Xy

Xytext – Turn LLM Prompts into Structured API Endpoints

Hacker News

Xytext – Turn LLM Prompts into Structured API Endpoints

I'm excited to share a tool we've been working on called Xytext. It's designed for developers and data analysts who want to harness the power of Large Language Models (LLMs) without getting bogged down in complexity. Xytext allows you to create API endpoint functions from simple prompts. Imagine defining input prompts and getting structured, predictable outputs that feel natural and manageable, even in production environments. Here's what makes Xytext different: Ease of Use: A straightforward UI to create and manage your functions. Hosted Function API: Quickly categorize, summarize, and recommend actions for inputs. Think low volume, high complexity (e.g. support tickets, google reviews) Customizable: Tailor your function logic, input prompts, and output schemas to your needs. We're looking for early users to try Xytext and provide feedback. To get started: Sign up at xytext.com > login Verify your email, then your account will be activated in a few hours with credits to play around with the system (unfortunately have to put this in place to prevent abuse of the free credits) Create your first function, test it out, and tell us what you think! We'd especially love to hear from HN users who have not worked with LLMs before and can provide insights on how Xytext compares to doing things manually or using other tools. Looking forward to your feedback and questions!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · 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: model, google, user · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, 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
58%58% 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 · Strong signals: reviews, host, users · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, users · Missing: mobile apps, ios, personal
40%40% 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 · 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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