Jonathan Roseland    Entrepreneurs, hustlers, and biz dev guys: Bedazzled by all the so shiny new thing growth hacks AI automation, becoming an influencer, etc , are you perhaps ignoring what might make the most difference today

BTCWire    OSYS CRM has unveiled an advanced integration designed to empower trading firms with cutting edge technology. By combining real time analytics, centralized management tools, and enhanced security protocols, this new solution addresses the challenges of modern proprietary trading.

Alex Holloway    Data visualization is a key method of communication that bridges the gap between data experts and decision makers. By turning data into stories, visualization helps communicate insights that drive strategy, improve efficiency, and create a shared understanding.

BTCWire    KTON is an institutional grade liquid staking protocol. KTON allows users to stake TON while receiving KTON, unlocking liquidity without sacrificing staking rewards. Users can start staking with as little as TON, and there is no lock up period. Kton is set to enter the United States and global...

Scott D. Clary    Entrepreneurs dream of reaching that point where decisions feel easy, where success feels certain, and where expertise eliminates doubt. But there’s a hidden cost to deep expertise that nobody talks about. The more you learn about building companies, the harder decisions get.

Chainwire    Silencio is the world’s first decentralized noise data engine, has officially launched. Silencio transforms smartphones into secure, anonymous sensors that crowdsource real time, hyper local noise data. The SLC token, powered by the Blocksound Foundation, is used for rewarding contributors,...

Chainwire    Flipster Superstars is a voluntary initiative designed for individuals passionate about crypto and community engagement. The program calls on creators, community influencers, and storytellers to team up with Flipster. Participants will have the chance to work closely with Flipsters users and...

Algorithmic Bias    This section details Algorithm for defeating the Follow the Leader opponent by leveraging the ellipsoid algorithm to approximate payoff matrices and predict actions. The strategy ensures nearly all rounds are won after learning best responses in an initial phase. A Limited History variant, which...

Algorithmic Bias    This section introduces the game setting: two player, repeated, zero sum games with limited payoffs and symmetric properties. It explores models of behaviorally biased opponents, including Myopic Best Responder, Gambler’s Fallacy, Win Stay Lose Shift, Follow the Leader, and Highest Average Payoff....

Algorithmic Bias    This section presents Algorithm for defeating the Limited History variant of the Follow the Leader opponent. By playing each action times in sequence, players record accurate best responses, then use the ellipsoid algorithm to predict and counter the opponent. The strategy ensures winning...

Algorithmic Bias    Future research will focus on characterizing which behaviorally biased strategies can be exploited without payoff observation, particularly in probabilistic settings where strategies like regret minimization challenge consistent wins. Additional avenues include performance benchmarking relative to...

Algorithmic Bias    This section explores generalizing strategies to beat behaviorally biased opponents in zero sum games. Predicting actions remains feasible with a halving algorithm, but learning best responses depends on the opponent’s strategy. A key condition is that opponents must eventually reveal best...

Algorithmic Bias    This study examines exploiting behavioral biases in opponents during zero sum games with limited information. Building on Feldman et al.’s copycat algorithm, the paper introduces strategies to predict and counter deterministic, biased opponents to win nearly every round. It highlights...

Algorithmic Bias    This section discusses the challenge of predicting opponents’ actions and learning best responses in zero sum games. While algorithms like halving can predict deterministic opponents, learning best responses requires exploiting their specific strategies. An example highlights how playing optimally...

Algorithmic Bias    This section introduces algorithms to counter the Tie Shift and Tie Stay variants of the Win Stay, Lose Shift opponent. Against Tie Shift, the strategy involves learning the opponent’s action order, recording best responses, and predicting their next moves. For Tie Stay, the approach is simpler, as...

Jon Stojan Journalist    The Hydra Doom Tournament highlighted Cardano’s Hydra solution, achieving M TPS and proving its scalability. With . B transactions processed in hours without failure, Hydra demonstrated its potential for gaming, DeFi, and P P applications. Its horizontal scaling via Hydra heads and...

Algorithmic Bias    This section introduces algorithms to exploit the Myopic Best Responder and Gambler’s Fallacy opponents in zero sum games. For the Myopic Best Responder, Algorithm guarantees wins after recording best responses in initial rounds. Against Gambler’s Fallacy, Algorithm uses patterns of overdue...

Algorithmic Bias    This section introduces Algorithm for defeating the Highest Average Payoff opponent. By learning best responses through sequential play and employing the ellipsoid algorithm for prediction, players can exploit the opponent’s average based strategy. Losses are limited to O n n log nr,...

Algorithmic Bias    This section explores scenarios where the opponent’s strategy is unknown but comes from a known set. Using a modified halving algorithm, predictions can be made with bounded errors. Learning best responses depends on the shared properties of the strategies in the set or distinguishing between them....

Algorithmic Bias    Algorithm outlines a strategy to exploit the Tie Stay variant of the Win Stay Lose Shift opponent in permissible games. By learning best responses and the opponent’s action order in the initial phases, players can predict actions and counter them effectively.