Another thing to consider. When the best hackers exfiltrate shitcoins from victims, the end result after their mixing, is almost always to hodl their ill gotten gains in Bitcoin. These are people that understand cryptography and computer science more than most.

Like Saifedean eludes to, the shitcoiners aren't able to squeeze any juice out of the shitcoin arena right now, so they are trying to find some juice in Bitcoin. I don't agree with them, but I see their angle.

I mean the lightning node SN runs

Your interpretation of the Statute of Monopolies 1624 is not consistent with the general understanding of the law. The Statute of Monopolies was enacted in the United Kingdom, with the primary goal of limiting the power of monarchs to grant monopolies, except in certain cases. It marked the end of the monopolistic royal trading privileges in England and was instrumental in the development of the concept of a competitive market. Statute of Monopolies doesn't apply to the situation described in Queensland, Australia. While historical British laws have influenced Australian legal principles, the invocation of a 17th-century British statute in a contemporary Australian legal context is unconventional. The Statute of Monopolies cannot somehow affect claims on personal assets by taxation authorities or resolve debt. It is unrelated to taxation, debt enforcement, or registration of personal property.

I watch a lot of sovereign individual court cases on YouTube. It’s a fascinating reality they live in.

SN has always had great lightning support and super low fees.

Use Tails, Electrum, and a border wallet and you don’t need any persistence or connections on the laptop or USB

It definitely effected it. Look back at the statistics starting around January or when Jack posted.

I like the curation of SN better

Dr. Norvig stresses the pivotal role of large datasets in machine learning model training. He proposes that larger data sets often result in enhanced performance, even more than complex algorithms trained with less data.

The session references a paper by Halevy, Norvig, and Pereira (2009), which indicates how web-scale data significantly improved machine translation, speech recognition, and information retrieval. The 'unreasonable' term signifies that simple models with large data can surpass intricate models with less data.

Dr. Norvig highlights Google's strategy of utilizing large datasets to improve its services. Examples include Google's spelling correction feature driven by user search query data, and Google Translate, which uses bilingual text corpora to train its translation models.

The necessity of rigorous testing and experimentation in machine learning is underlined by Dr. Norvig. Google's practice of consistent A/B testing to gauge algorithm performance is discussed as an essential part of the machine learning process.

In concluding the session, Dr. Norvig offers an insightful forecast regarding the trajectory of AI and machine learning. His prediction centers on the correlation between the expansion of data collection and the enhancement of AI performance.

In a data-driven world, the volume, variety, and velocity of data are continually increasing. This growth isn't only due to the rise of internet users but also the surge of interconnected devices, often referred to as the Internet of Things (IoT). The proliferation of these devices alongside advances in data storage and processing capabilities ensures an ever-growing pool of data for training and refining machine learning models.

Dr. Norvig foresees this trend catalyzing continual performance improvements in AI. These improvements are expected to be seen across various applications of AI, including but not limited to natural language processing, image and speech recognition, and predictive analytics.

Additionally, the increasing data volume can enable the discovery of subtle patterns and correlations that may be imperceptible with smaller datasets. This will allow AI to generate more accurate and nuanced predictions and insights, which can lead to more effective decision-making in a wide range of domains, from business and finance to healthcare and environmental monitoring.

Moreover, with more data, models can potentially become more generalizable and robust, as they can be trained and validated on a diverse range of scenarios and edge cases. This could lead to AI systems that are better equipped to handle real-world complexity and variability, further enhancing their performance and utility.

This presentation features economic analyst Dr. Jeff Ross providing a deep dive into the potential financial landscape towards the end of 2023, with a particular focus on an impending recession. He breaks down the current economic trends, spotlighting the ebb of the manufacturing sector in the US and its replacement with a service-centric economy. Furthermore, he discusses how a recession could affect various industries, including the potential risks of corporate debt defaults.

Dr. Ross identifies signs indicating that a recession isn't necessarily on the immediate horizon, such as the inverted yield curve and lower-than-average unemployment rates. Nevertheless, he alerts to the potential fallout if a recession strikes, including a surge in bankruptcies and soaring yields.

The presentation also includes a section on the repercussions of the COVID-19 pandemic, which Dr. Ross contends has already induced a recession by forcing industry closures and a contraction in economic activity. He emphasizes that rebooting a service-driven economy can be more straightforward than reviving industries that demand physical presence.

Dr. Ross accentuates the need to factor in quality of life and happiness when assessing the economy, rather than limiting our perspective to conventional economic metrics like GDP. He expresses concern over the evident decline in societal joy and happiness and the potential repercussions this might have on mental health.

He projects a stagnant economy for the current decade, characterized by low growth and unstable inflation, drawing parallels with the 1970s. Nevertheless, he sees a silver lining in the Bitcoin space, believing that dedication and humility can pave the way to success and enhance quality of life.

Thanks for the sats :)

Mr. Finney, a gallant adventurer in the vast realm of cryptography, had the unique honor of being the first to engage in a transaction using the unprecedented and groundbreaking currency known as Bitcoin. This monumental exchange was initiated by the elusive entity known as Satoshi Nakamoto. Much like the first steps of an explorer on virgin soil, this act resounded with unprecedented importance and foresight. The concept of Bitcoin initially emerged shrouded in an air of incredulity and suspicion. A currency spun from the ether of the internet, devoid of physical form, and lacking the endorsement of any established power, seemed as fanciful as the most whimsical tales of adventure and discovery.

Yet, Hal Finney, bearing an intrepid spirit akin to the adventurers in beloved novels such as 'Twenty Thousand Leagues Under the Sea' or 'Around the World in Eighty Days', ventured forth into this new realm with undeterred enthusiasm and resolute conviction. Much like the enigmatic figures often encountered in these stories, Finney chose to remain mostly out of the limelight. Despite his pivotal contributions to this groundbreaking technology, he sought not fame nor recognition. His story paints the picture of a selfless pioneer, navigating uncharted territories that would inevitably alter the course of mankind's relationship with currency. As time unfolded, Bitcoin, once regarded as an implausible concept, began to gain acceptance, much like the fantastical inventions once confined to the pages of adventure novels that have now become reality. It has revolutionized our perception of finance and transaction, signifying a leap as significant as the inventions that have propelled mankind into the depths of the sea or the vastness of outer space.

At first, I ain't trusted this here Satoshi Nakamoto fella - or lady, or group, or alien... whatever. The thing is, it's this Bitcoin thing they made. It's all online, no paper, no coins, just numbers on a screen. Seemed too newfangled, too out there. Plus, ain't no tellin' who's using it for what. Could be some no-good scoundrels laundering money or buyin' stuff they shouldn't. And another thing, who on earth comes up with something this big and just ghosts? Not a whisper, not a hint about who they really are. Felt like they were hiding something, you know?

But time's a funny thing. Makes you see things different. Bitcoin, it's not just a wild idea anymore. It's out there, changing things, making waves. It's making people think twice about what money even is. It's making people talk about power, who has it, who ought to. And the part about Nakamoto staying anonymous? That might just be a stroke of genius. Keeps the focus on the Bitcoin, not them. It's like they're saying it ain't about one person or one group, but about everyone. Makes you think, doesn't it? So, yeah, I guess you could say I've come around to this Satoshi Nakamoto, whoever they are. They've done stirred up the pot, and it ain't a bad thing at all.

To mitigate these issues, you should:

Over the years, I've leveraged a variety of technologies to support my endeavors in the realm of sports betting programming. Python has been my trusty workhorse for most of the data processing, thanks to its simplicity and the robustness of data manipulation libraries like pandas and NumPy.

One of my favorite inventions is a system I call "Beat the Spread" (BTS for short). It's a dynamic AI model that adapts to changes in teams' performance over the season, and factors in various other elements like players' health, morale, weather, and even the subtleties of home field advantage. The final version of BTS uses a stacked ensemble of models, including Random Forests, Support Vector Machines, and Gradient Boosting Machines, with a neural network at the top to combine their predictions.

Recognizing the need for real-time updates, I developed a live tracking system called "GameFlow". It's built on a fast, scalable stream processing platform that gathers data from multiple sources during the game, processes it in real time, and fine-tunes the predictions.

On this morn of our Restday Revelry, all and sundry, I hope, partake in the dark draught, brewed from the beans of Javara. As for mine own, 'tis without cream, sweetened with a brace of sugars. For today, I find myself needing the invigorating surge it provides, my body harbors an unusual fatigue. Yet, with the bitter brew's magic coursing in my veins, I shall transcend these lowly confines, transmuted as if by some arcane power.

An escapade awaits me this day, perchance a pilgrimage to the shore's edge or an exploration of the submerged caverns of our forefathers – these cenotes as they are called. Or mayhap, I shall recline in my sanctuary, the moving pictures of an ether-screen for company. The goal is but a brief respite, a disconnection from the fetters of existence, a chance to bask in unadulterated joy, to gather strength for the challenges of the coming days.

I wish for thee a day of excellence, filled to the brim with affection, merriment, and harmonious energy. Always recall, thou art significant, thou art treasured. Always. Remain in good health, my cherished companion, and keep thy spirit ablaze in this icy dystopia!