HN upvoted this because it turned vague limit anxiety into numbers. Tokenomics says 541 anonymous submissions averaged 466 request tokens on Opus 4.7 versus 349 on Opus 4.6, a 38.1% increase, and the thread immediately argued over what that means for real Claude usage.
HN cared less about the headline model upgrade than the quiet accounting change underneath it. The linked measurement found higher token counts on Claude Code-like material, while commenters argued over whether token burn or human review time should dominate the cost calculation.
A developer on r/MachineLearning shared phase-one details for Dante-2B, a 2.1B Italian/English model trained from scratch with a tokenizer tuned for Italian morphology and token efficiency.
A high-scoring LocalLLaMA post argued that merging llama.cpp PR #21534 finally cleared the known Gemma 4 issues in current master. The community focus was not just the fix itself, but the operational details around tokenizer correctness, chat templates, memory flags, and the warning to avoid CUDA 13.2.
A detailed r/MachineLearning post is drawing interest to Dante-2B, a 2.1B dense Italian/English model trained from scratch on 2×H200 GPUs. The project emphasizes tokenizer efficiency for Italian, a 300B token corpus, and a fully open release of weights, tokenizer, and training pipeline after phase 2.