Personal blog of Yzmir Ramirez

Every business needs to track fundamental financial information, but the data typically lives in a variety of silos, making it a constant challenge to understand a company’s overall financial health. DataJoy, an early-stage startup, wants to solve that issue. The company announced a $6 million seed round today led by Foundation Capital with help from Quarry VC, Partech Partners, IGSB, Bow Capital and SVB.

Like many startup founders, CEO Jon Lee has experienced the frustration firsthand of trying to gather this financial data, and he decided to start a company to deal with it once and for all. “The reason why I started this company was that I was really frustrated at Copper, my last company, because it was really hard just to find the answers to simple business questions in my data,” he told me.

These include basic questions like how the business is doing this quarter, if there are any surprises that could throw the company off track and where are the best places to invest in the business to accelerate more quickly.

The company has decided to concentrate its efforts for starters on SaaS companies and their requirements. “We basically focus on taking the work out of revenue intelligence, and just give you the insights that successful companies in the SaaS vertical depend on to be the largest and fastest growing in the market,” Lee explained.

The idea is to build a product with a way to connect to key business systems, pull the data and answer a very specific set of business questions, while using machine learning to provide more proactive advice.

While the company is still in the process of building the product and is pre-revenue, it has begun developing the pieces to ultimately help companies answer these questions. Eventually it will have a set of connectors to various key systems like Salesforce for CRM, HubSpot and Marketo for marketing, NetSuite for ERP, Gainsight for customer experience and Amplitude for product intelligence.

Lee says the set of connectors will be as specific as the questions themselves and based on their research with potential customers and what they are using to track this information. Ashu Garg, general partner at lead investor Foundation Capital, says that he was attracted to the founding team’s experience, but also to the fact they were solving a problem he sees all the time sitting on the boards of various SaaS startups.

“I spend my life in the board meetings. It’s what I do, and every CEO, every board is looking for straight answers for what should be obvious questions, but they require this intersection of data,” Garg said. He says to an extent, it’s only possible now due to the evolution of technology to pull this all together in a way that simplifies this process.

The company currently has 11 employees, with plans to double that by the middle of this year. As a longtime entrepreneur, Lee says that he has found that building a diverse workforce is essential to building a successful company. “People have found diversity usually [results in a company that is] more productive, more creative and works faster,” Lee said. He said that that’s why it’s important to focus on diversity from the earliest days of the company, while being proactive to make that happen. For example, ensuring you have a diverse set of candidates to choose from when you are reviewing resumes.

For now, the company is 100% remote. In fact, Lee and his co-founder, Chief Product Officer Ken Wong, who previously ran AI and machine learning at Tableau, have yet to meet in person, but they are hoping that changes soon. The company will eventually have a presence in Vancouver and San Mateo whenever offices start to open.

Google Cloud today announced a new operating mode for its Kubernetes Engine (GKE) that turns over the management of much of the day-to-day operations of a container cluster to Google’s own engineers and automated tools. With Autopilot, as the new mode is called, Google manages all of the Day 2 operations of managing these clusters and their nodes, all while implementing best practices for operating and securing them.

This new mode augments the existing GKE experience, which already managed most of the infrastructure of standing up a cluster. This ‘standard’ experience, as Google Cloud now calls it, is still available and allows users to customize their configurations to their heart’s content and manually provision and manage their node infrastructure.

Drew Bradstock, the Group Product Manager for GKE, told me that the idea behind Autopilot was to bring together all of the tools that Google already had for GKE and bring them together with its SRE teams who know how to run these clusters in production — and have long done so inside of the company.

“Autopilot stitches together auto-scaling, auto-upgrades, maintenance, Day 2 operations and — just as importantly — does it in a hardened fashion,” Bradstock noted. “[…] What this has allowed our initial customers to do is very quickly offer a better environment for developers or dev and test, as well as production, because they can go from Day Zero and the end of that five-minute cluster creation time, and actually have Day 2 done as well.”

From a developer’s perspective, nothing really changes here, but this new mode does free up teams to focus on the actual workloads and less on managing Kubernetes clusters. With Autopilot, businesses still get the benefits of Kubernetes, but without all of the routine management and maintenance work that comes with that. And that’s definitely a trend we’ve been seeing as the Kubernetes ecosystem has evolved. Few companies, after all, see their ability to effectively manage Kubernetes as their real competitive differentiator.

All of that comes at a price, of course, in addition to the standard GKE flat fee of $0.10 per hour and cluster (there’s also a free GKE tier that provides $74.40 in billing credits), plus additional fees for resources that your clusters and pods consume. Google offers a 99.95% SLA for the control plane of its Autopilot clusters and a 99.9% SLA for Autopilot pods in multiple zones.

Autopilot for GKE joins a set of container-centric products in the Google Cloud portfolio that also include Anthos for running in multi-cloud environments and Cloud Run, Google’s serverless offering. “[Autopilot] is really [about] bringing the automation aspects in GKE we have for running on Google Cloud, and bringing it all together in an easy-to-use package, so that if you’re newer to Kubernetes, or you’ve got a very large fleet, it drastically reduces the amount of time, operations and even compute you need to use,” Bradstock explained.

And while GKE is a key part of Anthos, that service is more about brining Google’s config management, service mesh and other tools to an enterprise’s own data center. Autopilot of GKE is, at least for now, only available on Google Cloud.

“On the serverless side, Cloud Run is really, really great for an opinionated development experience,” Bradstock added. “So you can get going really fast if you want an app to be able to go from zero to 1000 and back to zero — and not worry about anything at all and have it managed entirely by Google. That’s highly valuable and ideal for a lot of development. Autopilot is more about simplifying the entire platform people work on when they want to leverage the Kubernetes ecosystem, be a lot more in control and have a whole bunch of apps running within one environment.”

 

Back when I was a wee lad with a very security-compromised MySQL installation, I used to answer every web request with multiple “SELECT *” database requests — give me all the data and I’ll figure out what to do with it myself.

Today in a modern, data-intensive org, “SELECT *” will kill you. With petabytes of information, tens of thousands of tables (on the small side!), and millions and perhaps billions of calls flung at the database server, data science teams can no longer just ask for all the data and start working with it immediately.

Big data has led to the rise of data warehouses and data lakes (and apparently data lake houses), infrastructure to make accessing data more robust and easy. There is still a cataloguing and discovery problem though — just because you have all of your data in one place doesn’t mean a data scientist knows what the data represents, who owns it, or what that data might affect in the myriad of web and corporate reporting apps built on top of it.

That’s where Select Star comes in. The startup, which was founded about a year ago in March 2020, is designed to automatically build out metadata within the context of a data warehouse. From there, it offers a full-text search that allows users to quickly find data as well as “heat map” signals in its search results which can quickly pinpoint which columns of a dataset are most used by applications within a company and have the most queries that reference them.

The product is SaaS, and it is designed to allow for quick onboarding by connecting to a customer’s data warehouse or business intelligence (BI) tool.

Shinji Kim, the sole founder and CEO, explained that the tool is a solution to a problem she has seen directly in corporate data science teams. She formerly founded Concord Systems, a real-time data processing startup that was acquired by Akamai in 2016. “The part that I noticed is that we now have all the data and we have the ability to compute, but now the next challenge is to know what the data is and how to use it,” she explained.

She said that “tribal knowledge is starting to become more wasteful [in] time and pain in growing companies” and pointed out that large companies like Facebook, Airbnb, Uber, Lyft, Spotify and others have built out their own homebrewed data discovery tools. Her mission for Select Star is to allow any corporation to quickly tap into an easy-to-use platform to solve this problem.

The company raised a $2.5 million seed round led by Bowery Capital with participation from Background Capital and a number of prominent angels including Spencer Kimball, Scott Belsky, Nick Caldwell, Michael Li, Ryan Denehy and TLC Collective.

Data discovery tools have been around in some form for years, with popular companies like Alation having raised tens of millions of VC dollars over the years. Kim sees an opportunity to compete by offering a better onboarding experience and also automating large parts of the workflow that remain manual for many alternative data discovery tools. With many of these tools, “they don’t do the work of connecting and building the relationship,” between data she said, adding that “documentation is still important, but being able to automatically generate [metadata] allows data teams to get value right away.”

In addition to just understanding data, Select Star can help data engineers begin to figure out how to change their databases without leading to cascading errors. The platform can identify how columns are used and how a change to one may affect other applications or even other datasets.

Select Star is coming out of private beta today. The company’s team currently has seven people, and Kim says they are focused on growing the team and making it even easier to onboard users by the end of the year.

Aquarium, a startup from two former Cruise employees, wants to help companies refine their machine learning model data more easily and move the models into production faster. Today the company announced a $2.6 million seed led by Sequoia with participation from Y Combinator and a bunch of angel investors including Cruise co-founders Kyle Vogt and Dan Kan.

When the two co-founders CEO Peter Gao and head of engineering Quinn Johnson, were at Cruise they learned that finding areas of weakness in the model data was often the problem that prevented it from getting into production. Aquarium aims to solve this issue.

“Aquarium is a machine learning data management system that helps people improve model performance by improving the data that it’s trained on, which is usually the most important part of making the model work in production,” Gao told me.

He says that they are seeing a lot of different models being built across a variety of industries, but teams are getting stuck because iterating on the data set and continually finding relevant data is a hard problem to solve. That’s why Aquarium’s founders decided to focus on this.

“It turns out that most of the improvement to your model, and most of the work that it takes to get it into production is about deciding, ‘Here’s what I need to go and collect next. Here’s what I need to go label. Here’s what I need to go and retrain my model on and analyze it for errors and repeat that iteration cycle,” Gao explained.

The idea is to get a model into production that outperforms humans. One customer Sterblue offers a good example. They provide drone inspection services for wind turbines. Their customers used to send out humans to inspect the turbines for damage, but with a set of drone data, they were able to train a machine learning model to find issues. Using Aquarium, they refined their model and improved accuracy by 13%, while cutting the cost of human reviews in half, Gao said.

Aquarium currently has 7 employees including the founders, of which three are women. Gao says that they are being diverse by design. He understands the issues of bias inherent in machine learning model creation, and creating a diverse team for this kind of tooling is one way to help mitigate that bias.

The company launched last February and spent part of the year participating in the Y Combinator Summer 2020 cohort. They worked on refining the product throughout 2020, and recently opened it up from beta to generally available.

Virtual events platform Hopin is hopin’ for a mega valuation.

According to multiple sources who spoke with TechCrunch, the company, which was founded in mid-2019, is running around the fundraise circuit and perhaps nearing the end of a fundraise in which it is looking to raise roughly $400 million at a pre-money valuation of $5 billion for its Series C. The two names out in front, likely part of a joint ticket, are thought to be Andreessen Horowitz and General Catalyst.

Two sources implied that the valuation could have gone as high as $6 billion, but with greater dilution based on some offered terms the company has received. The deal is in flux, and both the round size and valuation are subject to change.

One source told TechCrunch that the company’s ARR has grown to $60 million, implying a valuation multiple of 80-100x if the valuation we’re hearing pans out. That sort of multiple wouldn’t be out of line with other major fundraises for star companies with SaaS-based business models.

Hopin has been on a fundraise tear in recent months. The company raised $125 million at a $2.125 billion valuation late last year for its Series B, which came just a few months after it raised a Series A of $40 million over the summer and a $6.5 million seed round last winter. All told, the roughly 20-month-old company has raised a known $171.4 million in VC according to Crunchbase.

When we last reported on the company, Hopin’s ARR had gone from $0 to $20 million, while its overall userbase had grown from essentially zero to 3.5 million users in November. The company reported then that it had 50,000 groups using its platform.

Hopin’s platform is designed to translate the in-person events experience into a virtual one, providing tools to recreate the experience of walking exhibition floors, networking one-on-one and spontaneously joining fireside chats and panels. It’s become a darling in the midst of the COVID-19 pandemic, which has seen most business and educational conferences canceled in the midst of mass restrictions on domestic and international travel worldwide.

It’s probably also useful to note that our business team uses Hopin to run all of TechCrunch’s editorial events, including Disrupt, Early Stage, Extra Crunch Live and next week’s TechCrunch Sessions: Justice 2021 event (these software selections and their costs are — thankfully — outside the purview of our editorial team).

Hopin may be the mega-leader of the virtual events space right now, but it isn’t the only startup trying to take on this suddenly vital industry. Run The World raised capital last year, Welcome wants to be the ‘Ritz-Carlton for event platforms,’ Spotify is getting into the business, Clubhouse is arguably a contender here, InEvent raised a seed earlier this month and Hubilo is another entrant which nabbed a check from Lightspeed a few months ago. Plus, quite literally dozens of other startups have either started in the space or are pivoting toward it.

We have reached out to Hopin for comment.

Post updated to report that Andreessen Horowitz and General Catalyst are in the lead.

Many companies spend a significant amount of money and resources processing data from logs, traces and metrics, forcing them to make trade-offs about how much to collect and store. Hydrolix, an early stage startup, announced a $10 million seed round today to help tackle logging at scale, while using unique technology to lower the cost of storing and querying this data.

Wing Venture Capital led the round with help from AV8 Ventures, Oregon Venture Fund and Silicon Valley Data Capital.

Company CEO and co-founder Marty Kagan noted that in his previous roles, he saw organizations with tons of data in logs, metrics and traces that could be valuable to various parts of the company, but most organizations couldn’t afford the high cost to maintain these records for very long due to the incredible volume of data involved. He started Hydrolix because he wanted to change the economics to make it easier to store and query this valuable data.

“The classic problem with these cluster-based databases is that they’ve got locally attached storage. So as the data set gets larger, you have no choice but to either spend a ton of money to grow your cluster or separate your hot and cold data to keep your costs under control,” Kagan told me.

What’s more, he says that when it comes to querying, the solutions out there like BigQuery and Snowflake are not well suited for this kind of data. “They rely really heavily on caching and bulk column scans, so they’re not really useful for […] these infrastructure plays where you want to do live stream ingest, and you want to be able to do ad hoc data exploration,” he said.

Hydrolix wanted to create a more cost-effective way of storing and querying log data, while solving these issues with other tooling. “So we built a new storage layer which delivers […] SSD-like performance using nothing but cloud storage and diskless spot instances,” Kagan explained. He says that this means that there is no caching or column scales, enabling them to do index searches. “You’re getting the low cost, unlimited retention benefits of cloud storage, but with the interactive performance of fully indexed search,” he added.

Peter Wagner, founding partner at investor Wing Venture Capital, says that the beauty of this tool is that it eliminates tradeoffs, while lowering customers overall data processing costs. “The Hydrolix team has built a real-time data platform optimized not only to deliver superior performance at a fraction of the cost of current analytics solutions, but one architected to offer those same advantages as data volumes grow by orders of magnitude,” Wagner said in a statement.

It’s worth pointing out that in the past couple of weeks SentinelOne bought high speed logging platform Scalyr for $155 million, then CrowdStrike grabbed Humio, another high speed logging tool for $400 million, so this category is getting attention.

The product is currently compatible with AWS and offered through the Amazon Marketplace, but Kagan says they are working on versions for Azure and Google Cloud and expect to have those available later this year. The company was founded at the end of 2018 and currently has 20 employees spread out over six countries with headquarters in Portland, Oregon.

Engineering teams face steep challenges when it comes to staying on schedule, and keeping to those schedules can have an impact on the entire organization. Acumen, an Israeli engineering operations startup, announced a $7 million seed investment today to help tackle this problem.

Hetz, 10D, Crescendo and Jibe participated in the round, designed to give the startup the funding to continue building out the product and bring it to market. The company, which has been working with beta customers for almost a year, also announced it was emerging from stealth today.

As an experienced startup founder, Acumen CEO and co-founder Nevo Alva has seen engineering teams struggle as they grow due to a lack of data and insight into how the teams are performing. He and his co-founders launched Acumen to give companies that missing visibility.

“As engineering teams scale, they face challenges due to a lack of visibility into what’s going on in the team. Suddenly prioritizing our tasks becomes much harder. We experience interdependencies [that have an impact on the schedule] every day,” Alva explained.

He says this manifests itself in a decrease in productivity and velocity and ultimately missed deadlines that have an impact across the whole company. What Acumen does is collect data from a variety of planning and communications tools that the engineering teams are using to organize their various projects. It then uses machine learning to identify potential problems that could have an impact on the schedule and presents this information in a customizable dashboard.

The tool is aimed at engineering team leaders, who are charged with getting their various projects completed on time with the goal of helping them understand possible bottlenecks. The software’s machine learning algorithms will learn over time which situations cause problems, and offer suggestions on how to prevent them from becoming major issues.

The company was founded in July 2019 and the founders spent the first 10 months working with a dozen design partners building out the first version of the product, making sure it could pass muster with various standards bodies like SOC-2. It has been in closed private beta since last year and is launching publicly this week.

Acumen currently has 20 employees with plans to add 10 more by the end of this year. After working remotely for most of 2020, Alva says that location is no longer really important when it comes to hiring. “It definitely becomes less and less important where they are. I think time zones are still a consideration when speaking of remote,” he said. In fact, they have people in Israel, the U.S. and eastern Europe at the moment among their 20 employees.

He recognizes that employees can feel isolated working alone, so the company has video meetings every day during which they spend the first part just chatting about non-work stuff as a way to stay connected. Starting today, Acumen will begin its go to market effort in earnest. While Alva recognizes there are competing products out there like Harness and Pinpoint, he thinks his company’s use of data and machine learning really helps differentiate it.

The concept of the “marketing cloud” — sold by the likes of Salesforce, Oracle and Adobe — has become a standard way for large tech companies to package together and sell marketing tools to businesses that want to improve how they use digital channels to grow their business.

Some argue, however, that “cloud”, singular, might be a misnomer: typically those tools are not integrated well with each other and effectively are run as separate pieces of software. Today a startup called Blueshift — which claims to offer an end-to-end marketing stack, by having built it from the ground up to include both traditional marketing data as well as customer experience — is announcing some funding, pointing to the opportunity to build more efficient alternatives.

The startup has closed a round of $30 million, a Series C that co-founder and CEO Vijay Chittoor said it will be using to expand to more markets (it’s most active in the U.S. and Europe currently) and also to expand its technology.

“The product already has a unified format, to ingest data from multiple sources and redistribute that out to apps. Now, we want to distribute that data to more last-mile applications,” he said in an interview. “Our biggest initiative is to scale out the notion of us being not just an app but a platform.”

The company’s customers include LendingTree, Discovery Inc., Udacity, BBC and Groupon, and it has seen revenue growth of 858% in the last three years, although it’s not disclosing actual revenues, nor valuation, today.

The round is being led by Fort Ross Ventures, with strong participation also from Avatar Growth Capital. Past investors Softbank Ventures Asia (which led its last round of $15 million), Storm Ventures, Conductive Ventures and Nexus Venture Partners also invested.

The concept for Blueshift came out of Chittoor’s direct experience at Groupon — which acquired his previous startup, social e-commerce company Mertado — and before that a long period at Walmart Labs — which Walmart rebranded after it acquired another startup where Chittoor was an early employee, semantic search company Kosmix.

“The challenges we are solving today we saw firsthand as challenges our customers saw at Groupon and Walmart,” he said. “The connected customer journey is creating a thousand times more data than before, and people and brands are engaging across more touchpoints. Tracking that has become harder with legacy channel-centric applications.”

Blueshift’s approach for solving that has been, he said, “to unify the data and to make decisions at customer level.”

That is to say, although the customer experience today is very fragmented — you might potentially encounter something about a company or brand in multiple places, such as in a physical environment, on various social media platforms, in your email, through a web search, in a vertical search portal, in a marketplace on a site, in an app, and so on — the experience for marketers should not be.

The company addresses this by way of a customer data platform (CDP) it markets as “SmartHub.” Designed for non-technical users although customizable by engineers if you need it to be, users can integrate different data feeds from multiple sources, which then Blueshift crunches and organises to let you view in a more structured way.

That data can then be used to power actions in a number of places where you might be setting up marketing campaigns. And Chittoor pointed out — like other marketing people have — that these days, the focus on that is largely first-party data to fuel that machine, rather than buying in data from third-party sources (which is definitely part of a bigger trend).

“Our mission is to back category-leading companies that are poised to dominate a market. Blueshift clearly stood out to us as the leader in the enterprise CDP space,” said Ratan Singh of Fort Ross Ventures in a statement. “We are thrilled to partner with the Blueshift team as they accelerate the adoption of their SmartHub CDP platform.” Singh is joining Blueshift’s board with this round.

Today we have the pleasure of talking with Tugboat CEO Matt Westgate.

www.talkingdrupal.com/283

Topics

• Brian – Gatsby and Bowsers Fury
• Stephen – John’s presentation
• John – Mailhog
• Nic – Clients in TX and OR
• Matt – Aruba Wifi
• What is Tugboat
• Origin story
• Pricing tiers
• SimplyTest.me
• FEDRamp
• Employee owned business
• Feature roadmap
• Where did the name come from

Resources

John’s session on Config Split

MailHog Helpful Links

• https://github.com/mailhog/MailHog
• https://docs.lando.dev/config/mailhog.html#supported-versions
• https://www.drupaleasy.com/blogs/ultimike/2018/01/testing-local-drupal-site-emails-lando-and-mailhog

Aruba instant on

Super Mario 3D World + Bowser’s Fury

Drupal Contrib

Drupal Providence

Linux for Everyone

Hosts

Stephen Cross – www.stephencross.com @stephencross

John Picozzi – www.oomphinc.com @johnpicozzi

Nic Laflin – www.nLighteneddevelopment.com @nicxvan

Brian Perry – @bricomedy   Brianperry.dev

Guest

Matt Westgate  @mettamatt  www.tugboat.qa

 

 

Slack took the workplace communications landscape by storm after it launched its integration-friendly, GIF-tastic chat platform in 2013. Within the space of a decade it entered into the pantheon of big tech: first with massive growth and usage, then a series of giant VC rounds and valuations, spawning controversial competition from incumbents, followed by a public listing and ultimately a $27.7 billion acquisition by Salesforce. Now that the cycle is complete, the decks are clear for a Slack disruptor!

Today, a new app quietly launched out of stealth called Quill, available by way of apps for the web, MacOS, Windows, Linux, Android and iOS.

Like Slack, Quill is a messaging app for co-workers to update each other on what they are doing, have conversations about projects and more.

Unlike Slack — the implication seems to be — the difference is that Quill is about delivering messaging in a non-distracting way that doesn’t take up too much of your time, your concentration, and your energy. Quill bills itself as “messaging for people that focus.”

So while you get a lot of the same features you have in Slack for chatting with workers, creating channels, integrating other apps, and having video and voice conversations — one of my colleagues quipped, “It looks like Slack, but more colorful!” — it also includes a bunch of features that put the focus on, well, focus.

“We grew exhausted having to skim thousands of messages every day to keep up, so we built a way to chat that’s even better than how we already communicate in person,” Quill notes on its website. “A more deliberate way to chat. That’s what Quill is all about.”

For example, “structured channels” let you enforce threads in a channel for different conversations rather than view chatter in a waterfall. Automatic sorting in the app moves up active conversations you’re in above others. Limitations on notifications mean you can have more nuance in what ultimately might end up distracting you, and senders for example can alter a setting (with a !!) to notify you if something is critical and needs to ping you. Video chats come automatically with a sidebar to continue texting, too.

Then, you get separate channels for social and non-work chat; and a series of features that let you manipulate conversations after they’ve already started: you can recast conversations into threads after they’ve already started and you have a fast way to reply to messages. There is an easier and more obvious way to pin important things to the tops of channels; and in addition to creating new threads after a conversation starts, you can also move messages from one channel or thread to another.

You can also interact with Quill chats using SMS and email, and like Slack, it offers the ability to integrate other app notifications into the process.

It’s also working on adding a Clubhouse-like feature for voice channels, end-to-end encryption, context-based search (it already has keyword search), and user profiles.

Managing “high load”

The app has been in stealth mode for nearly three years, and while some projects might never go noticed in that time, this one is a little different because of the pedigree and the context.

For starters, Quill was founded by the former creative director of Stripe, Ludwig Pettersson, who was given a lot of the credit for the simplicity and focus of the payment company’s flagship product and platform (simplicity that became the hallmark of the service and helped it balloon into a commerce behemoth).

His involvement signaled that the effort might get at least a little attention. In a landscape that seemed to be all but dominated by Slack and a few huge, well-funded rivals in the form of Microsoft and Facebook, it’s notable that when Quill was just an idea, it had already picked up $2 million in seed funding, from Sam Altman (at the time the head of Y Combinator) and General Catalyst.

Following that it raised a Series A of $12.5 million led by Sarah Cannon of Index Ventures, totaling some $14.5 million in funding in all. The Series A valued the company at $62.5 million, as we reported at the time.

Added to this is the story behind Quill and what brought Pettersson and others on his team to the idea of building it. From what we understand, the idea in its earliest inception was to capture something of the magic of communication that you get from messaging apps, and specifically from workplace communication tools like Slack, but without the distraction and resulting frustration that often come along with them.

By 2018, Slack was already a big product, valued at over $7 billion and attracting millions of users. But there was also a growing number of people criticizing it for being the opposite of productive. “It’s hard to track everything that’s going on in Slack, it can be distracting. Given the network effect, Slack has become powerful, but it was not designed as a high-load system,” Sam Altman, the investor and former head of both Y-Combinator and OpenAI, said to me back in 2018 when I asked him what he knew about Quill after I first got wind of it.

He said he was “super impressed” by Ludwig’s work at Stripe, and then OpenAI (where he stayed for a year after leaving Stripe), so much so that when Ludwig suggested building “a better version of Slack,” it seemed like a “credible idea” and one worth backing even without a product yet to be built.

It’s quite fitting that for an app focused on focus, Quill launched today quietly and without much fanfare: why worry about PR distraction when you can just get something out there?

In any case, we’re hoping to hear more and see what kind of momentum it picks up. We’ve asked Index if we can talk to Sarah Cannon about the investment, and we are still waiting to hear back. We are also trying to see if we can talk to Pettersson. But I should mention we have been trying to talk to him since first getting wind of this app back in August of 2018, so we’re not holding our breath (nor this story).