Secure Your Data Exchange with ReversingLabs & Kiteworks
reversinglabs.comΒ·4h
πŸ•ΈοΈMesh Archiving
The Copyright That Wasn't
jxself.orgΒ·16h
🏺ZIP Archaeology
New injectable gel shows promise as voice loss treatment
mcgill.caΒ·17hΒ·
Discuss: Hacker News
πŸ”ŠAcoustic Forensics
3 Best VPN for iPhone (2025), Tested and Reviewed
wired.comΒ·8h
🌐DNS over QUIC
TP-Link conducts successful Wi-Fi 8 trials, promises better reliability and lower latency
techspot.comΒ·1dΒ·
Discuss: r/technews
πŸ“‘Bluetooth Archaeology
Copy-and-Patch: A Copy-and-Patch Tutorial
transactional.blogΒ·1dΒ·
πŸ¦€Rust Macros
What's The Deal With GitHub Spec Kit
den.devΒ·2dΒ·
Discuss: Hacker News
πŸ”„Reproducible Builds
DeepMind’s New AI Is A Self-Taught Genius
youtube.comΒ·1d
πŸ”²Cellular Automata
Scientists achieve real-time control of quantum uncertainty using ultra-fast light
techspot.comΒ·1d
πŸ”¬Optical Physics
One-Punch Man season 3 is here, but is it still one of the best anime shows?
techradar.comΒ·10h
πŸ΄β€β˜ οΈPiracy
Intel unveils Crescent Island, an inference-only GPU with Xe3P architecture and 160GB of memory
tomshardware.comΒ·2h
πŸ–₯️Modern Terminals
Interactive Atmospheric Composition Emulation for Next-Generation Earth System Models
arxiv.orgΒ·15h
πŸ“œDocument Physics
Do LLMs Know They Are Being Tested? Evaluation Awareness and Incentive-Sensitive Failures in GPT-OSS-20B
arxiv.orgΒ·1d
πŸ”Concolic Testing
🧠 Day 9 β€” Back After 34 Days! Restarting My DSA + System Design Journey πŸš€
dev.toΒ·23hΒ·
Discuss: DEV
🌳Trie Structures
Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italy
arxiv.orgΒ·1d
🧠Machine Learning
DualResearch: Entropy-Gated Dual-Graph Retrieval for Answer Reconstruction
arxiv.orgΒ·1d
πŸ”Information Retrieval
Beyond Single-Granularity Prompts: A Multi-Scale Chain-of-Thought Prompt Learning for Graph
arxiv.orgΒ·1d
πŸ”’Denotational Semantics
Show HN: I've built a prototype for a physical e-ink todo list with voice input
github.comΒ·11mΒ·
Discuss: Hacker News
πŸŽ™οΈWhisper
Performance of Machine Learning Methods for Gravity Inversion: Successes and Challenges
arxiv.orgΒ·15h
πŸŒ€Differential Geometry