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1stdibs, the high-end online marketplace, just nabbed $76 million in Series D funding
1stdibs began pushing the antiques business into the 21st century long ago. Apparently, investors think it can push further and faster with $76 million in new funding. That’s how much the now-18-year-old, New York-based company says it just closed on for its Series D round, led by T. Rowe Price Associates, with participation from earlier backers Index Ventures, Benchmark and Spark Capital.
The company now boasts a valuation of well over $500 million, it tells the WSJ. Other investors in the new round include Sofina Group, Foxhaven Asset Management, and Allen & Company, as well as Michael Zeisser, who is the former chairman of U.S. Investments for Alibaba Group, and Groupe Artémis, which owns the auction house Christie’s.
1stdibs has always been an interesting startup, one that’s both loved by the antiques dealers who use it, and, apparently, feared. When, in 2016, 1stdibs became heavier-handed about enforcing the commissions from each sale on its platform — and on which it relies for revenue — more than 30 dealers reportedly met at a design store in lower Manhattan to grouse about the development, complaining that the company had begun prizing revenue growth over its relationships.
Of course, with venture-capital funding — and the company has now collected $170 million altogether — comes expectations. And despite pushback from dealers, they’ve apparently stuck with the platform. 1stdibs says an average of 50 items sell for more than $5,000 on its platform daily, and that 15 of these are items that sell for more than $10,000. (A quick scan suggests a very wide range of prices, with many vintage items priced at $5,000 or less, but plenty with far richer tags, like a three-carat ruby and diamond ring available right now on the site for a cool $200,000, and a chandelier dating back to roughly 1870 and selling, someone is hoping, for more than $300,000.)
With venture funding comes competition, too. Though 1stdibs may be the doyen of the online antiques market, other, newer companies eyeing its traction have since emerged on the scene, many of which have also since raised venture funding and are also growing fast, including The RealReal, which was founded in 2011 and is reportedly weighing a public offering; and Chairish, founded in 2013, which sells vintage and used decor.
Chairish has raised just $16.7 million from investors to date. The RealReal has raised $288 million.
In fact, a fight for brand recognition in what’s become an increasingly crowded playing field as the U.S. population ages (and more antiques are dispersed into the world) may ultimately lead 1stdibs to follow a growing number of formerly online-only marketplaces now extending their reach into the offline world.
Though the company already has a New York location, in a block-long, late-19th-century warehouse called the Terminal Stores building, CEO David Rosenblatt tells the WSJ that using its new funding, more brick-and-mortar showrooms may be in its future.
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Sketch, maker of popular design tools, just landed $20 million in Series A funding from Benchmark in its first outside round
You’ve probably noticed: Design has become central for many businesses that might have once considered it an afterthought. Indeed, with sales and marketing so thoroughly optimized at this point — and companies wondering how else to trounce the competition — there’s now a race afoot for numerous startups looking to become the Salesforce of design.
InVision is one of them. Just three months ago, the design collaboration startup raised $115 million in Series F funding at a $1.9 billion valuation. More recently, Figma, another design player, sealed up $40 million in Series C funding in a round that brings its total funding to $82.9 million and a valuation of $440 million.
Still, if the venture firm Benchmark has its way, Sketch — a seven-year-old, 42-person, Europe-based company — is going to win this race. Truth be told, Benchmark jumped at the chance to back Sketch founders Emanuel Sá and Pieter Omvlee when they reached out to the firm, says Chetan Puttagunta, the newest general partner at Benchmark. “We’d definitely known of Sketch and once we got a look at the company, we were blown away by it. There’s so much potential of what this could be that things moved fast. There wasn’t much of a negotiation. We were like, ‘What do you guys want to do? Let’s do it.’ ”
It helps that Sketch — which has a completely distributed workforce, with designers and other employees based around Europe and the U.S. — has been profitable from the outset, and that one million people have already paid $99 for a perpetual license (with one year of free updates).
Also impressive: those sales are entirely organic, and they are directly from Sketch’s site. Though its design tools were formerly available in the Mac App Store — Apple once gave it a design award and it routinely topped the Mac App Store charts — Sketch parted ways with the company back in 2015, including owing to Apple’s guidelines about what a Mac app can and can’t do, and the time Apple takes to approve app updates, among other things.
Benchmark — which isn’t sharing Sketch’s post-money valuation or how much of the company that $20 million is buying the venture firm — also sees a future wherein Sketch moves beyond its roots as a prototyping tool for both highly experienced and novice designers to build out their experience without the help of coders. The idea is for it to become a tool that teams big and small can gather around. In other words, like InVision and Figma (and Adobe and Autodesk), Sketch is going after the enterprise now, too.
In fact, Sketch is already planning some big upgrades that will be available this summer, as Sá and Omvlee told us yesterday from their respective offices in Portugal and The Netherlands. One major offering around the corner that builds on its existing cloud offering is team collaboration, via a tool called Sketch for Teams. As the two tell us, Sketch wants to be where all documents live and it will allow teams to make annotations and comments in the app.
Sketch is also bringing its tools to the browser starting later this year so users can render an entire document, add developer hand-off and allow editing along with collaboration, all without the need to leave the browser.
All of these features will be made available to anyone who downloads Sketch. In other words, then, as now, everyone gets the same functionality. Asked if there may eventually be features for enterprises that are not available to Sketch’s loyal base of current customers, Puttagunta says it’s a possibility, but that “at the moment, there’s no plan to bifurcate anything. Different modules, different charges — that’s all speculation at this point.”
Sá and Omvlee echo the point, telling us candidly that much remains to be seen. “We need to define a strategy,” says Sá. “So far, we’ve been focused on developing the product, but when the time comes, we’ll discuss [more of these business particulars] with Benchmark and the rest of the team and come up with the best solution.”
What won’t change, says Omvlee, is its focus on creating a product that users love so much that they tell others about it. “Our focus all along has been on making design available to pretty much anyone out there, and then get out of the way.”
Pictured above, left to right: Sketch founders Emanuel Sá and Pieter Omvlee.
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Backed by Benchmark, Blue Hexagon just raised $31 million for its deep learning cybersecurity software
Nayeem Islam spent nearly 11 years with chipmaker Qualcomm, where he founded its Silicon Valley-based R&D facility, recruited its entire team and oversaw research on all aspects of security, including applying machine learning on mobile devices and in the network to detect threats early.
Islam was nothing if not prolific, developing a system for on-device machine learning for malware detection, libraries for optimizing deep learning algorithms on mobile devices and systems for parallel compute on mobile devices, among other things.
In fact, because of his work, he also saw a big opportunity in better protecting enterprises from cyberthreats through deep neural networks that are capable of processing every raw byte within a file and that can uncover complex relations within data sets. So two years ago, Islam and Saumitra Das, a former Qualcomm engineer with 330 patents to his name and another 450 pending, struck out on their own to create Blue Hexagon, a now 30-person Sunnyvale, Calif.-based company that is today disclosing it has raised $31 million in funding from Benchmark and Altimeter.
The funding comes roughly one year after Benchmark quietly led a $6 million Series A round for the firm.
So what has investors so bullish on the company’s prospects, aside from its credentialed founders? In a word, speed, seemingly. According to Islam, Blue Hexagon has created a real-time, cybersecurity platform that he says can detect known and unknown threats at first encounter, then block them in “sub seconds” so the malware doesn’t have time to spread.
The industry has to move to real-time detection, he says, explaining that four new and unique malware samples are released every second, and arguing that traditional security methods can’t keep pace. He says that sandboxes, for example, meaning restricted environments that quarantine cyberthreats and keep them from breaching sensitive files, are no longer state of the art. The same is true of signatures, which are mathematical techniques used to validate the authenticity and integrity of a message, software or digital document but are being bypassed by rapidly evolving new malware.
Only time will tell if Blue Hexagon is far more capable of identifying and stopping attackers, as Islam insists is the case. It is not the only startup to apply deep learning to cybersecurity, though it’s certainly one of the first. Critics, some who are protecting their own corporate interests, also worry that hackers can foil security algorithms by targeting the warning flags they look for.
Still, with its technology, its team and its pitch, Blue Hexagon is starting to persuade not only top investors of its merits, but a growing — and broad — base of customers, says Islam. “Everyone has this issue, from large banks, insurance companies, state and local governments. Nowhere do you find someone who doesn’t need to be protected.”
Blue Hexagon can even help customers that are already under attack, Islam says, even if it isn’t ideal. “Our goal is to catch an attack as early in the kill chain as possible. But if someone is already being attacked, we’ll see that activity and pinpoint it and be able to turn it off.”
Some damage may already be done, of course. It’s another reason to plan ahead, he says. “With automated attacks, you need automated techniques.” Deep learning, he insists, “is one way of leveling the playing field against attackers.”
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Contentful raises $33.5M for its headless CMS platform
Contentful, a Berlin- and San Francisco-based startup that provides content management infrastructure for companies like Spotify, Nike, Lyft and others, today announced that it has raised a $33.5 million Series D funding round led by Sapphire Ventures, with participation from OMERS Ventures and Salesforce Ventures, as well as existing investors General Catalyst, Benchmark, Balderton Capital and Hercules. In total, the company has now raised $78.3 million.
It’s been less than a year since the company raised its Series C round and, as Contentful co-founder and CEO Sascha Konietzke told me, the company didn’t really need to raise right now. “We had just raised our last round about a year ago. We still had plenty of cash in our bank account and we didn’t need to raise as of now,” said Konietzke. “But we saw a lot of economic uncertainty, so we thought it might be a good moment in time to recharge. And at the same time, we already had some interesting conversations ongoing with Sapphire [formerly SAP Ventures] and Salesforce. So we saw the opportunity to add more funding and also start getting into a tight relationship with both of these players.”
The original plan for Contentful was to focus almost explicitly on mobile. As it turns out, though, the company’s customers also wanted to use the service to handle its web-based applications and these days, Contentful happily supports both. “What we’re seeing is that everything is becoming an application,” he told me. “We started with native mobile application, but even the websites nowadays are often an application.”
In its early days, Contentful focused only on developers. Now, however, that’s changing, and having these connections to large enterprise players like SAP and Salesforce surely isn’t going to hurt the company as it looks to bring on larger enterprise accounts.
Currently, the company’s focus is very much on Europe and North America, which account for about 80 percent of its customers. For now, Contentful plans to continue to focus on these regions, though it obviously supports customers anywhere in the world.
Contentful only exists as a hosted platform. As of now, the company doesn’t have any plans for offering a self-hosted version, though Konietzke noted that he does occasionally get requests for this.
What the company is planning to do in the near future, though, is to enable more integrations with existing enterprise tools. “Customers are asking for deeper integrations into their enterprise stack,” Konietzke said. “And that’s what we’re beginning to focus on and where we’re building a lot of capabilities around that.” In addition, support for GraphQL and an expanded rich text editing experience is coming up. The company also recently launched a new editing experience.
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Elastic’s IPO filing is here
Elastic, the provider of subscription-based data search software used by Dell, Netflix, The New York Times and others, has unveiled its IPO filing after confidentially submitting paperwork to the SEC in June. The company will be the latest in a line of enterprise SaaS businesses to hit the public markets in 2018.
Headquartered in Mountain View, Elastic plans to raise $100 million in its NYSE listing, though that’s likely a placeholder amount. The timing of the filing suggests the company will transition to the public markets this fall; we’ve reached out to the company for more details.
Elastic will trade under the symbol ESTC.
The business is known for its core product, an open-source search tool called ElasticSearch. It also offers a range of analytics and visualization tools meant to help businesses organize large data sets, competing directly with companies like Splunk and even Amazon — a name it mentions 14 times in the filing.
“Amazon offers some of our open source features as part of its Amazon Web Services offering. As such, Amazon competes with us for potential customers, and while Amazon cannot provide our proprietary software, the pricing of Amazon’s offerings may limit our ability to adjust,” the company wrote in the filing, which also lists Endeca, FAST, Autonomy and several others as key competitors.
This is our first look at Elastic’s financials. The company brought in $159.9 million in revenue in the 12 months ended July 30, 2018, up roughly 100 percent from $88.1 million the year prior. Losses are growing at about the same rate. Elastic reported a net loss of $18.5 million in the second quarter of 2018. That’s an increase from $9.9 million in the same period in 2017.
Founded in 2012, the company has raised about $100 million in venture capital funding, garnering a $700 million valuation the last time it raised VC, which was all the way back in 2014. Its investors include Benchmark, NEA and Future Fund, which each retain a 17.8 percent, 10.2 percent and 8.2 percent pre-IPO stake, respectively.
A flurry of business software companies have opted to go public this year. Domo, a business analytics company based in Utah, went public in June raising $193 million in the process. On top of that, subscription biller Zuora had a positive debut in April in what was a “clear sign post on the road to SaaS maturation,” according to TechCrunch’s Ron Miller. DocuSign and Smartsheet are also recent examples of both high-profile and successful SaaS IPOs.
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Computer vision researchers build an AI benchmark app for Android phones
A group of computer vision researchers from ETH Zurich want to do their bit to enhance AI development on smartphones. To wit: They’ve created a benchmark system for assessing the performance of several major neural network architectures used for common AI tasks.
They’re hoping it will be useful to other AI researchers but also to chipmakers (by helping them get competitive insights); Android developers (to see how fast their AI models will run on different devices); and, well, to phone nerds — such as by showing whether or not a particular device contains the necessary drivers for AI accelerators. (And, therefore, whether or not they should believe a company’s marketing messages.)
The app, called AI Benchmark, is available for download on Google Play and can run on any device with Android 4.1 or higher — generating a score the researchers describe as a “final verdict” of the device’s AI performance.
AI tasks being assessed by their benchmark system include image classification, face recognition, image deblurring, image super-resolution, photo enhancement or segmentation.
They are even testing some algorithms used in autonomous driving systems, though there’s not really any practical purpose for doing that at this point. Not yet anyway. (Looking down the road, the researchers say it’s not clear what hardware platform will be used for autonomous driving — and they suggest it’s “quite possible” mobile processors will, in future, become fast enough to be used for this task. So they’re at least prepped for that possibility.)
The app also includes visualizations of the algorithms’ output to help users assess the results and get a feel for the current state-of-the-art in various AI fields.
The researchers hope their score will become a universally accepted metric — similar to DxOMark that is used for evaluating camera performance — and all algorithms included in the benchmark are open source. The current ranking of different smartphones and mobile processors is available on the project’s webpage.
The benchmark system and app was around three months in development, says AI researcher and developer Andrey Ignatov.
He explains that the score being displayed reflects two main aspects: The SoC’s speed and available RAM.
“Let’s consider two devices: one with a score of 6000 and one with a score of 200. If some AI algorithm will run on the first device for 5 seconds, then this means that on the second device this will take about 30 times longer, i.e. almost 2.5 minutes. And if we are thinking about applications like face recognition this is not just about the speed, but about the applicability of the approach: Nobody will wait 10 seconds till their phone will be trying to recognize them.
“The same is about memory: The larger is the network/input image — the more RAM is needed to process it. If the phone has a small amount of RAM that is e.g. only enough to enhance 0.3MP photo, then this enhancement will be clearly useless, but if it can do the same job for Full HD images — this opens up much wider possibilities. So, basically the higher score — the more complex algorithms can be used / larger images can be processed / it will take less time to do this.”
Discussing the idea for the benchmark, Ignatov says the lab is “tightly bound” to both research and industry — so “at some point we became curious about what are the limitations of running the recent AI algorithms on smartphones”.
“Since there was no information about this (currently, all AI algorithms are running remotely on the servers, not on your device, except for some built-in apps integrated in phone’s firmware), we decided to develop our own tool that will clearly show the performance and capabilities of each device,” he adds.
“We can say that we are quite satisfied with the obtained results — despite all current problems, the industry is clearly moving towards using AI on smartphones, and we also hope that our efforts will help to accelerate this movement and give some useful information for other members participating in this development.”
After building the benchmarking system and collating scores on a bunch of Android devices, Ignatov sums up the current situation of AI on smartphones as “both interesting and absurd”.
For example, the team found that devices running Qualcomm chips weren’t the clear winners they’d imagined — i.e. based on the company’s promotional materials about Snapdragon’s 845 AI capabilities and 8x performance acceleration.
“It turned out that this acceleration is available only for ‘quantized’ networks that currently cannot be deployed on the phones, thus for ‘normal’ networks you won’t get any acceleration at all,” he says. “The saddest thing is that actually they can theoretically provide acceleration for the latter networks too, but they just haven’t implemented the appropriated drivers yet, and the only possible way to get this acceleration now is to use Snapdragon’s proprietary SDK available for their own processors only. As a result — if you are developing an app that is using AI, you won’t get any acceleration on Snapdragon’s SoCs, unless you are developing it for their processors only.”
Whereas the researchers found that Huawei’s Kirin’s 970 CPU — which is technically even slower than Snapdragon 636 — offered a surprisingly strong performance.
“Their integrated NPU gives almost 10x acceleration for Neural Networks, and thus even the most powerful phone CPUs and GPUs can’t compete with it,” says Ignatov. “Additionally, Huawei P20/P20 Pro are the only smartphones on the market running Android 8.1 that are currently providing AI acceleration, all other phones will get this support only in Android 9 or later.”
It’s not all great news for Huawei phone owners, though, as Ignatov says the NPU doesn’t provide acceleration for ‘quantized’ networks (though he notes the company has promised to add this support by the end of this year); and also it uses its own RAM — which is “quite limited” in size, and therefore you “can’t process large images with it”…
“We would say that if they solve these two issues — most likely nobody will be able to compete with them within the following year(s),” he suggests, though he also emphasizes that this assessment only refers to the one SoC, noting that Huawei’s processors don’t have the NPU module.
For Samsung processors, the researchers flag up that all the company’s devices are still running Android 8.0 but AI acceleration is only available starting from Android 8.1 and above. Natch.
They also found CPU performance could “vary quite significantly” — up to 50% on the same Samsung device — because of throttling and power optimization logic. Which would then have a knock on impact on AI performance.
For Mediatek, the researchers found the chipmaker is providing acceleration for both ‘quantized’ and ‘normal’ networks — which means it can reach the performance of “top CPUs”.
But, on the flip side, Ignatov calls out the company’s slogan — that it’s “Leading the Edge-AI Technology Revolution” — dubbing it “nothing more than their dream”, and adding: “Even the aforementioned Samsung’s latest Exynos CPU can slightly outperform it without using any acceleration at all, not to mention Huawei with its Kirin’s 970 NPU.”
“In summary: Snapdragon — can theoretically provide good results, but are lacking the drivers; Huawei — quite outstanding results now and most probably in the nearest future; Samsung — no acceleration support now (most likely this will change soon since they are now developing their own AI Chip), but powerful CPUs; Mediatek — good results for mid-range devices, but definitely no breakthrough.”
It’s also worth noting that some of the results were obtained on prototype samples, rather than shipped smartphones, so haven’t yet been included in the benchmark table on the team’s website.
“We will wait till the devices with final firmware will come to the market since some changes might still be introduced,” he adds.
For more on the pros and cons of AI-powered smartphone features check out our article from earlier this year.
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Benchmark’s Mitch Lasky will reportedly step down from Snap’s board of directors
Benchmark partner Mitch Lasky, who has served on Snap’s board of directors since December 2012, is not expected to stand for re-election to Snap’s board of directors and will thus be stepping down, according to a report by The Information.
Early investors stepping down from the board of directors — or at least not seeking re-election — isn’t that uncommon as once-private companies grow into larger public ones. Benchmark partner Peter Fenton did not seek re-election for Twitter’s board of directors in April last year. As Snap continues to navigate its future, especially as it has declined precipitously since going public and now sits at a valuation of around $16.5 billion. Partners with an expertise in the early-stage and later-stage startup life cycle may end up seeing themselves more useful taking a back seat and focusing on other investments. The voting process for board member re-election happens during the company’s annual meeting, so we’ll get more information when an additional proxy filing comes out ahead of the meeting later this year.
Benchmark is, or at least was at the time of going public last year, one of Snap’s biggest shareholders. According to the company’s 424B filing prior to going public in March last year, Benchmark held ownership of 23.1% of Snap’s Class B common stock and 8.2% of Snap’s Class A common stock. Lasky has been with Benchmark since April 2007, and also serves on the boards of a number of gaming companies like Riot Games and thatgamecompany, the creators of PlayStation titles flower and Journey. At the time, Snap said in its filing that Lasky was “qualified to serve as a member of our board of directors due to his extensive experience with social media and technology companies, as well as his experience as a venture capitalist investing in technology companies.”
The timing could be totally coincidental, but an earlier Recode report suggested Lasky had been talking about stepping down in future funds for Benchmark. The firm only recently wrapped up a very public battle with Uber, which ended up with Benchmark selling a significant stake in the company and a new CEO coming in to replace co-founder Travis Kalanick. Benchmark hired its first female general partner, Sarah Tavel, earlier this year.
We’ve reached out to both Snap and a representative from Benchmark for comment and will update the story when we hear back.
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Timescale is leading the next wave of NYC database tech
Data is the lifeblood of the modern corporation, yet acquiring, storing, processing, and analyzing it remains a remarkably challenging and expensive project. Every time data infrastructure finally catches up with the streams of information pouring in, another source and more demanding decision-making makes the existing technology obsolete.
Few cities rely on data the same way as New York City, nor has any other city so shaped the technology that underpins our data infrastructure. Back in the 1960s, banks and accounting firms helped to drive much of the original computation industry with their massive finance applications. Today, that industry has been supplanted by finance and advertising, both of which need to make microsecond decisions based on petabyte datasets and complex statistical models.
Unsurprisingly, the city’s hunger for data has led to waves of database companies finding their home in the city.
As web applications became increasingly popular in the mid-aughts, SQL databases came under increasing strain to scale, while also proving to be inflexible in terms of their data schemas for the fast-moving startups they served. That problem spawned Manhattan-based MongoDB, whose flexible “NoSQL” schemas and horizontal scaling capabilities made it the default choice for a generation of startups. The company would go on to raise $311 million according to Crunchbase, and debuted late last year on NASDAQ, trading today with a market cap of $2 billion.
At the same time that the NoSQL movement was hitting its stride, academic researchers and entrepreneurs were exploring how to evolve SQL to scale like its NoSQL competitors, while retaining the kinds of features (joining tables, transactions) that make SQL so convenient for developers.
One leading company in this next generation of database tech is New York-based Cockroach Labs, which was founded in 2015 by a trio of former Square, Viewfinder, and Google engineers. The company has gone on to raise more than $50 million according to Crunchbase from a luminary list of investors including Peter Fenton at Benchmark, Mike Volpi at Index, and Satish Dharmaraj at Redpoint, along with GV and Sequoia.
While web applications have their own peculiar data needs, the rise of the internet of things (IoT) created a whole new set of data challenges. How can streams of data from potentially millions of devices be stored in an easily analyzable manner? How could companies build real-time systems to respond to that data?
Mike Freedman and Ajay Kulkarni saw that problem increasingly manifesting itself in 2015. The two had been roommates at MIT in the late 90s, and then went on separate paths into academia and industry respectively. Freedman went to Stanford for a PhD in computer science, and nearly joined the spinout of Nicira, which sold to VMware in 2012 for $1.26 billion. Kulkarni joked that “Mike made the financially wise decision of not joining them,” and Freedman eventually went to Princeton as an assistant professor, and was awarded tenure in 2013. Kulkarni founded and worked at a variety of startups including GroupMe, as well as receiving an MBA from MIT.
The two had startup dreams, and tried building an IoT platform. As they started building it though, they realized they would need a real-time database to process the data streams coming in from devices. “There are a lot of time series databases, [so] let’s grab one off the shelf, and then we evaluated a few,” Kulkarni explained. They realized what they needed was a hybrid of SQL and NoSQL, and nothing they could find offered the feature set they required to power their platform. That challenge became the problem to be solved, and Timescale was born.
In many ways, Timescale is how you build a database in 2018. Rather than starting de novo, the team decided to build on top of Postgres, a popular open-source SQL database. “By building on top of Postgres, we became the more reliable option,” Kulkarni said of their thinking. In addition, the company opted to make the database fully open source. “In this day and age, in order to get wide adoption, you have to be an open source database company,” he said.
Since the project’s first public git commit on October 18, 2016, the company’s database has received nearly 4,500 stars on Github, and it has raised $16.1 million from Benchmark and NEA .
Far more important though are their customers, who are definitely not the typical tech startup roster and include companies from oil and gas, mining, and telecommunications. “You don’t think of them as early adopters, but they have a need, and because we built it on top of Postgres, it integrates into an ecosystem that they know,” Freedman explained. Kulkarni continued, “And the problem they have is that they have all of this time series data, and it isn’t sitting in the corner, it is integrated with their core service.”
New York has been a strong home for the two founders. Freedman continues to be a professor at Princeton, where he has built a pipeline of potential grads for the company. More widely, Kulkarni said, “Some of the most experienced people in databases are in the financial industry, and that’s here.” That’s evident in one of their investors, hedge fund Two Sigma. “Two Sigma had been the only venture firm that we talked to that already had built out their own time series database,” Kulkarni noted.
The two also benefit from paying customers. “I think the Bay Area is great for open source adoption, but a lot of Bay Area companies, they develop their own database tech, or they use an open source project and never pay for it,” Kulkarni said. Being in New York has meant closer collaboration with customers, and ultimately more revenues.
Open source plus revenues. It’s the database way, and the next wave of innovation in the NYC enterprise infrastructure ecosystem.
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Uber’s Frankenboard arrives
Today was a momentous day in Uber history. After much debate, rumors and strife over the past few weeks, it looks like the company and its shareholders have come to an agreement over a tender offer that will see SoftBank own nearly 15 percent of the company, while also injecting around $1 billion in fresh capital. That deal is expected to close in the new year. Read More
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Ex-Zynga execs raise $15 million for their new gaming studio, Manticore Games
In the eight years since Frederic Descamps and Jordan Maynard launched their last gaming startup, the industry they helped shape now brings in more than $100 billion in revenues globally. There’s been a resurgence in gaming on PCs. User-generated content has produced a string of wildly popular hits. Those trends are exactly what the two are hoping to harness with Manticore Games. Read More
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