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Find top resources on any topic, of any kind, curated by the community

Hacker News

Find top resources on any topic, of any kind, curated by the community

Hi, I'm Viraj! I built AwesomeHunt as a side project over a few months. Why am I building this? When we want to learn a new topic, we usually search for the topic name on Google and spend some time deciding the places we would learn the topic from. Although you can find pretty much anything you would want on Google, you still need to spend significant time curating the best resources. We usually need to depend on a bunch of blogs, and articles to decide what courses, books, podcasts, and videos are highly recommended for a topic. Manually curated lists of learning resources are incredibly helpful and save us a lot of time. One of the ways the Internet has tried to do this at scale is by creating a bunch of public repositories on GitHub as Awesome Lists ( https://github.com/sindresorhus/awesome ), where people add learning resources for a lot of topics as simple links in a text file. It's incredible that so many resources have been curated this way and a large number of people have been contributing to these repositories. As useful as this is, these lists are very much limited to the programming communities and do not have resources for most non-tech topics. These lists also do not have a way to rank these resources, and contributing to these lists is cumbersome. How does AwesomeHunt solve these problems? AwesomeHunt makes it easy to find any kind of resource on any kind of topic, ranked by their popularity. It's easy to contribute to the lists and discuss things related to a topic with other learners. Here's the list of key features: - Users can suggest a new topic if it doesn't exist on the website. Topics can be tagged to make it easy to find them - Users can suggest new resources for any topic/fix or remove incorrect information - Users can upvote the resources they find helpful - Users can take part in discussions under a topic or a learning resource, through threaded comments, which can also be voted on and ranked. - Every contribution will go through a manual screening process before going live on the site. - Every contribution by a user will be tracked, and awarded points (example: https://awesomehunt.org/users/virajclive ) Right now, the list of resources on the website is limited, but still quite useful. I can imagine the same website full of resources on any kind of topic, with top resources voted by a large number of people, with discussions around the best ways to learn a topic would be quite useful for the whole internet.

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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.
Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, new · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, month, google · 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
17%17% 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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