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Numscript, a declarative language to model financial transactions

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Numscript, a declarative language to model financial transactions

Numscript is a simple, declarative language that helps you model financial transactions. You can do quite a few things with it, such as modeling: * Payments involving vouchers and a user's prepaid balance * Complex funds destination scenario where the customer gets cash back * Configurable user credit balance spending transactions The main idea is to take the pain out of describing of a system dealing with money movements should behave in traditional languages such as JS/TS/Go/Ruby etc, landing an expressive way to model these movements of value. It is voluntarily broad in applicability—our customers use it today for use-cases ranging from marketplaces funds orchestration to home-grown loan management systems. Once those transactions are modeled, they are to be picked up and committed to a system-of-record, ledgering system or executed on a set of payments and banking APIs. It was initially a DSL we bundled into our Core Ledger product at Formance (YCS21) but we're giving it more autonomy now and started to make it standalone, with the idea that anyone could eventually bolt Numscript on top of their ledgering system. We're also exploring to make it natively compatible with other backends. As part of this un-bundling, we've just shipping a playground which lets you fiddle with it without installing anything at https://playground.numscript.org/ (it works best on desktop). Happy to chime in on any questions!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, compatible · Missing: supports, reddit linkedin, podcasting
90%90% 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: model, user, apis · Missing: mac, agents, macos
83%83% 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
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.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
23%23% 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
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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