🔢 My Favorite Compact Hash Table
An exceptional deep dive into the art of low-level data structure engineering — perfect for developers who appreciate algorithmic precision and performance tuning.
This article explores a high-performance hash table built with Robin Hood open addressing and power-of-two sizing. Through detailed C code examples, it explains lookup, insertion, and deletion logic while optimizing memory layout for modern CPUs like x86-64, ARM64, and RISC-V. The author also considers design extensions for handling larger or dynamic key-value pairs.
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⚙️ Multiplying Our Way Out of Division
A brilliant look into how compiler optimization turns math insight into tangible speed gains — a must-read for anyone who enjoys peeking under the hood of code generation.
Matt Godbolt explains how compilers replace integer divisions by constants with faster multiply-and-shift operations. Using assembly examples and detailed walkthroughs, he shows how these optimizations eliminate division instructions for better performance. The post belongs to his ‘Advent of Compiler Optimisations 2025’ series.
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🛡️ Offline Cybersecurity AI with RAG and Local LLM (Python, FAISS, Llama 3.1)
A fascinating example of how local AI can empower security professionals to perform smart analysis while staying fully disconnected — privacy-conscious engineering at its best.
Syd is an open-source offline cybersecurity assistant built for penetration testers in air-gapped or classified setups. It uses local LLMs like Llama 3.1 through Ollama to interpret outputs from Nmap, Volatility3, Metasploit, and YARA, offering CVE lookups and action suggestions entirely offline. The project values privacy, openness, and collaboration over production readiness.
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🍏 Apple’s Slow AI Strategy Turns Into a Market Advantage
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🕹️ Rebuilding the 1996 Space Jam Website with Claude — and Why It Failed
A humorous yet sharp demonstration of multimodal AI’s spatial limitations—these systems may ‘understand’ content, but still fail at true visual geometry. A smart and entertaining critique of current AI vision models.
Jonah details his unsuccessful effort to make Anthropic’s Claude accurately recreate the 1996 Space Jam website from screenshots and assets. Despite repeated refinements and tool assistance, Claude struggled with layout precision and pixel alignment, showing overconfidence and internal inconsistencies. The author concludes that Claude’s visual model captures semantic meaning but lacks the fine-grained spatial reasoning needed for faithful web reconstruction.
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