Gradient descent, SGD and Adam, backpropagation, vanishing/exploding gradients with residual connections, and learning rate schedules — organized around what each piece is doing at a high level. The goal is reading training logs and model card numbers, not computing anything.
A minimum set of calculus for reading AI and LLM articles — d/dx, e, the chain rule, partial derivatives, and gradients. Focus on what the symbols are doing, not on solving the formulas.
A minimum set of probability and statistics for reading AI and LLM articles — conditional probability, cross-entropy, perplexity, and temperature are the main ones; rigorous Bayes and MLE derivations stay out of scope.
A minimum set of vectors and matrices for reading AI and LLM articles — the dot product and matrix product are the main two; determinants, inverses, and eigenvalues stay out of scope.
A minimum set of math for reading AI, LLM, and image-generation articles — the aim isn't to derive anything, just to recognize weighted sums, S-curves, probabilities, and the 'nudge toward the answer' step of training.
Tested WAI-Anima v1 on Windows + RTX 4060 Laptop GPU (8GB VRAM). Headless execution via ComfyUI API hit a tqdm OSError on startup, but launching ComfyUI normally generates a single image in 55 seconds. Includes the workaround and timing notes.
Tested WAI-Anima v1, Anima preview3-base, and WAI-Illustrious v160 side by side on M1 Max 64GB ComfyUI with same seed/prompt. WAI-Anima inherits Anima's atmospheric lighting and natural running poses but still loses to WAI-Illustrious on tag control and character consistency. Includes i2i pipeline test (denoise 0.5), ~275s generation times, and how the Anima derivative ecosystem (WAI-Anima, CottonAnima, Kirazuri, RDBT) expanded in two months.
LLM safety stacks five layers — input filter, system prompt, RLHF, Constitutional AI, output filter — and each provider blocks at different layers. A breakdown of where abliterated vs uncensored models cut, and the default censorship level baked into local LLMs.
Google DeepMind's AI writing tool Fabula was demoed at CHI 2026 by Piotr Mirowski. Co-designed with 42 professional writers using convergent iteration for story structure. But the timeline shows Fabula was first demoed in May 2025 and entered early access in September 2025 — still a research prototype with no general availability.
Bryan Cantrill's 'The Peril of Laziness Lost' argues that LLMs have zero cost to write code and no motivation to abstract. Humans must serve as the 'deletion engine' or systems will bloat endlessly.
I tested local Vision LLMs (Gemma 3, Qwen2.5-VL, Llama 3.2 Vision, Gemma 4) to see if they could look at character illustrations and pixel art and generate RPG-style stats in JSON format.
colleague.skill, yourself-skill, nuwa-skill and other 'human distillation' OSS tools are exploding in popularity, primarily in China. Seeing a tool that distills colleagues, I wondered 'what if I distilled myself?' and researched how.
