Nv

Nv – workspace orchestrator for jj built for parallel agent workflows

Hacker News

Nv – workspace orchestrator for jj built for parallel agent workflows

howdy y'all, i've been deep in jj for a while and been experimenting with jj workspaces for parallel workflows. it's more intuitive than git worktrees but it still has a couple of gotchas that have been a hindrance to my ideal workflow. so I built jj-navi - a tiny rust based cli that makes jj workspace orchestration a lot less pain in the ass. key bits: - `navi switch <name>` -> creates/switchs workspaces and cds into them automatically (via shell integration) - `navi list` -> shows insertions/deletions across workspaces and also runs jj snapshot (so you don't see stale work) check it out here at: https://github.com/eersnington/jj-navi (note: url at the top is a git.new one) also, this is heavily inspired by worktrunk (still my daily driver replacement for git worktrees) and jj-ryu by dillon mulroy from the orange cloud forking company. would love y'alls feedback, especially from heavy jj + agent users. feel free to open up issues on gh or hit me up on X.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, new · Missing: mac, agents, macos
95%95% 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.
AppSumoStrong fit for a featured deal · Strong signals: intuitive, users · Missing: plus, platform, reviews
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
52%52% predicted probability of success on Indie Hackers, 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
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users, para · Missing: mobile apps, ios, personal
29%29% 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
18%18% 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.

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