Mi

Midjourney-Python-API

Hacker News

Midjourney-Python-API

This Python client is built for the unofficial MidJourney API, leveraging a Discord self bot and utilizing the Merubokkusu/Discord-S.C.U.M library. Please be mindful that there might be risks associated with the use of self bots, as per issue #66. Key Features: 1: Info retrieval 2: Imagine prompt 3: Image upscale and vectorization by label 4: All message returns via WebSocket, including banned words check and image processing 5: Auto reconnect WebSocket

Visit product View on Hacker NewsMay 19, 2023

Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

50%50% 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 HackersFits the IH revenue-focused audience · Strong signals: including · 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 · Missing: mobile apps, ios, personal

50%50% 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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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

9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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