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Practical writing on software architecture, SaaS products, AI automation, legacy modernisation, and the business of building reliable systems.

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Curated links from external sources — not 360Softy original articles.

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The Hacker News

Ransomware Groups Turn to Citrix Bleed 2, BYOVD, and Supply Chain Credentials

Threat actors associated with the Anubis ransomware operation have been observed exploiting the Citrix Bleed 2 (CVE-2025-5777) vulnerability to obtain initial access. "Although tactics differ between affiliates, common patterns emerged in tradecraft through use of legitimate Remote Management and Monitoring (RMM) tooling, credential access, and hands-on-keyboard procedures used for lateral

The Hacker NewsRead original
ExternalTechnology Trends
The Verge Tech

Weber marks down grills and griddles to their best prices ever for July 4th

The Weber Spirit E-325 grill with three burners. | Image: Weber If our recent Decoder interview with Weber Blackstone CEO Roger Dahle has you craving freshly grilled meats or veggies, Weber just so happens to have a variety of grills, smokers, griddles, and accessories selling at big discounts ahead of the July 4th holiday. It makes a bunch of models, but we’ve landed on a range of grills and griddles at varying price points. Weber is cheaper than other retailers by about $50 on most of these

The Verge TechRead original
ExternalSoftware Engineering
GitHub Changelog

Copilot agent session streaming is now in public preview

GitHub Enterprise Cloud customers with enterprise managed users can now access GitHub Copilot agent session data across all Copilot clients, including: Cloud agents operating on github.com and data resident deployments… The post Copilot agent session streaming is now in public preview appeared first on The GitHub Blog.

GitHub ChangelogRead original
ExternalSoftware Engineering
DZone

Real-Time AI Feature Engineering With Spark Structured Streaming and Databricks Feature Store

The Feature Engineering Problem Feature engineering is where most ML projects silently fail in production. Not because the model is wrong — but because the features the model sees at training time are different from the features it sees at inference time. This is called training-serving skew, and it's the #1 silent killer of ML systems. Three specific failure modes cause it:

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