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Ditto raises $1.5 million to help teams collaborate on copy
Even as remote software uptake has boomed during the pandemic, certain workflows have gotten prioritized for specialized toolsets while other team members have been left piecemealing their productivity. Employees designing the copy that directs users and encapsulates company messaging have been particularly forgotten at times, say the founders of Ditto, a young startup building software focused on finding a “single source of truth” for copy.
The startup was in Y Combinator’s winter 2020 batch (we selected it as one of our favorites from the class); now Ditto’s founders tell TechCrunch the team has raised a $1.5 million seed round from investors including Greycroft, Y Combinator, Soma Capital, Decent Capital, Twenty Two VC, Holly Liu and Scott Tong, among others.
While copy workflows are often very messy when it comes to design and implementation, even the most-organized teams are often left scouring through meandering email threads, screenshot dumps and slack DMs with disparate teams. The founders behind Ditto hope that their software can give copy teams the home they deserve to keep everything organized and synced across projects and applications, ensuring that language is actually finalized and ready to ship when the time comes.
The company’s founders Jessica Ouyang and Jolena Ma were Stanford roommates who saw a lingering opportunity to build a toolset that prioritized copy as its own vertical.
“It’s so easy to couple text with where it lives, like you may think of it as part of the design so a lot of writers have to manage it inside toolsets for design or you may already think of it as part of development so writers end up having to go into the codebase and figure out how to code or manage JSON even though they’re content designers,” Ouyang tells TechCrunch.
Out of the gate, Ditto has been built for Figma, meaning users can easily export text blocks from designs in the app and rework them inside the Ditto web app, pushing updates without having to dig through the designs themselves. The founders say they are currently working on building out integrations for Sketch and Adobe XD as well. Inside the Ditto web app users can access change logs and update the status of particular pieces of text inside a project so that approvals are always certain.
“We find there’s a lot more opportunity to integrate into all of the places where copy is being worked on,” Ma tells us. “We have a lot more we’re hoping to do with our developer integrations and just integrating to all of those places where copy lives, places like A/B testing, internationalization, localization and other workflows.”
Copy development has plenty of stakeholders and the team is looking to experiment with pricing tiers that address that. For now they split up users into editors and commenters paying $15 and $10 monthly (priced annually), respectively, on the startup’s Teams plan. Ditto has a free tier for teams of two, as well as pricing designed for larger enterprise clients.
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SESO Labor is providing a way for migrant farmworkers to get legally protected work status in the US
As the Biden administration works to bring legislation to Congress to address the endemic problem of immigration reform in America, on the other side of the nation a small California startup called SESO Labor has raised $4.5 million to ensure that farms can have access to legal migrant labor.
SESO’s founder Mike Guirguis raised the round over the summer from investors including Founders Fund and NFX. Pete Flint, a founder of Trulia, joined the company’s board. The company has 12 farms it’s working with and is negotiating contracts with another 46. The company’s other co-founder, Jordan Taylor, was the first product hire at Farmer’s Business Network and previously of Dropbox.
Working within the existing regulatory framework that has existed since 1986, SESO has created a service that streamlines and manages the process of getting H-2A visas, which allow migrant agricultural workers to reside temporarily in the U.S. with legal protections.
At this point, SESO is automating the visa process, getting the paperwork in place for workers and smoothing the application process. The company charges about $1,000 per worker, but eventually as it begins offering more services to workers themselves, Guirguis envisions several robust lines of revenue. Eventually, the company would like to offer integrated services for both farm owners and farm workers, Guirguis said.
SESO is currently expecting to bring in 1,000 workers over the course of 2021 and the company is, as of now, pre-revenue. The largest industry player handling worker visas today currently brings in 6,000 workers per year, so the competition, for SESO, is market share, Guirguis said.
America’s complicated history of immigration and agricultural labor
The H-2A program was set up to allow agricultural employers who anticipate shortages of domestic workers to bring to the U.S. non-immigrant foreign workers to work on farms temporarily or seasonally. The workers are covered by U.S. wage laws, workers’ compensation and other standards, including access to healthcare under the Affordable Care Act.
Employers who use the visa program to hire workers are required to pay inbound and outbound transportation, provide free or rental housing and provide meals for workers (they’re allowed to deduct the costs from salaries).
H-2 visas were first created in 1952 as part of the Immigration and Nationality Act, which reinforced the national origins quota system that restricted immigration primarily to Northern Europe, but opened America’s borders to Asian immigrants for the first time since immigration laws were first codified in 1924. While immigration regulations were further opened in the sixties, the last major immigration reform package in 1986 served to restrict immigration and made it illegal for businesses to hire undocumented workers. It also created the H-2A visas as a way for farms to hire migrant workers without incurring the penalties associated with using illegal labor.
For some migrant workers, the H-2A visa represents a golden ticket, according to Guirguis, an honors graduate of Stanford who wrote his graduate thesis on labor policy.
“We are providing a staffing solution for farms and agribusiness and we want to be Gusto for agriculture and upsell farms on a comprehensive human resources solution,” says Guirguis of the company’s ultimate mission, referencing payroll provider Gusto.
As Guirguis notes, most workers in agriculture are undocumented. “These are people who have been taken advantage of [and] the H-2A is a visa to bring workers in legally. We’re able to help employers maintain workforce [and] we’re building software to help farmers maintain the farms.”
Opening borders even as they remain closed
Farms need the help, if the latest numbers on labor shortages are believable, but it’s not necessarily a lack of H-2A visas that’s to blame, according to an article in Reuters.
In fact, the number of H-2A visas granted for agriculture equipment operators rose to 10,798 from October through March, according to the Reuters report. That’s up 49% from a year ago, according to data from the U.S. Department of Labor cited by Reuters.
Instead of an inability to acquire the H-2A visa, it was an inability to travel to the U.S. that’s been causing problems. Tighter border controls, the persistent global pandemic and travel restrictions that were imposed to combat it have all played a role in keeping migrant workers in their home countries.
Still, Guirguis believes that with the right tools, more farms would be willing to use the H-2A visa, cutting down on illegal immigration and boosting the available labor pool for the tough farm jobs that American workers don’t seem to want.
Photo by Brent Stirton/Getty Images.
David Misener, the owner of an Oklahoma-based harvesting company called Green Acres Enterprises, is one employer who has struggled to find suitable replacements for the migrant workers he typically hires.
“They could not fathom doing it and making it work,” Misener told Reuters, speaking about the American workers he’d tried to hire.
“With H-2A, migrant workers make 10 times more than they would get paid at home,” said Guirguis. “They’re taking home the equivalent of $40 an hour. The H-2A is coveted.”
Guirguis thinks that with the right incentives and an easier onramp for farmers to manage the application and approval process, the number of employers that use H-2A visas could grow to be 30% to 50% of the farm workforce in the country. That means growing the number of potential jobs from 300,000 to 1.5 million for migrants who would be under many of the same legal protections that citizens enjoy while they’re working on the visa.
Protecting agricultural workers through better paperwork
Interest in the farm labor nexus and issues surrounding it came to the first-time founder through Guirguis’ experience helping his cousin start her own farm. Spending several weekends a month helping her grow the farm with her husband, Guirguis heard his stories about coming to the U.S. as an undocumented worker.
Employers using the program avoid the liability associated with being caught employing illegal labor, something that crackdowns under the Trump administration made more common.
Still, it’s hard to deny the program’s roots in the darker past of America’s immigration policy. And some immigration advocates argue that the H-2A system suffers from the same kinds of structural problems that plague the corollary H-1B visas for tech workers.
“The H-2A visa is a short-term temporary visa program that employers use to import workers into the agricultural fields … It’s part of a very antiquated immigration system that needs to change. The 11.5 million people who are here need to be given citizenship,” said Saket Soni, the founder of an organization called Resilience Force, which advocates for immigrant labor. “And then workers who come from other countries, if we need them, they have to be able to stay … H-2A workers don’t have a pathway to citizenship. Workers come to us afraid of blowing the whistle on labor issues. As much as the H-2A is a welcome gift for a worker it can also be abused.”
Soni said the precarity of a worker’s situation — and their dependence on a single employer for their ability to remain in the country legally — means they are less likely to speak up about problems at work, since there’s nowhere for them to go if they are fired.
“We are big proponents that if you need people’s labor you have to welcome them as human beings,” Soni said. “Where there’s a labor shortage as people come, they should be allowed to stay … H-2A is an example of an outdated immigration tool.”
Guirguis clearly disagrees and said a platform like SESO’s will ultimately create more conveniences and better services for the workers who come in on these visas.
“We’re trying to put more money in the hands of these workers at the end of the day,” he said. “We’re going to be setting up remittance and banking services. Everything we do should be mutually beneficial for the employer and the worker who is trying to get into this program and know that they’re not getting taken advantage of.”
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Immunai raises $60M as it expands from improving immune therapies to discovering new ones, too
Just three years after its founding, biotech startup Immunai has raised $60 million in Series A funding, bringing its total raised to over $80 million. Despite its youth, Immunai has already established the largest database in the world for single cell immunity characteristics, and it has already used its machine learning-powered immunity analysts platform to enhance the performance of existing immunotherapies. Aided by this new funding, it’s now ready to expand into the development of entirely new therapies based on the strength and breadth of its data and ML.
Immunai’s approach to developing new insights around the human immune system uses a “multiomic” approach — essentially layering analysis of different types of biological data, including a cell’s genome, microbiome, epigenome (a genome’s chemical instruction set) and more. The startup’s unique edge is in combining the largest and richest data set of its type available, formed in partnership with world-leading immunological research organizations, with its own machine learning technology to deliver analytics at unprecedented scale.
“I hope it doesn’t sound corny, but we don’t have the luxury to move more slowly,” explained Immunai co-founder and CEO Noam Solomon in an interview. “Because I think that we are in kind of a perfect storm, where a lot of advances in machine learning and compute computations have led us to the point where we can actually leverage those methods to mine important insights. You have a limit or ceiling to how fast you can go by the number of people that you have — so I think with the vision that we have, and thanks to our very large network between MIT and Cambridge to Stanford in the Bay Area, and Tel Aviv, we just moved very quickly to harness people to say, let’s solve this problem together.”
Solomon and his co-founder and CTO Luis Voloch both have extensive computer science and machine learning backgrounds, and they initially connected and identified a need for the application of this kind of technology in immunology. Scientific co-founder and SVP of Strategic Research Danny Wells then helped them refine their approach to focus on improving efficacy of immunotherapies designed to treat cancerous tumors.
Immunai has already demonstrated that its platform can help identify optimal targets for existing therapies, including in a partnership with the Baylor College of Medicine where it assisted with a cell therapy product for use in treating neuroblastoma (a type of cancer that develops from immune cells, often in the adrenal glands). The company is now also moving into new territory with therapies, using its machine learning platform and industry-leading cell database to new therapy discovery — not only identifying and validating targets for existing therapies, but helping to create entirely new ones.
“We’re moving from just observing cells, but actually to going and perturbing them, and seeing what the outcome is,” explained Voloch. This, from the computational side, later allows us to move from correlative assessments to actually causal assessments, which makes our models a lot more powerful. Both on the computational side and on the lab side, this are really bleeding edge technologies that I think we will be the first to really put together at any kind of real scale.”
“The next step is to say, ‘Okay, now that we understand the human immune profile, can we develop new drugs?’,” said Solomon. “You can think about it like we’ve been building a Google Maps for the immune system for a few years — so we are mapping different roads and paths in the immune system. But at some point, we figured out that there are certain roads or bridges that haven’t been built yet. And we will be able to support building new roads and new bridges, and hopefully leading from current states of disease or cities of disease, to building cities of health.”
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Senti Bio raises $105 million for its new programmable biology platform and cancer therapies
Senti Biosciences, a company developing cancer therapies using a new programmable biology platform, said it has raised $105 million in a new round of financing led by the venture arm of life sciences giant Bayer.
The company’s technology uses new computational biological techniques to manufacture cell and gene therapies that can more precisely target specific cells in the body.
Senti Bio’s chief executive, Tim Lu, compares his company’s new tech to the difference between basic programming and object-oriented programming. “Instead of creating a program that just says ‘Hello world’, you can introduce ‘if’ statements and object-oriented programming,” said Lu.
By building genetic material that can target multiple receptors, Senti Bio’s therapies can be more precise in the way they identify genetic material in the body and deliver the kinds of therapies directly to the pathogens. “Instead of the cell expressing a single receptor… now we have two receptors,” he said.
The company is initially applying its gene circuit technology platform to develop therapies that use what are called chimeric antigen receptor natural killer (CAR-NK) cells that can target cancer cells in the body and eliminate them. Many existing cell and gene therapies use chimeric antigen receptor T-cells, which are white blood cells in the body that are critical to immune response and destroy cellular pathogens in the body.
However, T-cell-based therapies can be toxic to patients, stimulating immune responses that can be almost as dangerous as the pathogens themselves. Using CAR-NK cells produces similar results with fewer side effects.
That’s independent of the gene circuit, said Lu. “The gene circuit gets you specificity… Right now when you use a CAR-T cell or a CAR-NK cell… you find a target and hope that it doesn’t affect normal cells. We can build logic in our gene circuits in the cell that means a CAR-NK cell can identify two targets rather than one.”
That increased targeting means lower risks of healthy cells being destroyed alongside mutations or pathogens that are in the body.
For Lu and his co-founders — fellow MIT professor Jim Collins, Boston University professor Wilson Wong and longtime synthetic biology operator Phillip Lee — Senti Bio is the culmination of decades of work in the field.
“I compare it to the early days of semiconductor work,” Lu said of the journey to develop this gene circuit technology. “There were bits and pieces of technology being developed in research labs, but to realize the scale at which you need, this has to be done at the industrial level.”
So licensing work from MIT, Boston University and Stanford, Lu and his co-founders set out to take this work out of the labs to start a company.
“When the company was started it was a bag of tools and the know-how on how to use them,” Lu said. But it wasn’t a fully developed platform.
That’s what the company now has and with the new capital from Leaps by Bayer and its other investors, Senti is ready to start commercializing.
The first products will be therapies for acute myeloid leukemia, hepatocellular carcinoma and other, undisclosed, solid tumor targets, the company said in a statement.
“Leaps by Bayer’s mission is to invest in breakthrough technologies that may transform the lives of millions of patients for the better,” said Juergen Eckhardt, MD, head of Leaps by Bayer. “We believe that synthetic biology will become an important pillar in next-generation cell and gene therapy, and that Senti Bio’s leadership in designing and optimizing biological circuits fits precisely with our ambition to prevent and cure cancer and to regenerate lost tissue function.”
Lu and his co-founders also see their work as a platform for developing other cell therapies for other diseases and applications — and intend to partner with other pharmaceutical companies to bring those products to market.
“Over the past two years, our team has designed, built and tested thousands of sophisticated gene circuits to drive a robust product pipeline, focused initially on allogeneic CAR-NK cell therapies for difficult-to-treat liquid and solid tumor indications,” Lu said in a statement. “I look forward to continued platform and pipeline advancements, including starting IND-enabling studies in 2021.”
The new financing round brings Senti’s total capital raised to just under $160 million and Lu said the new money will be used to ramp up manufacturing and accelerate its work partnering with other pharmaceutical companies.
The current time frame is to get its investigational new drug permits filed by late 2022 and early 2023 and have initial clinical trials begun in 2023.
Developing gene circuits is a new and expanding field with a number of players, including Cell Design Labs, which was acquired by Gilead in 2017 for up to $567 million. Other companies working on similar therapies include CRISPR Therapeutics, Intellius and Editas, Lu said.
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Turing nabs $32M more for an AI-based platform to source and manage engineers remotely
As remote work continues to solidify its place as a critical aspect of how businesses exist these days, a startup that has built a platform to help companies source and bring on one specific category of remote employees — engineers — is taking on some more funding to meet demand.
Turing — which has built an AI-based platform to help evaluate prospective, but far-flung, engineers, bring them together into remote teams, then manage them for the company — has picked up $32 million in a Series B round of funding led by WestBridge Capital. Its plan is as ambitious as the world it is addressing is wide: an AI platform to help define the future of how companies source IT talent to grow.
“They have a ton of experience in investing in global IT services, companies like Cognizant and GlobalLogic,” said co-founder and CEO Jonathan Siddharth of its lead investor in an interview the other day. “We see Turing as the next iteration of that model. Once software ate the IT services industry, what would Accenture look like?”
It currently has a database of some 180,000 engineers covering around 100 or so engineering skills, including React, Node, Python, Agular, Swift, Android, Java, Rails, Golang, PHP, Vue, DevOps, machine learning, data engineering and more.
In addition to WestBridge, other investors in this round included Foundation Capital, Altair Capital, Mindset Ventures, Frontier Ventures and Gaingels. There is also a very long list of high-profile angels participating, underscoring the network that the founders themselves have amassed. It includes unnamed executives from Google, Facebook, Amazon, Twitter, Microsoft, Snap and other companies, as well as Adam D’Angelo (Facebook’s first CTO and CEO at Quora), Gokul Rajaram, Cyan Banister and Scott Banister, and Beerud Sheth (the founder of Upwork), among many others (I’ll run the full list below).
Turing is not disclosing its valuation. But as a measure of its momentum, it was only in August that the company raised a seed round of $14 million, led by Foundation. Siddharth said that the growth has been strong enough in the interim that the valuations it was getting and the level of interest compelled the company to skip a Series A altogether and go straight for its Series B.
The company now has signed up to its platform 180,000 developers from across 10,000 cities (compared to 150,000 developers back in August). Some 50,000 of them have gone through automated vetting on the Turing platform, and the task will now be to bring on more companies to tap into that trove of talent.
Or, “We are demand-constrained,” which is how Siddharth describes it. At the same time, it’s been growing revenues and growing its customer base, jumping from revenues of $9.5 million in October to $12 million in November, increasing 17x since first becoming generally available 14 months ago. Current customers include VillageMD, Plume, Lambda School, Ohi Tech, Proxy and Carta Healthcare.
Remote work = immediate opportunity
A lot of people talk about remote work today in the context of people no longer able to go into their offices as part of the effort to curtail the spread of COVID-19. But in reality, another form of it has been in existence for decades.
Offshoring and outsourcing by way of help from third parties — such as Accenture and other systems integrators — are two ways that companies have been scaling and operating, paying sums to those third parties to run certain functions or build out specific areas instead of shouldering the operating costs of employing, upsizing and sometimes downsizing that labor force itself.
Turing is essentially tapping into both concepts. On one hand, it has built a new way to source and run teams of people, specifically engineers, on behalf of others. On the other, it’s using the opportunity that has presented itself in the last year to open up the minds of engineering managers and others to consider the idea of bringing on people they might have previously insisted work in their offices, to now work for them remotely, and still be effective.
Siddarth and co-founder Vijay Krishnan (who is the CTO) know the other side of the coin all too well. They are both from India, and both relocated to the Valley first for school (post-graduate degrees at Stanford) and then work at a time when moving to the Valley was effectively the only option for ambitious people like them to get employed by large, global tech companies, or build startups — effectively what could become large, global tech companies.
“Talent is universal, but opportunities are not,” Siddarth said to me earlier this year when describing the state of the situation.
A previous startup co-founded by the pair — content discovery app Rover — highlighted to them a gap in the market. They built the startup around a remote and distributed team of engineers, which helped them keep costs down while still recruiting top talent. Meanwhile, rivals were building teams in the Valley. “All our competitors in Palo Alto and the wider area were burning through tons of cash, and it’s only worse now. Salaries have skyrocketed,” he said.
After Rover was acquired by Revcontent, a recommendation platform that competes against the likes of Taboola and Outbrain, they decided to turn their attention to seeing if they could build a startup based on how they had, basically, built their own previous startup.
There are a number of companies that have been tapping into the different aspects of the remote work opportunity, as it pertains to sourcing talent and how to manage it.
They include the likes of Remote (raised $35 million in November), Deel ($30 million raised in September), Papaya Global ($40 million also in September), Lattice ($45 million in July) and Factorial ($16 million in April), among others.
What’s interesting about Turing is how it’s trying to address and provide services for the different stages you go through when finding new talent. It starts with an AI platform to source and vet candidates. That then moves into matching people with opportunities, and onboarding those engineers. Then, Turing helps manage their work and productivity in a secure fashion, and also provides guidance on the best way to manage that worker in the most compliant way, be it as a contractor or potentially as a full-time remote employee.
The company is not freemium, as such, but gives people two weeks to trial people before committing to a project. So unlike an Accenture, Turing itself tries to build in some elasticity into its own product, not unlike the kind of elasticity that it promises its customers.
It all sounds like a great idea now, but interestingly, it was only after remote work really became the norm around March/April of this year that the idea really started to pick up traction.
“It’s amazing what COVID has done. It’s led to a huge boom for Turing,” said Sumir Chadha, managing director for WestBridge Capital, in an interview. For those who are building out tech teams, he added, there is now “No need for to find engineers and match them with customers. All of that is done in the cloud.”
“Turing has a very interesting business model, which today is especially relevant,” said Igor Ryabenkiy, managing partner at Altair Capital, in a statement. “Access to the best talent worldwide and keeping it well-managed and cost-effective make the offering attractive for many corporations. The energy of the founding team provides fast growth for the company, which will be even more accelerated after the B-round.”
PS. I said I’d list the full, longer list of investors in this round. In these COVID times, this is likely the biggest kind of party you’ll see for a while. In addition to those listed above, it included [deep breath] Founders Fund, Chapter One Ventures (Jeff Morris Jr.), Plug and Play Tech Ventures (Saeed Amidi), UpHonest Capital (Wei Guo, Ellen Ma), Ideas & Capital (Xavier Ponce de León), 500 Startups Vietnam (Binh Tran and Eddie Thai), Canvas Ventures (Gary Little), B Capital (Karen Appleton Page, Kabir Narang), Peak State Ventures (Bryan Ciambella, Seva Zakharov), Stanford StartX Fund, Amino Capital, Spike Ventures, Visary Capital (Faizan Khan), Brainstorm Ventures (Ariel Jaduszliwer), Dmitry Chernyak, Lorenzo Thione, Shariq Rizvi, Siqi Chen, Yi Ding, Sunil Rajaraman, Parakram Khandpur, Kintan Brahmbhatt, Cameron Drummond, Kevin Moore, Sundeep Ahuja, Auren Hoffman, Greg Back, Sean Foote, Kelly Graziadei, Bobby Balachandran, Ajith Samuel, Aakash Dhuna, Adam Canady, Steffen Nauman, Sybille Nauman, Eric Cohen, Vlad V, Marat Kichikov, Piyush Prahladka, Manas Joglekar, Vladimir Khristenko, Tim and Melinda Thompson, Alexandr Katalov, Joseph and Lea Anne Ng, Jed Ng, Eric Bunting, Rafael Carmona, Jorge Carmona, Viacheslav Turpanov, James Borow, Ray Carroll, Suzanne Fletcher, Denis Beloglazov, Tigran Nazaretian, Andrew Kamotskiy, Ilya Poz, Natalia Shkirtil, Ludmila Khrapchenko, Ustavshchikov Sergey, Maxim Matcin and Peggy Ferrell.
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Deep Vision announces its low-latency AI processor for the edge
Deep Vision, a new AI startup that is building an AI inferencing chip for edge computing solutions, is coming out of stealth today. The six-year-old company’s new ARA-1 processors promise to strike the right balance between low latency, energy efficiency and compute power for use in anything from sensors to cameras and full-fledged edge servers.
Because of its strength in real-time video analysis, the company is aiming its chip at solutions around smart retail, including cashier-less stores, smart cities and Industry 4.0/robotics. The company is also working with suppliers to the automotive industry, but less around autonomous driving than monitoring in-cabin activity to ensure that drivers are paying attention to the road and aren’t distracted or sleepy.
Image Credits: Deep Vision
The company was founded by its CTO Rehan Hameed and its Chief Architect Wajahat Qadeer, who recruited Ravi Annavajjhala, who previously worked at Intel and SanDisk, as the company’s CEO. Hameed and Qadeer developed Deep Vision’s architecture as part of a PhD thesis at Stanford.
“They came up with a very compelling architecture for AI that minimizes data movement within the chip,” Annavajjhala explained. “That gives you extraordinary efficiency — both in terms of performance per dollar and performance per watt — when looking at AI workloads.”
Long before the team had working hardware, though, the company focused on building its compiler to ensure that its solution could actually address its customers’ needs. Only then did they finalize the chip design.
Image Credits: Deep Vision
As Hameed told me, Deep Vision’s focus was always on reducing latency. While its competitors often emphasize throughput, the team believes that for edge solutions, latency is the more important metric. While architectures that focus on throughput make sense in the data center, Deep Vision CTO Hameed argues that this doesn’t necessarily make them a good fit at the edge.
“[Throughput architectures] require a large number of streams being processed by the accelerator at the same time to fully utilize the hardware, whether it’s through batching or pipeline execution,” he explained. “That’s the only way for them to get their big throughput. The result, of course, is high latency for individual tasks and that makes them a poor fit in our opinion for an edge use case where real-time performance is key.”
To enable this performance — and Deep Vision claims that its processor offers far lower latency than Google’s Edge TPUs and Movidius’ MyriadX, for example — the team is using an architecture that reduces data movement on the chip to a minimum. In addition, its software optimizes the overall data flow inside the architecture based on the specific workload.
Image Credits: Deep Vision
“In our design, instead of baking in a particular acceleration strategy into the hardware, we have instead built the right programmable primitives into our own processor, which allows the software to map any type of data flow or any execution flow that you might find in a neural network graph efficiently on top of the same set of basic primitives,” said Hameed.
With this, the compiler can then look at the model and figure out how to best map it on the hardware to optimize for data flow and minimize data movement. Thanks to this, the processor and compiler can also support virtually any neural network framework and optimize their models without the developers having to think about the specific hardware constraints that often make working with other chips hard.
“Every aspect of our hardware/software stack has been architected with the same two high-level goals in mind,” Hameed said. “One is to minimize the data movement to drive efficiency. And then also to keep every part of the design flexible in a way where the right execution plan can be used for every type of problem.”
Since its founding, the company has raised about $19 million and filed nine patents. The new chip has been sampling for a while, and even though the company already has a couple of customers, it chose to remain under the radar until now. The company obviously hopes that its unique architecture can give it an edge in this market, which is getting increasingly competitive. Besides the likes of Intel’s Movidius chips (and custom chips from Google and AWS for their own clouds), there are also plenty of startups in this space, including the likes of Hailo, which raised a $60 million Series B round earlier this year and recently launched its new chips, too.
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Pixie Labs raises $9.15M Series A round for its Kubernetes observability platform
Pixie, a startup that provides developers with tools to get observability into their Kubernetes-native applications, today announced that it has raised a $9.15 million Series A round led by Benchmark, with participation from GV. In addition, the company also today said that its service is now available as a public beta.
The company was co-founded by Zain Asgar (CEO), a former Google engineer working on Google AI and adjunct professor at Stanford, and Ishan Mukherjee (CPO), who led Apple’s Siri Knowledge Graph product team and also previously worked on Amazon’s Robotics efforts. Asgar had originally joined Benchmark to work on developer tools for machine learning. Over time, the idea changed to using machine learning to power tools to help developers manage large-scale deployments instead.
“We saw data systems, this move to the edge, and we felt like this old cloud 1.0 model of manually collecting data and shipping it to databases in the cloud seems pretty inefficient,” Mukherjee explained. “And the other part was: I was on call. I got gray hair and all that stuff. We felt like we could build this new generation of developer tools and get to Michael Jordan’s vision of intelligent augmentation, which is giving creatives tools where they can be a lot more productive.”
Image Credits: Pixie
The team argues that most competing monitoring and observability systems focus on operators and IT teams — and often involve a long manual setup process. But Pixie wants to automate most of this manual process and build a tool that developers want to use.
Pixie runs inside a developer’s Kubernetes platform and developers get instant and automatic visibility into their production environments. With Pixie, which the team is making available as a freemium SaaS product, there is no instrumentation to install. Instead, the team uses relatively new Linux kernel techniques like eBPF to collect data right at the source.
“One of the really cool things about this is that we can deploy Pixie in about a minute and you’ll instantly get data,” said Asgar. “Our goal here is that this really helps you when there are cases where you don’t want your business logic to be full of monitoring code, especially if you forget something — when you have an outage.”
Image Credits: Pixie
At the core of the developer experience is what the company calls “Pixie scripts.” Using a Python-like language (PxL), developers can codify their debugging workflows. The company’s system already features a number of scripts written by the team itself and the community at large. But as Asgar noted, not every user will write scripts. “The way scripts work, it’s supposed to capture human knowledge in that problem. We don’t expect the average user — or even the way-above-average developer — ever to touch a script or write one. They’re just going to use it in a specific scenario,” he explained.
Looking ahead, the team plans to make these scripts and the scripting language more robust and usable to allow developers to go from passively monitoring their systems to building scripts that can actively take actions on their clusters based on the monitoring data the system collects.
“Zain and Ishan’s provocative idea was to move software monitoring to the source,” said Eric Vishria, general partner at Benchmark. “Pixie enables engineering teams to fundamentally rethink their monitoring strategy as it presents a vision of the future where we detect anomalous behavior and make operational decisions inside the infrastructure layer itself. This allows companies of all sizes to monitor their digital experiences in a more responsive, cost-effective and scalable manner.”
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3 views on the future of geographic-focused funds
For many investors, the coronavirus has effectively taken geography out of the equation when it comes to vetting new opportunities.
While this dynamic opens up startups to more investment opportunities, venture capital firms that focus on a specific region are in a thornier spot. The competitive advantage they once had when raising — the notion that they’re focused on an area no one else is — is potentially threatened.
Natasha Mascarenhas, Danny Crichton and Alex Wilhelm of the TechCrunch Equity crew discussed the future of geographic-focused funds given the uptick of remote investing:
- Natasha: Early-stage regional funds can win if they remain focused
- Alex: Geo-focused venture funds will be weakened, but won’t die
- Danny: Geo-focused venture funds are dead (and should never have existed)
Natasha: Early-stage regional funds can win if they remain focused
Since 2014, Steve Case and his team have made an annual bus trip across the country to meet startups in emerging startup hubs. Five days, five cities and at least $500,000 of investment dollars given to startups. Case would even offer to fly out promising and hard-to-reach startups to have them join the trip.
The Rise of the Rest fund, with more than $300 million in assets under management, has invested in over 130 startups across 70 cities, including Austin, Chicago, Detroit, Los Angeles, New Orleans and Washington, D.C.
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Our 11 favorite companies from Y Combinator’s S20 Demo Day: Part I
Startup incubator and investment group Y Combinator today held the first of two demo days for founders in its Summer 2020 batch.
So far, this cohort contains the usual mix of bold, impressive and, at times, slightly wacky ideas young companies so often show off.
This was Y Combinator’s second online demo day, its first all-virtual class and the first time that it held live, remote pitches. The event largely went well, with founders dialing in from around the globe to share a few paragraphs of notes and a single slide. There were few technical hiccups, given the sheer number of startups presenting.
But if you are not in the mood to parse through dozens (and dozens) of entries detailing each startup that showed off its problem, solution and growth, the TechCrunch crew has collected our own favorites based on how likely a company seems to succeed and how impressed we were with the creativity of their vision. For each entry, one staffer made the call that the startup in question was among their favorites.
We’re not investors, so we’re not pretending to sort the unicorns from the goats. But if what you need is a digest of some of the day’s best companies to get a good taste of what founders are building, we have your back.
ZipSchool and Hellosaurus
The next wave of edtech startups is entering a market that demands a better remote-learning solution for younger learners. But that’s the obvious product gap, one that is already being tackled by the biggest names in the booming category.
The non-obvious product-market deficit is how teachers, also impacted by the pandemic, are searching for new ways to interact with students. Teachers are collaborating and cross-pollinating on successful lesson plans that work across stale Zoom screens, so why not monetize that same content?
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Mission Bio raises $70 million to help scale its tech for improving the development of targeted cancer therapies
California-based startup Mission Bio has raised a new $70 million Series C funding round, led by Novo Growth and including participation from Soleus Capital and existing investors Mayfield, Cota and Agilent. Mission Bio will use the funding to scale its Tapestri Platform, which uses the company’s work in single-cell multi-omics technology to help optimize clinical trials for targeted, precision cancer therapies.
Mission Bio’s single-cell multi-omics platform is unique in the therapeutic industry. What it allows is the ability to zero in on a single cell, observing both genotype (fully genetic) and phenotype (observable traits influenced by genetics and other factors) impact resulting from use of various therapies during clinical trials. Mission’s Tapestri can detect both DNA and protein changes within the same single cell, which is key in determining effectiveness of targeted therapies because it can help rule out the effect of other factors not under control when analyzing in bulk (i.e. across groups of cells).
Founded in 2012 as a spin-out of research work conducted at UCSF, Mission Bio has raised a total of $120 million to date. The company’s tech has been used by a number of large pharmaceutical and therapeutic companies, including Agios, LabCorp and Onconova Therapeutics, as well as at cancer research centers including UCSF, Stanford and the Memorial Sloan Kettering Cancer Center.
In addition to helping with the optimization of clinical trials for treatments of blood cancers and tumors, Mission’s tech can be used to validate genome editing — a large potential market that could see a lot of growth over the next few years with the rise of CRISPR-based therapeutic applications.
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