GitHub has unveiled a new product that leverages artificial intelligence to help you write code more efficiently. Named GitHub Copilot, today’s new product can suggest lines of code and even sometimes entire functions.

GitHub has partnered with OpenAI to develop this tool. It doesn’t replace developers, it’s just a tool that should improve productivity and make it easier to learn how to code. GitHub frames this new tool as an AI pair programmer.

The model behind GitHub Copilot has been trained on billions of lines of code — many of them are hosted and available publicly on GitHub itself. When you’re writing code, GitHub Copilot suggests code as you type. You can cycle through suggestions, accept or reject them.

In order to figure out what you’re currently coding, GitHub Copilot tries to parse the meaning of a comment, the name of the function you are writing or the past couple of lines. The company shows a few demos on its website.

In particular, you can describe a function in plain English in a comment and then convert it to actual code. If you’re getting started with a new language or you’ve been using no-code or low-code tools in the past, that feature could be useful.

If you’re writing code every day, GitHub Copilot can be used to work with a new framework or library. You don’t have to read the documentation from start to finish as GitHub Copilot already knows the specific functions and features of the framework you’re working with. It could also replace many Stack Overflow queries.

GitHub Copilot integrates directly with Visual Studio Code. You can install it as an extension or use it in the cloud with GitHub Codespaces. Over time, the service should improve based on how you interact with GitHub Copilot. As you accept and reject suggestions, those suggestions should get better.

Currently available as a technical preview, GitHub plans to launch a commercial product based on GitHub Copilot. It currently works best with Python, JavaScript, TypeScript, Ruby and Go.

Seattle-based Edge Delta, a startup that is building a modern distributed monitoring stack that is competing directly with industry heavyweights like Splunk, New Relic and Datadog, today announced that it has raised a $15 million Series A funding round led by Menlo Ventures and Tim Tully, the former CTO of Splunk. Previous investors MaC Venture Capital and Amity Ventures also participated in this round, which brings the company’s total funding to date to $18 million.

“Our thesis is that there’s no way that enterprises today can continue to analyze all their data in real time,” said Edge Delta co-founder and CEO Ozan Unlu, who has worked in the observability space for about 15 years already (including at Microsoft and Sumo Logic). “The way that it was traditionally done with these primitive, centralized models — there’s just too much data. It worked 10 years ago, but gigabytes turned into terabytes and now terabytes are turning into petabytes. That whole model is breaking down.”

He acknowledges that traditional big data warehousing works quite well for business intelligence and analytics use cases. But that’s not real-time and also involves moving a lot of data from where it’s generated to a centralized warehouse. The promise of Edge Delta is that it can offer all of the capabilities of this centralized model by allowing enterprises to start to analyze their logs, metrics, traces and other telemetry right at the source. This, in turn, also allows them to get visibility into all of the data that’s generated there, instead of many of today’s systems, which only provide insights into a small slice of this information.

While competing services tend to have agents that run on a customer’s machine, but typically only compress the data, encrypt it and then send it on to its final destination, Edge Delta’s agent starts analyzing the data right at the local level. With that, if you want to, for example, graph error rates from your Kubernetes cluster, you wouldn’t have to gather all of this data and send it off to your data warehouse where it has to be indexed before it can be analyzed and graphed.

With Edge Delta, you could instead have every single node draw its own graph, which Edge Delta can then combine later on. With this, Edge Delta argues, its agent is able to offer significant performance benefits, often by orders of magnitude. This also allows businesses to run their machine learning models at the edge, as well.

“What I saw before I was leaving Splunk was that people were sort of being choosy about where they put workloads for a variety of reasons, including cost control,” said Menlo Ventures’ Tim Tully, who joined the firm only a couple of months ago. “So this idea that you can move some of the compute down to the edge and lower latency and do machine learning at the edge in a distributed way was incredibly fascinating to me.”

Edge Delta is able to offer a significantly cheaper service, in large part because it doesn’t have to run a lot of compute and manage huge storage pools itself since a lot of that is handled at the edge. And while the customers obviously still incur some overhead to provision this compute power, it’s still significantly less than what they would be paying for a comparable service. The company argues that it typically sees about a 90 percent improvement in total cost of ownership compared to traditional centralized services.

Edge Delta charges based on volume and it is not shy to compare its prices with Splunk’s and does so right on its pricing calculator. Indeed, in talking to Tully and Unlu, Splunk was clearly on everybody’s mind.

“There’s kind of this concept of unbundling of Splunk,” Unlu said. “You have Snowflake and the data warehouse solutions coming in from one side, and they’re saying, ‘hey, if you don’t care about real time, go use us.’ And then we’re the other half of the equation, which is: actually there’s a lot of real-time operational use cases and this model is actually better for those massive stream processing datasets that you required to analyze in real time.”

But despite this competition, Edge Delta can still integrate with Splunk and similar services. Users can still take their data, ingest it through Edge Delta and then pass it on to the likes of Sumo Logic, Splunk, AWS’s S3 and other solutions.

“If you follow the trajectory of Splunk, we had this whole idea of building this business around IoT and Splunk at the Edge — and we never really quite got there,” Tully said. “I think what we’re winding up seeing collectively is the edge actually means something a little bit different. […] The advances in distributed computing and sophistication of hardware at the edge allows these types of problems to be solved at a lower cost and lower latency.”

The Edge Delta team plans to use the new funding to expand its team and support all of the new customers that have shown interest in the product. For that, it is building out its go-to-market and marketing teams, as well as its customer success and support teams.

 

Diversity and inclusion have become central topics in the world of work. In the best considerations, improving them is a holistic effort, involving not just conceiving of products with this in mind, but hiring and managing talent in a diverse and inclusive way, too. A new startup called Pequity, which has built a product to help with the latter of these areas, specifically in equitable compensation, has now raised some funding — a sign of the demand in the market, as well as how tech is being harnessed in aid of helping it.

The San Francisco-based startup has raised $19 million in a Series A led by Norwest Venture Partners. First Round Capital, Designer Fund, and Scribble Ventures also participated in the fundraise, which will be used to continue investing in product and also hiring: the company has 20 on its own books now and will aim to double that by the end of this year, on the heels of positive reception in the market.

Since launching officially last year, Pequity has picked up over 100 customers, with an initial focus on fast-scaling companies in its own backyard, a mark of how D&I have come into focus in the tech industry in particular. Those using Pequity to compare and figure out compensation include Instacart, Scale.ai and ClearCo, and the company said that in the last four months, the platform’s been used to make more then 5,000 job offers.

Kaitlyn Knopp, the CEO who co-founded the company with Warren Lebovics (both pictured, right), came up for the idea for Pequity in much the same way that many innovations in the world of enterprise IT come to market: through her own first-hand experience.

She spent a decade working in employment compensation in the Bay Area, with previous roles at Google, Instacart, and Cruise. In that time, she found the tools that many companies used were lacking and simply “clunky” when it came to compensation analysis.

“The way the market has worked so far is that platforms had compensation as an element but not the focus,” she said. “It was the end of the tagline, the final part of a ‘CRM for candidates.’ But you still have to fill in all the gaps, you have to set the architecture the right way. And with compensation, you have to bake in your own analytics, which implies that you have to have some expertise.”

Indeed, as with other aspects of enterprise software, she added that the very biggest tech companies sometimes worked on their own tools, but not only does that leave smaller or otherwise other-focused businesses out of having better calculation tools, but it also means that those tools are siloed and miss out on being shaped by a bigger picture of the world of work. “We wanted to take that process and own it.”

The Pequity product essentially works by plugging into all of the other tools that an HR professional might be using — HRIS, ATS, and payroll products — to manage salaries across the whole of the organization in order to analyse and compare how compensation could look for existing and prospective employees. It combines a company’s own data and then compares it to data from the wider market, including typical industry ranges and market trends, to provide insights to HR teams.

All of this means that HR teams are able to make more informed decisions, which is step number one in being more transparent and equitable, but is also something that Pequity is optimized to cover specifically in how it measures compensation across a team.

And in line with that, there is another aspect of the compensation mindset that Knopp also wanted to address in a standalone product, and that is the idea of building a tool with a mission, one of providing a platform that can bring in data to make transparent and equitable decisions.

“A lot of the comp tools that I’ve interacted with are reactive,” she said. “You may have to do, say, a pay equity test, you do your promotion and merit cycles, and then you find all these issues that you have to solve. We’re flagging those things proactively with our analytics, because we’re plugging into those systems, which will give you those alerts before the decisions need to be made.”

As an added step in that direction, Knopp said that ultimately she believes the tool should be something that those outside of HR, such as managers and emploiyees themselves, should be able to access to better understand the logic of their own compensation and have more information going into any kind of negotiation.

Ultimately, it will be interesting to see whether modernized products like Pequity, which are tackling old problems with a new approach and point of view, find traction in the wider market. If one purpose in HR is to address diversity and inclusion, and part of the problem has been that the tools are just not fit for that purpose, then it seems a no-brainer that we’ll see more organizations trying out new things to see if they can help them in their own race to secure talent.

“Compensation reflects a company’s values, affects its ability to hire talent, and is the biggest expense on its P&L. And yet, most comp teams run on spreadsheets and emails,” said Parker Barrile, Partner at Norwest, in a statement. “Pequity empowers comp teams to design and manage equitable compensation programs with modern software designed by comp professionals, for comp professionals.”

 

Time is your most valuable asset — as the saying goes — and today a startup called Memory.ai, which is building AI-based productivity tools to help you with your own time management, is announcing some funding to double down on its ambitions: It wants not only to help manage your time, but to, essentially, provide ways to use it better in the future.

The startup, based out of Oslo, Norway, initially made its name with an app called Timely, a tool for people to track time spent doing different tasks. Aimed not just at people who are quantified self geeks, but those who need to track time for practical reasons, such as consultants or others who work on the concept of billable hours. Timely has racked up 500,000 users since 2014, including more than 5,000 paying businesses in 160 countries.

Now, Memory.ai has raised $14 million as it gears up to launch its next apps, Dewo (pronounced “De-Voh”), an app that is meant to help people do more “deep work” by learning about what they are working on and filtering out distractions to focus better; and Glue, described as a knowledge hub to help in the creative process. Both are due to be released later in the year.

The funding is being led by local investors Melesio and Sanden, with participation from Investinor, Concentric and SNÖ Ventures, who backed Memory.ai previously.

“Productivity apps” has always been something of a nebulous category in the world of connected work. They can variously cover any kind of collaboration management software ranging from Asana and Jira through to Slack and Notion; or software that makes doing an existing work task more efficient than you did it before (e.g. Microsoft has described all of what goes into Microsoft 365 — Excel, Word, PowerPoint, etc. — as “productivity apps”); or, yes, apps like those from Memory.ai that aim to improve your concentration or time management.

These days, however, it feels like the worlds of AI and advances in mobile computing are increasingly coming together to evolve that concept once again.

If the first wave of smartphone communications and the apps that are run on smartphone devices — social, gaming, productivity, media, information, etc. — have led to us getting pinged by a huge amount of data from lots of different places, all of the time, then could it be that the second wave is quite possibly going to usher in a newer wave of tools to handle all that better, built on the premise that not everything is of equal importance? No-mo FOMO? We’ll see.

In any case, some bigger platform players also helping to push the agenda of what productivity means in this day and age.

For example, in Apple’s recent preview of iOS 15 (due to come out later this year) the company gave a supercharge to its existing “do not disturb” feature on its phones, where it showed off a new Focus mode, letting users customize how and when they want to receive notifications from which apps, and even which apps they want to have displayed, all organized by different times of day (e.g. work time), place, calendar items and so on.

“Today, iPhone plays so many roles in our lives. It’s where we get information, how people reach us, and where we get things done. This is great, but it means our attention is being pulled in so many different directions and finding that balance between work and life can be tricky,” said Apple’s Craig Federighi in the WWDC keynote earlier this month. “We want to free up space to focus and help you be in the moment.” How well that gets used, and how much other platforms like Google follow suit, will be interesting to see play out. It feels, in any case, like it could be the start of something.

And, serendipitously — or maybe because this is some kind of zeitgeist — this is also playing into what Memory.ai has built and is building. 

Mathias Mikkelsen, the Oslo-based founder of Memory.ai, first came up with his idea for Timely (which had also been the original name of the whole startup) when he was working as a designer in the ad industry, one of those jobs that needed to track what he was working on, and for how long, in order to get paid.

He said he knew the whole system as it existed was inefficient: “I just thought it was insane how cumbersome and old it was. But at the same time how important it was for the task,” he said.

The guy had an entrepreneurial itch that he was keen to scratch, and this idea would become the salve to help him. Mikkelsen was so taken with building a startup around time management, that he sold his apartment in Oslo and moved himself to San Francisco to be where he believed was the epicenter of startup innovation. He tells me he lived off the proceeds of his flat for two years “in a closet” in a hacker house, bootstrapping Timely, until eventually getting into an accelerator (500 Startups) and subsequently starting to raise money. He eventually moved back to Oslo after two years to continue growing the business, as well as to live somewhere a little more spacious.

The startup’s big technical breakthrough with Timely was to figure out an efficient way of tracking time for different tasks, not just time worked on anything, without people having to go through a lot of data entry.

The solution: to integrate with a person’s computer, plus a basic to-do schedule for a day or week, and then match up which files are open when to determine how long one works for one client or another. Phone or messaging conversations, for the moment, are not included, and neither are the contents of documents — just the titles of them. Nor is data coming from wearable devices, although you could see how that, too, might prove useful.

The basic premise is to be personalised, so managers and others cannot use Timely to track exactly what people are doing, although they can track and bill for those billable hours. All this is important, as it also will feed into how Dewo and Glue will work.

The startup’s big conceptual breakthrough came around the same time: Getting time tracking or any productivity right “has never been a UI problem,” Mikkelsen said. “It’s a human nature problem.” This is where the AI comes in, to nudge people towards something they identify as important, and nudge them away from work that might not contribute to that. Tackling bigger issues beyond time are essential to improving productivity overall, which is why Memory.ai now wants to extend to apps for carving out time for deep thinking and creative thinking.

While it might seem to be a threat that a company like Apple has identified the same time management predicament that Memory.ai has, and is looking to solve that itself, Mikkelsen is not fazed. He said he thinks of Focus as not unlike Apple’s work on Health: there will be ways of feeding information into Apple’s tool to make it work better for the user, and so that will be Memory.ai’s opportunity to hopefully grow, not cannibalize, its own audience with Timely and its two new apps. It is, in a sense, a timely disruption.

“Memory’s proven software is already redefining how businesses around the world track, plan and manage their time. We look forward to working with the team to help new markets profit from the efficiencies, insights and transparency of a Memory-enabled workforce,” said Arild Engh, a partner at Melesio, in a statement.

Kjartan Rist, a partner at Concentric, added: “We continue to be impressed with Memory’s vision to build and launch best-in-class products for the global marketplace. The company is well on its way to becoming a world leader in workplace productivity and collaboration, particularly in light of the remote and hybrid working revolution of the last 12 months. We look forward to supporting Mathias and the team in this exciting new chapter.”

As enterprise startups continue to target interesting gaps in the market, we’re seeing increasingly sophisticated tools getting built for small and medium businesses — traditionally a tricky segment to sell to, too small for large enterprise tools, and too advanced in their needs for consumer products. In the latest development of that trend, an Israeli startup called DataRails has raised $25 million to continue building out a platform that lets SMBs use Excel to run financial planning and analytics like their larger counterparts.

The funding closes out the company’s Series A at $43.5 million, after the company initially raised $18.5 million in April (some at the time reported this as its Series A, but it seems the round had yet to be completed). The full round includes Zeev Ventures, Vertex Ventures Israel and Innovation Endeavors, with Vintage Investment Partners added in this most recent tranche. DataRails is not disclosing its valuation, except to note that it has doubled in the last four months, with hundreds of customers and on target to cross 1,000 this year, with a focus on the North American market. It has raised $55 million in total. 

The challenge that DataRails has identified is that on one hand, SMBs have started to adopt a lot more apps, including software delivered as a service, to help them manage their businesses — a trend that has been accelerated in the last year with the pandemic and the knock-on effect that has had for remote working and bringing more virtual elements to replace face-to-face interactions. Those apps can include Salesforce, NetSuite, Sage, SAP, QuickBooks, Zuora, Xero, ADP and more.

But on the other hand, those in the business who manage finances and financial reporting are lacking the tools to look at the data from these different apps in a holistic way. While Excel is a default application for many of them, they are simply reading lots of individual spreadsheets rather than integrated data analytics based on the numbers.

DataRails has built a platform that can read the reported information, which typically already lives in Excel spreadsheets, and automatically translate it into a bigger picture view of the company.

For SMEs, Excel is such a central piece of software, yet such a pain point for its lack of extensibility and function, that this predicament was actually the germination of starting DataRails in the first place,

Didi Gurfinkel, the CEO who co-founded the company with Eyal Cohen (the CPO) said that DataRails initially set out to create a more general-purpose product that could help analyze and visualize anything from Excel.

“We started the company with a vision to save the world from Excel spreadsheets,” he said, by taking them and helping to connect the data contained within them to a structured database. “The core of our technology knows how to take unstructured data and map that to a central database.” Before 2020, DataRails (which was founded in 2015) applied this to a variety of areas with a focus on banks, insurance companies, compliance and data integrity.

Over time, it could see a very specific application emerging, specifically for SMEs: providing a platform for FP&A (financial planning and analytics), which didn’t really have a solution to address it at the time. “So we enabled that to beat the market.”

“They’re already investing so much time and money in their software, but they still don’t have analytics and insight,” said Gurfinkel.

That turned out to be fortunate timing, since “digital transformation” and getting more out of one’s data was really starting to get traction in the world of business, specifically in the world of SMEs, and CFOs and other people who oversaw finances were already looking for something like this.

The typical DataRails customer might be as small as a business of 50 people, or as big as 1,000 employees, a size of business that is too small for enterprise solutions, “which can cost tens of thousands of dollars to implement and use,” added Cohen, among other challenges. But as with so many of the apps that are being built today to address those using Excel, the idea with DataRails is low-code or even more specifically no-code, which means “no IT in the loop,” he said.

“That’s why we are so successful,” he said. “We are crossing the barrier and making our solution easy to use.”

The company doesn’t have a huge number of competitors today, either, although companies like Cube (which also recently raised some money) are among them. And others like Stripe, while currently not focusing on FP&A, have most definitely been expanding the tools that it is providing to businesses as part of their bigger play to manage payments and subsequently other processes related to financial activity, so perhaps it, or others like it, might at some point become competitors in this space as well.

In the meantime, Gurfinkel said that other areas that DataRails is likely to expand to cover alongside FP&A include HR, inventory and “planning for anything,” any process that you have running in Excel. Another interesting turn would be how and if DataRails decides to look beyond Excel at other spreadsheets, or bypass spreadsheets altogether.

The scope of the opportunity — in the U.S. alone there are more than 30 million small businesses — is what’s attracting the investment here.

“We’re thrilled to reinvest in DataRails and continue working with the team to help them navigate their recent explosive and rapid growth,” said Yanai Oron, general partner at Vertex Ventures, in a statement. “With innovative yet accessible technology and a tremendous untapped market opportunity, DataRails is primed to scale and become the leading FP&A solution for SMEs everywhere.”

“Businesses are constantly about to start, in the midst of, or have just finished a round of financial reporting — it’s a never-ending cycle,” added Oren Zeev, founding partner at Zeev Ventures. “But with DataRails, FP&A can be simple, streamlined, and effective, and that’s a vision we’ll back again and again.”