Prerequisites
- architecture and kernels
- runtime and agents
Learning sequence
- WASM memory and SIMD
- WebGPU device and kernel model
- Browser caching and distribution
- MLX unified memory
- Apple Foundation Models and Core ML boundaries
- Browser ASR case study
Hands-on lab
Compare the historical unqualified WebGPU curve and its missing receipt, Memory64 limit, Apple action-grounding result, and Parakeet browser receipt as four different capability and evidence boundaries.
Mastery gate
Choose the correct Mac or browser runtime from capability, memory, latency, energy, download, and ownership constraints.
CLI surface
- Run: posttrainllm hf-load
- Run: posttrainllm ane-validate
- Run: posttrainllm coreml-serve
- Run: posttrainllm vlm-smoke
How to read this dossier
This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method.
Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.
To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.
Source provenance
The normalized record comes from docs/learn/path-registry.json. The links below are the tracked evidence and explanatory sources preserved with the record.