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Write Haskell-inspired smart contracts with open source DAML

Hacker News

Write Haskell-inspired smart contracts with open source DAML

Hey HN! This is Paul from Digital Asset’s developer community team. We have recently added a web-based IDE for DAML so people can now try it out without having to download the SDK. We’d love your feedback on the experience and the language! https://webide.daml.com/ Thanks to Bolek Wisniewski from the DAML language team for making this happen. In case you’re not familiar with DAML, I have previously posted a few stories - it is basically an open source functional programming language designed specifically for use in coding multi-party business processes (‘smart contracts’ in the DLT space). In addition to using the new IDE you can download the DAML SDK, inspect and contribute to the source code, find fully-executed reference applications (also open source), and access the documentation from https://daml.com/ . DAML is very much a Haskell-inspired language, adding key language features (such as parties, signatories, and observers) that abstract away the underlying complexities of blockchains and database technologies. Because specifics of the underlying persistence mechanism are not exposed to the developer, applications built with DAML can run on a number of platforms, currently including VMware Blockchain, Hyperledger Sawtooth, and the Distributed Asset Canton ledger ( https://www.canton.io/ . More are in the works.

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Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
85%85% 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 · Strong signals: including · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, coding · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, 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 · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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