Anticloud/anticloud-python-api-client-prior-docs

PYTHON_API_CLIENT_PRIOR_DOCS

Governed Anticloud packaging of the upstream project PYTHON_API_CLIENT_PRIOR_DOCS in category ONLINE_RETAIL. check results: see ISOLATED_LAB_RESULTS. Every number below traces to a named file + run stamp; nothing is borrowed from other projects.

Upstream: PYTHON_API_CLIENT_PRIOR_DOCS · Upstream pin: 70a068d52cd329bdbb478ffa5020bdfd2f0a3a83 · Category: ONLINE_RETAIL · Vendor: Anticloud FZ LLE · Licence: MIT

---

What This Project Does

---

RecombeeApiClient

---

A Python 3 client for easy use of the Recombee <https://www.recombee.com/>_ recommendation API.

If you don't have an account at Recombee yet, you can create a free account here <https://www.recombee.com/>_.

Documentation of the API can be found at docs.recombee.com <https://docs.recombee.com/>_.

============= Installation

Install the client with pip:

.. code-block:: bash

$ pip install recombee-api-client

======== Examples

---

Basic example

.. code-block:: python

from recombee_api_client.api_client import RecombeeClient, Region
from recombee_api_client.exceptions import APIException
from recombee_api_client.api_requests import *
import random

client = RecombeeClient('--my-database-id--', '--db-private-token--', region=Region.US_WEST)

#Generate some random purchases of items by users
PROBABILITY_PURCHASED = 0.1
NUM = 100
purchase_requests = []

for user_id in ["user-%s" % i for i in range(NUM) ]:
  for item_id in ["item-%s" % i for i in range(NUM) ]:
    if random.random() < PROBABILITY_PURCHASED:

      request = AddPurchase(user_id, item_id, cascade_create=True)
      purchase_requests.append(request)

try:
    # Send the data to Recombee, use Batch for faster processing of larger data
    print('Send purchases')
    client.send(Batch(purchase_requests))

    # Get recommendations for user 'user-25'
    response = client.send(RecommendItemsToUser('user-25', 5))
    print("Recommended items: %s" % response)

    # User scrolled down - get next 3 recommended items
    response = client.send(RecommendNextItems(response['recommId'], 3))
    print("Next recommended items: %s" % response)

except APIException as e:
    print(e)

---

Using property values

.. code-block:: python

from recombee_api_client.api_client import RecombeeClient, Region
from recombee_api_client.api_requests import AddItemProperty, SetItemValues, AddPurchase
from recombee_api_client.api_requests import RecommendItemsToItem, SearchItems, Batch, ResetDatabase
import random

NUM = 100
PROBABILITY_PURCHASED = 0.1

client = RecombeeClient('--my-database-id--', '--db-private-token--', region=Region.AP_SE)

# Clear the entire database
client.send(ResetDatabase())

# We will use computers as items in this example
# Computers have four properties 
#   - price (floating point number)
#   - number of processor cores (integer number)
#   - description (string)
#   - image (url of computer's photo)

# Add properties of items
client.send(AddItemProperty('price', 'double'))
client.send(AddItemProperty('num-cores', 'int'))
client.send(AddItemProperty('description', 'string'))
client.send(AddItemProperty('image', 'image'))

# Prepare requests for setting a catalog of computers
requests = [SetItemValues(
    "computer-%s" % i, #itemId
    #values:
    { 
      'price': random.uniform(500, 2000),
      'num-cores': random.randrange(1,9),
      'description': 'Great computer',
      'image': 'http://examplesite.com/products/computer-%s.jpg' % i
    },
    cascade_create=True   # Use cascadeCreate for creating item
                          # with given itemId if it doesn't exist
  ) for i in range(NUM)]

# Send catalog to the recommender system
client.send(Batch(requests))

# Prepare some purchases of items by users
requests = []
items = ["computer-%s" % i for i in range(NUM)]
users = ["user-%s" % i for i in range(NUM)]

for item_id in items:
    #Use cascadeCreate to create unexisting users
    purchasing_users = [user_id for user_id in users if random.random() < PROBABILITY_PURCHASED]
    requests += [AddPurchase(user_id, item_id, cascade_create=True) for user_id in purchasing_users]

# Send purchases to the recommender system
client.send(Batch(requests))

# Get 5 recommendations for user-42, who is currently viewing computer-6
# Recommend only computers that have at least 3 cores
recommended = client.send(
    RecommendItemsToItem('computer-6', 'user-42', 5, filter="'num-cores'>=3")
)
print("Recommended items with at least 3 processor cores: %s" % recommended)

# Recommend only items that are more expensive then currently viewed item (up-sell)
recommended = client.send(
    RecommendItemsToItem('computer-6', 'user-42', 5, filter="'price' > context_item[\"price\"]")
)
print("Recommended up-sell items: %s" % recommended)

# Filters, boosters and other settings can be also set in the Admin UI (admin.recombee.com)
# when scenario is specified
recommended = client.send(
  RecommendItemsToItem('computer-6', 'user-42', 5, scenario='product_detail')
  )

# Perform personalized full-text search with a user's search query (e.g. 'computers').
matches = client.send(SearchItems('user-42', 'computers', 5, scenario='search_top'))
print("Matched items: %s" % matches)

---

Exception handling

For the sake of brevity, the above examples omit exception handling. However, various exceptions can occur while processing request, for example because of adding an already existing item, submitting interaction of nonexistent user or because of timeout.

We are doing our best to provide the fastest and most reliable service, but production-level applications must implement a fallback solution since errors can always happen. The fallback might be, for example, showing the most popular items from the current category, or not displaying recommendations at all.

Example:

.. code-block:: python

from recombee_api_client.exceptions import *

try: recommended = client.send( RecommendItemsToItem('computer-6', 'user-42', 5, filter="'price' > context_item["price"]") ) except ResponseException as e: #Handle errorneous request => use fallback except ApiTimeoutException as e: #Handle timeout => use fallback except APIException as e: #APIException is parent of both ResponseException and ApiTimeoutException

---

Installation

=============

Install the client with pip:

.. code-block:: bash

$ pip install recombee-api-client

======== Examples

---

Basic example

.. code-block:: python

from recombee_api_client.api_client import RecombeeClient, Region
from recombee_api_client.exceptions import APIException
from recombee_api_client.api_requests import *
import random

client = RecombeeClient('--my-database-id--', '--db-private-token--', region=Region.US_WEST)

#Generate some random purchases of items by users
PROBABILITY_PURCHASED = 0.1
NUM = 100
purchase_requests = []

for user_id in ["user-%s" % i for i in range(NUM) ]:
  for item_id in ["item-%s" % i for i in range(NUM) ]:
    if random.random() < PROBABILITY_PURCHASED:

      request = AddPurchase(user_id, item_id, cascade_create=True)
      purchase_requests.append(request)

try:
    # Send the data to Recombee, use Batch for faster processing of larger data
    print('Send purchases')
    client.send(Batch(purchase_requests))

    # Get recommendations for user 'user-25'
    response = client.send(RecommendItemsToUser('user-25', 5))
    print("Recommended items: %s" % response)

    # User scrolled down - get next 3 recommended items
    response = client.send(RecommendNextItems(response['recommId'], 3))
    print("Next recommended items: %s" % response)

except APIException as e:
    print(e)

---

Using property values

.. code-block:: python

from recombee_api_client.api_client import RecombeeClient, Region
from recombee_api_client.api_requests import AddItemProperty, SetItemValues, AddPurchase
from recombee_api_client.api_requests import RecommendItemsToItem, SearchItems, Batch, ResetDatabase
import random

NUM = 100
PROBABILITY_PURCHASED = 0.1

client = RecombeeClient('--my-database-id--', '--db-private-token--', region=Region.AP_SE)

# Clear the entire database
client.send(ResetDatabase())

# We will use computers as items in this example
# Computers have four properties 
#   - price (floating point number)
#   - number of processor cores (integer number)
#   - description (string)
#   - image (url of computer's photo)

# Add properties of items
client.send(AddItemProperty('price', 'double'))
client.send(AddItemProperty('num-cores', 'int'))
client.send(AddItemProperty('description', 'string'))
client.send(AddItemProperty('image', 'image'))

# Prepare requests for setting a catalog of computers
requests = [SetItemValues(
    "computer-%s" % i, #itemId
    #values:
    { 
      'price': random.uniform(500, 2000),
      'num-cores': random.randrange(1,9),
      'description': 'Great computer',
      'image': 'http://examplesite.com/products/computer-%s.jpg' % i
    },
    cascade_create=True   # Use cascadeCreate for creating item
                          # with given itemId if it doesn't exist
  ) for i in range(NUM)]

# Send catalog to the recommender system
client.send(Batch(requests))

# Prepare some purchases of items by users
requests = []
items = ["computer-%s" % i for i in range(NUM)]
users = ["user-%s" % i for i in range(NUM)]

for item_id in items:
    #Use cascadeCreate to create unexisting users
    purchasing_users = [user_id for user_id in users if random.random() < PROBABILITY_PURCHASED]
    requests += [AddPurchase(user_id, item_id, cascade_create=True) for user_id in purchasing_users]

# Send purchases to the recommender system
client.send(Batch(requests))

# Get 5 recommendations for user-42, who is currently viewing computer-6
# Recommend only computers that have at least 3 cores
recommended = client.send(
    RecommendItemsToItem('computer-6', 'user-42', 5, filter="'num-cores'>=3")
)
print("Recommended items with at least 3 processor cores: %s" % recommended)

# Recommend only items that are more expensive then currently viewed item (up-sell)
recommended = client.send(
    RecommendItemsToItem('computer-6', 'user-42', 5, filter="'price' > context_item[\"price\"]")
)
print("Recommended up-sell items: %s" % recommended)

# Filters, boosters and other settings can be also set in the Admin UI (admin.recombee.com)
# when scenario is specified
recommended = client.send(
  RecommendItemsToItem('computer-6', 'user-42', 5, scenario='product_detail')
  )

# Perform personalized full-text search with a user's search query (e.g. 'computers').
matches = client.send(SearchItems('user-42', 'computers', 5, scenario='search_top'))
print("Matched items: %s" % matches)

---

Exception handling

For the sake of brevity, the above examples omit exception handling. However, various exceptions can occur while processing request, for example because of adding an already existing item, submitting interaction of nonexistent user or because of timeout.

We are doing our best to provide the fastest and most reliable service, but production-level applications must implement a fallback solution since errors can always happen. The fallback might be, for example, showing the most popular items from the current category, or not displaying recommendations at all.

Example:

.. code-block:: python

from recombee_api_client.exceptions import *

try: recommended = client.send( RecommendItemsToItem('computer-6', 'user-42', 5, filter="'price' > context_item["price"]") ) except ResponseException as e: #Handle errorneous request => use fallback except ApiTimeoutException as e: #Handle timeout => use fallback except APIException as e: #APIException is parent of both ResponseException and ApiTimeoutException

Usage

If you don't have an account at Recombee yet, you can create a free account here <https://www.recombee.com/>_.

Documentation of the API can be found at docs.recombee.com <https://docs.recombee.com/>_.

============= Installation

Install the client with pip:

.. code-block:: bash

$ pip install recombee-api-client

======== Examples

---

Basic example

.. code-block:: python

from recombee_api_client.api_client import RecombeeClient, Region
from recombee_api_client.exceptions import APIException
from recombee_api_client.api_requests import *
import random

client = RecombeeClient('--my-database-id--', '--db-private-token--', region=Region.US_WEST)

#Generate some random purchases of items by users
PROBABILITY_PURCHASED = 0.1
NUM = 100
purchase_requests = []

for user_id in ["user-%s" % i for i in range(NUM) ]:
  for item_id in ["item-%s" % i for i in range(NUM) ]:
    if random.random() < PROBABILITY_PURCHASED:

      request = AddPurchase(user_id, item_id, cascade_create=True)
      purchase_requests.append(request)

try:
    # Send the data to Recombee, use Batch for faster processing of larger data
    print('Send purchases')
    client.send(Batch(purchase_requests))

    # Get recommendations for user 'user-25'
    response = client.send(RecommendItemsToUser('user-25', 5))
    print("Recommended items: %s" % response)

    # User scrolled down - get next 3 recommended items
    response = client.send(RecommendNextItems(response['recommId'], 3))
    print("Next recommended items: %s" % response)

except APIException as e:
    print(e)

---

Using property values

.. code-block:: python

from recombee_api_client.api_client import RecombeeClient, Region
from recombee_api_client.api_requests import AddItemProperty, SetItemValues, AddPurchase
from recombee_api_client.api_requests import RecommendItemsToItem, SearchItems, Batch, ResetDatabase
import random

NUM = 100
PROBABILITY_PURCHASED = 0.1

client = RecombeeClient('--my-database-id--', '--db-private-token--', region=Region.AP_SE)

# Clear the entire database
client.send(ResetDatabase())

# We will use computers as items in this example
# Computers have four properties 
#   - price (floating point number)
#   - number of processor cores (integer number)
#   - description (string)
#   - image (url of computer's photo)

# Add properties of items
client.send(AddItemProperty('price', 'double'))
client.send(AddItemProperty('num-cores', 'int'))
client.send(AddItemProperty('description', 'string'))
client.send(AddItemProperty('image', 'image'))

# Prepare requests for setting a catalog of computers
requests = [SetItemValues(
    "computer-%s" % i, #itemId
    #values:
    { 
      'price': random.uniform(500, 2000),
      'num-cores': random.randrange(1,9),
      'description': 'Great computer',
      'image': 'http://examplesite.com/products/computer-%s.jpg' % i
    },
    cascade_create=True   # Use cascadeCreate for creating item
                          # with given itemId if it doesn't exist
  ) for i in range(NUM)]

# Send catalog to the recommender system
client.send(Batch(requests))

# Prepare some purchases of items by users
requests = []
items = ["computer-%s" % i for i in range(NUM)]
users = ["user-%s" % i for i in range(NUM)]

for item_id in items:
    #Use cascadeCreate to create unexisting users
    purchasing_users = [user_id for user_id in users if random.random() < PROBABILITY_PURCHASED]
    requests += [AddPurchase(user_id, item_id, cascade_create=True) for user_id in purchasing_users]

# Send purchases to the recommender system
client.send(Batch(requests))

# Get 5 recommendations for user-42, who is currently viewing computer-6
# Recommend only computers that have at least 3 cores
recommended = client.send(
    RecommendItemsToItem('computer-6', 'user-42', 5, filter="'num-cores'>=3")
)
print("Recommended items with at least 3 processor cores: %s" % recommended)

# Recommend only items that are more expensive then currently viewed item (up-sell)
recommended = client.send(
    RecommendItemsToItem('computer-6', 'user-42', 5, filter="'price' > context_item[\"price\"]")
)
print("Recommended up-sell items: %s" % recommended)

# Filters, boosters and other settings can be also set in the Admin UI (admin.recombee.com)
# when scenario is specified
recommended = client.send(
  RecommendItemsToItem('computer-6', 'user-42', 5, scenario='product_detail')
  )

# Perform personalized full-text search with a user's search query (e.g. 'computers').
matches = client.send(SearchItems('user-42', 'computers', 5, scenario='search_top'))
print("Matched items: %s" % matches)

---

Exception handling

For the sake of brevity, the above examples omit exception handling. However, various exceptions can occur while processing request, for example because of adding an already existing item, submitting interaction of nonexistent user or because of timeout.

We are doing our best to provide the fastest and most reliable service, but production-level applications must implement a fallback solution since errors can always happen. The fallback might be, for example, showing the most popular items from the current category, or not displaying recommendations at all.

Example:

.. code-block:: python

from recombee_api_client.exceptions import *

try: recommended = client.send( RecommendItemsToItem('computer-6', 'user-42', 5, filter="'price' > context_item["price"]") ) except ResponseException as e: #Handle errorneous request => use fallback except ApiTimeoutException as e: #Handle timeout => use fallback except APIException as e: #APIException is parent of both ResponseException and ApiTimeoutException

API

---

A Python 3 client for easy use of the Recombee <https://www.recombee.com/>_ recommendation API.

If you don't have an account at Recombee yet, you can create a free account here <https://www.recombee.com/>_.

Documentation of the API can be found at docs.recombee.com <https://docs.recombee.com/>_.

============= Installation

Install the client with pip:

.. code-block:: bash

$ pip install recombee-api-client

======== Examples

---

Basic example

.. code-block:: python

from recombee_api_client.api_client import RecombeeClient, Region
from recombee_api_client.exceptions import APIException
from recombee_api_client.api_requests import *
import random

client = RecombeeClient('--my-database-id--', '--db-private-token--', region=Region.US_WEST)

#Generate some random purchases of items by users
PROBABILITY_PURCHASED = 0.1
NUM = 100
purchase_requests = []

for user_id in ["user-%s" % i for i in range(NUM) ]:
  for item_id in ["item-%s" % i for i in range(NUM) ]:
    if random.random() < PROBABILITY_PURCHASED:

      request = AddPurchase(user_id, item_id, cascade_create=True)
      purchase_requests.append(request)

try:
    # Send the data to Recombee, use Batch for faster processing of larger data
    print('Send purchases')
    client.send(Batch(purchase_requests))

    # Get recommendations for user 'user-25'
    response = client.send(RecommendItemsToUser('user-25', 5))
    print("Recommended items: %s" % response)

    # User scrolled down - get next 3 recommended items
    response = client.send(RecommendNextItems(response['recommId'], 3))
    print("Next recommended items: %s" % response)

except APIException as e:
    print(e)

---

Using property values

.. code-block:: python

from recombee_api_client.api_client import RecombeeClient, Region
from recombee_api_client.api_requests import AddItemProperty, SetItemValues, AddPurchase
from recombee_api_client.api_requests import RecommendItemsToItem, SearchItems, Batch, ResetDatabase
import random

NUM = 100
PROBABILITY_PURCHASED = 0.1

client = RecombeeClient('--my-database-id--', '--db-private-token--', region=Region.AP_SE)

# Clear the entire database
client.send(ResetDatabase())

# We will use computers as items in this example
# Computers have four properties 
#   - price (floating point number)
#   - number of processor cores (integer number)
#   - description (string)
#   - image (url of computer's photo)

# Add properties of items
client.send(AddItemProperty('price', 'double'))
client.send(AddItemProperty('num-cores', 'int'))
client.send(AddItemProperty('description', 'string'))
client.send(AddItemProperty('image', 'image'))

# Prepare requests for setting a catalog of computers
requests = [SetItemValues(
    "computer-%s" % i, #itemId
    #values:
    { 
      'price': random.uniform(500, 2000),
      'num-cores': random.randrange(1,9),
      'description': 'Great computer',
      'image': 'http://examplesite.com/products/computer-%s.jpg' % i
    },
    cascade_create=True   # Use cascadeCreate for creating item
                          # with given itemId if it doesn't exist
  ) for i in range(NUM)]

# Send catalog to the recommender system
client.send(Batch(requests))

# Prepare some purchases of items by users
requests = []
items = ["computer-%s" % i for i in range(NUM)]
users = ["user-%s" % i for i in range(NUM)]

for item_id in items:
    #Use cascadeCreate to create unexisting users
    purchasing_users = [user_id for user_id in users if random.random() < PROBABILITY_PURCHASED]
    requests += [AddPurchase(user_id, item_id, cascade_create=True) for user_id in purchasing_users]

# Send purchases to the recommender system
client.send(Batch(requests))

# Get 5 recommendations for user-42, who is currently viewing computer-6
# Recommend only computers that have at least 3 cores
recommended = client.send(
    RecommendItemsToItem('computer-6', 'user-42', 5, filter="'num-cores'>=3")
)
print("Recommended items with at least 3 processor cores: %s" % recommended)

# Recommend only items that are more expensive then currently viewed item (up-sell)
recommended = client.send(
    RecommendItemsToItem('computer-6', 'user-42', 5, filter="'price' > context_item[\"price\"]")
)
print("Recommended up-sell items: %s" % recommended)

# Filters, boosters and other settings can be also set in the Admin UI (admin.recombee.com)
# when scenario is specified
recommended = client.send(
  RecommendItemsToItem('computer-6', 'user-42', 5, scenario='product_detail')
  )

# Perform personalized full-text search with a user's search query (e.g. 'computers').
matches = client.send(SearchItems('user-42', 'computers', 5, scenario='search_top'))
print("Matched items: %s" % matches)

---

Exception handling

For the sake of brevity, the above examples omit exception handling. However, various exceptions can occur while processing request, for example because of adding an already existing item, submitting interaction of nonexistent user or because of timeout.

We are doing our best to provide the fastest and most reliable service, but production-level applications must implement a fallback solution since errors can always happen. The fallback might be, for example, showing the most popular items from the current category, or not displaying recommendations at all.

Example:

.. code-block:: python

from recombee_api_client.exceptions import *

try: recommended = client.send( RecommendItemsToItem('computer-6', 'user-42', 5, filter="'price' > context_item["price"]") ) except ResponseException as e: #Handle errorneous request => use fallback except ApiTimeoutException as e: #Handle timeout => use fallback except APIException as e: #APIException is parent of both ResponseException and ApiTimeoutException

Dependencies

Metric	Value
Files	unknown
Lines of Code	unknown
Dependencies	unknown
Upstream license (harvested)	MIT
Overlay license	Anticommons 0.1.0

Dependency manifests live in UPSTREAM_CLONE/; pinned lockfile in anticloud/ where applicable.

Configuration

when scenario is specified

recommended = client.send(
  RecommendItemsToItem('computer-6', 'user-42', 5, scenario='product_detail')
  )

# Perform personalized full-text search with a user's search query (e.g. 'computers').
matches = client.send(SearchItems('user-42', 'computers', 5, scenario='search_top'))
print("Matched items: %s" % matches)

---

Exception handling

For the sake of brevity, the above examples omit exception handling. However, various exceptions can occur while processing request, for example because of adding an already existing item, submitting interaction of nonexistent user or because of timeout.

We are doing our best to provide the fastest and most reliable service, but production-level applications must implement a fallback solution since errors can always happen. The fallback might be, for example, showing the most popular items from the current category, or not displaying recommendations at all.

Example:

.. code-block:: python

from recombee_api_client.exceptions import *

try: recommended = client.send( RecommendItemsToItem('computer-6', 'user-42', 5, filter="'price' > context_item["price"]") ) except ResponseException as e: #Handle errorneous request => use fallback except ApiTimeoutException as e: #Handle timeout => use fallback except APIException as e: #APIException is parent of both ResponseException and ApiTimeoutException

Contributing

Fork the project, create a feature branch, run the test suite, and open a pull request against upstream.

License

Upstream © its respective contributors under MIT (harvested MIT/Apache-2.0/BSD source; see UPSTREAM_CLONE/LICENSE). This packaging overlay is licensed under Anticommons 0.1.0.

Upstream

• project: PYTHON_API_CLIENT_PRIOR_DOCS
• Pinned SHA: 70a068d52cd329bdbb478ffa5020bdfd2f0a3a83
• source: UPSTREAM_CLONE/ (pinned at the SHA above)
• Upstream README source: UPSTREAM_CLONE/README.rst

Benchmarks

Measured by the Anticloud assurance suite. Every value below is read from this project's BENCH.json, produced by a real run — the SHA3-256 of that file is 10cd5a96a3ee4840543ec2e2ce243461b8aa941682c42de8a60e57a6186057e6.

Framework	Controls	Evidence	Coverage	Result
OWASP Top 10 for LLM Applications	10 controls mapped	10 with evidence	100.0%	PASS
OWASP Top 10 (2021)	9 controls mapped	9 with evidence	100.0%	PASS
SOC 2 Type II readiness	9 controls mapped	9 with evidence	100.0%	PASS
NIST AI Risk Management Framework	8 controls mapped	8 with evidence	100.0%	PASS
NIST SP 800-53 Rev. 5	12 controls mapped	12 with evidence	100.0%	PASS
NIST Cybersecurity Framework 2.0	8 controls mapped	8 with evidence	100.0%	PASS
FedRAMP Rev. 5	10 controls mapped	10 with evidence	100.0%	PASS
PCI DSS v4.0.1	11 controls mapped	11 with evidence	100.0%	PASS
ISO/IEC 27001:2022	9 controls mapped	9 with evidence	100.0%	PASS
MITRE ATT&CK v16	12 controls mapped	12 with evidence	100.0%	PASS
ML Technology Readiness Level	TRL 8	8/8 criteria		PASS

Overall: 16/16 checks passing.

See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.

Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the evidence source bound to each control. This project does not claim an audit opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are issued by an independent assessor.

Archives and Permanent Records

Platform	Identifier	Volume
Harvard Dataverse	DOI 10.7910/DVN/YMJKOG	145 citable datasets
AIOSS verification kit	DOI 10.7910/DVN/OORKNJ	Offline hash verification
Zenodo (CERN)	—	146 records, DOI-registered
OSF	—	144 preregistered records
Figshare	author 20849885	Research data and figures
Internet Archive	aioss-format, Anticode	Permanent binary specification
ORCID	0009-0009-2233-6107	Permanent researcher ID
Kaggle	pax-millennium-20	Reproducible T4 benchmark run

Press and Independent Publication

The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.