Alright, so you've got Cartesia humming and you're running a few experiments. Congrats. But let me guess: you're checking the results by… refreshing the dashboard? Maybe you've set up a Slack alert for when a variant hits 95% significance? That's cute, but it's also reactive, manual, and frankly, a bit amateur.
What you need is a basic monitoring pipeline. Something that polls the Cartesia API, checks your key metrics, and can alert or even take automated actions (like pausing a clearly losing variant) without you having to lift a finger. This isn't about replacing Cartesia's UI; it's about extending it into your own stack.
Here's a simple Python script to get you started. It's not production-grade—no error handling, no async—but it shows the bones. You'll need the `requests` library and an API key with at least read permissions.
```python
import requests
import time
from datetime import datetime
CARTESIA_API_KEY = "your_api_key_here"
EXPERIMENT_ID = "exp_your_experiment_id"
API_BASE = "https://api.cartesia.ai/v1"
headers = {"Authorization": f"Bearer {CARTESIA_API_KEY}"}
def fetch_experiment_results(experiment_id):
url = f"{API_BASE}/experiments/{experiment_id}/results"
response = requests.get(url, headers=headers)
if response.status_code == 200:
return response.json()
else:
print(f"Failed to fetch results: {response.status_code}")
return None
def check_conversion_delta(results, variant_name, threshold_delta=-0.02):
# A simple check: if a variant's conversion rate is below control by threshold, flag it.
# This assumes a specific structure in the 'results' response—you'll need to adapt.
variants = results.get('metrics', {}).get('conversion_rate', {})
control_rate = variants.get('control', 0)
test_rate = variants.get(variant_name, 0)
delta = test_rate - control_rate
if delta < threshold_delta:
return f"⚠️ Alert: {variant_name} is underperforming by {delta:.3f}"
return None
# Simple polling loop
while True:
print(f"nChecking at {datetime.now()}")
data = fetch_experiment_results(EXPERIMENT_ID)
if data:
alert = check_conversion_delta(data, "variant_b")
if alert:
print(alert)
# Here you could send to Slack, PagerDuty, or call Cartesia's API to pause the variant.
time.sleep(300) # Poll every 5 minutes
```
The real trick is what you do with the data. The script above just prints a warning, but you could easily hook it into your Slack bot, write a metric to a data warehouse for a unified dashboard, or even call Cartesia's API to pause a variant automatically if it's catastrophically bad (just be careful with that kind of power).
The Cartesia API docs are decent, but you'll likely need to massage the response to get the metrics you care about. I've found their `results` endpoint can be a bit of a nesting doll depending on your experiment setup. The key is to start simple: track one metric, one experiment. Then build out from there.
just sayin'
Data over dogma.