A “diff” tool for AI: Finding behavioral differences in new models - Anthropic
A “diff” tool for AI: Finding behavioral differences in new models Anthropic
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Empirical Evaluation of Structured Synthetic Data Privacy Metrics: Novel experimental framework
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Open Challenges for Secure and Scalable Wi-Fi Connectivity in Rural Areas
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Empirical Evaluation of Structured Synthetic Data Privacy Metrics: Novel experimental framework
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A technical, 100% local writeup on how I replicated and then surpassed the Secret Detection model from Wiz (and the challenges along the way) - including labeling an entire dataset with local AI
Hey everybody, I have a strong interest in offloading work to small, specialized models that I can parallelize - this lets me scale work significantly (plus, I am less dependent on proprietary APIs) Some time ago, I saw a blog post from Wiz about fine-tuning Llama 3.2-1B for secret detection in code. They got 86% Precision and 82% Recall. I wanted to see if I can replicate (or beat) those numbers using purely local AI and produce a local specialized model. After a couple of weekends of trying it out I managed to get a Llama 3.2-1B hitting 88% Precision and 84.4% Recall simultaneously! I also benchmarked Qwen 3.5-2B and 4B - expectedly, they outperformed Llama 1B at the cost of more VRAM and longer inference time. I’ve put together a full write-up with the training stats, examples, and a st
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