Taist - a platform to create addons for SaaS

Hacker News

Link: http://tai.st Guys, I'm building a service for web developers that like to improve things by hacking, and your feedback would be very useful: If you ever wanted to change anything in the SaaS you use, Taist can help you - with it you can create addons that add new functionality or change UX of any cloud application. The same things you could achieve by creating browser extensions or userscripts, but Taist makes it much easier to develop and maintain and adds some useful "out of the box" features like data storage. The platform works for several months. The website and API docs are not fancy yet, but the platform works and several addons have been created and used. By the way, I would be happy to get an advice also on creating docs for API - now I describe it in Google Docs to edit it fast, but as it grows constantly, it becomes less and less readable.

Visit product View on Hacker NewsSep 18, 2013

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, new · Missing: mac, agents, macos

81%81% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

63%63% 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, google, users · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · 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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