MCP Random Number and why it's more useful than it seems

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What? MCP to get random numbers within a defined range. It requests true random numbers from random.org (the randomness comes from atmospheric noise). Why? A couple of weeks ago, while working on another MCP, I noticed that Claude has a very strong preference for certain random numbers. Obviously, nobody expects perfect randomness from an LLM. But out of curiosity, I decided to test this by asking 3 LLMs for random numbers between 1-100, 100 times each. Result: all models heavily favored the number 73. Full charts are in the repo.

Visit product View on Hacker NewsJun 22, 2025

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Product HuntUnlikely to reach the leaderboard · Strong signals: claude, model, mcp · Missing: mac, agents, macos

44%44% 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.

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

40%40% 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

37%37% predicted probability of success on AppSumo, 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

34%34% 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: ios · Missing: supports, reddit linkedin, podcasting

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

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

24%24% 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

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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