CATCH #1302
DETECTED SEP 10, 2026 20:19 UTC
THE CATCHscore 10/10
EXHIBIT 1302 · FLASH
Sep 10, 2026 20:19 UTC
pypi
openai
OpenAI's latest `openai` package (version 3.13.0) hints at a new model, `gpt-4o-2024-08-06`, and new `realtime` and `responses` beta API connections, along with numerous other feature flags and textual changes.
READY TO POST:
looks like openai just dropped 3.13.0 and there's a reference to a new model: `gpt-4o-2024-08-06`. also, beta `realtime` and `responses` connections? spicy!
openai's new python sdk has a new model id and some beta api hints, including 'realtime' and 'responses' connections. wonder what openai is cooking with gpt-4o-2024-08-06!
EVIDENCE — the receipts
openai 3.12.0 -> 3.13.0: +1069 strings, -628 strings, 398 interesting
[model_id] (1)
+ py
with client.chat.completions.stream(
model="gpt-4o-2024-08-06",
messages=[...],
) as stream:
for event in stream:
if event.type == "content.delta":
print(event.delta, flush=True, end="")
[feature_flag] (5)
+ internal_error
+ py
connection = client.beta.realtime.connect(...).enter()
# ...
connection.close()
+ py
connection = client.beta.responses.connect(...).enter()
# ...
connection.close()
+ py
connection = client.beta.realtime.connect(...).enter()
# ...
connection.close()
+ py
connection = client.beta.responses.connect(...).enter()
# ...
connection.close()
[sentence] (397)
+ py
with client.chat.completions.stream(
model="gpt-4o-2024-08-06",
messages=[...],
) as stream:
for event in stream:
if event.type == "content.delta":
print(event.delta, flush=True, end="")
+ py
connection = client.beta.realtime.connect(...).enter()
# ...
connection.close()
+ py
connection = client.beta.responses.connect(...).enter()
# ...
connection.close()
+ py
connection = client.beta.realtime.connect(...).enter()
# ...
connection.close()
+ py
connection = client.beta.responses.connect(...).enter()
# ...
connection.close()
+ - Based on your data it seems like you're trying to fine-tune a model for {ft_type}
- For classification, we recommend you try one of the faster and cheaper models, such as `ada`
- For classification, you can estimate the expected model performance by keeping a held out dataset, which is not used fo
+ - There are {len(long_indexes)} examples that are very long. These are rows: {long_indexes}
For conditional generation, and for classificati
caught by the 20-minute sweep ·
the live wire · all receipts