Tootfinder

The Authoritarian Stack.
This is brilliant – if you’re interested in human rights, democracy, and tech, and want to understand technofascism/corporatocracy bookmark and read this.👇
#AuthoritarianStack #tech

#HotTake:
Liberalism is a syncretic religion where the highest deity is the State and all other deities are subject to it. Liberalism asserts this distinction by classifying political systems as deriving from "natural law" and religions as faith systems. Classical liberalism divided the world into "primitive" beliefs and "enlightened" beliefs. Atheistic anarchism aligns with this distinction by opposing the state and capitalism through logic, rather than asserting that the state and capitalism are themselves systems of faith. Atheistic anarchism further reinforces the narrative of the liberal state by aligning with the liberal understanding of religion as a "primitive" institution.
Graeber et al pointed out the connections between property rights as a form of fedishism (magic) and the evolution of currency, debt, taxes, and the state from temples.

HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents
Thomas Carta, Cl\'ement Romac, Loris Gaven, Pierre-Yves Oudeyer, Olivier Sigaud, Sylvain Lamprier
https://arxiv.org/abs/2508.14751

HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents
Open-ended AI agents need to be able to learn efficiently goals of increasing complexity, abstraction and heterogeneity over their lifetime. Beyond sampling efficiently their own goals, autotelic agents specifically need to be able to keep the growing complexity of goals under control, limiting the associated growth in sample and computational complexity. To adress this challenge, recent approaches have leveraged hierarchical reinforcement learning (HRL) and language, capitalizing on its compos…

Adaptive and Regime-Aware RL for Portfolio Optimization
Gabriel Nixon Raj
https://arxiv.org/abs/2509.14385 https://arxiv.org/pdf/2509.14385

Adaptive and Regime-Aware RL for Portfolio Optimization
This study proposes a regime-aware reinforcement learning framework for long-horizon portfolio optimization. Moving beyond traditional feedforward and GARCH-based models, we design realistic environments where agents dynamically reallocate capital in response to latent macroeconomic regime shifts. Agents receive hybrid observations and are trained using constrained reward functions that incorporate volatility penalties, capital resets, and tail-risk shocks. We benchmark multiple architectures, …