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Ply – Build internal features into your apps

Hacker News

Ply – Build internal features into your apps

Hey HN, I'm Guy, co-founder & CTO at Ply. Excited to share our internal feature builder. I've built a lot of internal products over the years, they can be really powerful when done right. After experimenting in our previous company with different approaches for this, we came up with a new approach that worked really well - internal products inside existing products. We've built a nocode platform that lets you build internal features into the apps you already use. (Think Gmail, HubSpot, Zendesk, etc). You can add buttons that trigger interfaces inside those apps (Chrome extension based). The features you build can use contextual data - data from the current item like an email in Gmail or a ticket in Zendesk. You can also build instances of ChatGPT that are fed with contextual data about the item you're on, so it can do more than just answer an email but also use data about the customer, your schedule or anything else. The product is geared towards "citizen developers" but you can add code (only javascript for now) and make API requests. A quick 4min overview from our website: https://www.loom.com/share/71e689c160cf4babbb3f2cb018c35b60 Happy to get some feedback!

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Actual performance

3points
1comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, email · Missing: mac, agents, macos
98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
83%83% 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: apps · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface, builder · 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
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: chat · Missing: web3, crypto, cryptocurrency
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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