Ez

Ezauth – trivial auth for a Go web app

Hacker News

Ezauth – trivial auth for a Go web app

https://github.com/mannders00/ezauth Basic bcrypt auth isn't that hard to learn for a Go net/http web app. Its true, but online forums are littered with desparate cries for a django-allauth equivalent in the Go ecosystem, which doesn't exist (in a way that is trivial to incorporate). These cries either come from beginners, or from time conscious individuals uninterested in reinventing wheels. Both are valid. I made the decision to implement auth fitting this description. By creating a public package, I can import future fixes into my disjoint projects, open the global community to make security suggestions, and help someone in the future get started more quickly.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, 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: exist, 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.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
47%47% 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
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% 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.

Incorrect prediction on native model

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