Lady Deirdre 2 – Rust Framework for Compilers and LSP Servers

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Greetings! I would like to share with you my project, Lady Deirdre. Lady Deirdre is a framework that helps you create new programming languages in Rust. It is specifically designed to develop compilers and interpreters with support for code editor extensions (LSP servers) from day one. The framework includes essential components to design parsers and semantic analyzers capable of incrementally reparsing dynamically evolving source code. Lady Deirdre can be seen as a replacement for preexisting projects with similar goals, such as Tree-Sitter, Rowan, or Salsa. However, Lady Deirdre aims to offer a unified framework API that guides you through the steps of programming language development, providing even more components necessary to develop a full-featured language ecosystem. For example, components to develop a source code formatter. I will be happy to answer any questions. Ilya

Visit product View on Hacker NewsJun 21, 2024

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

79%79% 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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · 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 · 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

34%34% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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