Begin by accessing the New York Times API. You will need to register for an API key on the New York Times Developer Network at https://developer.nytimes.com/. Once registered, select the appropriate API (e.g., Article Search API) to fetch the data you need.

Write a Python script to make HTTP requests to the New York Times API. Use the `requests` library to send GET requests and retrieve data in JSON format. Ensure you include your API key in the request header or as a query parameter.

Parse the JSON data returned by the API. Use Python’s built-in `json` library to convert the JSON response into a Python dictionary. Extract the relevant fields from the dictionary that you wish to store in Weaviate.

Install Weaviate on your local machine or a server. You can use Docker for a straightforward setup. Pull the Weaviate Docker image using the command `docker pull semitechnologies/weaviate`, and run it using `docker run -d -p 8080:8080 semitechnologies/weaviate`.

Define the schema in Weaviate that matches the structure of the data you've extracted from the New York Times. Use Weaviate's GraphQL-based schema endpoint to create classes and properties. Make sure the schema is flexible enough to accommodate the data fields from the New York Times.

Write a script to transform the parsed JSON data into the format specified by your Weaviate schema. Use the `requests` library again to send POST requests to Weaviate’s REST API, inserting the data into the appropriate classes and fields in your schema.

Once the data is loaded into Weaviate, perform queries using Weaviate’s GraphQL API to verify that the data has been correctly inserted. Check if all fields are populated as expected and troubleshoot any discrepancies by reviewing both the schema and the data transformation logic.

By following these steps, you can effectively move data from the New York Times to Weaviate without relying on third-party connectors or integrations.