Playground for OpenAI Function Calling

Hacker News

Hey everyone, I'm Petr. I'm excited to share LangTale Playground ( https://langtale.ai/playground ), a first-of-its-kind tool enabling anyone to experiment with OpenAI function calls without coding. Born out of a hackathon project, it's now a part of our broader LangTale platform aimed at tackling common developer challenges with Language Learning Models (LLM). These include prompt integration, testing and debugging, version control, auditing, and usage/cost management. Here's our tech stack: Next.js by Vercel, Tailwind CSS, OpenAI, PlanetScale's Vitess database, and the Radix UI & Shadcn's component library. We're eager to hear your feedback as we launch LangTale Playground today. Give it a spin and let us know what you think.

Visit product View on Hacker NewsJun 28, 2023

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

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

64%64% 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

45%45% 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

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

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, calls · Missing: plus, intuitive, reviews

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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