After three months of experimenting with quantization, pruning, and knowledge distillation, I compressed a 7B parameter language model by 85% while maintaining 96.3% of its original accuracy. This technical breakdown shares the specific techniques, tools, and performance benchmarks that made aggressive compression possible without destroying model utility.
After testing 500 articles through four major AI detection tools, I discovered false positive rates exceeding 20% and techniques that reliably fool even the best detectors. The accuracy claims don't match reality, and the implications for educators and publishers are more troubling than I expected.
Most AI speech recognition tools work great for native English speakers but fail dramatically with accented speech. I tested Whisper, Google Cloud Speech, Azure, and AWS Transcribe across eight accent profiles to find which tools actually understand non-native English speakers in real-world business contexts.
I tested AWS Comprehend, Google Cloud Natural Language, Azure Text Analytics, and MonkeyLearn on 5,000 real reviews from Amazon, Yelp, and Reddit. The results revealed surprising gaps between marketing claims and actual performance, especially on sarcasm, mixed emotions, and platform-specific language.
I tested 500 articles through GPTZero, Originality.ai, and Winston AI to determine which AI content detector actually works. The results revealed shocking false positive rates between 22-43%, with even pre-2021 human content flagged as AI-generated, proving these tools aren't ready for high-stakes decisions.
