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Selfhosted
- Any MythTV Users Here?
As a long-term MythTV user, I read all the discussion about Plex vs Jellyfin, but I'm still here... recording Live TV, watching films, listening to "me choonz" all on free, open-source software. What am I missing? Any other MythTV users out there?
- New apartment Internet has no port forwarding, admin login
Pro: 1Gb upload and download speeds on free Internet provided by the HOA. Con: As a self hoster, I have zero control over it. No port forwarding, no DMZ, no bridge mode. It's Starbucks free WiFi with a wired connection.
Option A: Buy Google Fiber and don't use free Internet. Option B: Create some elaborate tunnel through a VPS.
My public self hosted activities are fairly low bandwidth (password manager, SSH). I have a vague idea that I could point my domain to a low cost VPS that has a VPN tunnel into my home network for any incoming connection needs. That may require me to fill in port forwards on both systems but whatever. Tailscale is serving most of my remote needs but I still need a few ports. This does not fix the issue of online gaming port forwards (Nintendo Switch online requires a huge forwarded range for best performance) but oh well for now.
- Codeintegrity-ai/mutahunter: Open Source, Language Agnostic LLM-based mutation testing toolgithub.com GitHub - codeintegrity-ai/mutahunter: Open Source Language Agnostic LLM-based mutation testing tool
Open Source Language Agnostic LLM-based mutation testing tool - codeintegrity-ai/mutahunter
Check out our open-source, language-agnostic mutation testing tool using LLM agents here: https://github.com/codeintegrity-ai/mutahunter
Mutation testing is a way to verify the effectiveness of your test cases. It involves creating small changes, or “mutants,” in the code and checking if the test cases can catch these changes. Unlike line coverage, which only tells you how much of the code has been executed, mutation testing tells you how well it’s been tested. We all know line coverage is BS.
That’s where Mutahunter comes in. We leverage LLM models to inject context-aware faults into your codebase. As the first AI-based mutation testing tool, Our AI-driven approach provides a full contextual understanding of the entire codebase by using the AST, enabling it to identify and inject mutations that closely resemble real vulnerabilities. This ensures comprehensive and effective testing, significantly enhancing software security and quality. We also make use of LiteLLM, so we support all major self-hosted LLM models
We’ve added examples for JavaScript, Python, and Go (see /examples). It can theoretically work with any programming language that provides a coverage report in Cobertura XML format (more supported soon) and has a language grammar available in TreeSitter.
Here’s a YouTube video with an in-depth explanation: https://www.youtube.com/watch?v=8h4zpeK6LOA
Here’s our blog with more details: https://medium.com/codeintegrity-engineering/transforming-qa-mutahunter-and-the-power-of-llm-enhanced-mutation-testing-18c1ea19add8
Check it out and let us know what you think! We’re excited to get feedback from the community and help developers everywhere improve their code quality.