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      "title": "Adobe InDesign Expert Needed for Paperback and Kindle eBook Formatting",
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      "description": "I have a completed short manuscript that needs professional formatting for both print and digital publication.\n\nI’m seeking a skilled Adobe InDesign specialist who can transform my manuscript into a well-structured, visually appealing book suitable for Amazon KDP and Kindle.\n\nProject Requirements:\n\n•⁠ ⁠Format the manuscript using Adobe InDesign\n•⁠ ⁠Create a professional and readable interior layout\n•⁠ ⁠Set appropriate trim size, margins, and gutters\n•⁠ ⁠Apply consistent paragraph and chapter styles\n•⁠ ⁠Prepare a print-ready PDF for Amazon KDP\n•⁠ ⁠Create a properly formatted EPUB for Kindle\n•⁠ ⁠Provide the editable InDesign source file\n\nThe final files should be properly organized, free from formatting errors, and ready for publication.",
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      "title": "# AI/3D Computer Vision Engineer — Production Single-Image-to-3D Pipeline",
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The challenge is building a dependable production pipeline that preserves the object’s geometry and visible appearance instead of merely producing a plausible-looking 3D asset.\n\nHello,\n\n## What the workflow must accomplish\n\nGiven a single reference image, the system should:\n\n- Reconstruct accurate proportions, silhouette, thickness, curvature, and component placement.\n- Preserve thin structures such as legs, armrests, openings, supports, and decorative details.\n- Generate coherent geometry for surfaces not visible in the input.\n- Preserve visible texture information from the source image wherever possible.\n- Reproduce materials such as wood, marble, fabric, leather, and metal without replacing them with generic AI-generated patterns.\n- Produce stable UVs with controlled seams, stretching, and uncovered regions.\n- Generate high-resolution textures—ideally 2K or 4K—and appropriate PBR maps where useful.\n- Produce a clean, optimized GLB suitable for Blender, web rendering, and downstream visualization.\n- Operate reliably across different object categories, rather than being tuned for one example.\n- Support batch processing, automated quality checks, and a short human-review step where necessary.\n\n## Scope of work\n\nYou will be expected to:\n\n1. Evaluate and benchmark suitable image-to-3D models and pipelines. These may include Hunyuan3D, SAM 3D Objects, TRELLIS, or other relevant current approaches.\n2. Design a modular workflow covering:\n- Input preparation and object masking\n- Camera and scale handling\n- Shape reconstruction\n- Mesh cleanup, repair, and controlled simplification\n- UV unwrapping\n- Reference-image projection\n- Multiview texture generation\n- Completion of unseen surfaces\n- Texture baking and seam correction\n- PBR material estimation where appropriate\n- GLB validation and export\n3. Determine when pretrained models are sufficient and when fine-tuning, adapters, custom conditioning, or additional training data are required.\n4. Preserve reference-image pixels and material patterns on visible surfaces instead of allowing the generative texturing stage to unnecessarily repaint them.\n5. Provide reproducible deployment tooling for a GPU environment.\n6. Provide automated evaluation and failure detection for issues such as missing parts, incorrect proportions, texture drift, UV stretching, visible seams, incomplete coverage, excessive polygon counts, and invalid GLB exports.\n7. Review all models, code, and native dependencies for commercial-use restrictions and provide a clear license/component inventory.\n\nA ComfyUI interface may be part of the solution, but the underlying workflow must also be understandable, maintainable, and suitable for later automation. Python, Blender scripting, or headless services may be used where appropriate.\n\n## Required experience\n\n### Needed\n\nPlease apply only if you have hands-on experience with several of the following:\n\n- Single-image or multiview 3D reconstruction\n- PyTorch and CUDA-based inference pipelines\n- Mesh processing, repair, remeshing, and decimation\n- UV unwrapping and texture baking\n- Differentiable rendering or reference-image reprojection\n- Generative multiview texturing\n- Blender Python automation\n- GLB/glTF production pipelines\n- Evaluating or fine-tuning 3D foundation models\n\nYou must also be capable of diagnosing whether a failure comes from camera estimation, shape reconstruction, topology, UV layout, projection, diffusion-based texture synthesis, or postprocessing—and then modifying the pipeline accordingly.\n\n### Nice to have\n\n- PBR material generation\n- ComfyUI custom-node development\n- Experience with furniture, interior objects, product visualization, or similar asset categories\n- Experience building batch-processing systems, automated quality gates, or human-in-the-loop review workflows\n- Experience with commercial deployment and license/component audits for AI models and native dependencies\n- Experience optimizing GPU memory usage, inference speed, mesh complexity, and texture resolution for production environments\n\nExperience running pretrained models is not enough by itself.\n\n## Expected deliverables\n\n- Benchmark of candidate models using the same representative test set\n- Recommended production architecture and explanation of tradeoffs\n- Working and reproducible workflow with source code\n- Versioned installation or deployment script/container\n- Automated validation and evaluation tools\n- Sample textured GLB outputs with UV and material files\n- Documentation for installation, operation, and troubleshooting\n- Inventory of model and dependency licenses\n- Recommendations for improving the system with additional data or fine-tuning\n\n## Paid evaluation task\n\nShortlisted applicants may be offered a paid technical test using several supplied object images.\n\nAny manual corrections must be documented. We are not looking for someone to manually remodel every test object.\n\n## Questions to answer in your proposal\n\n1. Share two or three relevant image-to-3D examples. Where possible, include the input image, wireframe, UV map, texture atlas, and renders from multiple angles.\n2. Explain exactly which parts of those examples you personally implemented.\n3. How would you preserve high-resolution visible texture details while still generating plausible textures for hidden surfaces?\n4. If an output looks correct from the original camera but incorrect from the top or rear, how would you determine whether the problem is geometry, camera estimation, UV projection, or texture generation?\n5. What metrics and quality gates would you use for production acceptance?\n6. Which existing models would you benchmark first, and what limitations would you expect from each?\n\n## Important application instruction\n\nWe kindly ask you to start you response with three to five sentences describing one specific image-to-3D failure you personally debugged. Include the actual cause, the change you made, and how you verified that the result improved.\n\nThis is to filter automated AI replies, non specialized on this domain. We will not have a strong look at proposal that omit this instruction, only list model names, or provide generic descriptions of AI workflows.\n\n## Engagement\n\nWe expect to begin with a paid benchmarking and architecture milestone. Successful completion can lead to a longer-term engagement covering workflow development, optimization, deployment, and continued quality improvement.",
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      "title": "Project/ task Manager – Task Tracking, Board Optimization, Acountability",
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      "description": "I am a entrepreneur in healthtech & digital products. My strength is high-level vision, product strategy, and execution. My bottleneck is structure, task management and keeping sight and priority for longer term projects.\n\nI am looking for someone to:\n-Structure my work in Monday.com or Asana.\n-Proactively update the board throughout the week.\n-Proactively keep me acountable.\n\nI am thinking one meeting to discuss upcoming the week, and informal/ easy communication throughout the week (whatsapp, voice memo's).\n\n_______________________________\nAbout my work:\nI work as a consultant ánd as a founder of a start-up. The workload is roughly 50/50.\n\nThe consultancy work consist of working for 1 or 2 clinics which specialize in all sorts of medical treatments (from dermatology to plastic surgery). I work as application manager for the central healthcare system (EHR/EPR) in the clinics. This involves solving small to major issues, making rapports, implement new features and giving advice on workflows.\nThe challenge here is the workload versus the time I have for the job. I lose sight of all that needs to be done and therefore I cannot deliver consistent quality.\nThis work pays the bills, and is therefore important to execute as efficient as possible. The productivity hours have to be maximum efficient. Wasted time costs money directly.\nI have to be on location some days of the week. I also have a own office.\n\nThe start-up is focused in the same branche. We build a chatbox and dynamic forms for the clinic’s website to reduce workload at the reception. This product is almost to be launched.\nWe are also designing a software product which can be used in dermatology clinics. We are currently preparing a grant application for this product (it has not been build yet).\n\nThen, when there is time to spare, I have a side project, which is essentially a webshop for selling home-made candles. If I can spend just a few hours a month on this, it’s enough. I.e.: If there is a spare morning, I’d love to either do some sports or work on the candle project.\n\n_______________________________\nAbout me:\nI live in Rotterdam, The Netherlands with my wife, daughter (2 yo) and cat (12 yo).\nI am positive person.\nI am professional at the things I do.\nI am pretty happy the way things go in life.\nI have large ambitions.\nI have an ADHD diagnoses.\nI have learned myself to structure work, but I do not excel in this.\nI am good at making plans, but I fail to execute in the long run.\nI do not own a television.\nI like to read, play sports and have fun times with the family.\n\n_______________________________\nImportant note: I do not read AI/ LLM generated answers to this job post. I will know, so save yourself the time if that was your intend. It's okay to be human. :)\nBudget: $200 to $300 per month.",
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}