Lilit.dev - Java code intelligence on GitHub

Hacker News

Hey HN, Lilit ( https://lilit.dev ) is a "true" Java code intelligence (like jump-to-definition and find-usage) on Github. It is implemented with a browser extension and a backend. What sets Lilit apart from the alternatives is that Lilit understand Java's semantics tree and build systems. This means that Lilit is as good as your local IDE. Here are three concrete examples: (1) Lilit resolves overloaded method calls correctly, (2) Lilit resolves a complex generic-type-with-lambda-type-inference situation correctly, and (3) Lilit enables you to jump to a class within a jar or JDK. ---- You can try it here: https://lilit.dev/try Install our Chrome extension and head to https://github.com/stripe/stripe-java , https://github.com/algolia/algoliasearch-client-java-2 , or https://github.com/dotCMS/core Try jumping to a class within a jar. Trying finding all usages of java.lang.String. Try reviewing a pull request. And feel the power of Lilit :) ---- I've made Lilit to solve the pain of reading/reviewing Java code on our browsers. Lilit is especially helpful for a team that review each other's code regularly. We are expanding our private-beta user group right now. We are willing to support Gitlab and on-premises deployment in the near future as well. If you are interested in using Lilit on your repos, please send me a message through the contact form on https://lilit.dev Also, feedback is always welcome. Thank you!

Visit product View on Hacker NewsJun 19, 2019

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Actual performance

6points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, stripe, using · Missing: mac, agents, macos

80%80% 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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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