I

I made the easiest authentication library for Python. Seriously, Yes.

Hacker News

I made the easiest authentication library for Python. Seriously, Yes.

Haze is a blazing-fast Magic Link Authentication service for Python applications, designed for simplicity and performance. With a lightweight core and modern cryptography using JWT, Haze ensures secure, cross-device authentication without the need for background processes. Inspired by Neovim's configurability, it offers highly customizable options while maintaining minimal dependencies. Whether you need database flexibility with pluggable storage or efficient encoding with NanoID and MsgPack, Haze delivers modern defaults with near-zero overhead. Authenticate seamlessly—click on your phone, and log in on your desktop. Check it out on GitHub!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% 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: io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: efficient · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: using, coding · Missing: mac, agents, macos
50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency
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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