Một modder tên Ray Foss phát triển công cụ Auto-Charge Tracker dựa trên trình duyệt, sử dụng camera tracking và động cơ rung để tự động điều hướng Steam Controller quay trở lại dock sạc. Dự án được chia sẻ trên GitHub.
Vì sao nên đọc: Lập trình viên nên đọc bài này để khám phá cách kết hợp camera tracking và haptic feedback—các kỹ thuật cơ bản trong AI và IoT—để tạo ra giải pháp tự động hóa thú vị cho thiết bị ngoại vi, mở rộng kiến thức về cách ứng dụng công nghệ sensor và haptic trong thiết kế phần mềm thực tế.
Nguồn: https://www.xda-developers.com/steam-controller-mod-uses-vibration-guide-gamepad-back-to-its-charging-dock. 8sync News chỉ tóm tắt và dẫn link; bản quyền nội dung thuộc tác giả và nguồn gốc.

Netflix giới thiệu hai mô hình chỉnh sửa video AI giai đoạn đầu là Vera và VOID. Vera sử dụng mô hình diffusion phân lớp, chỉ tái tạo vùng chỉnh sửa (kèm alpha matte) thay vì toàn bộ clip, bảo toàn nội dung chưa chỉnh sửa. VOID chuyên xóa vật thể trong video với kỹ thuật inpainting hợp lý vật lý, tái tạo cảnh thực tế khi vật thể bị loại bỏ. Cả hai mô hình đều vượt trội so với các phương pháp hiện có trong nghiên cứu.
Lập trình viên muốn phát triển các giải pháp AI tiên tiến trong xử lý video nên tham khảo để hiểu cách thiết kế mô hình hiệu quả như Vera và VOID, từ kiến trúc đặc biệt đến kỹ thuật điều khiển chi tiết để nâng cao chất lượng và tính khả thi của các ứng dụng AI video trong tương lai.
Firefly has launched the AIBOX-9075, an industrial Edge AI box powered by the Qualcomm DragonWing IQ-9075 SoC. It delivers up to 200 TOPS of AI performance via a Hexagon Tensor Processor NPU, paired with 36GB LPDDR5 ECC memory and 128GB UFS 2.2 storage. The device targets edge AI workloads including private LLM deployment, robotics, and computer vision. It supports popular AI frameworks (TensorFlow, PyTorch, ONNX) and LLMs such as DeepSeek-R1, Llama, and Gemma, achieving up to 22 tokens/s with Llama2-7B. Connectivity includes dual 2.5GbE with TSN, optional Wi-Fi 6, 4G/5G, and 8x GMSL2 camera inputs. The rugged aluminum enclosure operates from -40°C to 85°C. It runs Ubuntu and Yocto Linux and is priced at $1,239 from the Firefly store.
Aseon Labs, a Redwood City startup from Y Combinator's 2026 spring cohort, has raised $10M in seed funding to build parking space-sized automated pods that clean, inspect, and charge robotaxis in-city. The core problem they address is 'deadhead miles' — empty trips robotaxis make to distant depots — which hurt fleet utilization and profitability. Their pods use robotic arms, cameras, and vision-language-action AI models to handle routine maintenance autonomously, while flagging complex issues for human handling at central depots. The units are designed as temporary structures to avoid lengthy permitting and can be relocated if a site underperforms. No robotaxi contracts have been signed yet, but the company plans to build five prototypes and grow its team with the new funding.
Aseon Labs has raised $10M in seed funding to build parking-space-sized automated pods that charge, clean, and inspect robotaxis in place. The Y Combinator-backed startup aims to solve the deadhead miles problem — robotaxis driving empty to distant depots for servicing — by deploying modular pods across city parking lots and gas stations. Using robotic arms, computer vision, and vision-language-action models, the pods can handle most routine maintenance autonomously. The company estimates its solution could cut reset costs by 50%, reduce downtime by 65%, and add over $50K in annual revenue per vehicle. Aseon Labs is pre-product with five prototypes as its immediate goal, targeting a market that Goldman Sachs projects will reach $415B by 2035.
Berlin startup Almetra (formerly Deltia) has raised €16.3M in Series A funding to expand its AI-powered factory floor analytics platform. The company mounts cameras above assembly lines at manufacturers like Bosch, Siemens Energy, and ABB, converting video footage into live production data — cycle times, output rates, equipment utilisation — without requiring IT system integration. Customers report productivity gains of 15–19%. The round was led by blisce/, with participation from Merantix Capital and others. Almetra has been accepted into Google DeepMind's Robotics Accelerator and an AWS/Nvidia/MassRobotics Physical AI Fellowship, positioning it as a potential data source for industrial robotics. The company plans to use the funding to expand into the US and build out robotics applications.
Researchers at EPFL's NeuroAI Lab have developed AI-based topographic neural network models that predict optimal brain stimulation patterns to evoke perception of complex visual objects — such as faces and houses — rather than just simple light flashes. The models were validated in live trials on sighted monkeys in Amsterdam, showing that model-guided cortical stimulation can bias visual object perception in predictable ways. While the team cannot yet create object perception from scratch (without any visual input), this is the stated next goal. The approach could also extend to improving cochlear implants for auditory prosthetics.
MIT Sports Lab, co-founded in 2015, has become a key technology partner for major sports organizations. The lab played a central role in validating FIFA's semi-automated offside technology (SAOT) used at the 2022 World Cup, processing over 108,900 skeletal data points per second to ensure accuracy. Beyond soccer, the lab developed an Expected Action Value (EAV) metric for the NBA to quantify player decision-making quality, helped Adidas optimize 3D-printed midsole designs using biomechanical models, and conducted a COVID-19 stadium attendance analysis for the NFL. The lab bridges academic research and industry needs, connecting MIT students and faculty with professional sports organizations.
BEVPoolV3 is a new CUDA kernel optimization for bird's-eye-view (BEV) pooling used in autonomous vehicles and robotics. The post walks through a practical GPU optimization workflow: classify whether the working set fits in L2 cache, remove redundant scatter traffic via a five-array INT32 scatter map, implement interval-owned scatter-reduce to avoid atomics, and validate with NVIDIA Nsight Compute. On RTX PRO 6000 Blackwell Max-Q (large L2), BEVPoolV3 FP8 achieves up to 42x speedup over the V2 baseline. On RTX A6000 (small L2, DRAM-bound), the adapted FP16 path reaches 19x speedup. The post also explains why FP8 outperforms NVFP4 for L2-resident scatter-reduce workloads, and how the same methodology applies to sparse embeddings, voxelization, and other irregular memory-bound kernels.