Xogs – local log aggregation in your terminal

Hacker News

Excited to share a new open source project I published recently – say hello to a tool you probably never thought you needed: xogs xogs is a terminal-based tool designed to enhance the way you work with local logs. It streamlines log aggregation, inspection and filtering, enabling you to handle logs more effectively and focus on what matters. Whether you're debugging, monitoring, or just exploring log data, xogs provides useful tools without leaving your terminal. This initial release contains all the basic features you need to start aggregating your local logs, but there is definitely a lot more to come. I am curious if anyone finds this useful - would love to hear any feedback :)

Visit product View on Hacker NewsJan 15, 2025

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

77%77% 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

53%53% 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: excited, open source, ide · Missing: https docs, just released, exist

38%38% 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

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

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

37%37% 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

11%11% 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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