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I made a 'Useless' GPT-4 plugin – Is it that useless?

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I made a 'Useless' GPT-4 plugin – Is it that useless?

Hey HN! I've been experimenting with GPT-4 and ended up creating a plugin that, at first glance, might seem utterly 'useless'. It's called Pulsetic, and its sole function is to check a website's availability worldwide. Now, you might be thinking, "Why on earth would I need that?" Here's how it works: 1. Visit the GPT-4 plugins store. 2. Search for Pulsetic: Once you're in the plugins store, look for "Pulsetic". When you find it, go ahead and install it. 3. Check a website's availability: After you've installed and activated Pulsetic, you can start checking website availability. Just input the URL of the website you're interested in into the chat field, and you'll get your results! Now, here's where the controversy comes in. Some might argue that this plugin is utterly useless. After all, there are already plenty of tools available that can check website availability. So why create another one? I'm curious to hear your thoughts. Do you think Pulsetic is a 'useless' GPT-4 plugin, or can you see its potential value? P.S. If you find Pulsetic useful (or amusingly useless), please consider sharing it with others. Your support is greatly appreciated! Question: In an age where there are countless tools and plugins available for almost every task, how do we determine what's 'useless' and what's not? Is there a place for seemingly 'useless' tools like Pulsetic's GPT-4 plugin in our digital toolbox?

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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: using · Missing: mac, agents, macos
48%48% 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: 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
46%46% 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
41%41% 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
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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