KNN алгоритм используя R

We are looking for a 3D GenAI Engineer / AI 3D Pipeline Developer We need to create a solution that can qualitatively generate 3D models from one or more images. It is important that this is not just a ready-made demo, but a clear and reproducible process: from the input image to a full-fledged 3D asset with mesh, geometry, textures, and the possibility of further use. What needs to be done: * test modern image-to-3D models and approaches; * determine which option is best suited for our task; * understand Trellis, Hunyuan3D, or similar solutions; * if necessary, use Gaussian Splatting in the 3D pipeline; * set up the conversion of Gaussian Splat / splat representation into a 3D mesh; * obtain usable geometry; * generate high-quality textures; * bring the result to a usable 3D asset state; * find the optimal balance between quality, generation speed, and pipeline complexity; * build a clear process that can be repeated for different images; * perform fine-tuning, LoRA, or other model adaptations for specific types of objects; * be able to work with ComfyUI: understand graphs, use the ComfyUI API, and write custom nodes if necessary. In your response, be sure to show a few examples in the format: input image and the resulting 3D model. The format does not matter: render, video, 3D viewer, GLB, OBJ, FBX, or Blender file. We are primarily interested in realistic objects: furniture, jewelry, interior items, decor, accessories, and other physical goods. Examples from anime or stylized characters are not suitable.

Hello everyone! Who knows how to work and has been working with Open Claw agents for coding and other tasks? I need to organize parallel work of open claw code agents for coding on a virtual server. Requirements: - help choose a server for the agents (there are many ready-made options) - help set up the environment - connect the API Who can do this today? Who is available? I found these options online: https://clawhost.cloud/

About the ProjectA manufacturing company with several warehouses in Ukraine. More than 40 suppliers, both domestic and from China. Each supplier has its own terms for order processing and logistics.What We Want to ImplementAn AI agent for automating purchases and managing the movement of goods between warehouses. The agent is planned to be trained to work with each supplier according to their individual conditions.Current InfrastructureA procurement report is operational on QlikView (BI analytics via browser). Data updates every 30 minutes, the report is generated in the browser on the fly. Excel files can be exported from it.How We See the ImplementationDecide on a platform (OpenClaw, Claude Cowork, or similar) for building the agentDeployment on a VPS serverConnection to the analytics report via browserTraining using 2–3 suppliers as a pilotAgent FunctionalityGenerating orders to suppliers (training for each supplier separately)Moving goods between internal warehouses to maintain stockCreating tasks for specific groups of goods (for example, cutting into pieces and filling missing positions by lengths)Required IntegrationsTelegram: management through chat by several employeesCreating tasks in our CRM via APISending tasks to specific employees in the Telegram chatFuture PlansAfter the implementation of this stage, the creation of a sales analysis agent is planned, also based on the QlikView report.

Hello! Task: Develop and place an agent on a WordPress page (the programmer will insert the code into WordPress). The user should be able to communicate with the AI agent in text or voice format (if possible) based on a specific uploaded topic (content: texts, photos). Functionality: The user visits the website page. Sees the AI agent interface: chat + voice communication button (if possible). Can request text. Can speak, and the agent should recognize the speech and respond. The agent responds only based on the uploaded materials on the specific topic. If the question goes beyond the topic, the agent should correctly inform that it can only respond based on materials related to Skovoroda. Responses should be lively, understandable, and educational. The administrator should be able to upload and update materials: texts, PDFs, images, audio, links. Interested in the cost and timeline for developing the agent.

Contact only those who can do this. And immediately provide deadlines and budget. Creating a personal Wiki database based on Obsidian Note. Registration on hosting, launching a local artificial intelligence OpenCl and connecting it to the created Wiki database. Explanation in a YouTube video. https://youtu.be/9DLyBdND22c?si=AqDvuTnteIBWAiyu