@Nikobar

I've been building a home robotic assistant with a goal of keeping it quiet, smooth, and simple to use. I plan to make both the hardware and software open source. I'm currently experimenting with voice control, remote operation, health monitoring integration, and optional camera tracking. It's still a work in progress, and I'd love to hear what features or improvements you'd find most useful. submitted by /u/Training_Income353 [link] [Kommentare]

I need to transfer a lot of force through this keyway into a 3d print, and am confused on the intended way to attach to this shaft. submitted by /u/IntroductionNo1700 [link] [Kommentare]

Researchers at the University of Toronto's Continuum Robotics Laboratory introduced CRAFT, a 3D-printed modular design library for tendon-driven continuum robots that allows a single robot to physically reconfigure its shape, stiffness, and degrees of freedom within minutes by snapping together six interchangeable modules. The same base robot was reconfigured into a long teleoperated probe for aircraft-wing inspection achieving 41% reduction in sag, a pipe-crawling robot capable of navigating 90-degree bends and 30-degree inclines, and a soft robotic hand that successfully cracked eggs with 85% accuracy. CRAFT eliminates the need to build entirely new robots for different tasks, replacing bespoke redesign with rapid modular composition. Credits: https://www.nature.com/articles/s44182-026-00107-x submitted by /u/Similar_Suit_3709 [link] [Kommentare]

Started testing a private qwen 35B moe capacity LLM runtime on s26 ultra, early testing shows that active model footprint can fit within the device’s memory limits.( not sharing the methods or architecture used) and results suggest roughly 90 input processing t/s achievable after optimisation and output generation is around 8 tokens/s on this mobile. Point is i learned ai ml based on my interest and no formal PhD , I have compute and resources to test. Anyone willing to join or collab to test on this I tried publishing papers on arxiv and 4 papers are still on hold as im first author and from no institution... submitted by /u/Severe_Post_2751 [link] [Kommentare]

Hi! I don't know if this has been posted about before or not, but for the past week, I've been trying to control my SPIKE Prime with custom commands via BLE. However, as far as the forums stated (from what I have read), it wasn't possible without Pybricks. However, while tinkering with the robot, I found that BLE control is just an "environment variable" that could be switched. Thus, we don't need a firmware to control BLE, we just need a *one-time setup" using a USB UART communication. If you want to use this for your own projects, please check out the code, and if you like it, please drop a star on the repo! https://github.com/Somespi/prime-connect submitted by /u/Formal-Arachnid-4247 [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]

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]

Hello guys. I want to introduce my recent research presented at ICML workshop. github link : genji970/SRM-LoRA: official implementation of "SRM-LoRA: Sub-Riemannian-Metric Updates for Mitigating LLM Hallucination in Low-Rank Adaptation" ICML2026 Workshop FoGen Shot summarization of Paper. """ SRM-LoRA is a sub-Riemannian-inspired LoRA method designed to reduce LLM hallucination. It builds a sensitivity-based Riemannian metric that reshapes backward gradients in the LoRA parameter space. This metric suppresses high-cost update directions while leaving the forward computation and inference cost unchanged. Trained only on HaluEval-QA, SRM-LoRA improves factual reliability on both related and out-of-distribution benchmarks. """ Experiment """ https://preview.redd.it/3uyvbtoa76dh1.png?width=740&format=png&auto=webp&s=4431233186dffae885db8e989f009bf37710fe22 """ In my view, the reason mathematics is not effectively used in the context of improving the performance of the latest AI systems, such as LLMs, is that progress in discussions about what should serve as the elements of mathematical theories has been slow. For example, suppose that we use a Riemannian metric. In the parameter space of an LLM, the update vector produced by backpropagation arises from a loss objective that contains the training data. However, if we introduce learnable parameters in order to construct the Riemannian metric and train those learnable parameters by passing through them the same signal as the main training signal, then this may simply amount to a more complicated form of training and may only increase the possibility of overfitting to the training data. Then, how can we obtain the benefits of mathematical theory while moving in a direction that can generalize? In this paper, the Riemannian metric is constructed based on the rate of change of the LLM model parameters with respect to the loss signal. The reason for defining it in this way is as follows. No matter how good the data or the distribution that can be learned may be, in practice there is still a high possibility of overfitting that results in hallucinations. Therefore, the cost used to construct the Riemannian metric, where a higher cost indicates a worse path, is defined using this sensitivity, which can be understood simply as gradient(loss)/gradient(parameter). In other words, rather than merely introducing a more complicated metric, the Riemannian metric acts as a brake on the updates generated from the training data, which is the main signal. I believe that mathematics can be incorporated more deeply into AI if, when using theory A and theory B, the elements of theory A and the elements of theory B are each designed appropriately for the specific situation. submitted by /u/Round_Apple2573 [link] [Kommentare]

https://www.youtube.com/@ALMA.Industries Chapter 2, a home theatre, 3D printed parts, motorized projector, home decoration, and DIY electronics -- if you know of anyone else that might be interested in this stuff, sharing to others would really help me out! Hope to see you around here or YouTube :) submitted by /u/ALMA_x11 [link] [Kommentare]

LLM's seem to make it so much easier to run experiments, write papers, etc. As a result, are we seeing phd students finish their phds sooner than ever before specifically in CS? If not, why not? submitted by /u/Alone_Reality3726 [link] [Kommentare]

Ranjay Krishna argues that language may be an unnecessary intermediary between perception and action in robotics. Humans do not translate every physical interaction into words before reacting. Catching a ball, pulling a hand away from something hot or moving through a room happens through a direct connection between perception and movement. He believes robotics models should work the same way, moving directly from visual and sensor input to action rather than relying on an LLM in the middle. submitted by /u/Responsible-Grass452 [link] [Kommentare]