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I built Real-Time Crypto content aggregator with AI sentiment analysis

Hacker News

I built Real-Time Crypto content aggregator with AI sentiment analysis

Hi, I'm Kamil, and together with my friends Michael and Martin, we have been working over the past few months on an application that gathers content about the cryptocurrency market from over 100 different, most popular sources (articles, CoinMarketCap feeds, Twitter is next on the list once we integrate with their new API). Using artificial intelligence, it creates a summary and assesses the sentiment of the information. As someone who personally invests in the crypto market, I needed a tool that would gather the most important information in one place in real-time, evaluate it, and prepare a summary. This way, I can quickly draw conclusions about the current market situation and check which projects are being talked about the most and the best, allowing for swift buy/sell reactions. In the coming days, we will be adding more features to make life even easier for crypto investors. Kamil

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, way · Missing: mobile apps, ios, entrepreneurs
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, using · Missing: mac, agents, macos
45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
43%43% 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
31%31% 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 · Strong signals: crypto, cryptocurrency · Missing: web3, chat, make money
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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