Cook for Mom

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Cook for Mom

Site: https://cookformom.com Source: https://github.com/anulman/cook-for-mom Creator here . I've been working on a free, six-week, online cooking class to help beginner and novice chefs build skills, toward the goal of preparing a 3-course gourmet meal guaranteed to impress mom for Mother's Day. I appreciate that landing pages are generally frowned upon as Show HNs; if you feel compelled to downvote, I understand. While I'm certainly hoping to recruit some cooks among us, I wanted to share this project because I want your feedback on this as a conversion tool, and I hope to share back any ideas or implementations you find interesting. Some "neat stuff" you might appreciate: - Though this is an Ember.js app, it loads _real_ quick due to a prebuilt index.html deployed (with all relevant assets) to Firebase CDN [1] - There are nudges and triggers all over the place. An exit intent nudge gets registered as soon as you scroll past "Menu". Try hovering over the nav bar before and after. This only works once, because we don't want to annoy the user. [2] - I set a consistent fingerprint across all analytics tools, using a lib espoused by adtech friends [3] - If on Chrome, the parallax is responsive to resizes, supports variable height, _and_ is hecka performant—even in light of all of the listeners + analytics code. [4] - The source for the welcome email is public too; it is generated with HEML [5] Thanks HN! [1] ember-cli-build.js#L13-L18 [2] app/pods/index/nav-bar/component.js#L35-L38 [3] app/metrics-adapters/segment.js#L23-L40 [4] app/pods/components/parallax-container/fluid/component.js#L24-L39; I'm hoping to extend this to Safari / Firefox too and have a halfway-working impl (they rely on odd DOM hacks and compute the transform differently); please message if you can help! [5] heml/welcome.heml; I plan to extend this into compiler tooling to better separate concerns & reuse components, in order to generate the weeks' content

Visit product View on Hacker NewsMar 21, 2018

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created

88%88% 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: user, email, using · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal

35%35% 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

14%14% 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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