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FastIndex, an open-source search engine indexation tool

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FastIndex, an open-source search engine indexation tool

There's a lot of paid search engine indexation tools out there and I wanted to create my own. Been working as an engineer for over a decade now and my open-source contribution has always been something I wanted to do. Thus I decided to create FastIndex, an open-source search engine indexation alternative to paid solutions such as TagParrot, URLMonitor, Omega Indexer and many more. Source: https://github.com/maurocasas/fastindex Wiki: https://github.com/maurocasas/fastindex/wiki

Visit productView on Hacker NewsSep 26, 2024
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
75%75% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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