Want to be notified to turn on the AC when a room reaches a certain temperature? Or when your laundry’s done? Well MIT Media Lab alumni Supermechanical have built Twine, a sleek 2.5″ rubber square which connects to Wifi and allows objects to “communicate” under certain conditions.

The Twine, which reminds me of a Square from a design simplicity perspective, comes with a web app, ‘Spool’ which allows you to program its sensors with natural language rules like “When: accelerometer is at rest, Then: Tweet” in the case of the laundry done thing, for example.

You can hook up the battery operated Twine to communicate through SMS, Twitter, Email and even HTTP requests if you’re into that sort of thing.

The basic Twine comes with an internal temperature sensor and an internal accelerometer, and the Twine guys are making optional external sensors including a magnetic switch for doors, a moisture sensor and a breakout board for those of you that want to create your own DIY sensor action. Supermechanical says that it will develop additional sensors for every $10K over its Kickstarter funding goal. Possible options include an RFID reader, a pressure sensor and/or current sensor.

After a stint on Hacker News, the project has received over $60K in funding, and with a donation of $90 you can order your own Twine through Kickstarter — A perfect gift for the person who has everything but a refrigerator door with its own Twitter account.

When asked if the thing would actually work, Supermechanical’s David Carr told me, ”Of course, we still have plenty of rough edges and features to fill out, but we are well along the path at this point. If you’re concerned about our ability to deliver, you might check out some of our past work and bios.”

Okay!

There’s a nasty, and very personal, lawsuit in progress between Radar Networks and a jilted investor, Kate Paley (daughter of CBS icon William Paley). The defendants include founder Nova Spivack, the company that eventually acquired Radar Networks (Evri) as well as investors Steve Hall, Ross Levinsohn and Baris Kardogan.

The company, known by most through it’s product Twine, was once doing quite well from an outsider’s perspective, but the reality, according to the lawsuit, is that the company was scrambling for ways to stay in business by 2008. The way that the company raised new money, and the details around its eventual sale to Evri, are a warning to any startup – sometimes it’s better to just shut down a failed company and move on.

The amended lawsuit is below. Plaintiff Kate Paley invested $5 million into Radar Networks in 2007 and 2008. Importantly, the second chunk, $3 million in 2008 – was convertible debt that would turn into equity only when the company raised a new round of financing of at least $4 million. The company did eventually raise more money, from existing investors, and converted that debt into equity. Apparently, though, only a small amount was raised and the company claimed the right to convert Paley’s debt because the total amount, including Paley’s $3 million, met the $4 million target. Paley asserts that the new equity had to equal $4 million without counting her $3 million. The new equity, from inside investors, was raised just days before Paley would have been able to call in her note.

What’s worse, after the financing and the conversion of Paley’s debt, the company raised new debt, including funds from Spivack, and that debt was paid off in the acquisition by Evri. Shareholders received nothing. Evri and Radar shared a common shareholder, Vulcan Capital, which raised additional issues of insider dealings.

There are lots of other allegations of broken promises and breached contracts in the lawsuit. The lawsuit alleges that Spivack painted a rosy picture of Radar Network’s prospects when raising money from Paley. But at the same time he was telling at least one existing investor that the company only had a month’s worth of cash left, and “we have to get a term sheet this month or the board will force a shutdown on us.”

Lots of companies spin tales to raise more cash and keep the dream alive for just a little bit longer. But it’s important not to say one thing to prospective investors and a different thing to existing investors. At least in writing – because it all comes out in the lawsuit.

Update: Spivak talks about the lawsuit here.

After shopping itself around to all the major search engines, Radar Networks finally found a buyer in another semantic search startup. Today, Evri is announcing that it will be acquiring Radar Networks, along with its core technical team and its main product, Twine. Rumors surfaced yesterday on ReadWriteWeb that Evri was being acquired, but that is not the case. Evri is the acquirer.

I spoke with both CEOs this morning. They would not disclose the terms of the deal, but it is safe to assume that it was largely an equity-based transaction. Both Evri and Radar Networks share Paul Allen’s Vulcan Capital as their largest shareholder. Radar has raised $24 million in total capital, while Evri has raised $8 million. (At least that is what has been publicly disclosed. Paul Allen has poured much more money into Evri almost single-handedly, perhaps even more than Radar raised). Radar was unable to raise more during the recession and kept pushing out the release of its next product, T2, an ambitious project to create a semantic index of the Web. Using this semantic index, T2 can do a better job understanding what each Web page it indexes is about.

Evri, on the other hand, has been focusing more on filtering the realtime Web and then creating a semantic index of those pages based on matching similar content. One of the big drivers of the deal was the promise of combining Evri’s realtime filtering with T2, which is ideal for more evergreen and authoritative content.

“We had to find a home,” explains Radar CEO Nova Spivack. “Fortunately, we had T2 and a portfolio of fundamentally valuable IP. And user growth is holding steady even though we are no longer working on Twine” He also confirmed that he was “in discussions” with larger companies. Why did he choose Evri? “At the end of the day, not only was it a better offer, but Evri is more compatible with our team. Joining one of the larger players was a possibility, but it meant we would not get to work on T2.” Spivack will be an advisor to the combined company. He wrote a blog post about the deal.

Semantic search is still in its infancy. Consolidation among startups could give the acquirers more firepower, but eventually the bigger search engines are going to start getting serious.

Yesterday, Bing released a surprisingly useful new feature around recipe search. If you search for “Chicken,” you can narrow the results down by “chicken recipes” and then a whole bunch of new filtering options appear down the left-hand column. You can further narrow results by recipe rating, cuisine (vegetarian, Spanish, Southwestern), convenience (quick/easy, family, entertaining), occasion (wedding, Valentine’s Day), main ingredient, course, or cooking method. Bing is big on guided search (showing relevant search categories to help narrow results), but this goes one step further towards semantic search (the ability to index and search the Web by different facets). Recipes are just the beginning, and it’s not just Bing. Google and a handful of startups, including  Evri, Hakia, and Radar Networks, are hard at work on making semantic search a reality.  The race is on to bring this type of semantic filtering for nearly every category of search across the Web.

In fact, Bing’s recipe search looks a hell of a lot like T2, the semantic search engine being developed in private by Radar Networks. The startup currently offers a semantic bookmarking application called Twine which is on autopilot, but T2 is much more ambitious.  Not many people have seen T2, but CEO Nova Spivack once gave me a demo and I took a bunch of screenshots like the one above (there are also slides on the Web).  When you search for “chicken” on T2, you can also narrow by difficulty level, meal, main ingredient, dietary option, cuisine, course, and so on.  Recipes happens to be one of T2′s strong suits.  It has perhaps the largest semantic indexes of recipes in the world with 300,000 recipes.  But it is also building out its semantic search index for video games, movies, music, travel, health, sports, and other category verticals.

Now here is where the plot thickens.  T2 was supposed to launch by the end of last year.  Part of the delay might be because semantic search is just a really hard problem.  But there could be another reason as well.  Rumors are swirling that Radar Networks is deep in M&A discussions with the “usual suspects.”  In search, the usual suspects are Google and Bing.  Spivack won’t comment on any rumors, but says that T2 is on track and that no other search engine is currently licensing it.  Earlier this week, for what it’s worth, he did Tweet about how he was running around to “insane” meetings, right after he Tweeted that “Google still has a great cafeteria.” And after Bing launched its new recipe feature, he sent out this enigmatic Tweet:

I bet that within 12 months Google and Bing will be fighting to enable best faceted search experiences. Just watch.

It’s easy to read too much into these idle Tweets.  Spivack is the CEO of a search startup. It stands to reason that he would have meetings with Google and and other big search engines about lots of things, ranging from licensing his semantic search technology to an outright sale.  The one thing it is pretty safe to conclude is that both Google and Bing are very interested in semantic search.  Bing seems to be further along than Google, as the launch of recipe search indicates.

But both are facing a buy-or-build decision when it comes to semantic search.  Bing’s recipe search is only based on partner feeds and tags, not a proper semantic index. Radar Networks is a serious player here, with a total of $24 million in capital raised since 2006.  It owns four patents on the technology, with 20 more pending.  Radar is essentially building a huge structured database for every page on the Web, which breaks down the information contained on those pages into well-defined facets.  For recipes, it is ingredients, cooking times, and difficulty levels. For video games, it can search by title, developer, character, and so on.  And since it is a structured database, it can jump from “Star Wars” video games to “Star Wars” recipes.  It is all linked together in the semantic index.

Radar is building that index across many different categories. each category requires its own ontology, or collection of facets which describe the things in that category.  Radar is building out its own ontologies and also wants to create a SourceForge for ontologies where Webmasters can contribute as well.  Some of the Webmaster tools Radar has developed will allow anyone who owns a Website to tell Radar about its Web pages.  For instance, on a site about movies, the Webmaster would use a browser plug-in to mark the title, actors, director, reviews, and so on.  Then Radar will take that template and apply it to the entire site.  If Google or Bing adopted a similar approach, Webmasters would have a big incentive to train their semantic indexes because it would create deep links and improve their SEO.

Like I said, the race is on.

While it appeared like Twine was finally getting some traction earlier this year, it’s fallen by the wayside. Traffic is way down (see chart below), partly because it is no longer buying traffic with ads and partly because of changes to the way Google indexes the site. Bottom line is that is that beyond a hardcore following of about 250,000, Twine does not have broad appeal.

But CEO Nova Spivack and his team at Twine have been busy working on something else entirely, to the point that the current Twine service is pretty much on autopilot. In the video above, Spivack gives a sneak peak at what his team has been working on. Codenamed T2, it is complete departure from the navel-gazing approach of Twine 1.0. It is a big step towards creating a semantic search engine that might eventually scale across the Web—exactly the kind of swing for the fences type of idea we like to see at TechCrunch.

When T2 launches, hopefully by the end of the year, it will be a demonstration of what semantic search could be. T2 will have a semantic index of the top 50 to 100 sites across major categories such as food, health, sports, music, finance, television, politics, tech and movies. In those categories, T2 should provide really good guided search. If you search for “baseball” you will get a list of baseball players, along with categories on the side to refine the list such as by position or team name. When you type in “thai food,” you can select the Recipes tab and then filter by food site, rating, main ingredient, and so on. Or you can select the restaurant tab and drill down by city, hours of operation, etc.

You’ll find this type of guided search on Bing, with the categories changing based on the initial search term. But Twine does things differently.

What Twine has done, basically, is speed up the rate at which it can look at a raw Web page and create semantic metadata for it. Bing sometimes does this via natural language processing, through the technology it bought with Powerset. That takes a lot of computation. It also employs other methods. Twine’s approach is more to create a set of semantic tags for each page.

There are already standards for doing this, such as RDF and OWL, but most Webmasters don’t bother adding such tags to their sites. If they happen to be there, Twine can read them, but it can also make a good guess as to what is on the page and assign its own tags to the page. In order to try to make it easier for Web developers to tag their sites, Twine is also working on developer tools such as an Ontologies Editor. This lets anyone with domain expertise define the different concepts and tags which would characterize a page about a particular topic, such as a recipe or a baseball player or a car. For example, a recipe might be contain concepts such as ingredients, difficulty level, an author, and a a date. There are literally millions of potential properties that can be matched to different concepts. The collection of all of these together for a specific topic is an ontology.

There can literally be hundreds of thousands of ontologies for every conceivable topic. If Twine knows what ontology to apply to a given Web page, it can do a better job applying semantic tags to it and extracting data. Twine wants to create an open directory of these things, which will be like a SourceForge for ontologies where anyone can contribute and make them better. You can watch this video for more details.

All of this might seem a bit abstract, but if we could ever get to the point where the most important pages on the Web have semantic tags, it will be a lot easier for computers to know what they are about. And to the extent that data is locked in those pages, the Semantic Web will turn that data into something that can be computed. As these tags get applied to more and more information, they could eventually help filter stream data as well that everyone is increasingly drowning in.

Whether or not Twine will be the company to deliver any or all of this is a long shot, but it is definitely something worth swinging for.

What is the best way to sift through a stream of information? The list view seems to be the most popular because it is information-dense and easy to scan, but it can be overwhelming. More visually appealing ways to manage data are needed. Twine, a site which lets you collect and subscribe to different interest feeds, just introduced a new way to wade through its streams.

The new Flash visualization presents your stream of shared links as a deck of headlines which you can shuffle through (see video below). A slider along the bottom, lets you cycle through the deck by time, and arrows underneath let you move sequentially, or you can just click on a deck in the background to move it forward. If you want to learn more, you can flip each deck to read a snippet and link to the full detail page. The semantic tags associated with each item also show up on the side and can be clicked on to navigate through the deck.

It feels like Coverflow on Apple’s iTunes, except that you navigate through the deck front to back instead of side to side. It also reminds me of the deck metaphor on the new Palm Pre. I can definitely see this as a good UI for mobile apps as well where screen size is more constrained. Don’t worry, though, the regular list view is still an option.

My only problem with the visualization is that it takes a while to load (Flash, why do you torture me?). But other than that, it makes sifting through each feed seem more like channel-surfing in the way that SearchMe does for search results and music search or StumbleVideo does for Web videos. But there must be a better way to wade through the stream. What is it?

What doesn’t kill you will only make you stronger. Adam Lindemann learned that the hard way with iMindi, a startup trying to create a “thought engine” that was skewered by our judges at last year’s TechCrunch50. “It almost destroyed us,” says Lindemann. But he and his team have completely redesigned the product, which creates a mind map of your thoughts based on semantic indexing technology, and lets you “merge” those thought maps with related ones created by other people.

It is still rough around the edges, but is a vast improvement over the original concept. Today, iMindi is launching in private beta, and we have 1,000 invites for TechCrunch readers (sign up here).

The drubbing iMindi received at TechCrunch50 last year was brutal. After Lindemann’s presentation (see video below), Mark Cuban, who was a judge, laid into him:

Maybe I’m missing something, but that just sounded like the biggest bunch of bullshit I’ve ever heard in my life. You want millions of people to create a virtual decision tree, and create a virtual mind meld, and then get advertisers to mine the virtual mind meld. Why would you want to invest the time?

Lindemann and his co-founder Galen Kaufman were devastated. Investors wanted to pull out. “It was a complete disaster,” says Lindemann, “but it was the best thing that could have happened because they were essentially right. If we would have launched in September, we would have failed.”

The main problem with the original design was that users were expected to manually connect their thought maps to other people’s thought maps. It was very labor intensive, and it wasn’t clear why anyone would invest the time. There are still some UI issues, but the connections are now automated, and it is easier to dump in data from other places on the Web.

Let me take a step back and explain what iMindi is today and what it hopes to become. When you create an account, you are encouraged to sign up for different “think tanks,” which are topic areas of interest. These include “Innovation & Technology,” “Wealth and Finance,” Fashion and Style,”"Travel and Adventure,” Sports,” “Pregnancy and Parenting,” and “Mind, Spirit, and Religion.” You can create your own think tank topics. Once you’ve signed up for a few think tanks, you are ready to enter your “thoughts” on that topic.

You can enter your thoughts directly, just like writing a blog post. Or you can cut and paste from blogs, articles, or other sources on the Web. (Soon it will be possible to ingest your existing thought stream from other sources, such as an RSS feed from your blog or your Twitter feed). Once you you are done entering your thought, iMindi runs it through its semantic index and creates tags for all the major concepts it recognizes. The concepts are hyperlinked in your post and presented as a thought map below. Clicking on any concept reveals other entries you have made with the same semantic tags. If they are truly related, you can “merge” the two thoughts and they will be connected on the mind map.

iMindi is still hit or miss in identifying and mapping the right concepts. For instance, I entered a post I wrote called “Jump Into The Stream” and it produced the mind map at right. It correctly identified the people mentioned in the post, a date, and the fact that the “stream” is a “metaphor” for information consumption related to another metaphor, “the page.” But other than “Yahoo” and “Google,” it failed to identify any of the smaller companies at the center of redefining teh Web in terms of information streams (Twitter, Facebook, FriendFeed, etc.). So its semantic index needs to get better, but it would be easy for iMindi to allow users to add their own tags or edit existing maps.

Where this becomes interesting is that you can also find other people whose posts/thoughts have created the same semantic tags, and merge your mind map with theirs. In this way, iMindi hopes to help you find like-minded people. Once you do find them, you can follow them and their thoughts in the think tanks where your interests overlap. The more people who merge their maps with yours, the greater your “mind rank.” You also can see everyone’s thoughts in a particular think tank as a stream when you explore that think tank. In this sense, iMindi shares an approach with Twine, which is also powered by a semantic index and lets you follow other people’s interest feeds.

Twine is much better funded and at a more mature stage of development. (Twine’s parent company, Radar Networks, has raised $18 million, whereas iMindi was built with only $500,000 so far). But iMindi’s focus on creating these mind melds is promising. We are increasingly drowning in people’s thought streams already (Twitter, Facebook, FreindFeed, you name it). iMindi today is still too much work. But if it follows through and lets you actually ingest these thought streams you are already creating and following elsewhere, it could be a valuable filter.

Imagine being able to map your entire Twitter stream, or the streams of individuals you are following, or just individual Tweets, and mapping those across time. Putting a semantic layer on top of Twitter, or any stream of content is a powerful way to explore related thoughts and concepts. The mind map connects related thoughts not by links, but by analyzing the underlying language used to express those thoughts. In a world of ever-larger information streams, we need better ways to navigate those streams. Semantic mind maps could be one way.

iMindi still has a lot of work to do before we get there. It needs to remove steps from the way it lets you create “thoughts” and link to other thoughts. Personally, I’d lose the think tanks concept or put it into the background. Right now, if you don’t look in the right think tank, you might miss a related thought if somebody entered it into another category. iMindi needs to let its thought engine do even more of the work in connecting thoughts together, or at least exposing them to users. Lindemann is aware of these limitations, and is keeping an open mind about how to make iMindi work. Sign up for the beta and give him your thoughts.

http://www.ustream.tv/flash/video/699938

It turns out that people are following more than just their friends online. Look at the comScore chart above comparing unique visitors in the U.S. to FriendFeed versus Twine. Yeah, I was shocked to see that Twine has more than three times as many unique monthly visitors as FriendFeed (714,000 vs. 188,000). On a worldwide basis, comScore shows FriendFeed still slightly ahead of Twine. ComScore doesn’t always do a great job with small sites, so I checked Compete, which shows Twine with 2.25 million monthly visitors in April versus 998,000 for FriendFeed (see embed below). Different numbers, same story.

While FriendFeed is organized around following feeds of your friends’ activities across the Web, Twine is organized around interest feeds. Essentially, Twine members create topic pages that others can follow. It requires more work than FriendFeed. You have to add items such as links,articles, videos, and notes. But once you do, Twine organizes them for you using an underlying semantic index and tagging technology combined with social inputs from the community. So in a sense it competes more with Mahalo or Squidoo in that it creates authoritative pages around topics, except that these pages are really constantly updated topic or interest feeds that anyone can add to. You can read more about the original concept here, which relaunched publicly in October, 2008. All the growth is from October.

I pinged Nova Spivack, CEO of Radar Networks, the company behind Twine, to ask what’s up. He says that both the Compete and comScore numbers are off, but the trend is right. The initial growth came simply from letting people in who had been on the waiting list. But even he is surprised by the growth rate. So far five million items have been bookmarked in Twine. There are now 200,000 registered users who have created Twines (its name for interest feeds) across 30,000 different interest groups. The rest of the traffic comes from people visiting those topic pages.

And it is not all SEO traffic. Spivack provides the following breakdown of traffic by source: 59 percent comes from people coming directly to Twine, 20 percent comes from search engines, and most of the rest comes from people who receive email notifications and daily digests tracking the interest feeds they’ve signed up for. About 2 percent of traffic comes from twitter, but that portion is “rising fast.”

Following interesting people is just a proxy for following your interests, and Twine lets you connect with like-minded people as well. It is the combination that is killer.

A year after launching its beta, Twine opened up today to the general public with a completely redesigned site. The relaunch got lots of coverage. Maybe you read some of it. Even if you did, you probably still don’t know what Twine does. Some semantic shit, right?

Exactly. Twine’s marketing department made the video above as a joke for their staff meeting today. (Warning:Turn the volume down, NSFW). In the voiceover, Candice Nobles, director of marketing at Radar Networks (which operates Twine) explains:

You use Twine to collect, find some shit, and share that shit with people you know. Twine ties it all together by topic, so you can have that shit in one place and it is easy for you to find it. You know what I’m sayin’?

I think that is the best explanation I’ve heard yet of what Twine does.

Click here to see the original version of the video.

Radar Networks is the company behind Twine, a site in private beta that helps you organize the Web and your personal information by automatically tagging and cataloging everything you save to it. (For more, see our write-up).

CEO Nova Spivack took me through a demo. On the surface, Twine is a place to organize information you find or create on the Web—bookmarks, notes, videos, photos,contacts, tasks. (A Web browser plug-in makes it easy to save stuff to your Twine wherever you may find it on the Web). You can also share that information with a private group or publicly. Once you ingest in all the information you want to organize, Twine applies a semantic analysis to it that creates tags for each document or video or photo. The tags match up to concepts that Twine’s algorithms associate with each piece of content, regardless of whether that concept is specifically mentioned in the Web page or other content being tagged. For example, you might bookmark this post and Twine would create tags for all the people mentioned in it (Nova Spivack, Paul Allen, Peter Rip, and Ron Conway). It would also create tags for the organizations related to the post, such as Radar Networks and DARPA, but also Paul Allen’s venture firm Vulcan Capital—even if Vulcan was never mentioned in the post.

What Twine does is automatically generate smart tags and connect them together. There is also a social element. If you share a Twine with others, each piece of content that someone brings into that online space is associated with that person. So when you do a search, the results that come back are influenced not just by the tags, but also by who put the information into the Twine in the first place. “It’s the wisdom of crowds plus the wisdom of computers working together,” says Spivack. The more closely related that person is to you, the higher the relevance. At the same time, Twine is creating a very detailed profile of your interests which it hopes to run highly targeted ads against.

Twine is putting structure onto all of this unstructured data that is out there by analyzing it and adding tags to it that are connected together. The network of links between these tags is something that Spivack calls the “semantic graph,” which includes the “social graph” that is made up only of those tags categorized as people. Bu the semantic graph is bigger than that, comprising other tags such as organizations, places, and other categories.

Rather than create a semantic index of the entire Web, which would be a huge undertaking, Spivack is starting with just those parts of the Web people feel are important enough to save in their collections. Then he applies natural language processing and semantic indexing to just that data. “If you just sucked in the whole Web,” he says, “you would get stuff people didn’t want. Here we are looking at who thought it was important and why.” It’s also cheaper to do it this way, since it’s a more limited set of data that needs to be run through Twine’s semantic engine.

Everything in Twine will become widgetizable and exportable elsewhere. There will also be a full set of APIs. All the data will be able to be taken in and out. Other search engines will be able to index anything in a public Twine, along with the smart tags that have been appended to the information there. “When you put stuff into Twine,” says Spivack, “Twine enriches it, but you can take it out.” Of course, all of those enriched tags will point right back to Twine. “We’re the only place that can even see the connections between things,” says Spivack. Well, not quite yet. People have to start using Twine first.