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Superfunctions – AI prompt templates as an API

Hacker News

Superfunctions – AI prompt templates as an API

Hi HN, https://superfunctions.com I'm working on a web app that allows Ai prompts to function as an API. I want to make it easier for developers to use Ai. I've found it painful to monitor, cache, and iterate on prompts. superfunctions.com is designed to be the simplest building block to create Ai powered apps and scripts. Simplest example I can think of: You want an api to convert human-named colors to hex You can write a prompt like: "convert {{query.color}} to color, only output hex for css" and then you can call your prompt with https://superfn.com/fn/color-to-hex?color=blue and the response will contain: #0000FF Watch a short video intro: https://www.youtube.com/watch?v=KdO1TBUbRuA Login without needing an account: https://superfunctions.com/login/anon I'm still sorting out a few bugs, but it's usable in it's current state. This is my first solo project, so I'm very open to feedback and suggestions. -Trent

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
58%58% 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.
TrustMRRFits verified-revenue profile · Strong signals: apps, video · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, open · Missing: mac, agents, macos
53%53% 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: ide, 000, io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
33%33% 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 · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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