Mr

MrMarkov (Markov Text Generator as an API)

Hacker News

MrMarkov (Markov Text Generator as an API)

http://mrmarkov.herokuapp.com/text Hi all, this is a personal project I've been working on for two weeks or so. I was interested in Markov Chains, and came across their use to generate text, and also wanted to learn how to create an API. So, I combined the two to make an API that generates random text based on text you submit to it. To generate your own text with this, just submit a POST request to the above URL with the original text you want to use as the base text to generate new text. For example (using Requests from Python): import requests payload = {'original_text':'''This is a test. This is only a test. This is my test.'''} requests.post("http://mrmarkov.herokuapp.com/text",data=payload) Longer pieces of text, such as paragraphs from Shakespeare or Wikipedia generate more realistic sentences. The response will have a field called "sentence", which will contain the generated text. Try it out! Let me know what you think!

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using · Missing: mac, agents, macos
67%67% 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 · Missing: https docs, excited, just released
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.
Indie HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, para · Missing: mobile apps, ios, entrepreneurs
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% 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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