Automatone — fact-checked guides, troubleshooting, and explainers for AI, dev, and systems
Fact-checked guides, troubleshooting, and explainers for AI, dev, and systems — clear answers to what you searched for.
FlashAttention Explained: Why Modern LLMs Run Faster
FlashAttention computes exact attention without ever materializing the N×N matrix in GPU memory, treating attention as a memory-movement problem instead of a FLOP-counting one. The payoff: up to 3× faster GPT-2 training, 15% off the BERT-large MLPerf record, and the long context windows modern LLMs rely on.
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Running TRELLIS.2 on a Mac (2/3): Five Bottlenecks I Guessed Wrong
Five confident guesses about where the Apple Silicon port was slow — and how honest measurement killed every one, plus the profiling methodology that made the numbers trustworthy.
DPO Explained: Aligning LLMs Without RLHF Complexity
Direct Preference Optimization (DPO) proves the RLHF objective has a closed-form solution, letting you express the reward in terms of the policy itself. The result: a single classification loss replaces the entire reward-model-plus-PPO pipeline, matching or beating RLHF on sentiment, summarization, and dialogue.
DeepSeek-R1 Explained: RL for Reasoning Models
DeepSeek-R1 showed that large language models can learn to reason through reinforcement learning alone — no supervised chain-of-thought data required. This review walks through the pure-RL R1-Zero result, the four-stage pipeline behind the production model, the benchmark numbers against OpenAI o1, and the honest list of what didn't work.
Stable Diffusion Paper Explained: Latent Diffusion Models
Latent Diffusion Models move diffusion out of pixel space: an autoencoder compresses images into a small latent grid, and the U-Net generates that instead, cutting compute by an order of magnitude. A cross-attention layer makes it a general conditional generator. This is the architecture behind Stable Diffusion.
Ollama vs LM Studio vs llama.cpp: The Best Way to Run LLMs Locally
Ollama, LM Studio, and llama.cpp aren't really rivals — two of them wrap the third, so token speed is nearly identical. The real choice is interface and control. Here's a side-by-side comparison and a clear pick for each kind of user.
How to Set Up a Python Virtual Environment with uv
uv replaces venv, pip, and virtualenv with one fast Rust binary. This guide walks through installing uv, creating a virtual environment with uv venv, installing packages, and when to move up to uv's project workflow.
What People Are Actually Building with Claude Fable 5 (and What It Teaches)
A tour of five real things people shipped with Claude Fable 5 in its first weeks: a browser-based procedural 3D world built ~99% autonomously, and four single-prompt UI pieces from a 550-project community gallery. Plus what the corpus teaches about one-shot prompting, verification tooling, cost, and hype.
What Is a Vector Database? (and When You Actually Need One)
A clear, jargon-light explanation of what a vector database is and how ANN indexing actually works — then the honest part most articles skip: when pgvector in the Postgres you already run is enough, and when scale, latency, or real-time indexing genuinely justify a dedicated engine like Pinecone, Qdrant, or Milvus.
Running TRELLIS.2 on a Mac (1/3): Porting a CUDA-Only Image-to-3D Model to Apple Silicon
How I got Microsoft's CUDA-only image-to-3D model (TRELLIS.2) to run on Apple Silicon: an idempotent patcher, swapping the CUDA kernels out one by one, and the first generation.