OpenAI Just Dropped Decisions API (7 WILD Use Cases)

OpenAI Just Dropped Decisions API (7 WILD Use Cases)

🎙 Pat Simmons 👥 29K 📅 October 8, 2026 ⏱ 25 min 👁 30K 📄 tutorial 🧭 2026-10-15
Available in: English (current) Français

Keywords

Decisions APIOpenAIJevreal-time decisionAI applications

Summary

The video provides an in-depth introduction to OpenAI’s new Decisions API, designed for ultra-fast and low-cost decision-making tasks. It explains the API’s mechanism: given a prompt and a list of options, it returns probabilities instead of generating text, enabling decisions in ~150 ms. The creator builds and tests seven real-world applications: a Pomodoro timer with screen monitoring, voice-based Mac control, an X feed cleaner, an auto-blur tool for privacy, a Wikipedia race between models, a photo-based calorie tracker, and an automated video editing tool. Each demo showcases the API’s speed, cost efficiency, and integration with coding agents like Claude. The video also compares the API to the competing model Jev, highlighting differences in cost, speed, and image support. The creator emphasizes the API’s transformative potential, particularly for real-time agent applications, and provides practical guidance for getting started. Overall, the video is a comprehensive and engaging demonstration of a significant AI development.

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Critical Evaluation

Value of the Information & Strength of the Argument

The value of the information lies in the hands-on demonstrations that illustrate the API’s capabilities and limitations. The creator provides concrete performance metrics (cost per million tokens, decision latency) and compares it to alternatives, strengthening the argument that the Decisions API enables new classes of applications. The argumentation is solid, as each use case is built and tested, offering empirical evidence. However, the evaluation is somewhat subjective, relying on personal experiences rather than controlled benchmarks. The creator also acknowledges minor issues, such as misclassifications, which adds credibility. The overall case for the API as a game-changer is persuasive, backed by tangible results.

Scientific Rigor, Source Quality, Title Accuracy

The scientific rigor is moderate; the video is a practical tutorial rather than a peer-reviewed study. The creator cites no external sources but relies on personal experiments and OpenAI’s documentation (implicitly). The title accurately reflects the content, as the video indeed covers seven use cases of the Decisions API. The inclusion of cost and speed comparisons with Jev adds depth, though the source of these figures (likely OpenAI’s pricing) is not explicitly named. The video would benefit from citing official documentation or benchmark studies for inflating reliability, but as a hands-on demo, it is informative and credible.

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Title / Content Match

The title accurately reflects the content: the video introduces the Decisions API and demonstrates seven concrete use cases.

Quality & Reliability

8/10

The video is a hands-on demonstration of the Decisions API, with reproducible examples and clear metrics. However, it lacks academic rigor and relies on personal testing, which is acceptable for a practical tutorial.

Chapters

Cited Sources

Contribution & Novelties

The contribution of this video is in showcasing the practical applications of a newly released API, making it accessible to developers and hobbyists. It demonstrates that the Decisions API can power real-time decision-making tasks at low cost, opening possibilities for autonomous agents and automation. The seven use cases serve as templates for further innovation. The comparison with Jev highlights different trade-offs, encouraging informed choices.

Pour aller plus loin :

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Radar Profile

The radar profile shows high scores in information quantity and quality, moderate technical depth, and good reliability, reflecting a well-structured tutorial with practical evidence.

Reliability 7/10