Integrating the Ruff Language Server

Hacker News

I wanted to learn how to create a code editor with linting support so I made one and added the code here on github: https://github.com/jackhodkinson/lsp-editor In the process I learned quite a lot about how the Language Server Protocol works and wrote a blog post about it (see the post link). I hope other people find this interesting. Although I don't go into it in the blog post, I found it quite challenging to synchronize the state of the code editor between the frontend, backend, and language server. I would be interested to know if anyone has suggestions for a more robust approach to synchonization the state of a code/text editor.

Visit product View on Hacker NewsJul 14, 2024

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

62%62% 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 · Missing: supports, reddit linkedin, podcasting

59%59% 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: code · Missing: mac, agents, macos

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% 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

26%26% 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

20%20% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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