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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
BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).