Have a side project that records my own mouse telemetry/clicks/keyboard inputs for league games synced with the video footage. Pulled these numbers from my sessions collected over roughly 350 games. Not an ML person but wondering if theres any use cases for this type of data. This is a sample from one of the games Data schema: “` { “games”: [ { “gameMode”: “Ranked Solo”, “champion”: “Varus”, “result”: “defeat”, “kda”: { “kills”: 23, “deaths”: 6, “assists”: 6 }, […]

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arXiv:2603.20317v1 Announce Type: new Abstract: Space-based compute is becoming plausible as launch costs fall and data-intensive AI workloads grow. This paper proposes a workload-centric framework for deciding which tasks belong in orbit versus terrestrial cloud, along with a phased adoption model tied to orbital data center maturity. We ground the framework with in-orbit semantic-reduction prototypes. An Earth-observation pipeline on Sentinel-2 imagery from Seattle and Bengaluru (formerly Bangalore) achieves 99.7-99.99% payload reduction by converting raw imagery to compact semantic […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## SERP Benchmarks: Success Rates and Latency at Scale By @brightdata [ 8 Min read ] ​​We benchmark SERP APIs for success rate, ​​speed, and stability under load. Learn which setup delivers consistent results for AI agents ​​and deep research. Read More. Create a Website […]

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arXiv:2603.08758v1 Announce Type: new Abstract: Many geometric learning problems require invariants on heterogeneous product spaces, i.e., products of distinct spaces carrying different group actions, where standard techniques do not directly apply. We show that, when a group $G$ acts transitively on a space $M$, any $G$-invariant function on a product space $X times M$ can be reduced to an invariant of the isotropy subgroup $H$ of $M$ acting on $X$ alone. Our approach establishes an explicit orbit equivalence […]

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Author(s): Mandar Karhade, MD. PhD. Originally published on Towards AI. When an AI model does in minutes what human researchers couldn’t do in decades, the market doesn’t just notice: it panics. Here is the nuclear option nobody in Silicon Valley wanted to talk about. For years, the cybersecurity industry has been a high stakes gambling ring built on a house of cards. You pay millions for “endpoint protection” and “zero trust” wrappers that essentially act as expensive digital […]

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arXiv:2602.07562v1 Announce Type: cross Abstract: Match-and-copy is a core retrieval primitive used at inference time by large language models to retrieve a matching token from the context then copy its successor. Yet, understanding how this behavior emerges on natural data is challenging because retrieval and memorization are entangled. To disentangle the two, we introduce Gaussian Match-and-Copy (GMC), a minimalist benchmark that isolates long-range retrieval through pure second-order correlation signals. Numerical investigations show that this task retains key qualitative […]

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The thing about 90% of TDMs [Technical Decision Makers] is that they’re motivated primarily by NOT GETTING FIRED. These aren’t people who browser Lobsters or push to GH on the weekend. These are people that work 9 to 5, get paid, go home, and NEVER THINK ABOUT WORK AGAIN. So to achieve all that, they follow secular trends supported by analysts and broad public sentiment. Oh, Gartner said that “AI strategy” is most important? McKinsey said “context” needs […]

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Concept learning is a form of supervised machine learning that operates on knowledge bases in description logics. State-of-the-art concept learners often rely on an iterative search through a countably infinite concept space. In each iteration, they retrieve instances of candidate solutions to select the best concept for the next iteration. While simple learning problems might require a few dozen instance retrieval calls to find a fitting solution, complex learning problems might necessitate thousands of calls. We alleviate the […]

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arXiv:2602.15950v1 Announce Type: new Abstract: We present a simple experiment that exposes a fundamental limitation in vision-language models (VLMs): the inability to accurately localize filled cells in binary grids when those cells lack textual identity. We generate fifteen 15×15 grids with varying density (10.7%-41.8% filled cells) and render each as two image types — text symbols (. and #) and filled squares without gridlines — then ask three frontier VLMs (Claude Opus, ChatGPT 5.2, and Gemini 3 Thinking) […]

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