it was slow—it worked all night just to identify a few articles matched to users’ interests. To be credible, a consumer-scale system would need to scour the whole Web, and it would need to process information in near-real time, ingesting newly published articles and matching them against users’ personal profiles in just seconds.
That was the goal that Notthaft and Griffiths—Notthaft’s old mentor from WebEx and LiteScape—set for themselves once they got SRI’s blessing to spin out the technology. Luckily, they had help from David Schairer, the former chief technology officer at Concentric, who became Trapit’s third co-founder. With both theoretical expertise in adaptive learning and natural language processing and practical experience building high-volume spam filters for Internet service providers, Schairer is a nearly unique commodity in Silicon Valley, Griffiths says. Even so, making the news assistant faster and scaling it up to monitor more sources (some 50,000 now) took more than a year. “It was a lot of work,” Griffiths says.
And the truth is that there’s more work to do. Trapit probably needs to learn even faster than it does, and do better at guessing users’ intentions when it doesn’t get direct feedback. When I created a trap for “Hitchcock,” one of my favorite directors, it took me forever to teach it that I meant Alfred Hitchcock, not Hitchcock, TX (a suburb of Galveston), not the singer-songwriter Robyn Hitchcock, and not the Arizona TV anchorwoman Tara Hitchcock. (I confess that I deliberately didn’t call the trap “Alfred Hitchock” from the start, because I wanted to see how long it would take to train it. But I had no idea how much non-Alfred news there would be.) When I created a trap for “vegetarian recipes,” the second item Trapit showed was a recipe for chicken with pesto and penne. I gave that one a thumbs-down, naturally, but will Trapit know it was because of the chicken? To be ready for prime time, in short, Trapit will need to work reliably even for users who aren’t as willing as early adopters like me to train it.
Notthaft says that the 16-employee company, which has an office in Portland, OR, as well as Palo Alto, is working on a series of feature enhancements that will show up on the site this fall. The service is free, but Nothhaft and Griffiths envision selling annual subscriptions to traps that include content that’s normally behind a paywall (e.g., the Wall Street Journal), or creating “sponsored traps”—imagine golf club maker Ping sponsoring a featured trap on the U.S. Open, for example.
Trapit, which recently raised $5.6 million in venture capital from a group including Horizons Ventures (which also invested in Siri), hasn’t said whether it’s working on native smartphone or tablet versions of its service. But that would be a natural direction to go. I do most of my own news browsing these days on my iPad rather than the desktop Web, and some of Trapit’s features don’t work well in a mobile browser. For me, a best-of-both-worlds news app would be something that’s as pretty and as user-friendly as Flipboard, but with the filtering smarts of Trapit underneath the hood. In fact, I won’t be surprised if Trapit gets scooped up quickly, just as Siri was, by a larger company with an interest in content curation—say, Flipboard, Google, Facebook, or even Apple.
Indeed, the bidding may already have begun. Nothhaft says the publicity around Siri has begun to rub off on Trapit, resulting in “increased visibility and opportunities” for the startup. “Just as Siri is revolutionizing the human-computer interaction on the mobile device, Trapit will revolutionize web search as we know it today,” he asserts. A bold claim—but one with some history to back it up.
