This list encompasses a range of engineering tools designed to enhance productivity and streamline processes in various fields of engineering. These tools are essential for tasks such as design, analysis, and project management, providing engineers with the resources needed to tackle complex challenges effectively.
Find the top 1% of engineers on GitHub by filtering by commit activity, weekend commit activity, cleaned up locations, and project contributions.
Amor makes it simple to answer "which front-end engineers committed to nextjs/astro/remix/react repos, commits regularly, and lives in NYC?" or any similar questions you might have as an engineering manager, recruiter, or sourcer looking for the perfect candidate for your role.
Save hours of time writing custom scripts or hunting around LinkedIn for data that's hidden in commit history.
LoadTester is a modern performance testing platform designed to help engineering teams understand how their applications behave under real-world traffic conditions. Instead of relying on complex setups, custom scripts, or heavy infrastructure, it provides a streamlined, browser-based experience that allows users to create, run, and analyze load tests in minutes. The platform focuses on simplicity without sacrificing power, enabling teams to validate performance before releases, campaigns, or traffic spikes. By eliminating the need to provision servers or manage distributed workers manually, LoadTester removes one of the biggest barriers to consistent and reliable performance testing.
At its core, LoadTester supports HTTP and API load testing with a clean and intuitive workflow. Users can define test scenarios by specifying endpoints, request methods, headers, and payloads, then configure how traffic should be generated using virtual users or requests per second. Once a test is launched, results are streamed live, giving immediate visibility into key performance metrics such as latency, throughput, and error rates. This real-time feedback allows teams to quickly identify bottlenecks and performance issues without waiting for reports after the test completes. Metrics like p50, p95, and p99 latency provide a detailed understanding of how the system performs under different load conditions.
One of the standout features of LoadTester is its ability to set performance thresholds and automate decision-making. Teams can define acceptable limits for latency, error rates, and success rates, and the system will automatically stop tests if those thresholds are exceeded. This makes it especially useful as a release gate in CI/CD pipelines, where performance regressions need to be detected early. Instead of manually analyzing results, teams can rely on clear pass-or-fail outcomes, ensuring that only stable and performant builds move forward in the deployment process.
LoadTester is also built with scalability and speed in mind. Tests can start in seconds, with distributed workers automatically handling the load generation behind the scenes. This allows users to simulate thousands of virtual users or high request rates without worrying about infrastructure. The platform is designed to handle both small-scale checks and large, high-intensity tests, making it suitable for startups as well as larger engineering teams. Its ability to run tests directly from a browser or integrate with automated pipelines ensures flexibility in how teams incorporate performance testing into their workflows.
Another important aspect of LoadTester is its focus on repeatability and comparison. Teams can run tests regularly, schedule baselines, and compare results across different runs to detect performance changes over time. This historical perspective is essential for understanding trends, identifying regressions, and ensuring that improvements are actually delivering value. With built-in analytics and export options, results can be easily shared across teams, improving collaboration and visibility.
Ultimately, LoadTester is designed to make performance testing accessible, fast, and actionable. By combining real-time analytics, automated thresholds, and seamless integration into development workflows, it empowers teams to make confident decisions about their applications. Whether validating a new feature, preparing for a product launch, or ensuring API reliability, LoadTester provides the tools needed to understand system limits and maintain high performance without unnecessary complexity.
DevIntern takes work from raw idea to merged pull request inside the tools your team already uses. @devintern/pm turns rough task descriptions, a paragraph of notes, a design, a bug report, into well-specced tickets your engineers can actually start from. @devintern/code picks those tickets up, implements them with your AI agent of choice, self-reviews its own diffs before a human sees them, and responds to reviewer comments on the resulting PR automatically.
The problem. AI has made writing code fast, but everything around it (drafting the spec, opening the PR, replying to review nits, keeping the tracker in sync) still takes hours. That overhead is where the productivity gains evaporate. DevIntern compresses the whole cycle so the speed your AI tooling already gives you turns into shipped tickets, not just faster keystrokes. It also opens a path for non-technical teammates (PMs, designers, founders, support) to ship real code and features end-to-end, without waiting on engineering bandwidth.
Who it's for. Engineering orgs that want their whole team to get measurably more effective without a multi-quarter platform migration. And individual contributors (engineers, PMs, founder-operators) who just want their own work to get done quicker, starting today, from their own laptop. Bring your own AI provider and keys. Runs locally for a single operator, or unattended on a server to drain the backlog 24/7. One-time perpetual licenses, not a subscription.
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Frequently asked questions
Amor is a powerful tool that helps engineering managers, recruiters, and sourcers identify the top 1% of engineers on GitHub by analyzing commit activity, project contributions, and locations. It simplifies the process of finding candidates who are actively contributing to specific repositories, such as nextjs, astro, remix, or react, saving users hours of time that would otherwise be spent writing custom scripts or searching through LinkedIn for hidden data in commit histories.
Using engineering tools like Amor can significantly enhance productivity by streamlining the recruitment process. It allows users to quickly filter and identify qualified candidates based on specific criteria, such as their commit activity and contributions to relevant projects. This targeted approach not only saves time but also increases the chances of finding the right fit for engineering roles.
Amor filters engineers by analyzing their commit activity on GitHub, including regular contributions, weekend activity, and the specific projects they have worked on. This allows users to pinpoint engineers who are not only active but also have relevant experience in the technologies and frameworks that matter for their projects.
