INSLIB

C11 is a dependency-free embedded navigation library.

LightNav-0 is an open-source generalist embodied-navigation model from the Light Origins Team. It uses a pretrained Qwen3-VL backbone with a single egocentric RGB stream and natural-language instructions to control humanoid, quadruped, wheeled, and aerial robots, transferring across tasks, embodiments, and scenes without per-task or per-benchmark fine-tuning. The model extends the vocabulary with dual-channel pointing tokens and residual vector-quantized action tokens rather than adding navigation-specific prediction heads. At each step, it emits an affordance point representing a feasible local direction or waypoint and an object point representing the goal, followed by three action tokens that decode to ten future SE(2) waypoints for an embodiment-specific low-level controller. Its temporally aware history compressor samples older frames less frequently, pools them more coarsely, and preserves ordering with timestamp tokens while bounding the visual context. The repository includes a released checkpoint and action decoder, command-line prediction, a vLLM-based server with WebSocket streaming, a MuJoCo simulation demo, evaluation harnesses, and a ROS 2 deployment stack with adapters for the Unitree Go2 and LimX TRON 1. The project is released under the Apache License 2.0; the included EVT-Bench is separately licensed CC BY-NC-SA 4.0.

LingBot-Map is a feed-forward 3D foundation model from the Robbyant Team for streaming 3D reconstruction from image sequences and video. Its Geometric Context Transformer unifies coordinate grounding, dense geometric cues, and long-range drift correction through anchor context, a pose-reference window, and trajectory memory. The system uses paged KV-cache attention for streaming inference and supports interactive browser-based reconstruction with point-cloud visualization, keyframe intervals, windowed processing for long sequences, and sky masking. It also provides an offline rendering pipeline for image folders or video, along with evaluation pipelines for datasets including KITTI and Oxford Spires. The repository reports approximately 20 FPS inference at 518×378 resolution on sequences exceeding 10,000 frames, and documents PyTorch installation with FlashInfer as the recommended attention backend and native SDPA as a fallback.

Dear ImGui is a self-contained, immediate-mode graphical user interface library for C++ developed for programmer-facing tools rather than typical end-user applications. Programs call UI functions each frame so the interface reflects live application state, making it suited to game-engine tooling, content-creation tools, visualization and debugging interfaces, real-time 3D applications, and embedded or fullscreen software. The library generates optimized vertex buffers that applications can render through their own 3D pipeline. Its core consists of platform-agnostic C++ source and header files that can be compiled directly into an existing project; platform and graphics API backends are provided separately, and custom backends can be used wherever textured triangles can be rendered. It is renderer-agnostic, portable, and has no external dependencies. Dear ImGui is distributed under a free, permissive license. The project emphasizes minimal state synchronization, setup, and maintenance, but does not provide some features common in higher-level GUI libraries, including full internationalization and accessibility support.

SGLang is an open-source serving framework and inference engine for large language models and multimodal models, developed by the SGLang project. It provides an alternative serving stack for deployed models, with runtime and kernel optimizations for inference workloads. Its documented capabilities include RadixAttention, a zero-overhead batch scheduler, cache-aware load balancing, structured-output decoding, speculative decoding, disaggregated prefill and decode, model parallelism, and support for GPU and TPU backends. The project also publishes integrations and optimizations for current open models and multimodal, image, and video-generation workloads.