technocracy

I’m a very heavy user of Claude Code on the web, Anthropic’s excellent but poorly named cloud version of Claude Code where everything runs in a container environment managed by them, greatly reducing the risk of anything bad happening to a computer I care about. I don’t use the web interface at all (hence my dislike of the name) – I access it exclusively through their native iPhone and Mac desktop apps. Something I particularly appreciate about the […]

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Symphony isn’t just helping developers by responding to prompts, it actually converts project boards into engines that get tasks automatically picked up, implemented, and submitted for review by AI. This marks a real shift in how software might be built soon. Tasks like interpreting requirements, coding, testing, and submitting aren’t the developer’s job anymore, the AI agents take care of all that inside orchestrated workflows. At the core of Symphony, it brings together familiar tools like issue trackers, […]

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Disentangled representation learning aims to capture the underlying explanatory factors of observed data, enabling a principled understanding of the data-generating process. Recent advances in generative modeling have introduced new paradigms for learning such representations. However, existing diffusion-based methods encourage factor independence via inductive biases, yet frequently lack strong semantic alignment. In this work, we propose a flow matching-based framework for disentangled representation learning, which casts disentanglement as learning factor-conditioned flows in a compact latent space. To enforce explicit […]

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Hey r/reinforcementlearning, I built a highly parallel CUDA implementation of Policy Iteration for continuous state/action spaces using barycentric interpolation. It solves complex systems like an Overhead Crane and Double CartPole without relying on standard deep RL methods. I’ve been working on this based on the theoretical framework from “Continuous RL via Dynamic Programming” (Dupuis & Kushner). I’m sharing it here because I think this approach is heavily underrepresented compared to DQN/PPO and deserves more attention. Most RL implementations […]

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Since the COVID-19 pandemic, online courses have expanded access to education, yet the absence of direct instructor support challenges learners’ ability to self-regulate attention and engagement. Mind wandering and disengagement can be detrimental to learning outcomes, making their automated detection via video-based indicators a promising approach for real-time learner support. However, machine learning-based approaches often require sharing sensitive data, raising privacy concerns. Federated learning offers a privacy-preserving alternative by enabling decentralized model training while also distributing computational load. […]

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Machine learning is a powerful method of extracting meaning from data; unfortunately, current digital hardware is extremely energy-intensive. There is interest in an alternative analog computing implementation that could match the performance of traditional machine learning while being significantly more energy-efficient. However, it remains unclear how to train such analog computing systems while adhering to locality constraints imposed by the physical (as opposed to digital) nature of these systems. Local learning algorithms such as Equilibrium Propagation and Coupled […]

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They’ve been a formidable force that’s driven the S&P 500 to new heights in recent years on the back of an AI boom period, but is Wall Street cooling on the Magnificent Seven? At the peak of the artificial intelligence rally that’s so far endured for three and a half years, the Magnificent Seven appeared to be unstoppable, and far more than the FAANG collective of market leaders that preceded it. Amounting to around one-third of the entirety […]

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The development of political fragmentation and the rigid division of groups into us and them has disrupted a primordial connection, one that once allowed humans to participate in something like a shared, distributed intelligence. Think of it as a legacy peer-to-peer network, running for millennia without central servers, gradually throttled to near-zero bandwidth by the demands of modernity. Ancient civilizations maintained what you might call a worldwide knowledge graph, coherent, interconnected, holistic, before it was shattered and replaced […]

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arXiv:2603.15655v1 Announce Type: new Abstract: In decentralized Multi-Agent Reinforcement Learning (MARL), steganographic collusion — where agents develop private protocols to evade monitoring — presents a critical AI safety threat. Existing defenses, limited to behavioral or reward layers, fail to detect coordination in latent communication channels. We introduce the Dynamic Representational Circuit Breaker (DRCB), an architectural defense operating at the optimization substrate. Building on the AI Mother Tongue (AIM) framework, DRCB utilizes a Vector Quantized Variational Autoencoder (VQ-VAE) bottleneck […]

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