Tysm, a Rust crate for using ChatGPT with Structured Outputs

Hacker News

Hi HN! This Christmas I made `tysm`, a rust crates which simplifies using ChatGPT with Structured Outputs. Essentially you just tell it the rust type you want the output to conform to, and it handles creating the JSON Schema, passing it to ChatGPT's chat-completions API, and deserializing the response. Structured Outputs are very useful because they guarantee that the response will conform to whatever type you expect. I'm already finding my crate very useful since I love using Structured Outputs but I hate writing JSON Schemas myself. Technical details for rust heads: To generate the JSON Schema, it uses an unreleased version of the `schemars` crate. (Hopefully it will be released soon and then I won't have to put up this disclaimer anymore.) For deserialization, it uses `serde`.

Visit product View on Hacker NewsDec 27, 2024

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AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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Product HuntOn track for Day 1 leaderboard · Strong signals: chatgpt, using · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

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