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Diffusion Models Beyond Images: Audio, Video, and 3D in 2026
Diffusion models beyond images in 2026: audio, video, and 3D. How diffusion transformers work, the sampling-step latency tax, and where autoregression wins.
Latest Intelligence
Curated technical papers and hands-on implementation guides for the modern AI engineer.
Multimodal LLMs in Production: What Native Vision Actually Costs
Multimodal LLMs in production: how image tokens drive cost and latency, why MMMU-Pro saturation hides gaps, and how to pick a model by the modality you ship.
Sparse Attention in 2026: Why It Finally Had to Be Native
Sparse attention in 2026: NSA, DeepSeek DSA, MoBA and MiniMax MSA. Why native trainable sparsity ships where post-hoc masking of a dense model stalls.
ArticleState Space Models in 2026: The Recall Gap, and What Finally Closed It
Research PaperLatent Reasoning: The Open Problem of Thinking Without Words
ArticleInkling Is Not Trying to Win: How to Measure an Open-Weights Fine-Tuning Base
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Step-by-step guides from neural network basics to advanced LLM fine-tuning.
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Peer-reviewed insights and white papers defining the frontier of artificial intelligence.
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High-fidelity training sets for natural language processing and computer vision.
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Guided sequences through our best content — structured to build understanding from the ground up.
Post-Training Modern LLMs
Pretraining produces a model that predicts text. Post-training is what turns it into something you can ship. This path walks the levers in the order you would actually reach for them: supervised fine-tuning and adapters, preference optimization without a reward model, the reinforcement-learning map from RLHF to verifiable rewards, RL against a verifier that cannot be talked out of its answer, and finally the inference-time compute that picks up where training leaves off. Every step names the ceiling it runs into.
Evaluating LLMs Honestly
A leaderboard number is a hypothesis, not a result. This path builds the habit of asking what a benchmark measured before quoting what it reported, starting with contamination and judge bias, moving through a case where the advertised figure and the measured one diverge, then to agents where a single passing run tells you almost nothing, and ending where the eval harness itself turns into attack surface.