HandyDash. A Simple Tree View Monitoring Tool

Hacker News

Author here, happy to answer questions about the tool. I've shared my motivations for writing the tool on the ReadMe page in GitHub, and I'll elaborate on this with a blog article in the coming week or two. Some background on the tool: HandyDash is a cross-platform HTTP, TCP, and IP monitoring tool, intended for desktop use. It is agent free, requires no installation, and saves all configuration to a single portable file. Simply unzip the package and run the executable, and within a few minutes you can view and edit nodes within the browser based UI. Thanks.

Visit product View on Hacker NewsJun 9, 2024

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

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

34%34% 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

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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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