
Super Maven vs GitHub Copilot: AI Code Completion Comparison This video features a detailed review of Super Maven, which is touted as the fastest AI code assistant, three times faster than GitHub Copilot. A standout difference is Super Maven's 300,000-token context window, allowing for higher code accuracy. The video also introduces Super Maven's creator, Jacob Jackson. Super Maven utilizes its own efficient neural network architecture rather than transformers. This architecture manages the vast context window without sacrificing speed or cost. In comparison tests, Super Maven was noted to produce highly accurate code suggestions, even with limited contexts, and demonstrated faster response times. I also explain how to access Super Maven through its VS code extension and join a free trial. While GitHub Copilot brought AI into coding, the video concludes that with its state-of-the-art design, Super Maven shows there is still considerable room for disruption in the AI coding tool market. 00:00 Introduction to Super Maven: The Fastest Code Co-Pilot 00:10 Comparing Super Maven and GitHub Copilot 01:11 The Power of Super Maven's 300,000 Token Context Window 01:39 Meet the Creator of Super Maven 02:00 Super Maven's Performance and Speed 03:00 Super Maven's Unique Neural Network Architecture 03:29 Getting Started with Super Maven 03:51 Conclusion: Super Maven's Impact on AI Coding 04:08 Call to Action and Video Wrap Up Links: https://supermaven.com/
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--- type: transcript date: 2024-02-24 youtube_id: JhmdYN1wbG0 --- # Transcript: Supermaven: The Fastest Copilot with a 300,000-Token Context Window! in this video I'm going to be showing you super Maven which is by far the fastest Cod and co-pilot that I've tried if we look at the results of the different coding tools out there this is three times faster than GitHub co-pilot one of the stand out features with super Maven is its 300,000 token Contex window if we look at the chart of comparison the big one that a lot of people are going to be comparing super Maven to is co-pilot it allows you to pass in more than 36 times the number of tokens that co-pilot allows you being able to pass in this many tokens into the context to get your code completion you're going to be able to reference other parts of your code and have higher accuracy I've used GitHub copilot for quite a long time now as soon as I installed this the first thing that I noticed is the latency super Maven really is much faster than GitHub co-pilot I have a video on GitHub co-pilot where I went over it quite a while ago and I've been using it ever since it does allow you to have really good code completion but this looks to take it a step further we're starting to enter an era where these response times back from these AI systems are going to be considerably faster so things like the grock chips that we're now starting to see with their lpus being deployed as well as overall improvements across how inference Works how do they get away with being able to pass in this 300,000 tokens one of the things super Maven is that they've developed and trained from scratch a new neural network which is more efficient than Transformers this architecture is designed for integrating information across long context Windows this is what allows them to have the 300,000 token context window while still keeping the cost and latency the same as a Transformer with a 4,000 token context window now a little bit about the Creator behind super MAV Jacob Jackson is formerly a researcher at open aai and he also started tab9 which was really a Trailblazer within code completion before these coding tools like GitHub co-pilot and GPT 3.5 and gp4 came out tab9 was at the Forefront of all of this when it came out it was one of the first tools to leverage deep learning Within code completion the heart of super maven's Innovation lies within its 300,000 token context window GitHub co-pilot recently expanded its context window to 8,192 tokens while GitHub copilot is an impressive tool the context window does pale in comparison to Super M's capacity to understand and integrate information from a vastly larger portion of your codebase immense context window allows super Maven to deliver highly accurate code suggestions for example in test where both super Maven and cod pilot were asked to generate a function with limited context super Maven succeeded in producing the compatible code by leveraging that broader understanding of the code base in terms of performance super Maven is not only smart but it's incredibly fast its latency as demonstrated in the response times within the chart are only 250 milliseconds have co-pilot lagged with it being about three times slower so this speed makes super Maven not only efficient but significantly enhances the coding experience by reducing in weight times super Maven employs a novel neural network architecture that outperforms the Transformer models commonly used today in tools like GitHub co-pilot this architecture is the key to managing the vast context window without sacrificing speed or cost but also as a sequence of edit like a get diff this perspective allows it to quickly grasp the developer intent and assist in real time this feature is going to be particularly beneficial when you're doing something like refactoring your code base so how can you get started with super Maven super Maven is currently available for download within the extension Marketplace on VSS codes once you've installed it you can go ahead sign up for an account and a free trial on their website you will need a credit card to get started so you can go ahead try this out see if you like it and then decide from there now in terms of the pricing it's $10 a month or $99 a year to use this tool to wrap up GitHub co-pilot has paved the way for AI encoding super Maven under Jacob Jackson's vision is setting a new Benchmark it's unpar paralled context window it's Innovative architecture challenges the status quo providing there's still plenty room for disruption if you found this video useful please like comment share and subscribe and otherwise until the next one
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