Weakest Link Reporter

Hacker News

This is my first post, and the first project I will be posting to a community... so be nice please. Also, I am not really interested in any other projects that do the exact same thing… I often like to reinvent the wheel, just for the experience. I work as a front-end web developer, and whenever I am about to push a site live, there is usually a huge list of bugs and general issues to fix, before a website can be seen by the public. The lists of bugs, are generally created by non-programmers, which means they really only care about the content of the site (ie grammar, spelling etc)… If I can fix all of the programming related errors before they get a chance to make these UAT lists (http://en.wikipedia.org/wiki/User_acceptance_testing#User_acceptance_testing), it makes my life and their lives easier. One of the most common problems are bad links, as I hardly ever click each link that I create. I created this tool to test all links found in every html file, found in a target directory, recursively; and generate a report on the results. If the script finds an internal/relative link, it checks to see if the file exists; if the link is external, it does an http request. I wrote this script in perl and made it work like a unix command line tool; I eventually want to make it a real command line tool, but I don’t know how yet. Also, my co-workers who are not unix friendly, suggested that I make a web form interface to activate the script. There are many things I can do to make this tool more useful; this version met a specific set of criteria for a project. Now that the project is over, I plan to expand this tool for more general use. Let me know what you think, if you would use a tool like this, and if you want to help dev it. Project: https://github.com/mathew-fleisch/Weakest-Link Script: https://github.com/mathew-fleisch/Weakest-Link/blob/master/weakest_link.v1.0.pl Sample Report: https://github.com/mathew-fleisch/Weakest-Link/blob/master/bh-eu-12-link-report.xls

Visit product View on Hacker NewsJul 23, 2012

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2points

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

86%86% 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.

AppSumoStrong fit for a featured deal · Strong signals: friendly, interface · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% 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 · 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

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.

Incorrect prediction on native model

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