PlugNFunc – Invoke ChatGPT Plugins as Functions by API

Hacker News

Hello HN Community, I'm thrilled to introduce you to a tool I've developed called PlugNFunc. - ChatGPT plugins are valuable but usually locked behind a paywall. - OpenAI's function-calling capabilities are powerful but cumbersome to implement and debug. PlugNFunc tackles both issues by enabling users to easily invoke ChatGPT plugins as functions, and what's more—it's completely free to use! Why PlugNFunc? We created PlugNFunc to democratize access to ChatGPT's advanced features and make it easier for developers to integrate these capabilities into their applications without the complications typically associated with OpenAI's function calls. Key Features Ease of Invocation: Invoke ChatGPT plugins as if you're making a simple function call. Contributing We'd love to hear your feedback and suggestions for improvement. We're looking for valuable input from the community: - Are there any usability improvements you can suggest? - Would you find a more detailed tutorial or video walkthrough beneficial? We're keen on receiving your insights to make PlugNFunc better.

Visit product View on Hacker NewsOct 21, 2023

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: video, users · Missing: mobile apps, ios, personal

47%47% 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

11%11% 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, introduce · Missing: web3, crypto, cryptocurrency

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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