Japan Mino Kogyo Ransomware Attack
pbxscience.com·3d·
Discuss: Hacker News
🌐Network Security
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TLA+ Modeling of AWS outage DNS race condition
muratbuffalo.blogspot.com·2d·
Discuss: Lobsters, Hacker News
📐TLA+
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LANDFALL: New Commercial-Grade Android Spyware in Exploit Chain Targeting Samsung Devices
unit42.paloaltonetworks.com·1d·
Discuss: Hacker News
🔓Binary Exploitation
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Show HN: Turn docs into tailored self-serve playgrounds to create aha-moments
visr.dev·2d·
Discuss: Hacker News
💬Prompt Engineering
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Using CRDTs and Sync as a Database
jackson.dev·1d·
Discuss: Hacker News
🔄CRDT
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Neuromorphic Computing: The Brain-Inspired Revolution Transforming AI
eproductempire.blogspot.com·2d
🤖Cyberpunk
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Interesting SPI Routing with iCE40 FPGAs
danielmangum.com·1d·
Discuss: Hacker News
🔐Hardware Security
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10 Smart Performance Hacks For Faster Python Code
blog.jetbrains.com·3d
⚡FastAPI
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One Size Does Not Fit All: Architecture-Aware Adaptive Batch Scheduling with DEBA
arxiv.org·1d
⚡Incremental Computation
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File Chunking: Why It Matters for Cybersecurity in Modern Applications
dev.to·4d·
Discuss: DEV
🔐ChaCha20
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Gen AI Grows Up: Building Production-Ready Agents on the JVM • Rod Johnson • GOTO 2025
youtube.com·3d
📱Edge AI
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Writing a DOS Clone in 2019
medium.com·5d·
Discuss: Hacker News
🖥️Emulation
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Principal Dev's Take On Vibe-Coding
dev.to·2d·
Discuss: DEV
🎭Program Synthesis
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Show HN: AI Coding Agents: Intent-Driven Development Guidelines
github.com·2d·
Discuss: Hacker News
🎭Program Synthesis
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Autonomous Swarm Optimization for Robotic Graffiti Removal in Urban Environments
dev.to·3h·
Discuss: DEV
🤖Robotics
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Rethinking My AWS Admin Access: Lessons Learned From Moving to Single-Sign-On
dev.to·2d·
Discuss: DEV
🏠Self-Hosting
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Agents of misfortune: The world isn't ready for autonomous software
theregister.com·2d·
Discuss: Hacker News
🛡️AI Security
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Tool-Driven Behavioral Directives: How to Scale LLM Agents Without Prompt Spaghetti
dev.to·1d·
Discuss: DEV
💬Prompt Engineering
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Composer-1 vs SWE-1.5: A detailed Comparison
dev.to·3d·
Discuss: DEV
🎮Game Engines
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Improving the Performance of Radiology Report De-identification with Large-Scale Training and Benchmarking Against Cloud Vendor Methods
arxiv.org·1d
🧠Machine Learning
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