A

A Solution to durably store your likes/dislikes counts

Hacker News

A Solution to durably store your likes/dislikes counts

Hi All, vDB is Solution to durably store your likes/dislikes count (or any count of your choice). Its takes O(1) time to read and put unsigned long values to DB, as it similar to Arrays (there are no keys but you use index to store counts). Single header only C++ file for storing and retreiving numbers. And one more thing it does this without increasing the file size, as its not append only DB. Which also means it can be slower on simultenous writes. Well if you task is just to store likes/dislikes counts, than it must not be much of a problem. As reads are done more than writes for such cases.

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3points
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
56%56% 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 · Missing: supports, reddit linkedin, podcasting
41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: single · Missing: mac, agents, macos
35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
33%33% 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
14%14% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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