Join us

ContentUpdates and recent posts about Magika..
 Activity
@thomas-byern started using tool Docker , 2 weeks, 4 days ago.
 Activity
@thomas-byern started using tool Caddy , 2 weeks, 4 days ago.
Story
@laura_garcia shared a post, 2 weeks, 5 days ago
Software Developer, RELIANOID

🔐 RELIANOID Load Balancer – Security Contributions

At RELIANOID, we actively and selflessly contribute to improving global cybersecurity, staying true to our open-source spirit. 🤝 We maintain close collaborations with security platforms, forums, and threat-intelligence communities, sharing our expertise to help strengthen protection across the Inter..

abuseipdb contributor relianoid
 Activity
@tiennm99 started using tool Java , 2 weeks, 5 days ago.
 Activity
@tiennm99 started using tool Go , 2 weeks, 5 days ago.
Story
@laura_garcia shared a post, 2 weeks, 6 days ago
Software Developer, RELIANOID

📍 RELIANOID at Bett UK 2026

We’re excited to take part in Bett UK 2026, the world’s leading EdTech event, bringing together educators, innovators, and decision-makers shaping the future of education. 🗓 January 21–23, 2026 📍 London, United Kingdom Join us to discover how RELIANOID enables secure, scalable, and highly available ..

bett_uk_event_london_2026_relianoid
 Activity
@nagarjun-avala started using tool Kubernetes , 2 weeks, 6 days ago.
 Activity
@nagarjun-avala started using tool GitHub Actions , 2 weeks, 6 days ago.
 Activity
@nagarjun-avala started using tool Docker , 2 weeks, 6 days ago.
Story
@laura_garcia shared a post, 3 weeks ago
Software Developer, RELIANOID

🚀 If you’re building AI systems, reliability is no longer optional

Many teams are rushing to adopt AI, but few are asking the most critical question: 👉 What happens when AI fails? Back in December, we published an article that remains more relevant than ever: AI is redefining Site Reliability Engineering (SRE). Why? Because AI inference workloads introduce new reli..

Magika is an open-source file type identification engine developed by Google that uses machine learning instead of traditional signature-based heuristics. Unlike classic tools such as file, which rely on magic bytes and handcrafted rules, Magika analyzes file content holistically using a trained model to infer the true file type.

It is designed to be both highly accurate and extremely fast, capable of classifying files in milliseconds. Magika excels at detecting edge cases where file extensions are incorrect, intentionally spoofed, or absent altogether. This makes it particularly valuable for security scanning, malware analysis, digital forensics, and large-scale content ingestion pipelines.

Magika supports hundreds of file formats, including programming languages, configuration files, documents, archives, executables, media formats, and data files. It is available as a Python library, a CLI, and integrates cleanly into automated workflows. The project is maintained by Google and released under an open-source license, making it suitable for both enterprise and research use.

Magika is commonly used in scenarios such as:

- Secure file uploads and content validation
- Malware detection and sandboxing pipelines
- Code repository scanning
- Data lake ingestion and classification
- Digital forensics and incident response