The AI SDK for TypeScript and Python

Hacker News

Hey HN! I'm Yoeven from JigsawStack, and I'm excited to share the launch of the AI SDK that lets you easily use a suite of fine-tuned AI utilities that can fully automate different aspects of your application in just three lines of code ``` import { JigsawStack } from "jigsawstack"; const jigsaw = JigsawStack({ apiKey: "your-api-key" }); await jigsaw.sentiment("I love how easy it is to get started"); //or you can run more complex tasks like an AI scraper await jigsaw.web.ai_scrape({ url: " https://www.amazon.com/Cadbury-Mini-Caramel-Eggs-Bulk/dp/B0C... ", element_prompts: ["prices"], }); ``` The goal is to make the DX enjoyable and as simple as possible, we would love feedback and suggestions on how we can make it better. The code is open source and always open for contribution!

Visit product View on Hacker NewsAug 26, 2024

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: tasks, code, open · Missing: mac, agents, macos

82%82% 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: started · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

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

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

57%57% 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

39%39% 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

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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