![]() We did a lot of customer discovery, and found a new direction - code search - that could leverage our AI technology and bottoms-up developer footprint.īut after seven years of intense work and early-stage-startup stress, our team was too tired to pursue that pivot, and we decided to find a soft landing. making their developers 18% faster when writing code did not resonate strongly enough. Their manager might, but engineering managers only want to pay for discrete new capabilities, i.e. Our diagnosis is that individual developers do not pay for tools. Our 500k developers would not pay to use it. ![]() Then, our product failed to generate revenue. We executed very well here, and grew our user base to 500,000 monthly-active developers, with almost zero marketing spend. It took many iterations and heavy engineering lifts to get there. We did not reach product-market fit until 2019, five years after starting the company. We sequenced building our business in the following order: First we built our team, then the product, then distribution, and then monetization.īecause our product was very difficult to build, we began by building a world-class engineering team. We failed to build a business because our product did not monetize, and it took too long to figure that out. Nonetheless, we could have built a successful business without 10×’ing developer productivity using AI, and we did not do that. It may cost over $100 million to build a production-quality tool capable of synthesizing code reliably, and nobody has tried that quite yet. We made some progress towards better models for code, but the problem is very engineering intensive. The largest issue is that state-of-the-art models don’t understand the structure of code, such as non-local context. ![]() As of late 2022, Copilot shows a lot of promise but still has a long way to go. You can see this in Github Copilot, which is built by Github in collaboration with Open AI. We built the most-advanced AI for helping developers at the time, but it fell short of the 10× improvement required to break through because the state of the art for ML on code is not good enough. While we built next-generation experiences for developers, our business failed in two important ways.įirst, we failed to deliver our vision of AI-assisted programming because we were 10+ years too early to market, i.e. Thank you to everyone who used our product, and thank you to our team members and investors who made this journey possible. We have stopped working on Kite, and are no longer supporting the Kite software. The source code is available on GitHub.From 2014 to 2021, Kite was a startup using AI to help developers write code. P圜harm Community Edition is distributed under Apache 2 license.There is also an Academic license, which is discounted for other educational use. P圜harm Professional Edition is free for open-source projects and for some educational uses.The Open Source variant is released under the name Community Edition – while the commercial variant, Professional Edition, contains closed-source modules. P圜harm became Open Source on 22 October 2013. Version 2.0 was released on 13 December 2011, version 3.0 was released on 24 September 2013, and version 4.0 was released on November 19, 2014. The beta version of the product was released in July 2010, with the 1.0 arriving 3 months later. P圜harm was released to the market of the Python-focused IDEs to compete with PyDev (for Eclipse) or the more broadly focused Komodo IDE by ActiveState. Scientific tools integration: integrates with IPython Notebook, has an interactive Python console, and supports Anaconda as well as multiple scientific packages including Matplotlib and NumPy.Version control integration: unified user interface for Mercurial, Git, Subversion, Perforce and CVS with changelists and merge.Integrated unit testing, with line-by-line coverage.Support for web frameworks: Django, web2py and Flask.Python code refactoring: including rename, extract method, introduce variable, introduce constant, pull up, push down and others. ![]()
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