TechCrunch’s Picks: The Top 8 Startups From Y Combinator W14 Demo Day

While today saw Paul Graham physically pass the reins to new Y Combinator president Sam Altman, the bigger buzz at Demo Day was about how most investors thought this was the best YC class yet. There were unsexy startups in industries ripe for disruption, deep B2B specialization to meet the needs of specific companies, and several non-profits out to make the world better, not themselves richer.

After Graham was awarded a pair of khaki shorts signed by the whole class, 68 startups took the stage at the Mountain View Computer History Museum. 55 presented on the record, and you can read about all of them in our overviews of Batch 1, Batch 2, and Batches 3 and 4. (If you want to be part of the next round of YC, the accelerator is currently accepting applications.)

Once the dust settled, TechCrunch’s team queried top investors in attendance and huddled together to choose our picks from this season. With fewer inane social apps and no-one-needs-this service startups, it was especially tough this time around, but here’s our selection:

Wit.ai – Voice Interface API

Voice is how we’ll control devices too small for a keyboard, like watches, earbuds, and much of the “Internet Of Things.” But it’s a ton of work for a developer to build their own voice system, with natural language processing, speech recognition, and other engineering requirements. So Wit.ai has created a voice interface API, and developers can pipe into their app to enable voice command. That could let them offer in-app voice search, hardware control without buttons, and more.

Its co-founder sold his last company to speech tech giant Nuance, and now Wit.ai has 3,000 developer-users like Pebble and Samsung and is growing 29 percent per week. Every app will soon need speech tech, and any startup that can offer an alternative to big providers like Nuance or (maybe) Google will be in a great position for traction or acquisition. Read more from TechCrunch about Wit.ai.

–

The Dating Ring – Matchmaker-Assisted Online Dating

“Dating should work like Uber, and with The Dating Ring, it does,” says the startup’s co-founder. Currently, dating online is a big hassle. You sift through profiles or Tinder cards, approve some people, wait for a match, and make chit chat. If you’re lucky, it progresses to a real date, but then your partner might look nothing like your partner.

With The Dating Ring, you apply to join. Pass the first bar and you’ll meet in-person with a Dating Ring matchmaker for five minutes. They’ll assess your style and open the ability to go on group dates with three men and three women (or a group of four for gay users). But unlike Grouper, a matchmaker has ensured you’re more likely to fall for one of your date mates. Users pay $25 for the initial matchmaker meeting and $20 per date. The Dating Ring certainly made a stir when it announced plans to crowdfund planes full of women to be delivered from New York City to lonely San Francisco guys.

The Dating Ring’s revenue is growing 60 percent per month for the last six months, it’s profitable, and 70 percent of users go on a second date. While only $2 billion a year is spent in the space, The Dating Ring wants to grow the pie. It seems reasonable that people would be willing to pay for love…or at least to go on real, match-made dates instead of endlessly browsing profiles online. Read more from TechCrunch about The Dating Ring.

–

Pushbullet – Synced Cross-Web And Mobile Push Notifications

It’s crazy that when you’re at your computer, you still have to open your phone to look at push notifications. Pushbullet syncs them so you can respond on the device you’re currently using, including the web thanks to a Chrome/Firefox extension. You can even send files back and forth between your phone and computer. But Pushbullet also lets you turn changes on websites and more into push notifications, even if a site doesn’t have a mobile app. For example, you could ask to get an alert the next time Nike puts a new line of shoe on sale.

Pushbullet is now handling 10 million notifications a day for 100,000 weekly users and 60,000 daily users. Push is quickly becoming the most powerful way to reach people and is eating email’s lunch. Pushbullet could hit the bullseye by enhancing the standard and bringing it to more devices and sites. Read more from TechCrunch about Pushbullet.

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AirHelp: Stick it to the airline 

AirHelp wants to help fliers get compensation that they are legally entitled to after an airline screws up and is late, or cancels their flight. The total dollar amount this sums to each year is $16 billion. And you can go back three years, meaning that there is another $48 billion potentially sitting there.

AirHelp handles the details. You put in your flight number, and if you can get paid, they’ll handle it. The company takes a one-fourth cut if it gets you money back. Not a bad business model unless you are an airline.

Weave: Integrated Phone/Software Systems For Doctors

Weave is a telephone service that better connects patients and their medical providers. Going to the dentist is roughly as fun as, well, going to the dentist. The company wants to make that better by helping dentists keep track of their patient information, and needs, integrating that into their communications system. When you call, the receptionist instantly sees your profile connected to your phone number. This way they don’t have to put you on hold, they can instantly see info about your past visits, and they can even spot revenue opportunities like that your spouse hasn’t had a teeth cleaning in two years (eww).

Before Y Combinator, Weave had yearly recurring revenue of $790,000. Post Y Combinator, that figure is now $1.8 million, up 38 percent per month during the period. Weave picked dentists to start because they are wealthy and low on regulation. The product will scale into other health verticals. Weave charges $300 per month per location for its service. There are 190,000 dental offices in North America, indicating that its addressable market is quite large. And Weave is determined to scale its system to other health verticals, making medical care better and friendlier across the board. Learn more about Weave on TechCrunch.

–

Boostable: Advertising Help For Marketplace Sellers

There are 30 million sellers on marketplaces like Airbnb and Etsy, and they all want more sales. Bettable makes it dead-simple for them to run ads promoting their apartment for rent, or homemade handicraft. Sellers just sign up and send Boostable the URL of their listing or item, select their budget, and Boostable runs efficient, A/B-tested ad campaigns on Facebook and soon Google using advanced retargeting to show them to the right people.

And since each sale also earns the marketplaces money, they’re happy to connect Boostable to their sellers. Airbnb has already partnered with the startup. With its current deals, Boostable could reach 500,000 sellers. If it can convince advertising novices they need to spend money to make money, Boostable could pull in enough small ad contracts to see huge revenue.

–

Kimono Labs: Web Scraping In One Click

Kimono turns websites into APIs. What that means is that you can point Kimono at a website, set parameters, and scrape data from it. Think of it like building a small Google just for your needs aimed at one website.

So far 20,000 devs have signed up to use the service, a figure that is growing 15 percent per week. In its short life of around 10 weeks, Kimono claims to have helped triple the number of public APIs online. I’m honestly interested to see what people could do with Kimono, TechCrunch, and CrunchBase data. Data becomes much more valuable when it’s structured. Read more from TechCrunch about Kimono Labs.

BatteryOS: Better battery-life management 

According to BatteryOS, if you charge a battery to 100 percent, it rapidly degrades the battery itself. After many cycles, batteries’ capacity declines. With BatteryOS, the company claims you will be able to charge your batteries to full capacity and not suffer that degradation.

The company claims that if the Chevy Volt used BatteryOS, it would have twice the range, and last 8 years longer. The company did not explain how its product works, but did allude to having orders for units in the tens of thousands. Learn more about BatteryOS from TechCrunch’s coverage here.

–

Honorable Mention

Noora Health: Healthcare Training For Families

Noora Health trains families to take care of their kin after they return from the hospital. For families living, say, more than one hundred miles from professional medical care, basic healthcare knowledge could be a scarcity. Imagine having a child with a serious heart condition and stressing that you might do something wrong with their medication, care, or diet that could land them back in the hospital or worse? Noora Health gives families training that can be taken back to their homes, leading to healthier outpatients.

They don’t go back as nurses, but the training that individuals receive from Noora Health can reduce complications by 36 percent. The non-profit estimates that its training thus far has saved 124 lives. The company has trained 7,000 families so far.

The company is selling its product to hospitals in the U.S. and using those funds to pay for its work in other places. The company told gathered investors and press that it is building something that is not dependent on donor dollars. It’s a smart model that makes Noora feel more like a startup than a charity. For a deeper look at Noora Health, check out TechCrunch’s past coverage.

Source: Source

Smartphone App Store Statistics FEB,2014 [Infographic]

Courtesy of: Blogmost.com

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Easy Ways To Get Your Kid Into Coding

Easy Ways To Get Your Kid Into Coding (via dotcomplicated.co)

Posted on 6/26/2013 Written by Liz Wassmann In 4th grade, I was presented with a choice: Would I like to study Spanish or French? In high school, my language options increased. I could take Spanish, French, Mandarin Chinese, Japanese, German or American…

---

Saving Tweets to MongoDB using Java.

MongoDB is the fastest growing NoSQL database. Apart from the advantages of NoSQL technology, it's JSON querying style, easy installation makes it more preferable. In this article I'm going to show you how to save tweets into MongoDB using Java.

Here I'm going to use Twitter Search API to get the tweets in the form of JSON and save it to the database. Previously I have tried this with the MySQL. I think MongoDB has some very good advantages.

• We don't have to create a schema for the database where we are going to store the tweets. If you closely see the structure of data returned by the search API, It would require you at least five tables to implement in RDBMS
• Each tweet has different set of attributes associated with it. Some containmention information, some contain retweet information, geo location, URLs and a lot more. This could be stored without designing complex database schema
• MongoDB drivers has built-in support for JSON. JSON being the primary choice of many web services, this becomes a great advantage.
• Twitter may add or remove some of the properties from the result returned by the search API. This will not affect our program.
• Querying the database has become easier and efficient. We don't need to useJoin any more.

You can download the Java driver for MongoDB here. The following simple code is enough to do our task.

import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.net.URL;
import java.net.URLConnection;

import com.mongodb.BasicDBList;
import com.mongodb.BasicDBObject;
import com.mongodb.DB;
import com.mongodb.DBCollection;
import com.mongodb.DBObject;
import com.mongodb.Mongo;
import com.mongodb.util.JSON;

public class Main {
    
    public static void main(String args[])throws Exception {
        System.setProperty("java.net.useSystemProxies", "true");
        
        //Connecting to MongoDB
        Mongo m = new Mongo();
        DB db = m.getDB("twitter");
        DBCollection coll = db.getCollection("tweets");

        //Fetching tweets from Twitter
        String urlstr = "http://search.twitter.com/" +
                "search.json?q=mongodb";
        URL url = new URL(urlstr);
        URLConnection con = url.openConnection();
        BufferedReader br = new BufferedReader(
                new InputStreamReader(con.getInputStream()));
        int c;
        StringBuffer content = new StringBuffer();
        while((c=br.read())!=-1) {
            content.append((char)c);
        }
        
        //Inserting tweets to database        
        BasicDBObject res = (BasicDBObject)
                JSON.parse(content.toString());
        BasicDBList list;
        list = (BasicDBList)res.get("results");
        for(Object obj : list) {
            coll.insert((DBObject)obj);
        }
        m.close();
    }

}

To verify, log into the database terminal and type the following commands and you will be able to see all the tweets in the form of JSON.

>use twitter
>db.tweets.find();

What was that Wiper thing?

In April 2012, several stories were published about a mysterious malware attack shutting down computer systems at businesses throughout Iran.

Several articles mentioned that a virus named Wiper was responsible. Yet, no samples were available from these attacks, causing many to doubt the accuracy of these reports.

Following these incidents, the International Telecommunication Union (ITU) asked Kaspersky Lab to investigate the incidents and determine the potentially destructive impact of this new malware.

After several weeks of research, we failed to find any malware that shared any known properties with Wiper. However, we did discover the nation-state cyber-espionage campaign now known as Flame and later Gauss.

It is our firm opinion that Wiper was a separate strain of malware that was not Flame. Although Flame was a highly flexible attack platform, we did not see any evidence of very destructive behavior. Given the complexity of Flame, one would expect it to be used for long-term surveillance of targets instead of direct sabotage attacks on computer systems. Of course, it is possible that one of the last stages of the surveillance was the delivery of a Wiper-related payload, but so far we haven-t seen this anywhere.

So, months later, we are left wondering: Just what was Wiper?

Enter Wiper

During the investigation of the mysterious malware attack in April, we were able to obtain and analyze several hard drive images that were attacked by Wiper. We can now say with certainty that the incidents took place and that the malware responsible for these attacks existed in April 2012. Also, we are aware of some very similar incidents that have taken place since December of 2011.

The attacks mostly took place in the last 10 days of the month (between the 21st and 30th ) although we cannot confirm that this was due to a special function being activated on certain dates.

The creators of Wiper were extremely careful to destroy absolutely every single piece of data which could be used to trace the incidents. So, in every single case we-ve analyzed, almost nothing was left after the activation of Wiper. It-s important to stress ?almost nothing here because some traces did remain that allowed us to get a better understanding of the attacks.

From some of the destroyed systems we were lucky enough to recover a copy of the registry hive. The registry hive did not contain any malicious drivers or startup entries. However, we came up with the idea to look into the hive slack space for deleted entries. This is what we found:

Interestingly, on 22 April, just before this system went down, a specific registry key was created and then deleted. The key was a service named ?RAHDAUD64. It pointed to a file on disk named ?~DF78.tmp, in the ?C:\WINDOWS\TEMP folder.

The moment we saw this, we immediately recalled Duqu, which used filenames of this format. In fact, the name Duqu was coined by the Hungarian researcher Boldizsár Bencsáth from the CrySyS lab because it created files named ?~dqXX.tmp. (see)

We tried to recover the file ?~DF78.tmp from the disk, but found that the physical space where it resided was filled with garbage data.

We found the same ?wiping pattern in several of the other systems we analyzed - a service named ?RAHDAUD64 which was deleted just before it is wiped - and its file filled with garbage data. In these other systems, the RAHDAUD64 service pointed to different filenames, such as ?~DF11.tmp and ?~DF3C.tmp. So it-s possible the names were random.

Another peculiarity of the wiping process was a specific pattern which was used to trash the files on disk:

Most of the files that were wiped contain this specific pattern that repeats over and over. Interestingly, it did not overwrite the entire file. In some cases some portions of the file remained intact, every header of the files were destroyed in the first place. This was probably caused by the size of the file. The wiping algorithm was designed to quickly destroy as many files as possible.

Based on the pattern that we know had been used when wiping files, we collected Kaspersky Security Network (KSN) statistics on which files had been destroyed.

In an attempt to reconstruct the Wiper algorithm we came up with the following sequence:

1. Searching for and wiping files based on their extensions.
   List of file extensions:
   
   

   accdb	cdx	dmp	H	js	pnf	rom	tif	wmdb
   acl	cfg	doc	hlp	json	png	rpt	tiff	wmv
   acm	chk	docx	hpi	lnk	pps	rsp	tlb	xdr
   amr	com	dot	htm	log	ppt	sam	tmp	xls
   apln	cpl	drv	html	lst	pptx	scp	tsp	xlsx
   asp	cpx	dwg	hxx	m4a	pro	scr	txt	xml
   avi	dat	eml	ico	mid	psd	sdb	vbs	xsd
   ax	db	exe	inc	nls	rar	sig	wab	zip
   bak	dbf	ext	Ini	one	rar	sql	wab~	=
   bin	dbx	fdb	jar	pdf	rdf	sqlite	wav	=
   bmp	dll	gif	jpg	pip	resources	theme	wma	=

2. Searching for and wiping all files in certain folders (e.g. in Documents and Settings, Windows, Program Files) and on all available USB drives connected to the computer.
3. Wiping disk sectors (possibly using a bootkit module).

Wiping a disk that is several hundred gigabytes in size might take hours. So the creators of the malware were careful to select wiping algorithms that could achieve maximum efficiency. For example, let-s take a look at the following disk which was wiped by Wiper. We-ve used a statistical representation (Shannon entropy in blocks of 256K) to represent entropy on disk. The lighter areas have higher entropy, the darker areas, lower. The areas in red have very high entropy (highly random data).

As you can see, Wiper managed to do a pretty good job of destroying most of the disk. One can observe a red-filled stripe at the top which indicates an area that has been cleaned well. Although no clear pattern is visible, a large amount of the disk has been filled with unusable data. The wiping operation obviously focused on the beginning of the disk, then it affected the middle of the disk, before the system finally crashed.

Another view can be obtained by looking for sectors which have been filled with the known ?%PNG / iHDR pattern. Red marks the sector blocks which have been overwritten with this pattern:

As you can see, more than three-quarters of the disk was affected by the wiper, with the vast majority of the data being lost forever.

In some cases, Wiper misfired - for instance, we saw one 64-bit system where Wiper failed to activate. In this case, we discovered two files in %TEMP% which were wiped with the known PNG/iHDR pattern, but not the whole disk:

We presume these two files, out of the thousands in %TEMP%, must have been destroyed because they contained data important to the Wiper attack. In another system we analyzed, in addition to these 20K-ish files, we saw two 512 byte files named ?~DF820A.tmp and ?~DF9FAF.tmp, which have also been wiped beyond recovery.

Interesting enough, on some systems we noticed that all PNF files in the INF Windows folder were wiped with a higher priority than other files. Once again, this is a connection to Duqu and Stuxnet, which kept their main body in encrypted ?.PNF files.

If the purpose of the attackers was to make sure the Wiper malware could never be discovered, it makes sense to first wipe the malware components, and only then to wipe other files in the system which could make it crash.

Links with Flame

While searching for this elusive malware, we came across something else. We suspected Wiper used filenames such as ?~DF*.tmp or ?~DE*.tmp in the TEMP folder, so we started looking for them via KSN. During this process we noticed that an abnormally large number of machines contained the same file name: ~DEB93D.tmp:

The name seemed like a good indicator that the file was part of the Tilded platform, and related to Duqu and Stuxnet. The file appeared to be encrypted, but we quickly noticed something:

Duqu (Nov 3, 2010):
00: ED 6F C8 DA 30 EE D5 01

~DEB93D:
00: 6F C8 FA AA 40 C5 03 B8

By complete chance, we noticed that this file started with bytes ?6F C8, which were also present at the beginning of the Duqu PNF main body, in encrypted format, loaded by the driver compiled on Nov 3, 2010. If it wasn-t for this, maybe we-d have never paid attention to ~DEB93D.tmp, since the content looked like trash.

The encryption algorithm was weak and a pattern appeared to be repeating every 4096 bytes. Since the algorithm was weak we managed to decrypt it by using statistical crypto-analysis, a common technique used for decrypting files during malware analysis. After decrypting the file, we noticed what appeared to be sniffer logs. This made us check further and we found other files, modified on the same date, with names such as ?mssecmgr.ocx, ?EF_trace.log or ?to961.tmp. The rest, as it is said, is history. This is exactly how we discovered Flame.

So, what was Wiper?

There is no doubt that there was a piece of malware known as Wiper that attacked computer systems in Iran (and maybe in other parts of the world) until late April 2012.

The malware was so well written that once it was activated, no data survived. So, although we-ve seen traces of the infection, the malware is still unknown because we have not seen any additional wiping incidents that followed the same pattern as Wiper, and no detections of the malware have appeared in the proactive detection components of our security solutions.

Conclusions:

• It may be possible that we will never find out what Wiper was but based on our experience, we are reasonably sure that it existed, and that it was not related to Flame.
• It-s possible that some machines exist somewhere where the malware has somehow escaped being wiped, but if there is such a case, we haven-t seen it yet.
• Wiper may have been related to Duqu and Stuxnet, given the common filenames, but we cannot be sure of this.
• What is certain is that Wiper was extremely effective and has sparked potential copycats such asShamoon.
• The fact that the use of Wiper led to the discovery of the 4- or 5-year-old Flame cyber-espionage campaign raises a major question. If the same people who created Duqu/Stuxnet/Flame also created Wiper, was it worth blowing the cover of a complex cyber-espionage campaign such as Flame just to destroy a few computer systems?

What Apple's victory over Samsung means

US firm may now go after Motorola and HTC, while Android considered likely to continue to dominate smartphone market

Despite Apple's victory over Samsung in the patent trial, Android is expected to keep control of the global smartphone market. Photograph: Ahn Young-Joon/AP

For Tim Cook, Apple's chief executive, who took over from Steve Jobs a year ago, the court victory over Samsung will have been sweet. Normally, patent disputes rarely produce clean victories. But the decision by the nine-member jury in San Jose, just a few miles from Apple's headquarters, is dramatic.

Apple had been suing Samsung for $2.5bn in damages, claiming Samsung's phones and tablets copied its devices' behaviour and appearance; Samsung counter-claimed about $200m, saying the iPhoneand iPad used its wireless 3G standard technologies, and methods for tasks such as sending a photo by email from a phone.

Apple won on almost every count it claimed; Samsung, on absolutely none. It was a dramatic demonstration of the home court advantage. Samsung Electronics can bear the $1.05bn in damages – in the second quarter of this year alone its operating profit was $5.86bn – but the hit to its reputation is substantial. Apple can portray it as a looter of intellectual property, a copyist, an unimaginative follower.

Apple can also now go after HTC and Motorola, its two principal smartphone rivals in the US, with renewed vigour. They may have to consider whether to sue for peace, for Samsung lost despite being the biggest of the mobile makers, the biggest smartphone maker, the biggest implementer of Google's Android mobile software, and extremely rich — it spent $9.5bn on market in the past 12 months, and is a major sponsor of the Olympics.

More than that, though, Apple will hope that this decision will put second thoughts and self-doubt into the minds of every industrial designer and software engineer competing in the smartphone and tablet business around the world.

Apple's key rival here is Google, whose Android software Samsung used to build its phones. But Apple can only go after the handset makers that implement Android, not the creators themselves; even so, by winning on Friday, it will have nervous engineers at the handset makers, who are using Android in ever-growing numbers, pausing as they compare their latest products with the next iPhone. Is it too similar? Will this trigger a lawsuit? Should I change it?

The patent wars have dismayed many, who find the legal fighting tedious, and wish the companies would just work on innovation. Apple, for its part, insists that it is happy with competition — but that rivals should do their own innovation. The rivals shoot back that Apple is trying to patent ideas that have been obvious and implemented before.

Despite this result, Android is likely to keep winning the battle to run the world's smartphones; it ran on 64% of the smartphones shipped worldwide in spring, and 80% of the smartphone shipped in China in that period; the iPhone had 12%. Android is running on phones that are getting cheaper and cheaper all the time. It wouldn't be worth Apple's time to sue every company using it.

But in the US, where its most valuable customers are, Apple definitely sees the effort as worth going to. The decision in San Jose may be the first of many. The question now is whether Google, whose Motorola subsidiary a week ago filed a fresh series of patent infringement claims against Apple — claims which could halt sales of the iPhone and iPad, if upheld — can manage to drive the war to a settlement. So far, there's no sign of that happening. And Apple is yet another billion dollars richer.