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CodeTogether VS Apple Machine Learning Journal

Compare CodeTogether VS Apple Machine Learning Journal and see what are their differences

Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

CodeTogether logo CodeTogether

Live share IDEs and coding sessions. See changes in real time.

Apple Machine Learning Journal logo Apple Machine Learning Journal

A blog written by Apple engineers
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.

  • Apple Machine Learning Journal Landing page
    Landing page //
    2022-12-13

CodeTogether

$ Details
paid Free Trial $10.0 / Monthly (Starter Plan, up to 25 users)
Platforms
Windows Mac OSX Linux
Release Date
2020 May

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

Apple Machine Learning Journal features and specs

  • Expert Insight
    The journal provides in-depth insights from Apple's own machine learning experts, offering unique and valuable perspectives on the latest research and applications in the field.
  • Practical Applications
    The content often focuses on real-world applications and implementations of machine learning within Apple's ecosystem, making it highly relevant for practitioners.
  • High-Quality Content
    The articles in the journal are meticulously reviewed and curated, ensuring high-quality and reliable information.
  • Cutting-Edge Research
    Readers get early access to cutting-edge research and innovations directly from Apple's R&D teams.
  • Free Access
    The journal is freely accessible to the public, removing barriers for anyone interested in learning from industry leaders.

Possible disadvantages of Apple Machine Learning Journal

  • Apple-Centric
    The focus is predominantly on Apple's ecosystem, which may limit the applicability of some insights and solutions for those working with other platforms.
  • Infrequent Updates
    The journal does not publish new content as frequently as some other machine learning blogs or journals, potentially limiting its usefulness for staying up-to-date with the latest in the field.
  • Technical Depth
    While the technical rigor is generally high, this can make the content less accessible to beginners or those without a strong background in machine learning.
  • Limited Interactivity
    The journal primarily provides static articles and lacks interactive elements or community features such as forums or comment sections for reader engagement.
  • Bias Towards Proprietary Solutions
    The solutions and approaches advocated often align closely with Apple's proprietary technologies, which may not always be applicable or optimal for all contexts and use cases.

Analysis of Apple Machine Learning Journal

Overall verdict

  • Yes, the Apple Machine Learning Journal is considered a valuable resource for those interested in applied machine learning, particularly in the context of consumer technology. The content is generally well-regarded for its quality and relevance to ongoing developments in the field.

Why this product is good

  • The Apple Machine Learning Journal offers insights into the cutting-edge machine learning advancements and applications at Apple. It features articles and research papers from Apple's machine learning teams, showcasing practical implementations in real-world products. This makes it an excellent resource for understanding how theoretical ML concepts are applied in industry settings.

Recommended for

  • Machine learning practitioners looking for industry applications of ML
  • Data scientists interested in Apple's ML innovations
  • Researchers seeking inspiration for practical ML implementations
  • Students learning about real-world applications of machine learning

CodeTogether videos

CodeTogether: The Complete Overview to Live Sharing your IDE

Apple Machine Learning Journal videos

No Apple Machine Learning Journal videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to CodeTogether and Apple Machine Learning Journal)
Code Collaboration
100 100%
0% 0
AI
0 0%
100% 100
Programming Tools
100 100%
0% 0
Developer Tools
0 0%
100% 100

User comments

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Social recommendations and mentions

Based on our record, Apple Machine Learning Journal should be more popular than CodeTogether. It has been mentiond 9 times since March 2021. 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.

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: about 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

Apple Machine Learning Journal mentions (9)

  • Why Appleโ€™s New Tools Are More Useful Than Hype
    Apple Machine Learning Research (papers, blog, research updates): Https://machinelearning.apple.com/ Https://ark-aquatics.com Https://anti-agingstore.com Https://androidtoitaly.com Https://amlaformulatorsschool.com. - Source: dev.to / 7 months ago
  • SimpleFold: Folding Proteins Is Simpler Than You Think
    Apple has an ML research group. They do a mixture of obviously-Apple things, other applications, generally useful optimizations, and basic research. https://machinelearning.apple.com/. - Source: Hacker News / 10 months ago
  • Apple Intelligence Foundation Language Models
    Https://machinelearning.apple.com Fun fact: Their first paper, Improving the Realism of Synthetic Images (2017; https://machinelearning.apple.com/research/gan), strongly hints at eye and hand tracking for the Apple Vision Pro released 5 years later. - Source: Hacker News / almost 2 years ago
  • Does anyone else suspect that the official iOS ChatGPT app might be conducting some local inference / edge-computing? [Discussion]
    For your reference, Apple's pages for Machine Learning for Developers and for their research. The Apple Neural Engine was custom designed to work better with their proprietary machine learning programs -- and they've been opening up access to developers by extending support / compatibility for TensorFlow and PyTorch. They've also got CoreML, CreateML, and various APIs they are making to allow more use of their... Source: about 3 years ago
  • Which papers should I implement or which Projects should I do to get an entry level job as a Computer vision engineer at MAANG ?
    We even host annual poster sessions of those PhD internโ€™s work while at our company, and itโ€™ll give you an idea of the caliber of work. It may not be as great as Nvidia, Stryker, Waymo, or Tesla (which are not part of MAANG but I believe are far more ahead in CV), but itโ€™s worth of considering. Source: about 3 years ago
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What are some alternatives?

When comparing CodeTogether and Apple Machine Learning Journal, you can also consider the following products

CodeShare.io - Realtime code sharing for developers

Amazon Machine Learning - Machine learning made easy for developers of any skill level

Visual Studio Live Share - Real-time collaborative development

Machine Learning Playground - Breathtaking visuals for learning ML techniques.

Teletype for Atom - Collaborate in real time in Atom

Lobe - Visual tool for building custom deep learning models