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Front End as a Service: Mason and Airtable

Hacker News

Front End as a Service: Mason and Airtable

Most (all?) website and app builders suck. They either try to give you templates for everything you might want, or they try to take designs and generate code that your engineers would never want to use. There's never been a builder good enough that a serious engineering team would use in production. Mason isn't an app builder. We're a frontend as a service platform that cares as much about delivering your production frontend layer as we do helping you build faster in the first place. Use Mason to build discrete, functional software features that look and function just like custom code. We're building the platform we want to build software with. We think in terms of components, and we let you build that way. Build functioning forms, connect to live data over your APIs, integrate with any tool the exposes a REST API, and generally stop reinventing solved wheels. Build functional components one-by-one that live in your codebase alongside the rest of your highly custom code. To commemorate our recent launch, here's a quick and dirty tutorial of how you can spin up a simple set of components to tie into an Airtable backend and build a full featured applicant tracking system. Ignore the lack of polish in the demos ;) we're working on it https://showcase.trymason.com/airtable/applicant-tracker PS - this is just a really simple first tutorial. Most of Mason is built using Mason. You can go as technically light or as technically deep as you want; just like in custom code.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, code, apis · Missing: mac, agents, macos
95%95% 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
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
47%47% 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: way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, 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
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.

Incorrect prediction on native model

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