PyTorch Profiler in Production: Finding the Real Bottleneck
The PyTorch Profiler reveals what is really slow in your training or inference. The 2026 patterns for diagnosing bottlenecks.
7 min read
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Quantization-Aware Training in PyTorch: FP4, INT8, and BF16 Mixed
QAT is how you get small models without quality regressions. The 2026 PyTorch patterns for FP4, INT8, and BF16 mixed-precision training.
7 min read
Quantizing Embeddings: int8, Binary, and Matryoshka
Embedding quantization cuts storage 4-32x at modest recall cost. The 2026 quantization techniques and where each one wins.
7 min read
Qwen3 Deep Dive: Agentic Tool Use and Multilingual Performance
Qwen3 is the strongest open-weights agentic model in 2026 by several measures. A deep dive on its tool use, multilingual capability, and architecture.
8 min read
RAG Caching Layers: Hit Rates and Cost Reduction Strategies
Cache the right RAG layer and you cut cost 60-80 percent. The 2026 multi-layer cache designs and what to cache where.
8 min read
RAG Evaluation Frameworks 2026: RAGAS, TruLens, and DeepEval in Practice
Three RAG evaluation frameworks compared on real production RAG pipelines: RAGAS, TruLens, and DeepEval. Strengths, weaknesses, when to use each.
8 min read
RAG Failure Mode Catalog: Why Pipelines Don't Find the Right Doc
Twelve recurring RAG failure modes from production deployments and the fixes for each in 2026.
8 min read
RAG for Code: Indexing Repos and Retrieving Relevant Snippets
Code RAG is different from text RAG. The 2026 patterns for AST-aware chunking, function-level embedding, and snippet ranking.
8 min read
RAG Privacy: Indexing Sensitive Data Without Leaking
RAG over sensitive data requires careful tenant isolation, embedding privacy, and access control. The 2026 patterns for safe RAG.
8 min read
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