This paper presents the first demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which could be used in future high-energy physics experiments. We present a three-fold contribution, with the PicoCal calorimeter, planned for the LHCb Upgrade II experiment, used as a test case. First, we develop a lightweight autoencoder to compress a 32-sample timing readout, representative of that of the PicoCal, into a two-dimensional latent space. Second, we introduce a systematic, hardware-aware quantization strategy […]

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A new OpenAI report maps how AI could reshape jobs across the EU, highlighting which occupations may face automation, growth, or workflow changes.

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arXiv:2603.28887v1 Announce Type: new Abstract: Data-driven autonomous driving simulation has long been constrained by its heavy reliance on pre-recorded driving logs or spatial priors, such as HD maps. This fundamental dependency severely limits scalability, restricting open-ended generation capabilities to the finite scale of existing collected datasets. To break this bottleneck, we present OccSim, the first occupancy world model-driven 3D simulator. OccSim obviates the requirement for continuous logs or HD maps; conditioned only on a single initial frame and […]

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Research: Claude system prompts as a git timeline Anthropic publish the system prompts for Claude chat and make that page available as Markdown. I had Claude Code turn that page into separate files for each model and model family with fake git commit dates to enable browsing the changes via the GitHub commit view. I used this to write my own detailed notes on the changes between Opus 4.6 and 4.7. Tags: system-prompts, anthropic, claude, generative-ai, ai, llms

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The energy management problem in the context of smart grids is inherently complex due to the interdependencies among diverse system components. Although Reinforcement Learning (RL) has been proposed for solving Optimal Power Flow (OPF) problems, the requirement for iterative interaction with an environment often necessitates computationally expensive simulators, leading to significant sample inefficiency. In this study, these challenges are addressed through the use of Physics-Informed Neural Networks (PINNs), which can replace conventional and costly smart grid simulators. The […]

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Over the years, we’ve come across hundreds of article submissions that promise deep technical insight right in their headlines. Common titles include: “How I built…,” “Learn how to…” and so forth. And honestly? We love content like this. When done right, it delivers lasting value and evolves into evergreen content that serves curious internet users year in, year out. Many such stories perform really well on HackerNoon. Stories like: Inside My $1,000 Homelab: How I Rebuilt Big Tech […]

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AI agents are reading executive email at Fortune 500 companies. They are moving stablecoins between corporate treasury wallets. They are closing six-figure procurement contracts on behalf of teams that have never seen the agent. They have been doing this for eighteen months, ever since Anthropic shipped Computer Use in October 2024 and OpenAI released Operator three months later. None of them have a verifiable identity. None of them have scoped authority that can be cryptographically verified. None of […]

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For more than a century, freight rail has evolved around one dominant constraint: energy cost. From steam to coal to diesel, each generation of rail has been defined by the same underlying challenge: how to move massive freight loads while managing one of the industry’s largest and most persistent operating expenses. Despite improvements in engine efficiency and logistics, energy has remained structurally difficult to reduce. In many rail systems, it can still account for up to ~20% of […]

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arXiv:2602.00970v1 Announce Type: new Abstract: Agents powered by large language models (LLMs) are increasingly deployed in settings where communication shapes high-stakes decisions, making a principled understanding of strategic communication essential. Prior work largely studies either unverifiable cheap-talk or fully verifiable disclosure, failing to capture realistic domains in which information has probabilistic credibility. We introduce MixTalk, a strategic communication game for LLM-to-LLM interaction that models information credibility. In MixTalk, a sender agent strategically combines verifiable and unverifiable claims to […]

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