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Anzu – Building Blocks for Product Teams

Hacker News

Anzu – Building Blocks for Product Teams

Hello Hacker News! We’re Bruno and Tim from Anzu [0]. We’re building a platform that makes it easier to build products by providing building blocks for repetitive work (think user management, services, databases, workers, error reporting, user analytics, and more). We believe that velocity is one of the most important goals for companies in any stage, so we’re building the tools for teams to reduce friction and move faster. When you’re in the early days of building a product, time is your most valuable asset. Getting to validate your idea quicker gives you the insights to build what people want. On the other hand, few things are as threatening to your motivation as having to set everything up to go live. Today, we’re excited to announce the first step in the long journey to reach this goal: User Management. Pretty much every product requires users to create an account and sign in, but customers expect a variety of sign-in options, including social networks and enterprise connections (SAML). With Anzu, you get a hosted auth page where your users can sign in with Google, GitHub, and Email (using magic links or a temporary code), with more auth providers coming soon. Your application is notified about new signups and can persist users in your database, while Anzu User Management takes care of authenticating users and issuing short-lived access tokens. Using Anzu, you don’t have to spend days understanding OAuth and sifting through auth provider docs — you can have it all set up in under an hour. Building blocks offer a unified integrated experience in one platform, but also give you the unique possibility of mixing and matching: You can choose which parts of your stack you want us to manage, and you can always switch to a different provider. While user management is the first block, it definitely is one of the most important ones to get right. When your users are in the same place as all other parts of the product lifecycle, you get unmatched context to work with. Track errors back to users, enrich metrics with session data and link customer support with user activity across the platform. Of course, there are specialized vendors for most of the areas we’re targeting but with those, you end up with the time-consuming work of integrating different services, understanding data models and concepts, and trying to make everything work. With Anzu, we want teams to spend less time choosing providers and gluing it all together — we provide everything you need to build reliable products that grow with your team. We understand that we’re working on highly-sensitive areas of your product and have to build trust first, so we’re committing ourselves to build in public, using and sharing open source tools, and enabling users to run Anzu on their own infrastructure. We’ll be offering a self-hosted option [1] in the coming weeks, based on measured interest (please reach out if you’d like to run Anzu on-premise!). Feel free to ask us anything and let us know what you think! [0]: https://anzuhq.com [1]: https://anzuhq.com/docs/platform/self-hosted

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
96%96% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, user · Missing: mac, agents, macos
91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, lua, open source · Missing: https docs, just released, exist
64%64% 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: google, users, way · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, soon · Missing: plus, intuitive, reviews
40%40% 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
13%13% 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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