Here is my test tokenURI.json file w/ the imageURI I pass to my token contract.setTokenURI():
{
"attributes": [
{
"trait_type": "location",
"value": "West Awesomeville"
},
{
"display_type": "date",
"trait_type": "created",
"value": 1535250800
}
],
"description": "My awesome NFT.",
"image": "https://ipfs.io/ipfs/QmaUXii41ESnUMxLJUoVcrEeXowz7RHcdTiumvrBmUvcwG?filename=test4.png",
"name": "NFT 1"
}
Which is the best IPFS uri form to use esp. if I want to load this NFT into Opensea?
The docs in IPFS recommend:
https://ipfs.io/ipfs/<CID>
but the docs in Opensea recommend:
ipfs://<CID>
Which form is better and why?
In the above json I'm using the first form recommended by IPFS. It works but loading into Opensea is slow/somewhat unpredictable.
The form Opensea recommends is shorter, no gateway. Would the image load faster in Opensea if I used the 2nd form?
IPFS docs: Address IPFS on the Web
Opensea docs:
If you use IPFS to host your metadata, your URL should be in the format ipfs://CID. For example, ipfs://QmTy8w65yBXgyfG2ZBg5TrfB2hPjrDQH3RCQFJGkARStJb.
The ipfs:// url is the better way. Because gateways can go down. Now the ipfs pinner that you're using (pinata.cloud?) can also go down, or you can stop paying them and they will disappear your stuff.
Opensea is not likely to care, as long as they can find your metadata / images from the uri returned by the contract they will list your thing, and there's a way somewhere to do a metadata refresh (if you do a reveal)
And if I can also suggest, it probably might be a good idea to include a way to update the baseURI in the contract just in case.
Related
We are developing an internal project to use the Windchill OData REST API to fill the eBOM for a given part. What we are trying to do here is to read data from another software we have to get the BOM info and send it to the part in windchill. But we cannot find an endpoint in servlet/odata to do it.
We guess the idea is to replicate the manual process. So we already know how to create, check out and check in a part. However we still cannot find an endpoint to modify the part and add the eBOM.
We know PartList, PartListItem, GetPartStructure in the PTC Product Management Domain. But these are GET endpoints and are only useful to retrieve data, including the BOM. But we cannot use them to modify the content.
I've found the solution.
The endpoint to use is:
POST /ProdMgmt/Parts('VR:wt.part.WTPart:xxxxxxxxx')/Uses
The body of the request must contain:
{
"Quantity": 1,
"Unit": {
"Value": "ea",
"Display": "Each"
},
"TraceCode": {
"Value": "0",
"Display": "Untraced"
},
"Uses#odata.bind": "Parts('OR:wt.part.WTPart:yyyyyyyyy')"
}
Where Uses#odata.bind contains the ID of the part we want to link
I have a little Python program that other people use and I would like to offer opt-in telemetry such that can get an idea of the usage patterns. Google Analytics 4 with the Measurement Protocol seems to be the thing that I want to use. I have created a new property and a new data stream.
I have tried to validate the request and set it to www.google-analytics.com/debug/mp/collect?measurement_id=G-LQDLGRLGZS&api_secret=JXGZ_CyvTt29ucNi9y0DkA via post and send this JSON payload:
{
"app_instance_id": "MyAppId",
"client_id": "TestClient.xx",
"events": [
{
"name": "login",
"params": {}
}
]
}
The response that I get is this:
{
"validationMessages": [
{
"description": "Cannot parse non Measurement Protocol hits.",
"validationCode": "INTERNAL_ERROR"
}
]
}
I seem to be doing exactly what they do in the documentation or tutorials. I must be doing something wrong, but I don't know, what is missing. What do I have to do in order to successfully validate the request?
Try to remove /debug part in the URL. In the example you followed it is not present so it is not quite exactly the same.
we just came across the same issue and the solution for us was to put https:// in front of the URL. Hope this helps.
I am using the google cloud vision api to analyze pictures. Is there a list of all the possible responses for the labelAnnotations method?
The API reference of Vision API gives an overview of all the possible JSON responses for the different image annotation requests.
The labelAnnotation request returns a generic EntityAnnotation response, you can find the JSON representation here, also containing more information about the JSON representation of BoundingPoly, LocationInfo and Property:
{
"mid": string,
"locale": string,
"description": string,
"score": number,
"confidence": number,
"topicality": number,
"boundingPoly": {
object(BoundingPoly)
},
"locations": [
{
object(LocationInfo)
}
],
"properties": [
{
object(Property)
}
],
}
I think you're asking whether you can get a look at the list of possible labels/entities that the Cloud Vision API will detect. If that's the case, the short answer is no, not in any manageable way.
The more complicated answer is sort of, since most labels will have a property for the knowledge graph entry (e.g., {desc: 'dog', mid: '/m/0bt9lr'}). This means that you can look-up more information about the label/entity using the Knowledge Graph API.
While you can't "store a copy" of Google's Knowledge Graph as a list of choices in a drop-down on a page, you can use the API to do a look-up after the Vision API responds with an ID.
I'm trying to integrate my CRM with Google Analytics to monitor lead changes (from lead to sell) and so on. As I understood, I need to use Google Measurement Protocol, to receive webhooks from CRM and translate it to Analytics Conversions.
But in fact, I don't really understand how to do it. I need to make some script, to translate webhook code to analytics, but where I need to place that script? Are there some templates? And so on.
So, If you know some tutorials/courses/freelancers to help me with intergrating webhooks with Analytics - I need your advice.
Example of webhook from CRM:
{
"leads": {
"status": {
"id": "25399013",
"name": "Lead title",
"old_status_id": "7039101",
"status_id": "142",
"price": "0",
"responsible_user_id": "102525",
"last_modified": "1413554372",
"modified_user_id": "102525",
"created_user_id": "102525",
"date_create": "1413554349",
"account_id": "7039099",
"custom_fields": [
{
"id": "427183",
"name": "Checkbox custom field",
"values": ["1"]
},
{
"id": "427271",
"name": "Date custom field",
"values": ["1412380800"]
},
{
"id": "1069602",
"name": "Checkbox custom field",
"values": ["0"]
},
{
"id": "427661",
"name": "Text custom field",
"values": ["Валера"]
},
{
"id": "1075272",
"name": "Date custom field",
"values": ["1413331200"]
}
]
}
}
}
"Webhook" is a fancy way of saying that your CRM can call a web based service whenever something interesting happens (i.e. the CRM can "hook" into a web based application). E.g. if a new lead is created you can call an url with the lead details as parameters.
Specifics depend on your CRM, but when you set up a webhook there should be a field to set a url; the script that evaluates the CRM data is located at the URL.
You have that big JSON thing as your example - No real way to tell without knowing your system, but I assume that is sent as request body. So in your script you evaluate the request body, extract the parameters you want to send to analytics (be mindful that you are not allowed to store personally identifiable information) and sent it via the measurement protocol as described in the documentation linked in the other answer.
Depending on the system you might even be able to call the measurement protocol without having a custom script in between (after all the measurement protocol is an url with a few parameters).
This is an awfully generic answer, but then the question is really broad.
I've done just this in my line of work.
You need to first decide your data model on how you would like the CRM data to look within Google Analytics. This could be just mapping Google Analytics' event category, event label, event action to your data, or perhpas using custom dimensions and metrics.
Then to make it most useful, you would like to be able to link the CRM activity of a customer to their online activity. You can do this if they login online. In that case, you can set the cid and/or uid of the user to your CRM id.
Then, if you send in a GA hit with the same cid/uid in your Measurement Protocol hit, you will link the online sessions with your offline CRM activity.
To make the actual record hit Google Analytics, you will need to program something that takes the CRM data and turns it into a Measurement Protocol hit, which is essentially just a URL with the correct parameters. Look here for reference: https://developers.google.com/analytics/devguides/collection/protocol/v1/reference
An example could be: http://www.google-analytics.com/collect?v=1&tid=UA-123456-1&cid=5555&t=pageview&dp=%2FpageA
We usually have this as a seperate process, that fires when the CRM data is written to its database (the webhook in your example). If its a lot of data, you should probably implement checks to see if the hit was sucessful, and caching in case the service is not online - you have an optional parameter that gives you 4 hours leeway in sending data.
Hope this gets you at least started.
So after successful upload of images via Filepicker we access the images at the given URL and in most places call convert with some parameters passed in. We use Amazon S3 Cloudfront as a CDN in front of filepicker as filepicker states convert is not meant for high traffic sites - fair enough.
The problem that I'm experiencing with convert though is that it's nailing our Page speed and we are getting the message "remove query strings from static resources" for all our Filepicker files.
The reason for the warning is "Resources with a "?" in the URL are not cached by some proxy caching servers."
Is there a way to avoid using query parameters while still retaining the capability of convert function?
I would love to improve my pagespeed but can't see how to fix this particular issue while still using Filepicker.
Similar to REST convert there is javascript filepicker.convert method which return new file link.
var blob = {
url: 'https://www.filepicker.io/api/file/H7KYuWy1S3e1qvG2M66i',
filename: 'customers.jpg',
mimetype: 'image/jpeg',
isWriteable: false,
size: 629454
};
filepicker.convert(
blob,
{
width: 200,
height: 200
},
function(converted_Blob){
console.log(converted_Blob.url);
}
);
Another solution would be to store the conversion REST results.
Curl example:
curl -X POST -d url="https://www.filepicker.io/api/file/H7KYuWy1S3e1qvG2M66i/convert?w=100" "https://www.filepicker.io/api/store/S3?key=yourApiKey&container=yourContainerName"
Sample response:
{"container": "yourContainerName", "url": "https://www.filepicker.io/api/file/383MY1piRQqTg1N9K5tw", "filename": "IMAG0038.jpg", "key": "XNeqfiMaRlm4OMAQIJ9y_IMAG0038.jpg", "type": "image/jpeg", "size": 1763}
In this way you can create a server-side conversions