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We write about maintainable software, debugging errors, and tools we're working on.

  • October 2019 Product Updates

    October 2019 Product Updates

    By Todd H Gardner on November 15, 2019

    We’ve got a big update about to launch for Ignore rules, but we still had some time to improve the little things last month. Here are all the things we launched:


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  • A Dumpster-Fire Alert for Your JavaScript Errors

    A Dumpster-Fire Alert for Your JavaScript Errors

    By Todd H Gardner on November 12, 2019

    Do you work with an app that’s a dumpster-fire of errors? Wishing for an appropriate alert when you need to fight down the flames? Look no further friend. Today, we’re creating a Dumpster fire notification for your JavaScript errors with Particle and TrackJS.

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  • The Ongoing State of JavaScript Errors

    The Ongoing State of JavaScript Errors

    By Todd H Gardner on November 4, 2019

    Today, we’re releasing TrackJS Global Error Statistics to the public. This aggregated production data is a useful measure of the state of client-side JavaScript errors and the quality of the web. We break it down by the most common errors, browsers, and operating systems.

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  • What is Developer Time Worth?

    What is Developer Time Worth?

    By Todd H Gardner on October 15, 2019

    It’s remarkable to me how many developers have no idea what their time is worth. I speak with a lot of developers, and when I mention my work on TrackJS, I frequently hear “I could build that”. Yeah, you could. Observability tools aren’t rocket science. But you shouldn’t. Your time is too valuable to build better mousetraps.

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  • September 2019 Product Updates

    September 2019 Product Updates

    By Todd H Gardner on October 15, 2019

    The TrackJS team was hard at working pushing out new features and improving UI responsiveness. Here are all the things we launched:


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  • Faster Elasticsearch Query Performance

    Faster Elasticsearch Query Performance

    By Eric Brandes on October 9, 2019

    We store all of our JavaScript error data in a large Elasticsearch cluster. This lets our customers slice and dice their error data in realtime, and perform full text searches over it. We push Elasticsearch to its limit, and we recently started querying more data for some of our core pages. We noticed that a certain set of our customers started experiencing unacceptably slow page response times. This is how we tracked down the problem and fixed it.

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