Runnaroo – A new web search engine

Hacker News

In the sprit of releasing to the public early, I would like to share a search engine/portal I have been building in my spare time: https://www.runnaroo.com Why: I started to not be able to easily tell the different between ads and organic search results in other search engines (even on DuckDuckGo), and I have been disappointed with recent UI changes in a lot of the major search engines. My main guiding rule when adding features has been to ask, "is it better for the user?" Important to know: I still believe Google currently returns the most relevant web results, so like StartPage I use Google's index as the base of the web results. Some interesting features: Deep Searching - The inclusion of relevant results from different vertical specific search engines depending on the search query. For example, if a user searches for 'python jobs NY', results from Indeed.com will be pulled in. The long term plan is to connect into the best vertical information sources for each type of query. Quick Directs - Automatic redirects for a limited number of commonly searched terms (facebook, google, etc.). This is similar to the Google "Feeling Lucky" button, but done by default. I found that most of the searches I did in Google were navigational. It's simple, but surprisingly has been the thing to resonate with normal (non-technical) users. Strict Search - Optional selection to force all the search terms in the query to be present in the web results. Full URL paths - The full URL path of the result is available on the SERP under a toggle. Privacy - The site doesn't do any tracking. There will most likely be a need to put some things in place at some point to prevent abuse, but user search data will never be used for ad tracking. The number of things I still have to add to the site is incredibly long, but I have gotten to the point where I have switched over to using it full time as my default search engine.

Visit product View on Hacker NewsFeb 26, 2020

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

85%85% 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.

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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

64%64% 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 · Strong signals: users · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google, users · Missing: mobile apps, ios, personal

35%35% 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

13%13% 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.

Correct prediction on native model

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