A small group of AI researchers (Reactor) have released Open Dreamer, an open implementation of the Dreamer 4 world-model pipeline written in JAX and Flax NNX. Two repositories were released: next-state/open-dreamer holds the training pipeline including a causal video tokenizer, action-conditioned latent dynamics model, rollout generation, and FVD scoring, while reactor-team/open-dreamer holds a minimal local rollout harness. A browser demo hosted on the Reactor runtime streams a generated Minecraft world in real time with a Game ⟷ Dream toggle. The stated objective was to reproduce the Dreamer 4 research, deliberately avoiding methods outside that paper. The team started on CoinRun, then scaled to Minecraft/VPT-style gameplay video. Both the tokenizer and dynamics model use the same block-causal transformer backbone alternating space layers and causal time layers. The tokenizer is a transformer-based Masked Autoencoder reporting roughly 100× compression. The dynamics model performs next-frame prediction trained with diffusion forcing, flow matching, and shortcut models. The shipped Minecraft configs specify a dynamics model of 1.6B parameters: 30 block-causal layers, d_model 1920, 30 attention heads, and 3 KV heads. Training runs for 200,000 steps with Muon, a WSD schedule, and a peak learning rate of 3e-4. Shortcut/bootstrap samples switch on at step 100,000 at a 0.25 batch