Updates and recent posts about Magika..

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@rafidbottler gave 🐾 to Android Architecture: Components, Patterns & Best Practices Guide , 3 days, 18 hours ago.

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@rafidbottler gave 🐾 to Artificial Intelligence vs Machine Learning: What's the Difference? , 3 days, 18 hours ago.

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@anjali5 shared a link, 4 days, 14 hours ago

How to Fix Developer Productivity at 50+ Engineers

You ship a feature. It works. A week later, someone asks why it's not in staging yet, and you realize it's behind an infrastructure request that's still in review. The ticket isn't urgent enough to escalate. It's also not small enough to ignore. So it waits.

That's what a developer productivity problem feels like at 50 engineers.

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@laura_garcia shared a post, 4 days, 14 hours ago

Software Developer, RELIANOID

AI Reliability Engineering: The New Era of SRE

🤖 As AI becomes part of critical business operations, reliability is no longer just an infrastructure concern. From latency and model drift to observability and trust, AI workloads introduce a new set of challenges for modern SRE teams. In our latest article, we look at how reliability engineering i..

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@pluto_native started using tool Terraform , 1 week ago.

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@pluto_native started using tool Kubernetes , 1 week ago.

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@pluto_native started using tool Google Cloud Platform , 1 week ago.

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@pluto_native started using tool Amazon Web Services , 1 week ago.

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@eon01 shared a link, 1 week ago

Founder, FAUN.dev

A curated list of free AI models, APIs, and tools you can use without paying a cent.

Running AI shouldn't require a credit card. This list curates genuinely free models — open-weight models you can self-host, free API tiers from major providers, and tools to run everything locally.

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@eon01 added a new tool Unsloth , 1 week ago.

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