LATEST INVESTIGATIONS
A technical source audit tracing Retrieval-Augmented Generation from Lewis et al. to 2026 production patterns: dense indexing, hybrid search, re-ranking, and Graph RAG.
A foundational source audit comparing extractive graph algorithms and abstractive neural sequence models for long-form audio-visual transcription and content synthesis.
A source audit comparing LangChain LCEL/LangGraph with LlamaIndex data frameworks, evaluating retrieval graphs, agent loops, and production trade-offs.
A source audit analyzing Consistency Large Language Models (CLLMs), evaluating parallel decoding, Jacobi trajectories, collocation learning, and inference speed.
A foundational source audit of generative AI paradigms, contrasting autoregressive transformers, latent diffusion SDEs, and flow matching for continuous representation learning.
A technical guide and source audit configuring VS Code for C++ on Apple Silicon, evaluating Clang build tasks, LLDB debugging, and arm64 IntelliSense pitfalls.
Examine foundational algorithmic principles—from randomized Quicksort and the Master Theorem to cache locality and asymptotic limits in AI systems.
A source audit evaluating Phi-3-mini browser deployment via Transformers.js and WebGPU, analyzing memory constraints, ONNX runtimes, and client-side execution.
Deconstruct foundational LLM terminology—from multi-token prediction and LoRA adaptation to RAG grounding, speculative decoding, and quantization architectures.
Audit real-time LLM pipelines for unstructured information extraction—from Kafka streaming ingestion and schema validation to cost-tiered model routing.
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