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Symphony – Use GPT-4 to call functions in sequence

Hacker News

Symphony – Use GPT-4 to call functions in sequence

Hey HN! I'm excited to introduce Symphony – a toolkit designed to help developers write functions and let GPT-4 call them in whatever sequence that makes most sense based on conversation. I've been quite amazed[1] by GPT-4's recent ability to both detect when a function needs to be called and to respond with JSON that adheres to the function's signature. Since developers currently append descriptions of functions to API calls[2], I often found myself wishing for a toolkit that would automatically create these descriptions as I added and debugged functions during development. You can get started by cloning the repository and adding functions by following the guide at https://symphony.run/docs As of now, the toolkit supports functions in TypeScript. I'll be adding support for more languages and features based on your feedback :) [1] - Symphony Showcase: https://symphony.run/showcase [2] - Function calling and other API updates from OpenAI: https://openai.com/blog/function-calling-and-other-api-updat...

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Product HuntOn track for Day 1 leaderboard · Strong signals: openai, open · Missing: mac, agents, macos
81%81% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
56%56% 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 · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
31%31% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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