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@nelly96 shared a post, 6 days, 12 hours ago
Marketing specialist, Winston AI

How Accurate Are AI Detectors? (What the Data Actually Shows in 2026)

Do you also wonder, “Are AI detectors accurate?” and think the answer is a simple yes or no? The problem lies in the expectation. AI detectors don’t work like switches. They assign a probability of the text being AI-generated. The job of an AI detector is to estimate the likelihood, not to give verdicts. 

how-accurate-are-AI-detectors
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@nelly96 started using tool Winston AI , 6 days, 12 hours ago.
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@nelly96 added a new tool Winston AI , 6 days, 13 hours ago.
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@laura_garcia shared a post, 6 days, 18 hours ago
Software Developer, RELIANOID

🌍 In case you missed it

the $26 billion losses caused by global tech outages in 2025 highlight a hard truth — our digital infrastructure is more fragile than we’d like to believe. In this article, I dive into the real impact of these failures, the key lessons for businesses, and how RELIANOID actively contributes to preven..

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@laura_garcia shared a post, 1 week ago
Software Developer, RELIANOID

RELIANOID aligned with ISO/IEC 15408 (Common Criteria) principles

At RELIANOID, security is not just a feature — it’s a design principle. Our load balancing platform and organizational controls are aligned with ISO/IEC 15408 (Common Criteria), the internationally recognized framework for evaluating IT security in government and critical infrastructure environments..

ISOIEC 15408 common criteria COMPLIANCE RELIANOID
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@laura_garcia shared a post, 1 week, 1 day ago
Software Developer, RELIANOID

Chicago Cybersecurity Conference 2026

Chicago, USA | Jan 29, 2026 A must-attend event for CISOs and security leaders tackling today’s cyber threats. Expert insights, executive panels, up to 10 CPEs — and meetRELIANOIDsupporting secure and resilient application delivery. #Cybersecurity #CISO #FutureCon #ChicagoEvents #InfoSec #RELIANO..

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@varbear shared a link, 1 week, 3 days ago
FAUN.dev()

Replacing Protobuf with Rust to go 5 times faster

PgDog ditched Protobuf for raw C-to-Rust integration inpg_query.rs. The new setup usesbindgenand recursive FFI wrappers - no serialization, no handoffs. The payoff? Query parsing is 5× faster. Deparsing hit 10×. Evenpgbenchsaw a 25% bump across major ops... read more  

Replacing Protobuf with Rust to go 5 times faster
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@varbear shared a link, 1 week, 3 days ago
FAUN.dev()

A Social Filesystem

The AT Protocol flips social apps inside out. Instead of locking posts and profiles inside platform silos, it treats them as files -JSON-based records, stored in your own decentralized, app-neutral repo. Everything you do - posts, follows, likes - gets logged as a signed, timestampedrecordin your pe.. read more  

A Social Filesystem
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@varbear shared a link, 1 week, 3 days ago
FAUN.dev()

YOLO Mode: Hidden Risks in Claude Code Permissions

A scrape of 18,470 Claude Code configs on GitHub shows a pattern: developers are handing their AI agents the keys to the castle. Unrestricted file, shell, and network accessis common. Among them: - 21.3% let Claude runcurl - 14.5% allowarbitrary Python execution - 19.7% give itgit pushprivileges Tha.. read more  

YOLO Mode: Hidden Risks in Claude Code Permissions
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@varbear shared a link, 1 week, 3 days ago
FAUN.dev()

ASCII characters are not pixels: a deep dive into ASCII rendering

A fresh take on programmatic ASCII rendering brings inhigh-dimensional shape vectors,supersampling, andcontrast tricksto keep edges crisp and animations clean. Under the hood:k-d tree nearest-neighbor lookups,vector quantization, andGPU-powered samplinghelp push sharp ASCII frames without tanking pe.. read more  

ASCII characters are not pixels: a deep dive into ASCII rendering
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