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Dopt – State machines and SDKs/APIs for your user flows

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Dopt – State machines and SDKs/APIs for your user flows

Hi everyone, Dopt lets you visually model flows (which are state machines) to power your product’s onboarding and education experiences. We offer a React SDK that lets you create instances of those flows for any user of your product and exposes methods for transitioning the state of the user—effectively moving them through the flow. Dopt then acts as a persistence layer for the users' flow state. Dopt lets you send in data about your users (identify calls/properties) and lets non-devs define rules based on that data for which users should enter the flows. Non-devs can also update content and other custom properties that can be referenced via the SDK. For example, with Dopt’s SDK you can build: - a multi-step, interactive product walkthrough that helps users learn how to use a product by using it https://www.dopt.com/examples/learn-by-doing-onboarding - a getting started checklist that helps a team get setup and activated https://www.dopt.com/examples/getting-started-checklist You can see a short 4 minute demo of how Dopt at https://www.youtube.com/watch?v=1gRvAp6Cnls We make iteration on your state modeling easy with flow versioning, and we even handle migrating your users between versions on the fly. Dopt's UI lets everyone easily track how users move through your flows and provides controls for enabling/disabling or resetting them. We’ll work right alongside your analytics and experimentation tools. We started Dopt because we’ve seen the teams building onboarding/education flows struggle with a similar set of issues that Dopt sets out to solve: - Homegrown onboarding is time-consuming to maintain (and it changes a lot!). The logic of the flows is complex and opaque to non-developers and devs are forced to be in the loop for every small copy, targeting, and logic change. - The customer and user data needed to show people relevant experiences lives in multiple places and is hard to build interactive experiences with (e.g. what’s their use case, what plan they’re on, what they’ve done in the product, what other teammates have done in the product, etc…). - Persisting and migrating user flow state is hard. The existing tools aimed at helping people put onboarding into their products frankly suck. They’re not built for developers, only offer boilerplate components, and have brittle integration points that are hard to troubleshoot. They end up producing mostly low-quality tooltip tours. We’re in a closed beta period right now but are looking to find more people building product onboarding and education that are willing to give Dopt a spin. We’re not charging for Dopt during our beta period and are just asking for feedback to help us learn how to make Dopt better. We’ll eventually start charging later this year (by monthly tracked users) when we go fully self-service but are planning on having a “free in-perpetuity” tier for small use cases to make it easy to evaluate whether Dopt’s valuable. We won’t pull the rug out from anyone who’s built with us during the closed beta. You can signup for our waitlist on https://dopt.com or drop us a note at info@dopt.com. If you mention HN we’ll skip you to the head of the line. We’d love any feedback on the site, docs, and examples too!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
95%95% 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: mac, model, user · Missing: agents, macos, agent
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, lua, existing · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month, monthly, users · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users, calls · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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