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Telegram message when HN string match

Hacker News

Telegram message when HN string match

Hey HN, I’ve had it on my todo list to share a project on HN for far too long, so posting this quick project I made this morning. The tool looks for a string in the post titles and comments and sends a telegram message with a link to the match. I can imagine a few use cases, but for me it’s interesting to see when $employer is mentioned in the comments. It could also be interesting for $potential_employer mentions. I’m sure there are better or easier ways to do this, and do feel empowered to comment with a bash one-liner that uses 1/1,000,000th the resources :)

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
56%56% 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.
Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released
51%51% 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
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
21%21% 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
11%11% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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