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Why I love GPT-5.5 for hard problems

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:* 1. How I think about GPT 5.5 Pro’s pricing vs engineering time, and when I believe the “intelligence tax” is worth paying 2. 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 3. The exact prompt pattern I use to unlock a long-running autonomous subagent loop 4. 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 5. Why I’m now throwing GPT 5.5 at tech debt, flaky tests, and security backlogs first 6. 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 7. How I use the /personality command inside Codex to swap the default “baked potato” tone for something I actually enjoy working with *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 *Tools referenced:* • GPT 5.5 and GPT 5.5 Pro: https://openai.com/index/introducing-gpt-5-5/ • Codex: https://openai.com/codex/ • ChatGPT: https://chat.openai.com/ • Claude Code: https://claude.ai/code • Sentry: https://sentry.io/ • Divoom MiniToo: https://divoom.com/products/minitoo *Other references:* • OpenAI Codex Security: https://openai.com/index/codex-security-now-in-research-preview/ *Where to find Claire Vo:* ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email jordan@penname.co._

Claire Vohost
Apr 23, 202623mWatch on YouTube ↗

Episode Details

EPISODE INFO

Released
April 23, 2026
Duration
23m
Channel
How I AI
Watch on YouTube
▶ Open ↗

EPISODE DESCRIPTION

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:*

  1. How I think about GPT 5.5 Pro’s pricing vs engineering time, and when I believe the “intelligence tax” is worth paying
  2. 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
  3. The exact prompt pattern I use to unlock a long-running autonomous subagent loop
  4. 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
  5. Why I’m now throwing GPT 5.5 at tech debt, flaky tests, and security backlogs first
  6. 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
  7. How I use the /personality command inside Codex to swap the default “baked potato” tone for something I actually enjoy working with

*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 *Tools referenced:*

*Other references:*

• OpenAI Codex Security: https://openai.com/index/codex-security-now-in-research-preview/ *Where to find Claire Vo:* ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email jordan@penname.co._

SPEAKERS

  • Claire Vo

    host

    Product leader and AI enthusiast; host of the How I AI podcast.

EPISODE SUMMARY

In this episode of How I AI, featuring Claire Vo, Why I love GPT-5.5 for hard problems explores gPT-5.5 Pro excels at autonomous coding, migrations, and device hacking GPT‑5.5 and GPT‑5.5 Pro feel meaningfully more capable and token-efficient on complex work, but their pricing makes them an “intelligence tax” that needs clear ROI.

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