i made an ant simulation powered by reinforcement learning agent in pure Rust in a Bevy environment
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digitado
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2026 Is the Make-or-Break Year for the New Space Race
From the Gobi Desert to the Arctic Circle, a generation of private rocket startups is finally bending metal. 2026 is the year they either reach orbit—or run out of cash. (title image credit: Rockets of the World by Nick Stevens) The Monopoly in the Sky Look up on a clear night in 2026 and you’ll see them: not just Orion or Cassiopeia, but the synchronized trains of Starlink, drifting across the sky like a slow-motion barcode. One company, […]
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Stop Calling It an AI Assistant. It’s Already Managing Your Company
The next enterprise AI risk is not that a chatbot writes a bad email. It is that an AI agent quietly enters the operational layer of the company and starts ranking priorities, routing approvals, classifying risk, delaying purchases, escalating tickets, flagging customers, and shaping managerial decisions before anyone calls it management. Companies still describe these systems as “assistants” because the word sounds harmless. But once a system can trigger action inside an ERP, CRM, inventory platform, purchasing workflow, […]
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Why AI Products Lose Users After the First Try — And How to Design for Adoption
Most AI products don’t fail because the model is bad. They fail because the product decisions around the model are wrong — or absent. Activation looks healthy in week one, then collapses in week two. Generation counts climb while export and approval rates stay flat. Users try the AI feature once, mark it as “interesting,” and never come back. If you’re shipping AI products in 2026, you’ve seen this pattern. The question is what to do about it. […]
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Building self-improving tax agents with Codex
See how OpenAI, Thrive, and Crete built a self-improving tax agent with Codex, automating filings, improving accuracy, and accelerating workflows.
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Quoting Kyle Ferrana
PICARD: Data, shields up DATA: Brilliant! Shields can reduce damage we sustain. Not immunity. Not hubris. Just prudence. It’s not precaution—it’s strategy. [camera shakes] WORF: HULL BREACHES ON NINE DECKS DATA: Here’s what happened: you told me to raise shields, and I didn’t — Kyle Ferrana, @KyleTrainEmoji Tags: ai-misuse, coding-agents, ai, llms
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La soberanía digital deja de ser una abstracción
Mi columna de esta semana en Invertia se titula «Europa descubre el botón de apagado» (pdf), y trata sobre cómo Europa ha empezado, por fin, a comprender que su dependencia tecnológica de las grandes compañías estadounidenses no era simplemente una cuestión de comodidad o eficiencia, sino un problema estratégico de primer orden. El detonante de esa toma de conciencia lo analiza muy bien un reciente artículo en Wired, «The EU Is going through a Trump-fueled breakup with Big […]
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Why Can’t Transformers Multiply Beyond Their Training Length? (And a Fix: 80.6% on Unseen Digits)
submitted by /u/ZhenBoYan [link] [comments]
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Transformers Can Learn Posterior Predictive Distributions In-Context
arXiv:2605.26713v1 Announce Type: new Abstract: Prior-data fitted networks (PFNs) have recently emerged as a powerful approach for Bayesian prediction tasks, approximating the posterior predictive distribution (PPD) through in-context learning. Despite their strong empirical performance and ability to go beyond point predictions, theoretical understandings of the algorithmic capability of transformers to learn distributions in context are still lacking. Focusing on Gaussian process regression problems, we show by construction that transformers can implement a gradient descent algorithm targeting the posterior […]
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CART Random Forests as Sequential Allocation over Random Opportunity Sets: A Stochastic-Control Theory of Ensemble Risk
arXiv:2605.26675v1 Announce Type: new Abstract: CART random forests are among the most widely used modern predictive methods, with well-documented empirical success. Yet, at the mechanistic level, the algorithm is often treated as a black box because of its complexity. In this paper, we develop a stochastic-control perspective on feature-subsampled CART random forests, named CART random opportunity-set allocation (CART-ROSA). At each node, the random subset of features is interpreted as a random feasible action set, and the CART split […]
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