In this mini episode, I break down OpenAI’s new GPT 5.5 and GPT 5.5 Pro after weeks of early testing. I walk through three real jobs I threw at the model: building an app for me to teach my second grader more advanced subtraction concepts, tackling a tech debt problem in the ChatPRD codebase, and hacking into a proprietary Bluetooth pixel display that every other model had failed me on. My verdict: higher intelligence, better efficiency, and genuinely autonomous long-running loops that change what I think is worth tackling.

What you’ll learn:

How I think about GPT 5.5 Pro’s pricing vs engineering time, and when I believe the “intelligence tax” is worth paying
Why I treat GPT 5.5 as a developer model first, and why I couldn’t find a consumer use case that justified its intelligence
The exact prompt pattern I use to unlock a long-running autonomous subagent loop
How I got a near-six-hour autonomous run to one-shot 98% of edge cases in a migration over millions of chat threads and drop my Sentry error rate to the floor
Why I’m now throwing GPT 5.5 at tech debt, flaky tests, and security backlogs first
How I combined a Bluetooth packet sniffer and GPT 5.5 to reverse-engineer a proprietary pixel speaker after Claude Code and GPT 5.4 both gave up
How I use the /personality command inside Codex to swap the default “baked potato” tone for something I actually enjoy working with

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In this episode, I cover:

(00:00) Introduction to GPT 5.5 testing

(00:40) What is GPT 5.5 and how much does it cost?

(03:23) Testing GPT 5.5 in ChatGPT: the intelligence overhang problem

(07:12) Moving to Codex: where GPT 5.5 really shines

(16:01) Hacking a Chinese Bluetooth speaker

(21:47) Final thoughts on GPT 5.5’s intelligence and efficiency

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Tools referenced:

• GPT 5.5 and GPT 5.5 Pro: https://openai.com/index/introducing-gpt-5-5/

• Codex: https://openai.com/codex/

GPT 5.5 just did what no other model could

How I AI