scripod.com

Why Dwarkesh is Wrong about Computer Use + How OpenAI shipped its Jev competitor in 1 Week

OpenAI’s DevDay announcements frame a rapidly evolving agent stack: persistent cloud computers, faster and more capable Computer Use, responsive tool-calling APIs, and infrastructure for long-running agents.
Ari Weinstein explains why Computer Use has changed dramatically through better models, self-debugging, richer UI representations, generated code, and improved harnesses. The conversation then turns to trust, consent, software testing, and the path from human-level speed to superhuman operation. Nikunj Handa details OpenAI’s API work on asynchronous tool calls, WebSockets, low-latency inference, the rapidly built Decisions API, prompt caching, compaction, and higher-level agent abstractions. Together, the guests describe OpenAI’s attempt to make agents reliable, fast, persistent, and easier to build on.
03:01
03:01
Every Dot gets its own cloud computer
05:23
05:23
Computer use is 180 degrees different now
10:00
10:00
Computer use can see the whole application
12:32
12:32
Computer use is already faster than average humans
15:36
15:36
Agents are now trusted with real-world payments
18:16
18:16
The agent becomes its own QA
20:18
20:18
Models no longer have to pause while tools run
24:26
24:26
Jev inspired OpenAI’s Decisions API sprint
27:40
27:40
The Decisions API starts with Luna, not a new model
30:49
30:49
The Decisions API is already classifying support tickets
33:34
33:34
A 12-hour cache guarantee is coming
35:47
35:47
Compaction is built into the Agents API harness
38:39
38:39
Building an AI-native cloud