Technology

Knock Knock, a startup building games for platforms like Facebook Messenger and WeChat, is announcing that it has raised $2 million in seed funding.

The goal isn&t to build interactive chat fiction, but rather fully fledged mobile games that are accessed from messaging apps, while also taking advantages of the opportunities offered by incorporating messaging and chatbots into the game mechanics.

&This is the most frictionless an experience can get,& said CEO Andrew Friday. &Thereno download, ithooked up to a fast messaging medium that you&re already using and people can bring their friends into the experience seamlessly.&

Friday was a senior product manager for chat games at Zynga, while his co-founder Andrew N. Green was previously the head of business operations at TinyCo. They plan to release their first game for Facebook Messenger later this year, and then a WeChat title in early 2019.

When I asked if there are any specific genres that will do best on messaging, Friday suggested that thereactually &an embarrassment of riches.&

&Most great mobile game genres, and game genres in general, are good for the platform,& he said. &Itjust that if you try to just port those designs to the platform, itnot going to work. If you rethink or reimagine these mechanics, how they would work best, how they would be most fun on the platform, there are so many genres that can work on chat.&

He also suggested that compared to FRVR, another recently funded startup looking to build chat games, Knock Knock is less focused on &hypercasual& games and instead taking &a deeper, more thoughtful approach.& Although thoughtfulness and depth are relative — Friday suggested that Knock Knock could still create the initial versions of its games in 90 days.

The funding was led by Raine Ventures, with participation from London Venture Partners, Ludlow Ventures and Gregory Milken.

&Knock Knock has the potential to usher in the next wave of chat games that will redefine the market,& said Courtney Favreau, a venture capital partner at Raine, in the funding announcement. &The founding team has an impressive track record in the mobile and chat gaming spaces and we&re very excited to help them bring their vision to life.&

Mental health startup Lantern, which raised more than $20 million in funding, is winding down its commercial operations after a couple of acquisition deals fell through, TechCrunch has learned. As part of the wind-down, Lantern is laying off about 25 people, which is the majority of the staff, with a handful of former team members to focus on whatnext for the company. Their last days will be August 1, 2018.

Lantern, which offers tools to deal with stress, anxiety and body image for about $50 a month, will continue to be available for paid customers through the end of the year.However, coaches are no longer available to users.

Allof Lanternprograms were based oncognitive behavioral therapy techniques, which examines the relationship between thoughts, feelings and behaviors. The programs were designed to empower people to learn how to manage their anxiety, stress and/or body images on a daily basis.

Lantern also employed coaches, who were experienced behavioral change professionals trained in CBT to guide patientsthrough the programs, give them feedback and help keep them accountable for reaching their goals.

Since its founding in 2013, Lantern — formerly known as ThriveOn — served hundreds of thousands of people. Lanternultimate goal was to sell to insurers, but in the interim, first sold directly to consumers and then through employers like Facebook and Intuit. But Lantern needed more customers in order to survive, Lantern CEO Alejandro Foung told TechCrunch.

&What we&ve built, I still really believe in the value of it,& Foung told me. &We&re committed to finding a path forward for us.&

Lanterngoal was to bridge the gap that exists between those who need mental health services and those who receive them.If Foung knew what the end goal was for Lantern when he first started the company, he would&ve kept a smaller team, he said. He also, of course, would&ve started focusing on insurers a lot earlier in the process.

&What I feel sad about is that we weren&t able to make that vision come to reality,& Foung said. &What I&m left with is knowledge that the product we built has a place in society. It can best be used in the future for markets people ignore.&

As lantern winds down, Foung is focused on what comes next for the company with a handful of remaining team members. He wouldn&t get in to too much detail, but told me his team will be focused &on addressing gaps that exist for underserved populations.&

When Lantern first came on the scene, there were very few startups addressing mental health issues. Since then, a number of startups have emerged, including Meru Health, Pacific Labs and startups more strictly focused on mindfulness and meditation.

Figure Eight, a platform that helps developers train, test and fine-tune their machine learning models, today announced a major new collaboration with Google that essentially turns Figure Eight into the de facto standard for creating and annotating machine learning data for Google CloudAutoML service.

As Figure EightCEO Robin Bordoli told me, Google had long been a customer, but the two companies decided to work closer together now that AutoML is launching in beta and expanding its product portfolio, too. As Bordoli argues, training data remains one of the biggest bottlenecks for developers who want to build their own machine learning models — and Google recognized this, too. &Ittheir recognition that the lack of training data is a fundamental bottleneck to the adoption of AutoML,& he told me.

Since AutoMLfirst product focuses on machine vision, itmaybe no surprise that Figure Eightpartnership with Google is also currently mostly about this kind of visual training data. Its service is meant to help relatively inexperienced developers collect data, prepare it for use in AutoML and then experiment with the results.

What makes Figure Eight stand out from other platforms is that it keeps the human in the loop. Bordoli argues that you can&t simply use AI tools to annotate your training data, just like you can&t fully rely on humans either (unless you want to employ entire countries as image taggers). &Human labeling is a key need for our customers, and we are excited to partner with Figure Eight to enhance our support in this area,& said Francisco Uribe, the product manager for Google Cloud AutoML at Google.

As part of this partnership, Figure Eight has developed a number of AutoML-specific templates and processes for uploading the data. It also offers its customers assistance with creating the training data (while also ensuring AI fairness). Google Cloud users can use the Figure Eight platform to label up to 1,000 images and they do, of course, get access to the companydata labeling annotators if they don&t want to do all the work themselves.

Ahead of todayannouncement, Figure Eight had already generated more than 10 billion data labels and todayannouncement will surely accelerate this.