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Hugging Face VS CodeTogether

Compare Hugging Face VS CodeTogether and see what are their differences

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Hugging Face logo Hugging Face

The AI community building the future. The platform where the machine learning community collaborates on models, datasets, and applications.

CodeTogether logo CodeTogether

Live share IDEs and coding sessions. See changes in real time.
  • Hugging Face Landing page
    Landing page //
    2023-09-19
Not present

CodeTogether is the perfect blend of functionality and simplicity, designed by a team of remote developers that rely on collaborative development. Whether you are on an Agile team that uses pair programming as part of your regular software development flow or you just like to live share your code in the occasional troubleshooting session, CodeTogether is the best tool for pair programming, mob programming, code review, and more! If youโ€™ve been using screen sharing or an online code editor for collaborative coding, youโ€™ll be amazed at the difference! Seeing is believingโ€”watch our linked videos to see CodeTogether in action.

CodeTogether

$ Details
paid Free Trial $10 / Monthly (Starter Plan, up to 25 users)
Platforms
Windows Mac OSX Linux
Release Date
2020 May

Hugging Face features and specs

  • Model Availability
    Hugging Face offers a wide variety of pre-trained models for different NLP tasks such as text classification, translation, summarization, and question-answering, which can be easily accessed and implemented in projects.
  • Ease of Use
    The platform provides user-friendly APIs and transformers library that simplifies the integration and use of complex models, even for users with limited expertise in machine learning.
  • Community and Collaboration
    Hugging Face has a robust community of developers and researchers who contribute to the continuous improvement of models and tools. Users can share their models and collaborate with others within the community.
  • Documentation and Tutorials
    Extensive documentation and a variety of tutorials are available, making it easier for users to understand how to apply models to their specific needs and learn best practices.
  • Inference API
    Offers an inference API that allows users to deploy models without needing to worry about the backend infrastructure, making it easier and quicker to put models into production.

Possible disadvantages of Hugging Face

  • Compute Resources
    Many models available on Hugging Face are large and require significant computational resources for training and inference, which might be expensive or impractical for small-scale or individual projects.
  • Limited Non-English Models
    While Hugging Face is expanding its availability of models in languages other than English, the majority of well-supported and high-performing models are still predominantly for English.
  • Dependency Management
    Using the Hugging Face library can introduce a number of dependencies, which might complicate the setup and maintenance of projects, especially in a production environment.
  • Cost of Usage
    Although many resources on Hugging Face are free, certain advanced features and higher usage tiers (like the Inference API with higher throughput) require a subscription, which might be costly for startups or individual developers.
  • Model Fine-Tuning
    Fine-tuning pre-trained models for specific tasks or datasets can be complex and may require a deep understanding of both the model architecture and the specific context of the task, posing a challenge for less experienced users.

CodeTogether features and specs

  • End-to-End Encryption
  • On-Premises
    Available
  • Cross-platform support
    Across multiple IDEs and browsers, no vendor lock-in
  • Host-provided intelligence
    Advanced content assist, validation, navigation, etc.
  • Simultaneous Coding
    Code in any group (even in the same file at the same time) or on your own
  • Shared servers, terminals & consoles
    Hosts can share servers for remote access, and terminals that optionally allow guests to execute commands
  • Run Tests & Launches
    Guests can remotely run tests and analyze results. They can also execute run configurations from the host IDE.
  • Audio/Video & Screen Sharing
    Option to invite guests that aren't part of the coding session

Analysis of Hugging Face

Overall verdict

  • Hugging Face is generally considered an excellent resource for both learning and implementing NLP technologies. Its robust and comprehensive range of tools and models support various applications, making it highly recommended in the field.

Why this product is good

  • Hugging Face is widely recognized for its contributions to the development and democratization of natural language processing (NLP). They offer a user-friendly platform with a variety of pre-trained models and tools that are highly effective for numerous NLP tasks, such as text classification, translation, sentiment analysis, and more. The community-driven approach, extensive documentation, and active forums make it accessible and supportive for both beginners and experienced users. Furthermore, Hugging Face's Transformers library is one of the most popular resources for implementing state-of-the-art NLP models.

Recommended for

  • Data scientists and machine learning engineers interested in NLP and AI.
  • Research professionals and academic institutions involved in language technology projects.
  • Developers seeking to integrate advanced language models into their applications with ease.
  • Beginners looking for accessible resources and community support in the AI and NLP space.

Hugging Face videos

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CodeTogether videos

CodeTogether: The Complete Overview to Live Sharing your IDE

Category Popularity

0-100% (relative to Hugging Face and CodeTogether)
AI
100 100%
0% 0
Code Collaboration
0 0%
100% 100
Social & Communications
100 100%
0% 0
Programming Tools
0 0%
100% 100

User comments

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Social recommendations and mentions

Based on our record, Hugging Face seems to be a lot more popular than CodeTogether. While we know about 329 links to Hugging Face, we've tracked only 4 mentions of CodeTogether. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Hugging Face mentions (329)

  • How Much Does It Cost to Self-Host Open Models on AWS?
    Download from Hugging Face with a single command. Models come in different quantization levels (compression trade-offs). A 4-bit quantized version is roughly 4x smaller than the full-precision version, with minor quality loss. For most team use cases, the quantized versions are the practical choice because they fit in less GPU memory. - Source: dev.to / 14 days ago
  • Ask HN: What are you using for LLM inference in production?
    There are a couple of options. One good way to find inference providers for open models is through hugging face (https://huggingface.co). You can select a model and see which inference providers serve it. You can even access it through hugging face. If you just wanted to test a model or have super light work you can get some free access to alot of open source models through nvidia (https://build.nvidia.com). There... - Source: Hacker News / 19 days ago
  • VIDRAFT Releases Aether-7B-5Attn: A Fully Open-Source MoE LLM with Five Heterogeneous Attention Mechanisms
    Both the base and instruct variants of Aether-7B-5Attn, plus a live interactive demo, are publicly available on Hugging Face. Search for VIDRAFT or Aether-7B-5Attn on huggingface.co to find the model cards and repository. - Source: dev.to / 28 days ago
  • Integration with Hugging Face Inference API
    Hugging Face hosts thousands of open models for NLP, vision, and other tasks. The Inference API (via Inference Providers) lets you call those models over HTTP. The @huggingface/inference package from huggingface.js is the Node.js client. - Source: dev.to / 3 months ago
  • How I built pairwise AI model compare pages with Claude Haiku and a budget cap
    Right now, I don't. If model foo is deleted from HuggingFace but its compare rows are still in the DB, those compare pages will still be served at build time. They'll have the old data until the model's row in models.json is removed โ€” which only happens if the model falls out of the top-500 in the nightly fetch. It's a known gap. For now, the risk is low; popular models don't disappear. A more robust system would... - Source: dev.to / 3 months ago
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CodeTogether mentions (4)

  • Hey! Are there any coding platforms where you can share a simple link with other people to use an app? I keep wanting to find something other than code.org (which makes sharing pretty easy and accessible to anyone)
    Looking for collaboration and advanced features? Most decent ones cost money ... Start with replit.com, also look at codeanywhere.com, and also codetogether.com (requires download, free+paid plans). Source: over 4 years ago
  • QUESTION: How to manage pair programming?
    Are you using the right tools? Screen sharing isn't great for longer sessions, and you need a code focused tool like Live Share, or one we make - CodeTogether, especially if you need to work across IDEs. Source: over 5 years ago
  • dual keyboard / mouse input?
    Just addressing the pair programming aspect of this - if you were doing this remotely, you could use something like codetogether.com Each of you would have your own machines and screens, but be looking at the same piece of code (if you want) or investigate / code in different areas of the project too. Source: over 5 years ago
  • PhpStorm 2021.1 Released: Preview for PHP and HTML Files, 20+ New Inspections, Improvements in All Subsystems, and Pair Programming via Code With Me
    If any of you are looking for a pair/mob programming solution that works across IDEs, do try codetogether.com. Host in IntelliJ, join from VS Code or Eclipse if you want. We just added the support for writeable shared terminals. Video covering all the features is here: https://youtu.be/OgCWc3hTBc0. Source: over 5 years ago

What are some alternatives?

When comparing Hugging Face and CodeTogether, you can also consider the following products

OpenAI - GPT-3 access without the wait

CodeShare.io - Realtime code sharing for developers

Eden AI - Regrouping the best AI APIs for 10mn integration in your code

Visual Studio Live Share - Real-time collaborative development

Gemini - Gemini, formerly known as Bard, is a generative artificial intelligence chatbot developed by Google. Based on the large language model (LLM) of the same name, it was launched in 2023 in response to the rise of OpenAI's ChatGPT.

Teletype for Atom - Collaborate in real time in Atom