Free-Attractions.com – potential for a multi million dollar auto pilot website

Free-Attractions.com

Was formerly a very popular site with backlinks on USAToday and tripadvisor etc.

Collected 300+ leads on the coming soon page. Has traffic. Content has been done for 3 States.

Why are you selling this site?

I don't have the time to complete this project

How is it monetized?

Currently being rebuilt, I envisage monetization via affiliate deals, private advertising for local businesses relevant to attractions.

If you sold just one adspace at a meagre sum…

Free-Attractions.com – potential for a multi million dollar auto pilot website

Subtitle Database website for sale! more than 4 million subtitles 80000 visitors per month

Why are you selling this site?
I have tons of others things to take care of, lack of time.

How is it monetized?
Advertisement.

Does this site come with any social media accounts?
No.

How much time does this site take to run?
This does not take any time to run. People can upload subtitles so it is automatic.

What challenges are there with running this site?
Building it and making it work. Have signup system, commenting system, small CMS.

Best approach to send 0.5 million emails with lowest cost [closed]

I am from engineering background. I have to send 0.5 million emails to users. I already have email addresses of the users.

I have few confusions

  • Sending mail through sendgrid etc, do I need to buy subscription which can hold 0.5 contacts?

  • I want to send via API, is there any API only approach?

  • What if I send via gmail SMTP?

Any kind of information is highly appreciated.

Querying a table with 200+ million rows by filtering on the clustered index AND additional column is super slow

We have one table that has clustered index on two columns: first one is NVARCHAR(50) and second one is on DATETIME2(3) type.

There are few other columns. When we query the table with WHERE clause on the clustered index columns, the result is received immediately, even when the result set is tens of thousands of rows.

However, if we add one more predicate on the where clause, the query is super slow. After ten seconds I just stop it, because that is not useful to us.

The table has 200+ million rows.

This is the query that is super fast:

SELECT * FROM Table WHERE Col1 = 'blah' AND Col2 BETWEEN 'date1' AND 'date2' 

This is the query that is super slow:

SELECT * FROM Table WHERE Col1 = 'blah' AND Col2 BETWEEN 'date1' AND 'date2' AND BooleanColumn = 1 

In my mind, the second query should use the clustered index to search for rows and then simply scan the result set to filter out what’s needed.

Is it possible to (somehow) make the second query work fast without creating additional non clustered index that would include other columns that we need to filter on?

[ Politics ] Open Question : Why are we concerned about 200k americans dying from coronavirus when 1 million americans are aborted each year?

I think it’s great that we are concerned about 200k americans dying from coronavirus… but i think it’s illogical to be concerned about 200,000 americans dying from that virus whereas at the same time each year around 1 million american babies are aborted.. why so much incoherence?

How did Microsoft discover 44 million user passwords were breached? [closed]

In December 2019, tons of new sites reported Microsoft ran a security research that found out over 44 million of user passwords were breached. The news sites said Microsoft used third-party resources and public databases in order to discover this, and forced all these users to change their passwords (which is nice!), but I still don’t get it.

If the password is properly hashed, how did they manage to look them up on these databases? I’m not a security expert or anything, but the only possibility I could come up on my mind was to hash the passwords on these public databases and compare with the users’ hashed passwords, but that sounds absurd considering salt (they would have to hash every leaked password to every account, right?). Does anyone understand how they did that?

EDIT: @schroeder’s comment and closing the question doesn’t make sense. The question is valid – how could they check so many password to so many accounts, if that’s how they did it.

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