There’s a HD from mining in the early 2Ks also it’s in another country. Is it worth flying 12+hrs to retrieve & what you do once I have it? submitted by /u/Professional-God2379 [link] [Kommentare]
Inventory robots are moving beyond isolated pilots and becoming part of larger systems that combine computer vision, electronic shelf labels, AI, cleaning robots, and real-time analytics. Tesco, Kroger, Harmons, BJ’s, Stop & Shop, Wakefern, Schnucks, and others are using autonomous shelf-scanning systems to track out-of-stocks, misplaced products, pricing errors, promotional compliance, and shelf conditions. Harmons says autonomous shelf scanning reduced out-of-stock conditions by more than 50% and pricing errors by 75%. submitted by /u/Responsible-Grass452 [link] [Kommentare]
Building a sports prediction model ,I found consistent edge when backtesting against closing lines. At inference time tho, I predict 12-24 hours before the event where closing lines don't exist yet. I use the current line instead. My strongest feature is line movement (opening to closing implied probability). At prediction time this feature is incomplete as the market hasn't fully moved yet. This creates a paradox: Closing lines are considered nearly impossible to beat because they contain all available information : sharp money, injury news, everything. Yet the backtest shows consistent edge against them. If closing lines are truly efficient, beating them implies genuine model signal. But at inference time we're betting against earlier, less efficient lines with an incomplete version of our strongest feature. The question: does edge against closing lines transfer to earlier bets where lines are less efficient ? Or does the incomplete line movement signal hurt prediction enough that the edge disappears before close? My intuition is the edge is smaller earlier because the market is less efficient but the model signal is also weaker. These two effects might cancel out or one might dominate. Curious if anyone has studied this tradeoff in sports or financial prediction. submitted by /u/MrProbability101 [link] [Kommentare]
N/A. Contribute to MSNightmare/LegacyHive development by creating an account on GitHub.
Dear Secretary of State, Protect children online without making the Internet less secure Protecting children online is an objective we all share.
Written pieces, talks, and other bits by Zach Holman.
Operator, integrator, and packager reference for satd — a Bitcoin Core-compatible full node in Rust.
I built this Snake demo to show how easy it can be to go from data preparation to training and evaluation with FeynRL. The model is overkill for Snake, but that’s not the point. This example walks through the full VLM training pipeline in a simple, visual, and fun setting, showing how FeynRL makes it easier to understand how large models like LLMs and VLMs are built, trained, and optimized end to end. https://i.redd.it/9j0t2bukg8dh1.gif GitHub: https://github.com/FeynRL-project/FeynRL Check out the examples section to build something similar yourself, and feel free to share feedback or contribute. submitted by /u/murdock_aubry [link] [Kommentare]
Can LLM agents coordinate in long-horizon, open-ended worlds? We evaluate 13 modern LLMs in a new benchmark where agents must work together to explore, communicate, trade resources, craft tools, build structures, and fight mobs. TL;DR: Most agents struggle, averaging only ~6% normalised return. Yet on the hardest setting, zero-shot Gemini 3.1 Pro performs comparably to the best MARL agent trained for 1 billion environment steps. More broadly, we find coordination is a distinct bottleneck beyond long-horizon task competence, with communication having the largest effect in our harness ablations. Paper: https://arxiv.org/abs/2606.08340 Project page and leaderboard: https://alem-world.github.io Code: https://github.com/alem-world/alem-env Interactive traces: https://alem-world.github.io/traces.html Feel free to ask any questions! submitted by /u/ktessera [link] [Kommentare]