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Aidmin – Web-based database GUI

Hacker News

Aidmin – Web-based database GUI

Hi All, I’ve been working on Aidmin for quite some time now, and finally released a beta version of it. I’ve focused most of my efforts on security, making sure that Aidmin can’t negatively impact your database. I talk about that a little bit in the Security Overview ( https://github.com/aidmin-io/docs/blob/main/security-overvie... ). I would love to know if Aidmin would be helpful at your workplace, and if not, why? Are there any features that are missing which would make it much more useful? Thank you for checking out the project!

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
73%73% 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: io · Missing: https docs, excited, just released
60%60% 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
44%44% 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
42%42% 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
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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