Si

Simple Git Workflow for Effective Teams

Hacker News

Simple Git Workflow for Effective Teams

Hi HN, My friends and I worked at Meta & Google. Once we’d left we realised a lot of the internal tooling didn’t have parity on the outside. Specifically, version control tools outside big tech are much harder to use than Google’s Critique and Facebook’s Phabricator, etc. Git is powerful but notoriously difficult to use [0]. Existing Git clients don’t make life much easier, exposing all the complexity by default. In practice we don’t need an equally complex GUI, and version control can be much easier to use. In our experience, it works best to expose a small number of features for an efficient workflow: commit, submit changes for review, and rebase (those are actively used in trunk-based development). Further, a “tree of commits” is a sufficient mental model, there’s no need to worry about branches, merges, etc. We have built a simple prototype called TrunkFlow that works inside Visual Studio Code. It borrows UI from Sapling (which is similar to internal tooling at Meta) but is fully compatible with Git. Would appreciate any feedback on the prototype [1] or on the challenges you’ve had using Git! [0]: https://news.ycombinator.com/item?id=25121416 [1]: https://www.trunkflow.com/

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, new · Missing: mac, agents, macos
90%90% 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 · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
80%80% 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
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: efficient · Missing: plus, platform, intuitive
60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
12%12% 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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