If UCE shows up in your inbox or ham gets filtered to Junk, you can use a keyboard shortcut or other trigger to inform SpamSieve that it guessed wrong. Integrated mail apps lets you manually change the analysis of a message.
#Airmail spamsieve code
This lets it performs its analysis, scoring, and tagging in such a way that the mail app can filter messages or run rules against them-and even have the app color code them by likelihood of spamminess. You can integrate SpamSieve with nearly all major mail apps, including Apple’s Mail, Outlook, AirMail, Gyazmail, MailMate, and others. You barely need to touch the app after setup.Ī few preferences let you control aspects of tagging behavior. It’s like training a drug-sniffing dog, only much more rapidly and with no treats required. A tip: save spam you’ve manually identified for a bit so that you can prime the SpamSieve pump with about 350 bad messages alongside 650 good ones you’ve filed.
#Airmail spamsieve software
SpamSieve is a largely set-it-up-and-forget-it utility after some initial training, about which C-Command Software offers detailed guidance. And SpamSieve includes other filtering options, like an option to block all HTML encoded to be unreadable to the human eye until viewed in a browser. Spammers years ago developed techniques to partially defeat Bayesian analysis-in case you wondered why some spam has extracts of random passages of text at the bottom-but there’s only so far a message can go in defeating probability before it’s unreadable by a recipient. After training my filters for years, SpamSieve is often 100 percent certain a message is desirable or that it’s unwanted, and it’s correct nearly all of the time. As you train SpamSieve’s filter, it increasingly identifies messages as good or bad more accurately. SpamSieve scores every word and some aspects of embedded images by how frequently they appear in messages marked as spam or ham. (It’s used broadly across many disciplines-not just email quality detection.) The app uses Bayesian inference, which is the mathematical equivalent of an educated guess based on which words or phrases appear more frequently in mail you want and mail you don’t. It arrived after a certain kind of frequency analysis became a popular way for server software to distinguish spam (unwanted email) and ham (the good stuff) with some reliability.
#Airmail spamsieve mac os x
Its advent was near the beginning of the Mac OS X transition. SpamSieve is nearly two decades old, arriving on the Mac not too far after spam first became insufferable. I last reset the statistics in 2007, and SpamSieve has helped with nearly 600,000 messages since then.
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