Introduction The concept of land value tax (LVT) has gained traction in recent discussions surrounding property tax regimes. LVT, proposed by Henry George, aims to address issues related to investment discouragement and wealth consolidation. This article delves into the debate surrounding LVT and its potential implications for cities, with particular focus on the case of Detroit. Understanding Land Value Tax Under current property tax regimes, the value of improvements and land are taken into consideration. However, LVT suggests taxing only the value of the land, not the structures or improvements on it. This approach seeks to remove the perverse incentive of holding onto idle land for speculative purposes. By taxing the land component and not the improvements made by investors, LVT aims to promote economic growth and discourage the concentration of wealth.

Introduction: Inflation is an important economic indicator that affects the purchasing power of individuals and the overall health of the economy. However, recent discussions on social media have raised questions about the accuracy of inflation measurements, particularly when it comes to the cost of housing and food. Some argue that the Consumer Price Index (CPI), which is commonly used to measure inflation, fails to capture the true extent of price increases over the years. Let’s delve deeper into this debate and explore the different perspectives.

The digital landscape of the 21st century has given birth to a phenomenon most Internet users are painfully aware of: clickbait. Today’s online world, dominated primarily by Google’s search algorithms, encourages the production of content engineered to appeal to these algorithms more than to genuine human curiosity or intellectual pursuits. A user might set out with genuine intentions of learning or researching on Google, only to find themselves mired in masses of irrelevant or low-quality content. Finding real travel advice on Google, for instance, has become increasingly arduous. Most writers seem unaware of the destinations they write about, their content reading more like uninspired middle school essays than thorough travel guides.

The internet has revolutionized the way we access information, but it has also brought about some significant challenges. One of these challenges is the overwhelming amount of clickbait and low-quality content that plagues search engine results. This issue is not new, but it has become increasingly prevalent in recent years. The rise of Google and its search algorithms has played a significant role in the deterioration of search results. The open web that we access through Google is largely influenced by ranking algorithms that prioritize certain types of content. This prioritization has led to an influx of articles and videos created solely to please Google’s algorithms, rather than providing valuable and reliable information.

Introduction: Time series forecasting has long been a subject of interest for businesses and researchers alike. With the rise of deep learning models in various domains, it is natural to explore their potential in time series forecasting. However, recent discussions and experiences from professionals in the field have cast doubt on the efficacy of these specialized “time series” deep learning models. In this article, we delve into the criticisms raised by experts and shed light on the current state of time series forecasting.