Stories with Tag private

• Open source and self hostable/private file converter

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  Posted: 2025-04-12 12:40:13

  Verbose tl;dr

  Vert.sh is an open-source, self-hostable file conversion service. It leverages LibreOffice in the backend to handle a wide array of document, image, and presentation formats. Users can easily deploy Vert.sh using Docker and configure it to their specific needs, maintaining complete control over their data privacy. The project aims to provide a robust and versatile alternative to cloud-based conversion tools for individuals and organizations concerned about data security and vendor lock-in.

  The Vert.sh project introduces a versatile, open-source file conversion solution designed for self-hosting and private use. This locally-operated system eliminates reliance on external cloud services, ensuring data privacy and security. Vert.sh leverages the power of LibreOffice in the background, providing robust support for a wide array of document, spreadsheet, presentation, and image formats. This means users can convert files like DOCX, XLSX, PPTX, ODT, ODS, ODP, and various image types without transmitting sensitive information over the internet. The system is built with user-friendliness in mind, offering a straightforward command-line interface for direct interaction and an API for integration with other applications or workflows. Furthermore, Vert.sh is packaged as a Docker container, simplifying deployment and ensuring portability across different systems. This containerized approach streamlines installation and management, allowing users to quickly set up and maintain their private file conversion server. The project emphasizes its commitment to remaining open-source, providing transparency and allowing community contributions for ongoing improvement and expansion of its capabilities. In essence, Vert.sh empowers users to reclaim control over their file conversions, offering a secure, flexible, and locally-managed alternative to cloud-based services.

  • file conversion
  • file converter
  • Open Source
  • self-hosted
  • private
  • document conversion
  • cloud alternative
  • privacy
  • Security
  • data control
  • web application
  • online converter
  • local hosting
  • on-premise
  • offline converter

  Summary of Comments ( 66 )
  https://news.ycombinator.com/item?id=43663865

  Verbose tl;dr

  Hacker News users generally expressed enthusiasm for the open-source, self-hostable file converter Vert.sh, praising its simplicity and potential usefulness. Several commenters highlighted the benefit of avoiding uploads to third-party services for privacy and security reasons, with some mentioning specific use cases like converting ebooks. A few users questioned the project's long-term viability and maintainability given the potential complexity of handling numerous file formats and dependencies. Some also suggested alternative self-hosted solutions like Pandoc and Soffice/LibreOffice. The discussion also touched on the challenges of sandboxing potentially malicious files uploaded for conversion, with some proposing using Docker or virtual machines for enhanced security.

  The Hacker News post discussing the open-source, self-hostable file converter Vert.sh generated a moderate amount of discussion, with several commenters expressing interest in the project and exploring its potential use cases and limitations.

  Several users appreciated the simplicity and self-hostable nature of Vert.sh. One commenter highlighted the advantage of using a tool like this for sensitive data, avoiding the privacy concerns associated with uploading files to third-party online converters. Another user mentioned their existing use of Pandoc for similar conversion tasks but expressed interest in exploring Vert.sh due to its potentially streamlined interface and focus on web-based conversion. The self-hosting aspect was repeatedly praised, allowing users to maintain control over their data and avoid potential costs associated with cloud-based services.

  Some commenters discussed the technical aspects of Vert.sh. One pointed out that the project relies on LibreOffice running in the background, suggesting that users would need to have it installed and functioning correctly. This sparked a brief discussion about the resource requirements of running LibreOffice and its potential impact on performance, especially for complex conversions. Another user inquired about the possibility of containerizing Vert.sh for easier deployment and management, which another user confirmed was possible through the provided Dockerfile.

  The limitations of relying on LibreOffice were also brought up. One user questioned the efficiency of using LibreOffice for simple conversions like Markdown to HTML, suggesting that a dedicated tool might be faster. Another commenter mentioned potential issues with font handling in LibreOffice, which could affect the fidelity of converted documents.

  Finally, the discussion touched upon alternative solutions and potential improvements. One user suggested using specialized tools for specific conversion tasks, pointing out the superior performance and quality compared to a general-purpose solution like LibreOffice. Others expressed interest in features like batch conversion and direct integration with cloud storage services. While acknowledging the current limitations, several commenters expressed optimism about the project's future development and potential to become a valuable tool for privacy-conscious users.

• Complete hardware and software setup for running Deepseek-R1 locally

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  Posted: 2025-01-29 14:56:57

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  This Twitter thread details a comprehensive guide to setting up Deepseek-R1, a retrieval-based question-answering system, on a local machine. It outlines the necessary hardware, recommending a powerful GPU (like an RTX 4090) with substantial VRAM (24GB+) for optimal performance and a hefty amount of RAM (128GB or more). The guide covers software prerequisites, including CUDA, cuDNN, Python, and various libraries, along with the steps to download and install Deepseek's specific dependencies. Finally, it provides instructions on how to download and convert the Large Language Model (LLM) and retriever components, offering different options depending on available hardware resources. The thread also includes tips on configuring the setup and troubleshooting potential issues.

  The Twitter post by @carrigmat details a comprehensive guide for setting up the Deepseek-R1 AI coding assistant locally, covering both hardware and software requirements and installation. The author emphasizes the non-trivial nature of the process, particularly for those unfamiliar with such setups.

  Hardware-wise, the guide recommends a powerful machine equipped with an NVIDIA RTX 4090 GPU due to the model's substantial VRAM demands exceeding 24GB. While technically possible to run on cards with less VRAM, performance will be significantly impacted and might necessitate offloading to CPU or disk, leading to much slower processing. A high-core-count CPU is also suggested to complement the GPU, though specific recommendations aren't provided. Sufficient RAM, likely upwards of 64GB, is also implied, although not explicitly stated, given the resource-intensive nature of large language models. Storage requirements are not explicitly mentioned but likely depend on the size of the model being used.

  The software setup involves a multi-step process. Initially, users need to obtain specific versions of PyTorch and CUDA, highlighting the importance of version compatibility for optimal performance and stability. The CUDA toolkit, essential for leveraging the GPU's capabilities, must be correctly installed and configured. Additionally, transformers and accelerate libraries are required, hinting at the use of a pre-trained transformer model and utilizing the accelerate library for distributed training or optimized inference. The guide then directs users to a comprehensive "how-to" document which presumably provides detailed instructions for configuring these software components. Finally, the post suggests a specific startup command for launching Deepseek-R1, incorporating various parameters likely related to model loading, resource allocation, and other runtime configurations. This command hints at the complexity of running the model and the need for fine-tuning these parameters based on the specific hardware and desired performance. Overall, the post presents a challenging yet achievable path to running Deepseek-R1 locally, provided one has the appropriate hardware and follows the detailed instructions.

  • deepseek
  • deepseek-r1
  • local setup
  • Hardware
  • Software
  • Installation
  • configuration
  • AI
  • artificial intelligence
  • Large Language Model
  • LLM
  • Open Source
  • offline
  • private
  • self-hosted

  Summary of Comments ( 153 )
  https://news.ycombinator.com/item?id=42865575

  Verbose tl;dr

  HN users discuss the practicality and cost of running the Deepseek-R1 model locally, given its substantial hardware requirements (8x A100 GPUs). Some express skepticism about the feasibility for most individuals, highlighting the significant upfront investment and ongoing electricity costs. Others suggest cloud computing as a more accessible alternative, albeit with its own expense. The discussion also touches on the potential for smaller, quantized models to offer a compromise between performance and resource requirements, with some expressing interest in seeing benchmarks comparing different model sizes. A few commenters question the necessity of such a large model for certain tasks and suggest exploring alternative approaches. Overall, the sentiment leans toward acknowledging the impressive technical achievement while remaining pragmatic about the accessibility challenges for average users.

  The Hacker News post "Complete hardware and software setup for running Deepseek-R1 locally" has a modest number of comments, focusing primarily on the practicality and cost of running large language models (LLMs) locally. No one expresses having tried the setup described.

  One commenter points out the significant hardware requirements and associated costs, questioning the feasibility for most individuals. They highlight the need for a powerful GPU, ample RAM, and substantial storage, estimating a total cost exceeding $5,000, and potentially much higher depending on GPU choice. This commenter implicitly argues that cloud services offer a more economical alternative for most users.

  Another commenter builds on this point by suggesting that even with the necessary hardware, the ongoing electricity costs for running such a system could be substantial, further strengthening the case for cloud-based solutions. They emphasize the difference between the initial hardware investment and the less obvious but continuing power consumption expenses.

  One comment briefly mentions an alternative approach, suggesting using a smaller quantized model that could potentially run on less powerful hardware. However, they don't elaborate on specific models or performance expectations, leaving it as an open-ended suggestion.

  A further commenter notes the rapid pace of development in the LLM space, predicting that the hardware requirements for running these models locally will likely decrease over time due to ongoing optimizations and smaller model sizes. They express hope that this evolution will eventually make local deployment more accessible to a wider audience.

  Overall, the comments reflect a cautious perspective on the practicality of the proposed local setup, primarily due to the cost and resource intensiveness of running large language models. The discussion highlights the economic advantages of cloud-based solutions for most users while acknowledging the potential for future improvements in local deployment accessibility.