Wranglr – A programmable CLI for tracking dev work

Hacker News

As a software engineer and open source maintainer, I've found that signal gets lost in the noise pretty frequently. Especially GitHub notifications. To stay organized I've tried things like GitHub Projects, Notion, and Pen + Paper. So far, pen and paper has proven to work the best for me but I had this nagging feeling that there is a "better" way to stay organized. I've been working on wranglr to help me cut through the noise and get a focused view of what needs my attention across both GitHub and Jira. Let me know what you've tried to stay organized, what has worked, and what you think of wranglr if you give it a try.

Visit product View on Hacker NewsOct 27, 2025

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

84%84% 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: open source, io · Missing: https docs, excited, just released

61%61% 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: way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

32%32% 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

23%23% 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

15%15% 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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