KnowledgeForAI – remote MCP for various data sources

Hacker News

Hosted (remote) MCP enabling ChatGPT, Claude, and other LLMs or agents to search Reddit, Twitter, App Store, Play Store I was tired of paying to different services just to be able to search and anlalyze data from different sources. I wanted to analyze appstore and playstore reviews for a category of apps, and saw that multiple startups charge subscription fees for what essentially, should be a pay as you go MCP, to be used with your AI applications. So, made knowledgeforai.com, with which your AI will be able to access reddit, hackernews, play store and app store reviews, twitter and be able to analyze them Please do check it out and any thoughts will be appreciated.

Visit product View on Hacker NewsJan 29, 2026

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

79%79% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, host · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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