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The post Texas Senate’s Bitcoin Reserve Hearing: A Game Changer or Symbolic Move? appeared first on Coinpedia Fintech News The Texas Senate is all set to hold a public hearing, tomorrow, to discuss creating a Bitcoin reserve. However, market analysts believe that the move may be symbolic unless the state announces a clear investment plan. The first public hearing for the establishment of a potential strategic Bitcoin reserve in Texas is scheduled for Feb. 18, less than a week after the bill was received by the Secretary of the Senate, according to the Texas government’s website. “A Symbolic Move” Despite being a positive step for Bitcoin adoption, the hearing may only represent a ‘symbolic move’ for cryptocurrency markets, according to Iliya Kalchev, dispatch analyst at Nexo. “Texas considering Bitcoin as a reserve asset is another chess move in BTC’s march toward institutional legitimacy, but state-level initiatives often make ripples, not waves, compared to ETF inflows or corporate treasury allocations,” Kalchev noted. Kalchev pointed out that unless Texas announces specific actions, like buying Bitcoin soon or a major policy shift, the market won’t react strongly. This is because Texas is already known for its pro-crypto stance, and hence the news itself won’t be surprising. Lately, Bitcoin has lacked upside momentum, trading under the $100,000 for over nine days since Feb. 7. COO of Bitget, believes that Bitcoin will continue to face pressure in the short term due to the ongoing trade tensions between the US and China. Upcoming Events Could Reignite Interest Nevertheless, Key events this week like the upcoming FOMC minuted could stir and reignite the markets. The Federal Reserve will release the minutes from its January meeting on Wednesday, where it decided to keep interest rates steady. Besides, Trump’s tariffs are also adding inflationary pressure. Nevertheless, Bitcoin has displayed a resilient performance. While the positive developments surrounding Bitcoin reserve legislation signal growing institutional adoption, Kalchev noted that macro forces like fed policy and regulatory shifts will ultimately shape Bitcoin’s trajectory. Texas’ push for a Bitcoin reserve marks a significant shift in both economic and political landscapes, with other states following suit. Kentucky, for example, has become the 16th state to propose legislation that would allocate up to 10% of excess state reserves into digital assets. These moves highlight the growing trend toward crypto adoption across the U.S.

A bit afraid of mine and the next generation's ability to connect the dots. If I ask GPT to explain something, it breaks it down, and I get it. Pre-GPT, I'd read maybe 10 things for a few hours, and think about how something works and connect the dots. That thinking part where I consider how disjoint resources are related to explain the topic I wanted to learn is no longer a part of the equation. Yes, we'll all be more efficient and save time, but I'm seriously concerned about the long-term implications on mine, and everyone else's, ability to think. The solution seems to be to use GPT less for research, or spend more time playing chess or doing something the requires lots of computational brainpower.

XRP closed in profit again Are u ready for next week (active trading) Copy with fixed ratio Enjoy $BTC $ETH $ETC $PROPS $CHESS $BLUR $CLORE $DUEL $PONKE $SWELL $INVITE $NEIROETH $U2U $PSTAKE $PNUT $JELLYJELLY

The $MTOS chart is starting to look like a game of chess, and every move counts. With the price steady at $0.010 and a tight range between $0.008 and $0.012, it feels like we’re in the calm before the storm. But the question is, which way will the storm blow? The trading volume of 8.63M $MTOS in the past 24 hours shows there’s still interest in the token, yet the lack of a clear breakout has kept many traders on edge. Looking at the historical movement, the current pattern could suggest a quiet accumulation phase where smart money is positioning itself for a bigger move. At the same time, it could also be a setup to trap buyers before a further drop. For me, as someone holding $MTOS, I’m keeping an eye on a few critical signals. First, whether the price can close above the $0.012 resistance zone with strong volume—this would be a solid bullish sign. Second, if the price slips below $0.008 with heavy selling, it might indicate a bearish turn. What’s fascinating is the psychology behind the current market structure. Fear and doubt dominate when prices consolidate for too long. But this is where opportunities often lie for patient holders. Are you ready to buy more on the dips, or are you waiting for a clear breakout to join the rally? $MTOS .

AI agents have made significant strides in mastering games like chess and Go, showcasing advanced problem-solving capabilities. However, tackling football presents a more substantial challenge, requiring understanding intricate concepts such as passing, dribbling, and strategic positioning. These agents must learn to coordinate with teammates, adapt tactics dynamically, and develop winning strategies in a continuously changing environment. Using Reinforcement Learning, we can train for football and create systems capable of handling real-time decision-making and teamwork. Project Idea: To implement a football-playing AI agent, Google's Football Environment can be used, which provides a reinforcement learning framework for training agents in a simulated football setting. The problem can be approached using Deep Q-Networks (DQN), a self-learning algorithm that uses rewards to optimise actions, and LightGBM, a supervised learning technique trained on football match datasets from sources like Kaggle. Combining these approaches will allow the agent to learn complex skills autonomously while refining strategies based on data-driven insights.  To create a highly intelligent AI agent, you can integrate multi-agent collaboration, adaptive strategies, and self-supervised learning to navigate the game's complex ever-changing environment. Unlike conventional AI systems that follow preset actions, this autonomous agent learns dynamically, coordinating with teammates and adapting play based on game conditions, opponent strategies, and past experiences. With real-time decision-making and human-like behavioural modeling capabilities, the agent executes tasks and optimizes them autonomously, making it a genuinely adaptive, self-driven system.