Popular video calling and messaging app JusTalk claims to be both secure and encrypted. But a security lapse has proven the app to be neither secure nor encrypted after a huge cache of users’ unencrypted private messages was found online.
The messaging app is widely used across Asia and has a booming international audience with 20 million users globally. Google Play lists JusTalk Kids, billed as its child-friendly and compatible version of its messaging app, as having more than 1 million Android downloads.
JusTalk says both its apps are end-to-end encrypted — where only the people in the conversation can read its messages — and boasts on its website that “only you and the person you communicate with can see, read or listen to them: Even the JusTalk team won’t access your data!”
But a review of the huge cache of internal data, seen by TechCrunch, proves those claims are not true. The data includes millions of JusTalk user messages, along with the precise date and time they were sent and the phone numbers of both the sender and recipient. The data also contained records of calls that were placed using the app.
Security researcher Anurag Sen found the data this week and asked TechCrunch for help in reporting it to the company. Juphoon, the China-based cloud company behind the messaging app said it spun out the service in 2016 and is now owned and operated by Ningbo Jus, a company that appears to share the same office as listed on Juphoon’s website. But despite multiple efforts to reach JusTalk’s founder Leo Lv and other executives, our emails were not acknowledged or returned, and the company has shown no attempt to remediate the spill. A text message to Lv’s phone was marked as delivered but not read.
Because each message recorded in the data contained every phone number in the same chat, it was possible to follow entire conversations, including from children who were using the JusTalk Kids app to chat with their parents.
The internal data also included the granular locations of thousands of users collected from users’ phones, with large clusters of users in the United States, United Kingdom, India, Saudi Arabia, Thailand and mainland China.
According to Sen, the data also contained records from a third app, JusTalk 2nd Phone Number, which allows users to generate virtual, ephemeral phone numbers to use instead of giving out their private cell phone number. A review of some of these records reveal both the user’s cell phone number as well as every ephemeral phone number they generated.
We’re not disclosing where or how the data is obtainable, but are weighing in favor of public disclosure after we found evidence that Sen was not alone in discovering the data.
This is the latest in a spate of data spills in China. Earlier this month a huge database of some 1 billion Chinese residents was siphoned from a Shanghai police database stored in Alibaba’s cloud and portions of the data were published online. Beijing has yet to comment publicly on the leak, but references to the breach on social media have been widely censored.
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AI music generators could be a boon for artists — but also problematic • TechCrunch
It was only five years ago that electronic punk band YACHT entered the recording studio with a daunting task: they would train an AI on fourteen years of their music, then synthesize the results into the album “Chain Tripping.”
“I’m not interested in being a reactionary,” YACHT member and tech writer Claire L. Evans said in a documentary about the album. “I don’t want to return to my roots and play acoustic guitar because I’m so freaked out about the coming robot apocalypse, but I also don’t want to jump into the trenches and welcome our new robot overlords either.”
But our new robot overlords are making a whole lot of progress in the space of AI music generation. Even though the Grammy-nominated “Chain Tripping” was released in 2019, the technology behind it is already becoming outdated. Now, the startup behind the open source AI image generator Stable Diffusion is pushing us forward again with its next act: making music.
Harmonai is an organization with financial backing from Stability AI, the London-based startup behind Stable Diffusion. In late September, Harmonai released Dance Diffusion, an algorithm and set of tools that can generate clips of music by training on hundreds of hours of existing songs.
“I started my work on audio diffusion around the same time as I started working with Stability AI,” Zach Evans, who heads development of Dance Diffusion, told TechCrunch in an email interview. “I was brought on to the company due to my development work with [the image-generating algorithm] Disco Diffusion and I quickly decided to pivot to audio research. To facilitate my own learning and research, and make a community that focuses on audio AI, I started Harmonai.”
Dance Diffusion remains in the testing stages — at present, the system can only generate clips a few seconds long. But the early results provide a tantalizing glimpse at what could be the future of music creation, while at the same time raising questions about the potential impact on artists.
The emergence of Dance Diffusion comes several years after OpenAI, the San Francisco-based lab behind DALL-E 2, detailed its grand experiment with music generation, dubbed Jukebox. Given a genre, artist and a snippet of lyrics, Jukebox could generate relatively coherent music complete with vocals. But the songs Jukebox produced lacked larger musical structures like choruses that repeat, and often contained nonsense lyrics.
Google’s AudioLM, detailed for the first time earlier this week, shows more promise, with an uncanny ability to generate piano music given a short snippet of playing. But it hasn’t been open sourced.
Dance Diffusion aims to overcome the limitations of previous open source tools by borrowing technology from image generators such as Stable Diffusion. The system is what’s known as a diffusion model, which generates new data (e.g., songs) by learning how to destroy and recover many existing samples of data. As it’s fed the existing samples — say, the entire Smashing Pumpkins discography — the model gets better at recovering all the data it had previously destroyed to create new works.
Kyle Worrall, a Ph.D. student at the University of York in the U.K. studying the musical applications of machine learning, explained the nuances of diffusion systems in an interview with TechCrunch:
“In the training of a diffusion model, training data such as the MAESTRO data set of piano performances is ‘destroyed’ and ‘recovered,’ and the model improves at performing these tasks as it works its way through the training data,” he said via email. “Eventually the trained model can take noise and turn that into music similar to the training data (i.e., piano performances in MAESTRO’s case). Users can then use the trained model to do one of three tasks: Generate new audio, regenerate existing audio that the user chooses, or interpolate between two input tracks.”
It’s not the most intuitive idea. But as DALL-E 2, Stable Diffusion and other such systems have shown, the results can be remarkably realistic.
For example, check out this Disco Diffusion model fine-tuned on Daft Punk music:
Or this style transfer of the Pirates of the Caribbean theme to flute:
Or this style transfer of Smash Mouth vocals to the Tetris theme (yes, really):
Or these models, which were fine-tuned on copyright-free dance music:
Jona Bechtolt of YACHT was impressed by what Dance Diffusion can create.
“Our initial reaction was like, ‘Okay, this is a leap forward from where we were at before with raw audio,’” Bechtolt told TechCrunch.
Unlike popular image-generating systems, Dance Diffusion is somewhat limited in what it can create — at least for the time being. While it can be fine-tuned on a particular artist, genre or even instrument, the system isn’t as general as Jukebox. The handful of Dance Diffusion models available — a hodgepodge from Harmonai and early adopters on the official Discord server, including models fine-tuned with clips from Billy Joel, The Beatles, Daft Punk and musician Jonathan Mann’s Song A Day project — stay within their respective lanes. That is to say, the Jonathan Mann model always generates songs in Mann’s musical style.
And Dance Diffusion-generated music won’t fool anyone today. While the system can “style transfer” songs by applying the style of one artist to a song by another, essentially creating covers, it can’t generate clips longer than a few seconds in length and lyrics that aren’t gibberish (see the below clip). That’s the result of technical hurdles Harmonai has yet to overcome, says Nicolas Martel, a self-taught game developer and member of the Harmonai Discord.
“The model is only trained on short 1.5-second samples at a time so it can’t learn or reason about long-term structure,” Martel told TechCrunch. “The authors seem to be saying this isn’t a problem, but in my experience — and logically anyway — that hasn’t been very true.”
YACHT’s Evans and Bechtolt are concerned about the ethical implications of AI – they are working artists, after all – but they observe that these “style transfers” are already part of the natural creative process.
“That’s something that artists are already doing in the studio in a much more informal and sloppy way,” Evans said. “You sit down to write a song and you’re like, I want a Fall bass line and a B-52’s melody, and I want it to sound like it came from London in 1977.”
But Evans isn’t interested in writing the dark, post-punk rendition of “Love Shack.” Rather, she thinks that interesting music comes from experimentation in the studio – even if you take inspiration from the B-52’s, your final product may not bear the signs of those influences.
“In trying to achieve that, you fail,” Evans told TechCrunch. “One of the things that attracted us to machine learning tools and AI art was the ways in which it was failing, because these models aren’t perfect. They’re just guessing at what we want.”
Evans describes artists as “the ultimate beta testers,” using tools outside of the ways in which they were intended to create something new.
“Oftentimes, the output can be really weird and damaged and upsetting, or it can sound really strange and novel, and that failure is delightful,” Evans said.
Assuming Dance Diffusion one day reaches the point where it can generate coherent whole songs, it seems inevitable that major ethical and legal issues will come to the fore. They already have, albeit around simpler AI systems. In 2020, Jay-Z ‘s record label filed copyright strikes against a YouTube channel, Vocal Synthesis, for using AI to create Jay-Z covers of songs like Billy Joel’s “We Didn’t Start the Fire.” After initially removing the videos, YouTube reinstated them, finding the takedown requests were “incomplete.” But deepfaked music still stands on murky legal ground.
Perhaps anticipating legal challenges, OpenAI for its part open-sourced Jukebox under a non-commercial license, prohibiting users from selling any music created with the system.
“There is little work into establishing how original the output of generative algorithms are, so the use of generative music in advertisements and other projects still runs the risk of accidentally infringing on copyright, and as such damaging the property,” Worrall said. “This area needs to be further researched.”
An academic paper authored by Eric Sunray, now a legal intern at the Music Publishers Association, argues that AI music generators like Dance Diffusion violate music copyright by creating “tapestries of coherent audio from the works they ingest in training, thereby infringing the United States Copyright Act’s reproduction right.” Following the release of Jukebox, critics have also questioned whether training AI models on copyrighted musical material constitutes fair use. Similar concerns have been raised around the training data used in image-, code-, and text-generating AI systems, which is often scraped from the web without creators’ knowledge.
Technologists like Mat Dryhurst and Holly Herndon founded Spawning AI, a set of AI tools built for artists, by artists. One of their projects, “Have I Been Trained,” allows users to search for their artwork and see if it has been incorporated into an AI training set without their consent.
“We are showing people what exists within popular datasets used to train AI image systems, and are initially offering them tools to opt out or opt in to training,” Herndon told TechCrunch via email. “We are also talking to many of the biggest research organizations to convince them that consensual data is beneficial for everyone.”
But these standards are — and will likely remain — voluntary. Harmonai hasn’t said whether it’ll adopt them.
“To be clear, Dance Diffusion is not a product and it is currently only research,” said Zach Evans of Stability AI. “All of the models that are officially being released as part of Dance Diffusion are trained on public domain data, Creative Commons-licensed data, and data contributed by artists in the community. The method here is opt-in only and we look forward to working with artists to scale up our data through further opt-in contributions, and I applaud the work of Holly Herndon and Mat Dryhurst and their new Spawning organization.”
YACHT’s Evans and Bechtolt see parallels between the emergence of AI generated art and other new technologies.
“It’s especially frustrating when we see the same patterns play out across all disciplines,” Evans told TechCrunch. “We’ve seen the way that people being lazy about security and privacy on social media can lead to harassment. When tools and platforms are designed by people who aren’t thinking about the long term consequences and social effects of their work like that, things happen.”
Jonathan Mann — the same Mann whose music was used to train one of the early Dance Diffusion models — told TechCrunch that he has mixed feelings about generative AI systems. While he believes that Harmonai has been “thoughtful” about the data they’re using for training, others like OpenAI have not been as conscientius.
“Jukebox was trained on thousands of artists without their permission — it’s staggering,” Mann said. “It feels weird to use Jukebox knowing that a lot of folks’ music was used without their permission. We are in uncharted territory.”
From a user perspective, Waxy’s Andy Baio speculates in a blog post that new music generated by an AI system would be considered a derivative work, in which case only the original elements would be protected by copyright. Of course, it’s unclear what might be considered “original” in such music. Using this music commercially is to enter uncharted waters. It’s a simpler matter if generated music is used for purposes protected under fair use, like parody and commentary, but Baio expects that courts would have to make case-by-base judgements.
According to Herndon, copyright law is not structured to adequately regulate AI art-making. Evans also points out that the music industry has been historically more litigious than the visual art world, which is perhaps why Dance Diffusion was explicitly trained on a dataset of copyright-free or voluntarily-submitted material, while DALL-E mini will easily spit out a Pikachu if you input the term “Pokémon.”
“I have no illusion that that’s because they thought that was the best thing to do ethically,” Evans said. “It’s because copyright law in music is very strict and more aggressively enforced.”
Gordon Tuomikoski, an arts major at the University of Nebraska-Lincoln who moderates the official Stable Diffusion Discord community, believes that Dance Diffusion has immense artistic potential. He notes that some members of the Harmonai server have created models trained on dubstep “webs,” kicks and snare drums and backup vocals, which they’ve strung together into original songs.
“As a musician, I definitely see myself using something like Dance Diffusion for samples and loops,” Tuomikoski told TechCrunch via email.
Martel sees Dance Diffusion one day replacing VSTs, the digital standard used to connect synthesizers and effect plugins with recording systems and audio editing software. For example, he says, a model trained on ’70s jazz rock and Canterbury music will intelligently introduce new “textures” in the drums, like subtle drum rolls and “ghost notes,” in the same way that artists like John Marshall might — but without the manual engineering work normally required.
Take this Dance Diffusion model of Senegalese drumming, for instance:
And this model of snares:
And this model of a male choir singing in the key of D across three octaves:
And this model of Mann’s songs fine-tuned with royalty-free dance music:
“Normally, you’d have to lay down notes in a MIDI file and sound-design really hard. Achieving a humanized sound this way is not only very time-consuming, but requires a deeply intimate understanding of the instrument you’re sound designing,” Martel said. “With Dance Diffusion, I look forward to feeding the finest ’70s prog rock into AI, an infinite unending orchestra of virtuoso musicians playing Pink Floyd, Soft Machine and Genesis, trillions of new albums in different styles, remixed in new ways by injecting some Aphex Twin and Vaporwave, all performing at the peak of human creativity — all in collaboration with your own personal tastes.”
Mann has greater ambitions. He’s currently using a combination of Jukebox and Dance Diffusion to play around with music generation, and plans to release a tool that’ll allow others to do the same. But he hopes to one day use Dance Diffusion — possibly in conjunction with other systems — to create a “digital version” of himself capable of continuing the Song A Day project after he passes away.
“The exact form it’ll take hasn’t quite become clear yet … [but] thanks to folks at Harmonai and some others I’ve met in the Jukebox Discord, over the last few months I feel like we’ve made bigger strides than any time in the last four years,” Mann said. “I have over 5,000 Song A Day songs, complete with their lyrics as well as rich metadata, with attributes ranging from mood, genre, tempo, key, all the way to location and beard (whether or not I had a beard when I wrote the song). My hope is that given all this data, we can create a model that can reliably create new songs as if I had written them myself. A Song A Day, but forever.”
If AI can successfully make new music, where does that leave musicians?
YACHT’s Evans and Bechtolt point out that new technology has upended the art scene before, and the results weren’t as catastrophic as expected. In the 1980s, the UK Musicians Union attempted to ban the use of synthesizers, arguing that it would replace musicians and put them out of work.
“With synthesizers, a lot of artists took this new thing and instead of refusing it, they invented techno, hip hop, post punk and new wave music,” Evans said. “It’s just that right now, the upheavals are happening so quickly that we don’t have time to digest and absorb the impact of these tools and make sense of them.”
Still, YACHT worries that AI could eventually challenge work that musicians do in their day jobs, like writing scores for commercials. But like Herndon, they don’t think AI can quite replicate the creative process just yet.
“It is divisive and a fundamental misunderstanding of the function of art to think that AI tools are going to replace the importance of human expression,” Herndon said. “I hope that automated systems will raise important questions about how little we as a society have valued art and journalism on the internet. Rather than speculate about replacement narratives, I prefer to think about this as a fresh opportunity to revalue humans.”
SaaS platform klikit saves restaurant kitchens from “tablet hell” • TechCrunch
The proliferation of delivery services give customers many options, but means chaos for busy restaurants that need to manage orders across multiple apps and channels. Many kitchens handle this by juggling several devices at a time, one for each app. Klikit wants to save Southeast Asian food businesses from “tablet hell” by aggregating order information from all apps into one platform. Based in Singapore, the startup just exited stealth mode with $2 million in pre-seed funding.
The round was co-led by Global Founders Capital and Wavemaker Partners, with participation from Gentree Fund, AfterWork Ventures, Reshape Ventures, Nordstar, Pentas Ventures, Moving Capital, Gojek co-founder Kevin Aluwi, NasDaily’s Nuseir Yassin, YouTuber Lazar Beam and Radish Fiction founder Seung-yoon Lee. Strategic angel investors include executives from Gojek, YouTube and Flash Coffee.
Since launching seven months ago, klikit’s SaaS platform, klikit Cloud, has been used to service more than $2.8 million in orders across 150 brands in the Philippines, Malaysia, Indonesia, Singapore, Taiwan and Australia.
Klikit was founded in 2021 by Christopher Withers, who has a lot of experience in the on-demand space—he was previously vice president of marketplaces at GoJek, chief strategy officer at Bangladesh ride-hailing platform Pathao and launched UberEats in the Asia Pacific.
During the pandemic, while at GoJek, Withers moved home to Australia to work remotely. He also owned and operated a ghost kitchen.
Withers told TechCrunch he’s always been fascinated by the food delivery space.
“I started my ghost kitchen because I have always wanted to truly experience the difficulties of running a restaurant firsthand, rather than sit hypothesizing on the sidelines or from behind my laptop as I built out many of these super app marketplaces,” he said.
During that time, Withers was overwhelmed by the number and cost of platforms, devices, software, ads and social media he had to juggle. As a result he wanted to find more effective ways to manage them and launch new brands.
Withers explains that existing F&B software aren’t suited for many delivery restaurants and cloud kitchens, and less than 2% of merchants in Asia have integrated their delivery orders with legacy point-of-sale systems. This leaves kitchens and staff managing orders across different apps and devices, which is not only time-consuming but also results in missed orders, errors, confusion and general chaos.
“Many operators refer to this as ‘tablet hell’ and some of our clients had as many as 20+ devices—taking up an entire pantry closet’s worth of real estate—for a single kitchen location!” Withers said.
Klikit differentiates from legacy POS systems, which were created for single-brand companies, by enabling restaurants and ghost kitchens to manage multiple food brands across locations and channels on a single device. Features include updating menus across delivery apps, which klikit is able to do quickly because it has official API agreements with apps like GrabFood, foodpanda, GoFood and UberEats. It gives on-demand access to historical data analytics (in contrast, many F&B software systems restrict data to time-limited viewings), including daily sales, product mixes and channel breakdown.
Since many restaurants in Southeast Asia often process delivery orders through social media like WhatsApp, SMS or audio messages, klikit also enables these orders to be added to its order dashboard so they are included in its analytics.
If one of klikit’s clients has spare capacity and equipment, they can sign-up for access to its virtual brand partnerships with creators and consumer brands. Klikit is now working with creators who have a combined following of 38 million in the Philippines and Australia to launch two “creator drops” in late 2022. Withers says klikit connected with top YouTubers because they have the clout to compete against fast food giants, marketing-wise.
Klikit’s closest competitors include Deliverect and NextBite, but Withers says he believes a regional startup like klikit will succeed because it can cement API partnerships with major delivery apps.
The startup’s new funding was used during stealth mode to hire 30 people in six countries. It will also use the capital for regional expansion and adding more features by building its engineering team.
In a statement, Wavemaker Partners managing partner Paul Santos said, “We see klikit solving widely unaddressed problems for restaurateurs everywhere, while also creating unique solutions for creators and brands to earn revenue and engage with fans in entirely new ways. Their vision strategically brings together the converging and only growing trends in food delivery and the creator economy.”
Sub-Optimus • TechCrunch
I sat out Friday’s big Tesla AI event. I was actually looking forward to seeing what the company had cooked up after months of teasing, but a combination of rogue stomach virus and the most inconvenient event timing (Friday at 9:15 PM EDT) outside of something held on the other side of the world meant I had to watch the whole thing over the weekend.
I’m not sure one can call Optimus “disappointing,” exactly. Disappointing implies higher expectations than I think most of us had going into the thing. Elon Musk has largely proven himself to be a great hype man and self-promoter over the years, but in the lead-up to last week’s official unveiling, I didn’t encounter many serious roboticists who believed we would see much more than what Tesla showed.
I’m always happy to be pleasantly surprised by this stuff, but no one can claim that Musk didn’t — at the very least — set a reasonably low expectation when Optimus (nee Tesla Bot) was unveiled by way of a spandex-clad dancer. If nothing else, the company has given us a nice visual shorthand for the difficult task of building robots.
It’s been fascinating watching the robotics world react. Impressions have ranged from high school science fair project to suggestions that Tesla’s in the early stages of a real game changer here. Ultimately, it’s true that the event was, in large part, designed to recruit future employees, and despite Musk’s best efforts to the contrary, Tesla still has cache among engineers — particularly those looking to prove themselves fresh out of school.
I still take issue with his initial suggestions that people simply don’t understand the world-changing impact of a humanoid robot that can do everything people can, but better. The pushback thus far has largely been focused on a feeling that past proclamations have discounted just how difficult it is to get there, and the prototypes offered a kind of physical manifestation of that.
Lots of questions linger, and at the very least, it will be fun to see how this all progresses. Top of mind is how much of the company’s work in vehicle autonomy and AI is ultimately transferable to robots. And getting back to last week’s rant, I’m still not entirely convinced that an all-purpose humanoid robot is the right short-term path to widespread robotic adoption. While I both understand and respect the opinion that human form factor makes as we’ve built our world to best suit ourselves, the question is whether the world we’ve built is optimized for automation.
If we accept that much of today’s work is going to be automated (which seems like a fairly safe assumption based on current trends), an important thing to consider is whether there are better and more efficient methods for building the workplace for robots, rather than the other way around.
I’d be curious to hear how last week’s event struck engineers — especially if you’re new to the workforce. Did the whole thing make you more or less excited about the potential of working on team Optimus?
So, here’s a poorly kept secret: I generally write these newsletters a day in advance, so my copyeditor, Carrie, doesn’t fly to New York and murder me in my sleep. Every once in a while, an important bit of news drops in that liminal zone between writing and publishing that would have made a marked impact on Actuator that week. Last week, I wrote a bunch of words about Amazon’s robotics play and made a passing reference to potential regulatory scrutiny over the iRobot deal.
Not long before the newsletter arrived on your doorstep, we got word that Massachusetts senator Elizabeth Warren is leading a group of six congressional Democrats asking the FTC to block the deal. The lawmakers note, “Rather than compete in a fair marketplace on its own merits, Amazon is following a familiar anticompetitive playbook: leveraging its massive market share and access to capital to buy or suppress popular products.”
Following publication, Amazon offered TechCrunch the following statement: “The letter contains a number of falsehoods and is broadly inaccurate. We will continue to cooperate with regulators, and we are confident that this deal is procompetitive and will make customers’ lives better and easier.”
Expect some fireworks there.
News of another sizable round for another pizza robotics firm. Stellar Pizza raised a $16.5 million Series A, led by Jay-Z’s Marcy Venture Partners. That joins a thus-far undisclosed $9 million in funding from the firm, which follows in the footsteps of one-time pioneer in the category, Zume, by developing delivery trucks with on-board pizza robots. The company says that it is “in the process of building a fleet of mobile pizza restaurants with the intention of building a nationwide brand over the next few years.”
Cionic announced its own Series A this week. The $12.5 million round led by BlueRun Ventures will go toward accelerating the company’s first product, Neural Sleeve. The system is, in effect, a soft, wearable robotic exoskeleton designed to increase mobility in people who have a range of conditions, including stroke, cerebral palsy and multiple sclerosis.
The company describes the product thusly:
Cionic builds bionic clothing that can analyze and augment human movement, enabling the body to move with more freedom and control than with crutches, walkers, or wheelchairs. Cionic thoughtfully combines the diagnostic power of a gait lab with the therapeutic power of Functional Electrical Stimulation (FES) into a lightweight, durable garment that can be worn anywhere and work everywhere.
And closing out this week is news that Nauticus Robotics signed a deal with the U.S. Defense Innovation Unit to create an autonomous amphibious robot. The company’s Director of Business Development for Defense Systems says in a post:
We are thrilled with the additional work the DIU and the U.S. Marine Corps have awarded us to continue providing leading maritime robotics and autonomy solutions to assist the warfighter. We are humbled and honored to be doing our part to advance the usage of robotics and autonomous systems to remove servicemembers from harm’s way.
Allow me to reintroduce myself — Actu-to-the-ator. Subscribe here.
Microsoft explores investment in Indian gaming platform Zupee • TechCrunch
Microsoft has held conversations to invest in the Indian play-to-earn gaming platform Zupee in recent weeks and proposed to potentially lead a funding round of over $100 million, two people familiar with the matter told TechCrunch, the latest in a series of bets from the cloud services giant to expand its business in the key overseas market.
The two firms haven’t reached an agreement and there is a reasonable chance the deal will not materialize, the people cautioned. A team within Microsoft has expressed apprehension about optics around betting and advised the global tech giant to steer away from the deal, a person briefed on the details said.
Zupee declined to comment. Microsoft did not respond to a request for comment late last month. Like many other startups including Oyo in which Microsoft has invested in India, the Zupee deal sought to have the startup use Azure and other Microsoft cloud services as part of the deal, two people familiar with the matter said.
The New Delhi-headquartered Zupee — which has raised over $120 million to date, including about $100 million in its Series B round that it closed in January this year — operates what it describes as a “skill-based casual gaming” platform. The firm, which was last valued at $600 million, has garnered over 70 million downloads across over a dozen games including with themes around cricket, football, chess, rummy and board titles.
In January, it formed what it described as a “first-of-its-kind strategic” partnership with Jio Platforms, India’s largest telecom operator with over 420 million subscribers. The two firms will work to build an “ecosystem that will facilitate faster and more efficient development and distribution of products and services,” Zupee said in a press release earlier.
Google will use private subsea cable to launch its first full-scale cloud region in Africa • TechCrunch
To get a roundup of TechCrunch’s biggest and most important stories delivered to your inbox every day at 3 p.m. PDT, subscribe here.
The TechCrunch Top 3
- Cloudy day: Google’s first cloud region in Africa launched in South Africa. Annie and Tage write that this move “allows for the localization of applications and services” and for businesses to more quickly deploy capabilities — for example, artificial intelligence, machine learning and data analytics.
- Duck, duck, goose: French food tech startup Gourmey took in $48 million of new funding to cook up its slaughter-free and lab-grown foie gras. Romain has more.
- Show a little more, show a little less: Facebook is testing out a new feature with Reels to let users say how much or how little they want to see of certain things in their feed, Aisha reports.
Startups and VC
Headline included, I enjoyed Paul’s story today on Whistleblower Software, an aptly named company that took in a $3 million seed round to continue developing its product aimed at making it easier for workers to report corporate wrongdoings.
Enjoy five more:
Dear Sophie: Any tips for negotiating visa and green card sponsorship?
I’m currently on an F-1 student visa. I’ll receive my bachelor’s degree in computer science in December and will apply for OPT. I’d like to stay and work in the U.S.
Do you have any tips for negotiating visa and green card sponsorship? Anything else I should remember as I start contacting prospective employers?
— Shy Student
Three more from the TC+ team:
Big Tech Inc.
A cybersecurity incident disrupted business at U.S. hospital chain CommonSpirit Health, causing it to take some of its information technology systems offline, Carly reports. It is not yet known what kind of incident it was or what, if any, personal information was taken in the process.
Here’s five more for you:
- How we doing?: It’s been one year since Google’s $1 billion pledge of support to Africa’s digital economy, and Tage and Annie do a little performance review.
- M&A action: Spotify acquires Kinzen, a content moderation tech company, in a move that Sarah writes will address platform safety issues.
- We take your sale and raise you one: Walmart is not letting Amazon’s Prime Early Access Sale get the best of it and is countering with its own sale, Aisha reports.
- This is only the beginning: Just as Elon Musk announced he was moving forward with the Twitter deal, a judge was granting the social media giant time to do some digging. A lot of twists and turns indeed, as Taylor put it. Also, Alex weighs in on how expensive Elon’s Twitter buy is.
- Fight, for your right, to partAI: The White House is proposing an AI Bill of Rights, which “mandates that AI systems be proven safe and effective through testing and consultation with stakeholders, in addition to continuous monitoring of the systems in production,” Kyle writes.
Now kettles have Wi-Fi too, apparently • TechCrunch
If you’ve been swimming in the toasty waters of high-end pour-over kettles, you’ve no doubt come across Fellow, and its Stagg EKG kettle. The company uses power-pulsing technology to keep the water temperature at a very precise level, which has attracted a number of award-winning baristas to use their products: It turns out that 204ºF is different than 199 or 210, and if you are the kind of person who cares about that, these kettles have you covered. The newest additions to the line — Stagg EKG Pro and Stagg EKG Pro Studio Edition — add Wi-Fi to the mix, so you can dial in a finely crafted brew from bed.
The core of the kettle is more or less the same as the existing version, but being able to schedule water heating, or dialing in presets for specific types of coffees and teas, is new. You also can program in what altitude you are at — water boils at slightly different temperatures based on the pressure, which is a function of altitude (it’s pretty wild to think about, but if you are at 10,000 feet, you have 10,000 fewer feet of weight of air above you — humans don’t mind the difference much, but boiling water does).
“In 2017, our iconic Stagg EKG Electric Kettle revolutionized the coffee industry and set the undisputed standard in electric pour-over kettles. Five years later, we are raising that bar again,” said Jake Miller, founder and CEO of San-Francisco based Fellow in an email to TechCrunch. “We’re continuously thinking of ways to improve and innovate our products to give consumers access to cafe-quality coffee and gear within the comfort of their own homes. These kettles are a vision into the future of pour-over brewing.”
The company raised a round of funding a few months ago, and is continuing to crank out new products.
Available now, the Stagg EKG Pro starts at $195 and Stagg EKG Pro Studio Edition starts at $225. Both kettles can be configured with beautiful walnut wood handles and other design-y upgrades that ups the price further. The original kettle has a number of special editions and designs to make the coffee-pouring experience more your own, and the new kettles will no doubt show up in a rainbow of colors over time, too.
The Pro and Pro Studio Edition are the same, functionality-wise. The only difference is in the materials; the Pro Studio Edition features upgraded materials including a metal base with a glass top with a metal button and other details. The app that’s compatible with the new kettles has yet to launch, but it will launch in mid-October (exact date TBD) when the kettles begin shipping to customers. As you might expect from a kettle, you can use the product without connectivity, as well.
Now, do we really need kettles with Wi-Fi? I suppose that’s your choice. If the answer is “no,” but you still want high-precision control over your water, the original is available at $165, or you can boil your water in a pan on the stove, like a philistine.
Party Round’s rebrand is banking on founder bank accounts • TechCrunch
Party Round wants you to know that the party isn’t over. In fact, it just rebranded, put the music just a little bit lower and finally put out some appetizers. After a certain point, don’t we all get peckish?
Party Round announced today that it has rebranded to Capital to underscore its product expansion. Now, the startup won’t just make it easier for other startups to raise their own party round. Capital wants to build a tech stack for the modern founder to handle their finances, a crowded space, but one always in need of more disruption.
Up until this point, the startup was focused on automating seed deals for the likes of Diagram, Popshop, JuneShine and Yuga Labs. Plus, as CEO and co-founder Jordi Hays will admit, lots and lots of marketing.
“Party Round was this amazing, living breathing meme that was evolving and meant to entertain the community,” Hays, who built the company alongside Sarah Chase, said. “But the thing is, even our ambition as a company, and what we want to do on the product side, is [different]. Fundraising and investing gets so much attention in the startup media, but it’s maybe like 1-5% of what it actually takes to build a company.”
“We were very comfortable saying that in the first 18 months of building this company, we’re going to ignore every single possible channel except tech Twitter, and that was like the best possible strategy we could have done,” the founder said. “There’s 100,000 early-stage founders and investors signed up for our email list.”
Capital wants to take that trust and expressed interest and give the same founders a place to raise, hold and spend that earned capital. It’s a maturation for the company, which raised $7 million months ago from Alexis Ohanian’s Seven Seven Six fund, Anish Acharya from a16z, Shrug Capital, Packy McCormick, Nik Sharma and Austin Rief.
Here’s the simplest way to describe what Capital does today: Founders can turn to the platform to create and set terms for SAFE notes, and then invite potential investors to contribute through the platform. Investors, meanwhile, can select to link their banking account to invest in the company through either USD or crypto with specific allocation; all while Capital handles back-end documents. There’s an NFT to verify the investment if investors are interested in NFTs that verify the investment.
Once the money is wired, founders can use Capital to create a business checking account, get a debit card and conduct payments. Hays explained how a founder who uses Ramp for creidt cards can then connect their Ramp account to Capital; same goes for if someone was using Rippling for payroll. Capital’s utility is that it gives all those fintech tools one home to live, or, some would say, one living room to party at.
Hays isn’t too intimidated by the unicorns in the space, noting that many (such as Brex and Ramp) started with expense tracking and are heavily focused on the enterprise, while Capital seeks to work with smaller startups at the point of their first fundraise.
“Before you need a bank account, you need money to put in that bank account. And unless you’re bootstrapping, or generating revenue, really, really early on and self funding, typically those funds are coming from your investors,” Hays said. “We are exclusively focused on companies at the inflection point and figuring out how we can be the first place that they raise, hold and spend their money.”
The challenge for Capital is if it can prove that its users, a number which remains undisclosed, are sticky enough to stay. Up until now, the company’s fundraising tool was free with some simple steps: create a round, configure the SAFE terms and invite investors. Hays says that they will monetize new products over time, but ease of use will stay a focus for the business.
“I think that being funny and entertaining is great, but in the long term, we think the most important [thing] is building the best products and software for founders period. And to do that we need a brand that’s going to resonate more broadly and outside of our bubble,” the founder said.
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