Logwise – AI Powered Log Analysis with context from all your apps

Hacker News

Hey HN! We're excited to introduce Logwise, our new AI-powered log analysis tool. (built by two devs who hate logs) Product page: https://logwise.framer.website/ Logwise makes debugging and incident response faster for developers. It uses natural language processing to automatically parse log data, surface insights, and detect anomalies. We built Logwise to eliminate the manual sifting of log analysis. Key features: - Search logs in plain English - no complex queries needed - Auto-generated alerts highlight potential issues - Contextual debugging advice speeds incident response - Centralized access to all your log data sources - Continuous learning improves analysis over time Logwise saves developers hours or even days wasted on manual log searches. We want to help resolve incidents 2x faster with accelerated insights Reduce context switching by aggregating all log data Let developers focus on building, not log mining Get ahead of problems with predictive anomaly detection We're currently working on: Customizable log parsing for different data formats Integrations with PagerDuty, Datadog, and other tools An API for accessing analysis results Try out the Logwise beta today! We'd love your feedback on how we can improve. Let us know if you have any feature requests. Our goal is to make AI-powered log analysis seamless and maximize developer productivity. Thanks for any feedback and support!

Visit product View on Hacker NewsJul 18, 2023

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

89%89% 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: maximize · 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: excited, ide, io · Missing: https docs, just released, exist

58%58% 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: apps · Missing: mobile apps, ios, personal

40%40% 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

36%36% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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