The RAMGPT library / 33 articles
Clear explanations of the concepts behind modern AI systems.
AI Foundations #28 explains static batching, continuous batching, request scheduling, chunked prefill, head-of-line blocking, fairness, and why server throughput and user latency can pull in opposite directions.
AI Foundations #27 explains time to first token, time per output token, prefill, decode, batching, throughput, context length, and why one tokens-per-second number can hide the real user experience.
AI Foundations #26 explains production monitoring, data and concept drift, feedback loops, human review, and why a model that passed evaluation can still fail after deployment.
AI Foundations #25 explains alignment, preference training, guardrails, instruction hierarchy, runtime policy, and why capability and safe system behavior are different goals.
AI Foundations #24 explains tool use, function calling, agents, action loops, observations, permissions, failure modes, and why an LLM does not execute a tool by itself.
AI Foundations #23 explains retrieval-augmented generation, embeddings, chunking, retrieval, context injection, citations, failure modes, and why RAG is different from training.
AI Foundations #22 explains test sets, metrics, benchmark design, sampling variance, contamination, and why a fair model comparison must control more than one score.
AI Foundations #21 explains reasoning tokens, inference-time compute, reward signals, RLHF, PPO, and how reinforcement-learning post-training changes model behavior.
AI Foundations #20 builds a practical mental model of multimodal AI: how text, image, audio, and video representations are converted, aligned, and fed into transformer-style systems.
AI Foundations #19 explains Mixture of Experts models: routers, top-k expert selection, active versus total parameters, load balancing, and why MoE changes inference economics.
AI Foundations #18 builds quantization from number representation and rounding error, then connects 4-bit weights to model size, speed, calibration, and quality tradeoffs.
AI Foundations #17 explains context windows and KV cache: what the model can see, what keys and values are cached, why decoding gets faster, and why long chats consume more memory.
AI Foundations #16 explains how logits become generated text through greedy decoding, temperature, top-k, and top-p sampling, with simple probability intuition and worked examples.
AI Foundations #15 explains inference as the trained model's forward-only phase: tokenize a prompt, compute logits, choose a token, append it, and repeat without backpropagation or weight updates.
AI Foundations #14 explains how fine-tuning continues gradient-based training from pretrained weights, using narrower data to change model behavior without relearning language from scratch.
A practical research playbook for reconstructing CVE-centric remediation event logs from change, incident, request, and vulnerability records before forming hypotheses.
AI Foundations #13 connects the Transformer architecture to the training objective that turns random parameters into a useful language model.
AI Foundations #12 assembles attention, residual connections, normalization, and MLP layers into the repeating Transformer block behind modern language models.
AI Foundations #11 follows tokenization and embeddings into attention: queries, keys, values, similarity scores, softmax weights, and context-dependent representations.
AI Foundations #10 connects embeddings to tokenization: how text is split into tokens, mapped to vocabulary IDs, and then looked up as learned vectors.
AI Foundations #9 explains embeddings as learned vectors that give neural networks a useful numerical space for tokens and other objects.
AI Foundations #8 explains backpropagation as repeated chain-rule bookkeeping that carries loss information backward through a neural network to every trainable parameter.
AI Foundations #7 explains gradient descent as the rule that turns loss into a direction for changing weights, using one small numerical example before backpropagation.
AI Foundations #6 connects the forward pass to training by showing why a model needs targets and a loss function before its weights can improve.
AI Foundations #5 follows one input through a tiny neural network to show what weights, biases, activations, and layers actually do during a forward pass.
A cybersecurity research report on the gap between fast-moving exploitation and slow enterprise remediation, and why compensating controls should be measured by survival rather than deployment alone.
AI Foundations #4 explains tensors through shapes, axes, images, batches, model weights, and memory, without assuming you already know linear algebra or PyTorch.
AI Foundations #3 explains what parameters, weights, and biases actually are, why large models have billions of them, and how precision changes model size.
AI Foundations #1 builds a clear map from artificial intelligence to machine learning, neural networks, deep learning, generative AI, and LLMs.
AI Foundations #2 explains what machine learning actually changes inside a model, using simple math, examples, and a student's point of view.
Learn what LLM context length means, what counts toward the context window, how tokens, chat history, KV cache, VRAM, truncation, and long-context models work.
Learn how LLM quantization reduces model memory, what FP16, INT8 and INT4 mean, and how GGUF, AWQ, GPTQ, scales, groups, and quality trade-offs work.
Learn what an LLM KV cache stores, why it speeds up token generation, how to estimate its VRAM use, and how context length and quantization affect memory.