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Error Monitoring

raygun

Seamless error monitoring with Spring Boot and Raygun

Sep 9, 2024 By Raydhitya Yoseph In Raygun
This guest post comes from long-time Raygun customer Midtrans, a leading payment gateway in Southeast Asia. As Midtrans grew, so did the number of applications requiring error monitoring. To tackle the challenges of scaling and standardizing Raygun across multiple teams and services, they created a custom Spring Boot starter for Raygun. Now, Midtrans is excited to share this open-source Spring Boot auto-configuration with the community.
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appsignal

Track Errors in Phoenix for Elixir with AppSignal

Jul 9, 2024 By Aestimo Kirina In AppSignal
AppSignal is a powerful error tracking and performance monitoring tool that can help you maintain reliability and speed in your Elixir applications. In this tutorial, the first of a two-part series, you'll learn how to integrate AppSignal into your Elixir application, configure it for error tracking, interpret error reports, and leverage AppSignal's features to debug and resolve issues.
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raygun

AI engineering for AI Error Resolution

Jun 4, 2024 By Panos Patros In Raygun
Smart engineering teams are working out how to use Large Language Models (LLMs) to solve real business problems. At Raygun, we're no exception, and we're committing our time and effort to developing AI software applications that bring value to our customers. Our first AI-powered release is AI Error Resolution (AIER), a novel Crash Reporting feature that takes debugging with ChatGPT to the next level. We know that LLMs have already dramatically increased software engineers' productivity.
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appsignal

Track Errors in Your Python Flask Application with AppSignal

May 29, 2024 By Nik Tomazic In AppSignal
In this article, we'll look at how to track errors in a Flask application using AppSignal. We'll first bootstrap a Flask project, and install and configure AppSignal. Then, we'll introduce some faulty code and demonstrate how to track and resolve errors using AppSignal's Errors dashboard. Let's get started!
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raygun

Announcing AI Error Resolution

Apr 22, 2024 By Laura Marwick In Raygun
After months of anticipation (and invaluable input from our beta testers!) we’re so excited to officially share AI Error Resolution. We can say firsthand that this tool helps developers resolve issues with renewed speed and accuracy, using AI-powered suggestions on the root cause of errors and how to fix them. Testing has shown how effectively this feature can pinpoint the source of an error and produce the most efficient method to resolve it, accelerating the entire debugging process.
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How to detect new errors in production

Apr 5, 2024 By Rollbar In Rollbar
How to improve your release quality by using Rollbar to detect new and reactivated errors from production, staging or qa environments. Go beyond crash reporting, error tracking, logging and error monitoring. Get instant and accurate alerts — plus a real-time feed — of all errors, including unhandled exceptions. Our automation-grade grouping uses machine learning to reduce noise and gives you error signals you can trust.
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datadog

Simplify production debugging with Datadog Exception Replay

Mar 15, 2024 By Candace Shamieh In Datadog
Debugging errors in production environments can frustrate your team and disrupt your development cycle. Once error tracking detects an exception, you then need to identify which specific line of code or module is responsible for the error. Without access to the inputs and associated states that caused the errors, reproducing them to find the root cause and a solution can be a lengthy and challenging process.
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raygun

Symbolicating stack traces from Apple system libraries

Dec 6, 2023 By Jared Scholz In Raygun

In the world of software development, quickly finding and fixing errors drives better experiences for both end-users and developers. One key tool in this process is the symbol map, which records debugging information that was lost in the compilation process. Symbol maps (or source maps if we're talking JavaScript) connect the code developers write to the minified code in production, making it easier to decipher crashes by pinpointing the exact source code that caused the error.

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