Legal Hold Is Here: Protect Your Business When It Matters Most
backblaze.com·1d
⚖️Legal Compliance
iOS 26 preview: Liquid Glass is more than just a visual refresh
engadget.com·21h
🖥️Vintage Macintosh
CVE-2025-25200: this Koa ReDoS is not a serious vulnerability
sjoerdlangkemper.nl·2d
📦Container Security
A new era of software engineering, cybersecurity, & AI
softwareheritage.org·1d
🏺Archaeological Computing
Leveraging AI Multi-Agent Systems in Financial Analysis
cacm.acm.org·23h
🧮Z3 Applications
Fast(er)API: Optimizing Processing Time
fabridamicelli.github.io·3d·
Discuss: Hacker News
✅Format Verification
The AI Safety Puzzle Everyone Avoids: How To Measure Impact, Not Intent.
lesswrong.com·2d·
Discuss: Hacker News
✨Effect Handlers
Fixed Ivanti Bugs Still Haunt Japan Orgs 6 Months Later
darkreading.com·1d
🔓Hacking
ChatLoopBackOff: Episode 64 (Bank-Vaults)
youtube.com·1d
🌳Archive Merkle Trees
I tested Canon’s biggest multifunction tank printer and loved it
techradar.com·1d
📸TIFF Evolution
Technical approach for classifying human-AI interactions at scale
microsoft.com·1d·
Discuss: Hacker News
🌀Brotli Internals
The Young and the Restless: Young Cybercriminals Raise Concerns
darkreading.com·1h
🛡Cybersecurity
Banking Trojan Coyote Abuses Windows UI Automation
darkreading.com·1d
🦠Malware Analysis
Reasoning about Rare-Event Reachability in Stochastic Vector Addition Systems via Affine Vector Spaces
arxiv.org·1d
🎯Performance Proofs
💡 Supercharge Your Git Workflow with AI — Introducing ai-git-cli (Open for Contributions!)
dev.to·11h·
Discuss: DEV
🌳Git Internals
Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance
arxiv.org·10h
🧠Machine Learning
10 Best AI App Builders in 2025
dev.to·1h·
Discuss: DEV
🌀Brotli Internals
Harnessing RLHF for Robust Unanswerability Recognition and Trustworthy Response Generation in LLMs
arxiv.org·1d
💻Local LLMs
Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance
arxiv.org·1d
🔗Constraint Handling
Exploring the Frontiers of kNN Noisy Feature Detection and Recovery for Self-Driving Labs
arxiv.org·1d
🧠Machine Learning