A mechanism-first guide to optimizers: what SGD, momentum, RMSProp, Adam, and AdamW each solve, why AdamW became a strong default for modern deep learning, and when other optimizers still matter.

A source-reading walkthrough of vLLM V1 Scheduler: how it decides across running/waiting queues, token budget, KV cache blocks, prefix-cache hits, and preemption to produce SchedulerOutput for ModelRunner.

A concise map of floating point, integer quantization, storage dtype, compute dtype, and accumulation dtype in neural networks.

A source-reading walkthrough of vLLM V1 GPUModelRunner: how SchedulerOutput becomes input batches, attention metadata, KV slot mappings, model forward, logits, and sampled tokens.

A practical guide to loss functions: when to use MSE, MAE, Huber, binary cross entropy, cross entropy, KL divergence, hinge loss, contrastive loss, and triplet loss.

A small 5-token example for understanding temperature, top-p, and top-k during LLM inference, with source-reading notes from the vLLM V1 sampler.

A mechanism-first guide to activation functions: why neural networks need nonlinearities, how sigmoid, tanh, ReLU, GELU, and SiLU differ, and why a 400-function survey is best read as a map rather than a menu.

A compact map of embodied model development: VLA policies, JEPA-style world models, and WAM-style models that tie actions to predicted future states.

A practical model for upload-side and download-side streaming: transport moves bytes, while the application layer defines boundaries, progress, recovery, idempotency, backpressure, and business meaning.

A source-reading walkthrough of the vLLM V1 request path: OpenAI-compatible HTTP entrypoint, serving render, AsyncLLM, EngineCore client, Tensor IPC, scheduler, and one GPUModelRunner forward pass.

Why LLM inference splits into a compute-bound prefill phase and a memory-bandwidth-bound decode phase, and how that explains TTFT, TPOT, batching, KV cache pressure, and serving-engine design.

A series index for LLM attention kernels and GPU primitives: fused softmax, online softmax, FlashAttention, PagedAttention kernels, Triton/CUDA, and memory-access optimization.

A series index for LLM quantization and low-precision serving: INT8/INT4, GPTQ, AWQ, SmoothQuant, NF4, AQLM, KV cache quantization, FP8 serving, and quality/speed/memory tradeoffs.

An LLM inference experiment series index: vLLM/SGLang benchmarks, TTFT/TPOT, prefix cache, chunked prefill, PagedAttention, quantization, and a profiler dashboard.

A vLLM / SGLang source-reading series index: request lifecycle, scheduler, KV cache allocation, block manager, radix cache, and benchmarks.

A series index for core LLM serving mechanisms: prefill/decode, KV cache, PagedAttention, continuous batching, prefix caching, and disaggregated prefill.

A practical survey of LLM quantization, covering linear quantization, codebook quantization, LLM.int8(), SmoothQuant, GPTQ, AWQ, NF4, AQLM, KV cache quantization, and FP8.

A step-by-step explanation of positional encoding in Transformers, from absolute embeddings to sinusoidal encodings, Euler's formula, and rotary position embeddings.

A back-of-the-envelope framework for estimating LLM training FLOPs, inference FLOPs, weight memory, KV cache, and training memory.

A practical note on managing agent skills: how to install, create, remove, disable, update, and evolve skills, plus which meta-skills are worth installing first.