Commits search and more for GitHub

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GitSense is a free Chrome extension that I've developed, which makes it possible to search for commits, diffs and more, all from within GitHub. You can learn more about it here: http://gitsense.github.io GitSense relies on my indexing technology, which was designed specifically to index Git repositories at the branch level. So far I'm focused on indexing GitHub repositories with 300 or more stars, which has been pushed to within the last 6 months. Right now, I've indexed 36,375 repositories, 128,357 branches, 3,682,9480 commits, 14,155,250 diffs and 19,016,254 blobs. From a technology point of view, I use Java, JavaScript, Postgres, Lucene and RocksDB. The RocksDB is more of an experiment right now, to find the right persistent key/value store DB. The indexer is designed to scale horizontally and I'm finding a single machine with 32GB of RAM and 1TB of SSD can easily process 10,000 - 20,000 unique repositories. All of this depends on the repo sizes of course. Something like the linux kernel is a beast and consumes 8GB of RAM during the initial index and takes about 8 hours to fully index. If you have any technology questions, ask away.

Visit product View on Hacker NewsDec 21, 2015

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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: mac, single · Missing: agents, macos, agent

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.

Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

68%68% 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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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

10%10% 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.

Incorrect prediction on native model

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