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Quantization Deep Dive: FP8 Training, FP4, and the Outlier Problem
A technical guide to LLM quantization: FP8 training, NVFP4 and MXFP4, W4A4 inference, the outlier problem, and where low-bit precision quietly breaks accuracy.
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Curated technical papers and hands-on implementation guides for the modern AI engineer.
The LLM Evaluation Crisis: Contamination, Saturation, and the Judge Problem
LLM evaluation is breaking down: benchmark saturation, contamination, and biased LLM-as-a-judge setups make leaderboard numbers misleading. Here is what to measure instead.
Optimizing CUDA Kernels for Generative Adversarial Networks
Learn to optimize CUDA kernels for GAN training: memory coalescing, occupancy tuning, mixed-precision training, custom fused kernels, Triton compiler, and profiling with Nsight. Practical code included.
Research PaperNeural Symbiosis: The Path to AGI via Recurrent Feedback Loops
ArticleArchitecting the Sentient Web: How AI Agents Are Reshaping the Internet
TutorialFine-Tuning Transformer Models with Low-Rank Adaptation (LoRA)
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