En

Engineering team accidentally changes SEO elements...so I built a tool

Hacker News

Engineering team accidentally changes SEO elements...so I built a tool

I can't tell you how many times some folks in engineering/UI/product teams accidentally change/break on-page SEO elements (or analytic tracking codes) - so I built a tool to monitor pages and let me know when a change is detected. It's probably best suited for larger companies that have multiple teams and frequent release cycles. It's easy to forget to verify every single element. I don't care how much training or cheat sheets the other teams have, accidents happen - it's pretty much unavoidable at large companies. I thought I'd share it here. It will also monitor Google PageSpeed (Desktop/Mobile), competitor sites, and track pages that require a username/password like a QA environment. The way I use it is to look at my page-types (homepage, product detail page, state-specific product page, etc.) and then track 1 'representive url' for each of those page types/templates. This cuts down on noise and allows me to track sites that are small or large. Lastly, you can toggle sensitivity/elements tracked. Anyways, hope you like it and would like any feedback! Here's the link for SEO Tripwire: https://www.seotripwire.com There's a 30 day free trial if you just want to kick the tires.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, single · Missing: mac, agents, macos
78%78% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: google, way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
44%44% predicted probability of success on Indie Hackers, 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 · Strong signals: training · Missing: arr, mrr, revenue
20%20% 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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