Discovery
<link rel="alternate" href="/api" type="application/vnd.microforms">
Self-described payloads
{
Discovery
<link rel="alternate" href="/api" type="application/vnd.microforms">
Self-described payloads
{
JSON-LD encapsulation of forms:
{
@context: "https://example.com",
@type: MyType,
action: {
@context: "https://w3c.org/2018/forms",
@type: Form,
...
CoreML/ONNX/TF JSON-LD based for serving.
{
@context: "https://w3c.org/2018/deeplearning",
@type: Model,
network: {
@type: NeuralNetwork,
...
}
ATOM feeds in JSON-LD.
{
"@context": "http://www.w3.org/2005/Atom",
"@type": "Feed",
"title": "Example Feed",
"subtitle": "A subtitle.",
"links": [{
Alternatives considered
TODO(goto): go over this.
ARML is an XML-based data format to describe an interact with AR scenes.
ARML
10-15% cost of interpreation seems like a non-starter to me when they are trying to squeeze in every level of performance. I'm seeing some convergence in the industry regarding "inference models" file formats / representations
I would challenge that assertion. Your going to get a larger drop on an android phone when thermal throttling kicks in.
Things that could be interesting to control/censor:
Things that "use strict" control: