Pa

Patchmark – LSP for reviewing code changes/diffs in text

Hacker News

Patchmark – LSP for reviewing code changes/diffs in text

Increasingly I've been needing to review changes inside text editor, typically as a part of editing a prompt in code harness, though sometimes for other purposes (editing md documents, PRs, etc.). Manipulating diffs, quoting text, removing hunks, jumping to definition to verify was clumsy and cumbersome, and it occurred to me that most of it could be done very well as a language server protocol component, and wouldn't even require knowledge or special support for the language being under review. So I've built it, and I've been slowly tweaking and improving for few days now.

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2points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
85%85% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% 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
29%29% 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
19%19% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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