number of Bitcoin ATMs spiked for the first time

zycryptoОпубліковано о 2023-06-05Востаннє оновлено о 2023-06-05

Анотація

The number of Bitcoin ATMs globally has seen a notable increase over the past month, reaching a new high with over 35,000 active machines.

According to the latest Coin ATM Radar activity data, in May 2023 alone, the number of ATMs increased significantly with 35,069 units, reflecting a sudden growth since the great volatility in March. 2023, when the number of machines drops below 34,000 units.

Meanwhile, in April 2023, Coin ATM Radar studied the entire cryptocurrency market with only 33,389 Bitcoin ATMs, the lowest level in the past 2 years.

Comparing the data to December 2022, when the number of ATMs exceeded 39,000, it is clear that there was a drop despite the Bitcoin (BTC) price increase during that period. As of March there were about 3,600 Bitcoin ATMs disconnected worldwide.

Bitcoin ATMs spike in May after dropping in March

The trend has been down since December, as depicted in the Coin ATM Radar chart. However, this situation has been reversed with an increase of nearly 1,400 units in May and an additional 278 units in the first days of June.

Currently, when it comes to the global distribution of ATMs, the United States holds the leading position, with over 30,000 ATMs installed within its borders, representing more than 85% of the total number of machines in operation today. now. According to Coin ATM Radar, Canada is the only other country to surpass 1,000 ATMs, accumulating around 2,745.

Europe is in second place with about 1,400 ATMs installed across the region, with Spain leading the list with 286 ATMs. By comparison, the UK has a meager 16 ATMs installed despite its position as a financial hub in the region.

US and Canada Dominate Bitcoin ATM Distribution

Spain tops the list of Spanish-speaking countries with 285 ATMs, followed by El Salvador with 212. Mexico came in third with 52 ATMs, while Colombia and Puerto Rico rounded out the top five with 37 and 32, respectively.

Hong Kong leads the way with 148 ATMs in Asia, while Japan has only three installed despite its Bitcoin-friendly regulations. El Salvador unsurprisingly leads in Latin America with 272 ATMs after adopting Bitcoin as legal tender.

Africa has the lowest number of Bitcoin ATMs, with South Africa leading with 17, while the regional giant Nigeria has only two ATMs installed in its jurisdiction.

Пов'язані матеріали

Jensen Huang: Prompts are Becoming Obsolete, Loops are the New Paradigm

Jensen Huang, alongside AI leaders like Peter Norvig, Boris Cherny, and Andrew Ng, is advocating for a shift from "prompt engineering" to "loop engineering" as the new paradigm for AI development. Instead of manually crafting individual prompts, the focus is now on designing autonomous loops—systems where AI agents execute tasks, self-validate results, and iterate until completion without constant human oversight. A loop is a management framework that enables agents to operate independently. Key implementations are seen in Claude Code (with features like /loop, /goal, and /schedule) and OpenAI Codex, which employ multiple agents working in parallel within isolated environments. A core principle is the separation of roles: one agent (or model) performs the task, while an independent agent (or a smaller, separate model) validates the output to ensure objectivity. The article outlines a practical roadmap for implementing loops, starting with a "four-condition test" to assess suitability, building a minimal viable loop, and emphasizing critical pitfalls to avoid, such as lacking hard stop conditions or allowing loops to handle tasks requiring human judgment. This evolution is framed as the fourth major shift in AI interaction: from Prompt Engineering (crafting instructions) to Context Engineering (providing background information), then to Harness Engineering (building tool-enabled environments), and finally to Loop Engineering (creating self-sustaining systems). This progression reflects a consistent trend of increasing abstraction, moving human involvement from direct instruction to system design and rule-setting. The concept has academic roots in frameworks like ReAct, which formalized the "reason-act-observe" cycle. While loop engineering promises greater automation, experts caution about managing token costs and warn against outsourcing understanding—AI can assist, but deep problem comprehension remains essential.

marsbit34 хв тому

Jensen Huang: Prompts are Becoming Obsolete, Loops are the New Paradigm

marsbit34 хв тому

MemeCore Token Crashes As ZachXBT Warning Puts Insider Supply Back In Focus

MemeCore's M token experienced a severe crash, plunging over 70% in a single session. This sudden sell-off revived concerns highlighted by on-chain investigator ZachXBT regarding tokens with concentrated insider supply and thin liquidity. The incident underscores the fragility of many memecoins, where large paper valuations can vanish quickly when selling pressure hits illiquid markets. It also brings exchange listing standards into question, as listings can imply a legitimacy not supported by the underlying token structure. The crash serves as a stark warning for traders that social traction and high market capitalization do not guarantee resilient liquidity, emphasizing the risks inherent in high-beta crypto assets.

bitcoinist58 хв тому

MemeCore Token Crashes As ZachXBT Warning Puts Insider Supply Back In Focus

bitcoinist58 хв тому

GPT Designs GPT

OpenAI has unveiled its first custom AI chip, Jalapeño, a move signaling a strategic shift beyond being a mere model company. While many see it as a challenge to NVIDIA, its core aim is to control the entire intelligent production pipeline—from models and chips to data centers and energy. The key driver is the evolving competitive landscape: model advantages are shrinking, while the computational gap in areas like cost-per-token, system throughput, and energy efficiency is becoming the true long-term barrier. Jalapeño is primarily an inference chip, targeting the massive and growing "inference tax"—the daily operational cost of generating tokens for services like ChatGPT and APIs. By designing its own hardware optimized for its specific workloads and future product roadmaps (even using AI to aid the chip design process), OpenAI aims to drastically reduce token generation costs and improve system efficiency. This creates a potential flywheel: better models help design better chips, which lower costs for running next-generation models, supporting more users and products, which in turn provides more data to refine future chips. The strategy mirrors Apple’s integrated approach, building a closed loop where hardware, software, and applications are co-optimized. In the long term, OpenAI is not trying to become the next NVIDIA (a supplier of "shovels" to all AI companies) but to own and operate the entire "mine"—selling the end product of intelligence itself. This move marks OpenAI's ambition to evolve from creating the smartest models to controlling the foundational infrastructure of AI production.

marsbit58 хв тому

marsbit58 хв тому

Ethereum Foundation Interim Executive Director Speaks Out: What Is Our Mission?

The Ethereum Foundation's core mission is to ensure Ethereum remains a truly permissionless, censorship-resistant, private, and open infrastructure for large-scale, sovereign coordination. The article clarifies the EF's focus and dismisses irrelevant objectives, such as pursuing institutional popularity or short-term speculation. Its core work centers on eliminating systemic weaknesses. This involves fortifying Ethereum across multiple layers—protocol, access, user, and institutional—against exploitation, control, or surveillance. Key initiatives include minimizing harmful MEV and preventing privileged control over transaction flow, making unconditional privacy a foundational default, ensuring staking remains permissionless and decentralized, and strengthening user-facing access points to uphold autonomy. Concurrently, the EF aims to seize strategic opportunities. These include leading the transition to post-quantum cryptography, achieving a fully verifiable protocol stack, establishing Ethereum as private digital cash, integrating user-owned AI agents with personal wallets, and demonstrating that trusted-neutral infrastructure can competitively handle disintermediated coordination at an institutional scale. The article also addresses recent organizational changes, stating that personnel departures were due to strategic realignment, role fit, or natural evolution. It clarifies the approach to spin-outs, emphasizing that external funding will be provided only for work critical to the EF's mission that reduces Ethereum's dependency without creating new risks or mission drift. Ultimately, the EF is committed to building an enduring, neutral system that reshapes global coordination, focusing relentlessly on the principles of censorship resistance, openness, privacy, and sovereignty (CROP).

链捕手1 год тому

Ethereum Foundation Interim Executive Director Speaks Out: What Is Our Mission?

链捕手1 год тому

Switching Research Fields in the Final Year of My PhD, I Landed an Offer from OpenAI: My Interview Journey Was Full of 'Surprises'

Yong Zheng-Xin, a final-year PhD student at Brown University, secured an OpenAI offer after pivoting from multilingual models to AI safety research in his last year. In his blog post "Surprising lessons from my research scientist job search," he shares six unexpected insights from his job hunt. He found that often only one or two key papers matter for securing an interview, not the total number. Interview formats were highly diverse, including unexpected rounds on system design and AI agent usage. "Work trials," common in AI startups, involved paid, sometimes week-long collaborative tasks that disrupted other interview preparations. Timing proved crucial, with opportunity windows sometimes lasting less than a month. Return offers for research roles were rare compared to software engineering. Surprisingly, many interview rounds were unrelated to his specific AI safety focus, instead testing broader research competencies. Zheng-Xin concludes that the ability to demonstrate relevant expertise in a new field can unlock opportunities, regardless of one's previous publication record.

marsbit1 год тому

Switching Research Fields in the Final Year of My PhD, I Landed an Offer from OpenAI: My Interview Journey Was Full of 'Surprises'

marsbit1 год тому

Торгівля

Спот

Ф'ючерси