[/caption] The title looks like a potpourri. It’s normal. When my cat is allowed to go out in the morning, the first thing it does is to scan the neighbourhoods from the terrace. It stands on a small pillar and turns its head, slowly and zooms on the propitious spots. Mouse, lizards, cats are on the radar. Trees and high grass are hiding them. It takes 30 seconds and here it goes. It jumps into the garden to get a close eye on something. I have about the same attitude when I check my mail or the RSS aggregator in the morning, a mug of coffee in my hand, looking to cat on the terrace. The first thing that pops to my eyes and tries to capture my attention is spam. Obviously those behind this plague know that orders, banks, credit cards are high on our agenda. For this reason scanning the Inbox is usually very deceptive and useless. So the very first task is to run the spam filter(s). What the spam filter does (after obvious steps) is to take a suspicious look into the contents, scanning for outrageous hints. Any deceiving term would flag it as spam. Scarcity of terms in our field of business and abundance of engaging term (free, money) are impacting as well. The point is that scanning to remove spam is similar but different in the fact that we look for exaggerated proliferation or out of subject terms instead of informative messages. It becomes more and more the same with RSS. RSS are not cluttered by spam but with 3 million and counting bloggers it is over abundant. Twitter started a new era and we get now from our friendfeed a continuous flow of messages about all and everything. I was not going to spend time looking for a pearl in this sea. We have a nice tool in Kneaver which takes a text and evaluates it against our personal knowledge. Basically a natural language parser will take it, identify terms available in the corpus, compute their frequency. Up to here pretty common. Now what is less common is that we compute some sort of distance between the terms in the text but also in the corpus. For this we use graph distances and semantic vectors. What we are really looking at are texts mostly residing in our Knowledge Frontier. They are the most likely to bring us something new but still in the focus. This is really similar to the cat approach. Is this grey color likely to be a cat, if yes I should see another hint nearby: something moving or another grey patch. Now we are testing an RSS valuator filter based on this feature. What you get is exactly the original feed but some styles are added so that your RSS newsreader will display it in colors depending on the foreseen interest. It is now easy for the relax reader to spot the most interesting news. Nice feature and very personal. However what you will not get are the hidden pearls. The interesting piece of news, totally out of context, lost in the stream of professionally oriented streams. To illustrate this when I reviewed my daily delivery of news from Guy Kawasaki (I follow him for informations and hints in the perspective of his books) , filtered by Kneaaver, I could see, lost like a bottle in ocean, a piece about Chris Ware. I like his work and so do my son but this piece was completely outside of my job interest. Kneaver couldn’t catch it, I could. We, human, remain better than software. We can adapt at once any procedure and incorporate extraneous exceptions on the fly. Yes but without Kneaver raising the visibility of interesting pieces, I wouldn’t have the spare time to look for an out of band piece. Wow! the cat was stung by a wasp while I was writing. She’s young and need also to enhance her scanning procedure.