Bu

Build Apps with Automated Workflows

Hacker News

Build Apps with Automated Workflows

Hey HN! We [1] just released a new thing: http://tines.com/product/build-apps. tl;dr – it's a way to build simple-but-slick web apps that are powered by API automation behind the scenes, without writing code. No code? It's because our traditional users are technical-but-not-engineer types like Security Analysts and IT Managers. So this new way of building front-end experiences follows that lead. But as a more technical maker myself, I've been really surprised at how well this works and scales, and we’re seeing teams like Engineering and DevOps increasingly adopt also. I think maybe it's because us software people most intimately understand the long-term/hidden costs of code… maintaining, bug-fixing, deployment, etc. Anyway, If you’d like to check it out… 1. there's a usable version of a demo app mid-way down the page above, and then clicking the "How was this built?" button shows you the equivalent of source-code, i.e. the visual automation underpinning everything. 2. we have a forever-free tier, which you can sign up for here and play around for real: https://www.tines.com/sign-up I’d love to hear any and all feedback! We love a good dose of HN-style tough love, which we certainly received our fair share of last time we surfaced here :-) [2] - Stephen --- [1]: Tines – no code automation [2]: https://news.ycombinator.com/item?id=30249518

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, new · Missing: mac, agents, macos
93%93% 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.
Hacker NewsStrong engagement from HN community · Strong signals: just released, io · Missing: https docs, excited, exist
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, users, way · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
35%35% 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
17%17% 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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