So Google is basically admitting PyTorch/XLA on TPUs didn't work — TorchTPU looks like them rebuilding what should have worked day one. Its hard to run production ML on a toolchain engineers can't trust, no matter how fast the silicon is.
DeepSeek is commoditizing frontier capability... Opus 4.6-level benchmarks at a fraction of the cost changes also who can access these tools.
Stuff that was prohibitive six months ago is now up for grabs. We keep on working on the infra level now, swithcing models whenever we run out of credits, or want a different result. The question is how do we build context, architecture and ensure the agent is effective and efficient..... wouldn't it be good if we simply used less energy to make these AI calls?
Anthropic keeps conflating two distinct strategies — be the best model for developers to build on, or be the company that ships Claude Code. Those two have opposite policy conclusions. Restricting third-party harnesses maximizes Claude Code revenue; allowing them maximizes model-layer lock-in through developer habit. The whiplash is the symptom of not picking. Pick for crying out loud!
Most 'runs on Mac' ports are a wrapper around a cloud call or a quantized shell of the original model. Going after the CUDA-specific kernels with pure-PyTorch alternatives is the kind of work that ages well, because the next CUDA-locked research release is three weeks away. One question: how much of the gather-scatter sparse conv is reusable for other TRELLIS-like architectures, or is it bespoke to this one?
An interesting question is whether the tokenizer is better at something measurable or just denser. A denser tokenizer with worse alignment to semantic boundaries costs you twice, higher bill and worse reasoning. A denser tokenizer that actually carves at the joints of the model's latent space pays for itself in quality. Nobody outside Anthropic can answer which it is without their eval suite, so the rugpull read is fair but premature. Perhaps the real tell will be whether 4.7 beats 4.6 on the same dollar budget on the benchmarks you care about, not on the per-token ones Anthropic publishes.