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LogMerge – View multiple log files in a merged view

Hacker News

LogMerge – View multiple log files in a merged view

Hey HN! I needed a tool to view multiple log files in a merged view, and easily filter based on the specified fields. Spent a good amount of time searching, but couldn’t find any open source tool that quite did what I wanted. So, ended up building a custom solution instead (I would appreciate suggestions on tools that have similar functionality). I don't know much about GUIs (most all my PC based utilities are CLI) - but I did have the following: - I know enough Python to spot obviously wrong things - Some knowledge of how to make programs performant in general - ... and tokens to burn :) GitHub : https://github.com/faisal-shah/pylogmerge Usage Video: https://youtu.be/37V_kZO2TLA Key Features: - Merge and display multiple log files in a single, chronologically ordered view - Live log monitoring with auto-scroll - Add files individually or discover them recursively with regex filtering - Plugin-based system to support any log format (easy to extend!) - Filtering: discrete values, numeric ranges, regex/text, and time-based queries - Color-coded file identification - Configurable columns and ordering - Built-in plugins for syslog, CANKing (CAN Bus monitoring tool), and another custom log format called dbglog. If you have any feedback or questions, let me know! Hope someone else finds it useful.

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3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntUnlikely to reach the leaderboard · Strong signals: single, code, open · Missing: mac, agents, macos
49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
38%38% 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
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.

Correct prediction on native model

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