I

I made an AI data Analyst to process n number of connections

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I made an AI data Analyst to process n number of connections

Hey guys, I’m a uni undergraduate pursuing my bachelors in data science and I’ve taken a keen interest in working with LLMs to generate data visualisations easily. I’ve recently noticed that GPT doesn’t allow a lot of generations due to its rate limiting and to counter this I’ve made a tool which utilises GPT API(bit of an overkill tbh) to process prompts and provide an output (with option to modify those outputs as well) I hope you guys like it and feel free to give me any advise or feedback about running this app. https://www.sheetbot.ai/

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual · Missing: mac, agents, macos
70%70% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
39%39% 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 · Missing: supports, reddit linkedin, podcasting
38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
26%26% 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
12%12% 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.

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