Personal blog of Yzmir Ramirez

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.

Image Credits: GitHub

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.

Image Credits: GitHub

Iterative, an open-source startup that is building an enterprise AI platform to help companies operationalize their models, today announced that it has raised a $20 million Series A round led by 468 Capital and Mesosphere co-founder Florian Leibert. Previous investors True Ventures and Afore Capital also participated in this round, which brings the company’s total funding to $25 million.

The core idea behind Iterative is to provide data scientists and data engineers with a platform that closely resembles a modern GitOps-driven development stack.

After spending time in academia, Iterative co-founder and CEO Dmitry Petrov joined Microsoft as a data scientist on the Bing team in 2013. He noted that the industry has changed quite a bit since then. While early on, the questions were about how to build machine learning models, today the problem is how to build predictable processes around machine learning, especially in large organizations with sizable teams. “How can we make the team productive, not the person? This is a new challenge for the entire industry,” he said.

Big companies (like Microsoft) were able to build their own proprietary tooling and processes to build their AI operations, Petrov noted, but that’s not an option for smaller companies.

Currently, Iterative’s stack consists of a couple of different components that sit on top of tools like GitLab and GitHub. These include DVC for running experiments and data and model versioning, CML, the company’s CI/CD platform for machine learning, and the company’s newest product, Studio, its SaaS platform for enabling collaboration between teams. Instead of reinventing the wheel, Iterative essentially provides data scientists who already use GitHub or GitLab to collaborate on their source code with a tool like DVC Studio that extends this to help them collaborate on data and metrics, too.

Image Credits: Iterative

“DVC Studio enables machine learning developers to run hundreds of experiments with full transparency, giving other developers in the organization the ability to collaborate fully in the process,” said Petrov. “The funding today will help us bring more innovative products and services into our ecosystem.”

Petrov stressed that he wants to build an ecosystem of tools, not a monolithic platform. When the company closed this current funding round about three months ago, Iterative had about 30 employees, many of whom were previously active in the open-source community around its projects. Today, that number is already closer to 60.

“Data, ML and AI are becoming an essential part of the industry and IT infrastructure,” said Leibert, general partner at 468 Capital. “Companies with great open-source adoption and bottom-up market strategy, like Iterative, are going to define the standards for AI tools and processes around building ML models.”

With more than 200,000 customers, a market cap of nearly $56 billion, and the recent acquisition of Segment for $3.2 billion, Twilio is a SaaS behemoth.

It’s hard to imagine companies like Twilio as anything but a giant. But everybody starts out small, and you can usually trace success back to key decisions made in the early days.

For Twilio, a big differentiator was being one of the first technology-focused SaaS organizations that focused on empowering and building for the end user (which in their case is developers) with a self-service function. Another differentiator was, the executive team designed the organization to create tight feedback loops between sales and product with national roadshows, during which CEO Jeff Lawson frequently met with users.

Moreover, Twilio’s “secret sauce” per their S-1 is a developer-focused model and a strong belief in the future of software. They encourage developers to explore and innovate with Twilio’s flexible offering, which led to an incredible 155% net-dollar expansion rate at the time of the IPO.

Most importantly, Twilio put the product in the hands of teams before the sale happened, standing by to answer hard questions about how Twilio would fit into their infrastructure. This was pretty rare at the time — sales engineering resources aren’t cheap — and it was a strong differentiating factor. So much so that when the company went public, they were growing at 106% annually.

Twilio sells to developers at large enterprises by solving a problem that developers come up against regularly: Getting in touch with customers.

But as more successful public software companies emerge, it’s clear that Twilio’s secret sauce can and will be replicated.

Why traditional marketing doesn’t work on developers

1. Don’t hire marketing (or sales, either). If you do, hire someone super experienced from an enterprise sales background. And then fire them within three to six months.
2. Just hire someone who’s passionate about the product to “manage the community.” What is community management? Lots of swag. Cool meetups. Publish 1–2 articles as a stab at content (bonus points if they’re listicles). Oh, wait. How can we show the ROI here? Make the community manager do that until she quits. Repeat.

Israeli security startup Cycode, which specializes in helping enterprises secure their DevOps pipelines and prevent code tampering, today announced that it has raised a $20 million Series A funding round led by Insight Partners. Seed investor YL Ventures also participated in this round, which brings the total funding in the company to $24.6 million.

Cycode’s focus was squarely on securing source code in its early days, but thanks to the advent of infrastructure as code (IaC), policies as code and similar processes, it has expanded its scope. In this context, it’s worth noting that Cycode’s tools are language and use case agnostic. To its tools, code is code.

“This ‘everything as code’ notion creates an opportunity because the code repositories, they become a single source of truth of what the operation should look like and how everything should function, Cycode CTO and co-founder Ronen Slavin told me. “So if we look at that and we understand it — the next phase is to verify this is indeed what’s happening, and then whenever something deviates from it, it’s probably something that you should look at and investigate.”

Cycode Dashboard. Image Credits: Cycode

The company’s service already provides the tools for managing code governance, leak detection, secret detection and access management. Recently it added its features for securing code that defines a business’ infrastructure; looking ahead, the team plans to add features like drift detection, integrity monitoring and alert prioritization.

“Cycode is here to protect the entire CI/CD pipeline — the development infrastructure — from end to end, from code to cloud,” Cycode CEO and co-founder Lior Levy told me.

“If we look at the landscape today, we can say that existing solutions in the market are kind of siloed, just like the DevOps stages used to be,” Levy explained. “They don’t really see the bigger picture, they don’t look at the pipeline from a holistic perspective. Essentially, this is causing them to generate thousands of alerts, which amplifies the problem even further, because not only don’t you get a holistic view, but also the noise level that comes from those thousands of alerts causes a lot of valuable time to get wasted on chasing down some irrelevant issues.”

What Cycode wants to do then is to break down these silos and integrate the relevant data from across a company’s CI/CD infrastructure, starting with the source code itself, which ideally allows the company to anticipate issues early on in the software life cycle. To do so, Cycode can pull in data from services like GitHub, GitLab, Bitbucket and Jenkins (among others) and scan it for security issues. Later this year, the company plans to integrate data from third-party security tools like Snyk and Checkmarx as well.

“The problem of protecting CI/CD tools like GitHub, Jenkins and AWS is a gap for virtually every enterprise,” said Jon Rosenbaum, principal at Insight Partners, who will join Cycode’s board of directors. “Cycode secures CI/CD pipelines in an elegant, developer-centric manner. This positions the company to be a leader within the new breed of application security companies — those that are rapidly expanding the market with solutions which secure every release without sacrificing velocity.”

The company plans to use the new funding to accelerate its R&D efforts, and expand its sales and marketing teams. Levy and Slavin expect that the company will grow to about 65 employees this year, spread between the development team in Israel and its sales and marketing operations in the U.S.

Microsoft today announced Power Fx, a new low-code language that takes its cues from Excel formulas. Power Fx will become the standard for writing logic customization across Microsoft’s own low-code Power Platform, but since the company is open-sourcing the language, Microsoft also hopes that others will implement it as well and that it will become the de facto standard for these kinds of use cases.

Since Power Platform itself targets business users more so than professional developers, it feels like a smart move to leverage their existing knowledge of Excel and their familiarity with Excel formulas to get started.

“We have this long history of programming languages and something really interesting happened over the last 15 years, which is programming languages became free, they became open source and they became community-driven,” Charles Lamanna, the CVP of Power Platform engineering at Microsoft, told me. He noted that even internal languages like C#, TypeScript or Google’s Go are good examples for this.

“That’s been an ongoing trend. And what’s interesting is: that’s all for pro devs and coders. If we go back and look at the low-code/no-code space, there actually are programming languages, like the Excel programming language, or in every low-code/no-code platform has its own programming language. But those aren’t open, those aren’t portable, and those are community-driven,” Lamanna explained.

Microsoft says the language was developed by a team led by Vijay Mital, Robin Abraham, Shon Katzenberger and Darryl Rubin. Beyond Excel, the team also took inspiration from tools and languages like Pascal, Mathematica and Miranda, a functional programming language developed in the 1980s.

Microsoft plans to bring Power Fx to all of its low-code platforms, but given the focus on community, it’ll start making appearances in Power Automate, Power Virtual Agents and elsewhere soon.

But the team clearly hopes that others will adopt it as well. Low-code developers will see it pop up in the formula bars of products like Power Apps Studio, but more sophisticated users will also be able to use it to go to Visual Studio Code and build more complex applications with it.

As the team noted, it focused on not just making the language Excel-like but also having it behave like Excel — or like a REPL, for you high-code programmers out there. That means formulas are declarative and instantly recalculate as developers update their code.

Most low-code/no-code tools these days offer an escape hatch to allow users to either extend their apps with more sophisticated code or have their tool export the entire code base. Because at the end of the day, you can only take these tools so far. By default, they are built to support a wide range of scenarios, but since every company has its own way of doing things, they can’t cover every use case.

“We imagine that probably the majority of developers — and I say ‘developers’ as business users to coders that use Power Platform — will ultimately drop into writing these formulas in some form. The idea is that on that first day that you get started with Power Platform, we’re not going to write any formulas, right? […] It’s a macro recorder, it’s templates. Same thing for Power Apps: it’s pure visual, drag and drop, you don’t write a single formula. But what’s great about Power Platform, in week number two, when you’re using this thing, you learn a little bit more sophistication. You start to use a little bit more of the advanced capabilities. And before you know it, you actually have professionals who are Power Platform or low-code developers because they’re able to go down that spectrum of capability.”

Early Stage is the premiere ‘how-to’ event for startup entrepreneurs and investors. You’ll hear firsthand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. We’ll cover every aspect of company-building: Fundraising, recruiting, sales, legal, PR, marketing and brand building. Each session also has audience participation built-in — there’s ample time included in each for audience questions and discussion.

Census, a startup that helps businesses sync their customer data from their data warehouses to their various business tools like Salesforce and Marketo, today announced that it has raised a $16 million Series A round led by Sequoia Capital. Other participants in this round include Andreessen Horowitz, which led the company’s $4.3 million seed round last year, as well as several notable angles, including Figma CEO Dylan Field, GitHub CTO Jason Warner, Notion COO Akshay Kothari and Rippling CEO Parker Conrad.

The company is part of a new crop of startups that are building on top of data warehouses. The general idea behind Census is to help businesses operationalize the data in their data warehouses, which was traditionally only used for analytics and reporting use cases. But as businesses realized that all the data they needed was already available in their data warehouses and that they could use that as a single source of truth without having to build additional integrations, an ecosystem of companies that operationalize this data started to form.

The company argues that the modern data stack, with data warehouses like Amazon Redshift, Google BigQuery and Snowflake at its core, offers all of the tools a business needs to extract and transform data (like Fivetran, dbt) and then visualize it (think Looker).

Tools like Census then essentially function as a new layer that sits between the data warehouse and the business tools that can help companies extract value from this data. With that, users can easily sync their product data into a marketing tool like Marketo or a CRM service like Salesforce, for example.

Image Credits: Census

“Three years ago, we were the first to ask, ‘Why are we relying on a clumsy tangle of wires connecting every app when everything we need is already in the warehouse? What if you could leverage your data team to drive operations?’ When the data warehouse is connected to the rest of the business, the possibilities are limitless,” Census explains in today’s announcement. “When we launched, our focus was enabling product-led companies like Figma, Canva, and Notion to drive better marketing, sales, and customer success. Along the way, our customers have pulled Census into more and more scenarios, like auto-prioritizing support tickets in Zendesk, automating invoices in Netsuite, or even integrating with HR systems.“

Census already integrates with dozens of different services and data tools and its customers include the likes of Clearbit, Figma, Fivetran, LogDNA, Loom and Notion.

Looking ahead, Census plans to use the new funding to launch new features like deeper data validation and a visual query experience. In addition, it also plans to launch code-based orchestration to make Census workflows versionable and make it easier to integrate them into an enterprise orchestration system.

Nobody likes dealing with taxes — until the system works in your favor. In many countries, startups can receive tax credits for their R&D work and related employee cost, but as with all things bureaucracy, that’s often a slow and onerous task. Boast.ai aims to make this process far easier, by using a mix of AI and tax experts. The company, which currently has about 1,000 customers, today announced that it has raised a $23 million Series A round led by Radian Capital.

Launched in 2012 by co-founders Alex Popa (CEO) and Lloyed Lobo (president), Boast focuses on helping companies — and especially startups — in the U.S. and Canada claim their R&D tax credits.

“Globally, over $200 billion has been given in R&D incentives to fund businesses, not only in the U.S. and Canada, but the U.K., Australia, France, New Zealand, Ireland give out these incentives,” Lobo explained. “But there’s huge red tape. It’s a cumbersome process. You got to dive in and figure out work that qualifies and what doesn’t. Then you’ve got to file it with your taxes. Then if the government audits you, it’s like a long, laborious process.”

Image Credits: Boast.ai

After working on a few other startup ideas, the co-founders decided to go all-in on Boast. And in the process of working on other ideas, they also realized that AI wasn’t going to be able to do it all, but that it was getting good enough to augment humans to make a complex process like dealing with R&D tax credits scalable.

“The way I think to bootstrap a company is three things,” Lobo explained. “One, customers are looking for an outcome. Get them that outcome in the fastest, cheapest way possible. Two, when you’re doing that, you may have to do a lot of manual work. Figure out what those manual touch points are and then build the workflow to automate that. And once you have those two things, then you’ll have enough data to start working on artificial intelligence and machine learning. Those are the key learnings that we learned the hard way.”

So after doing some of that manual work, Boast can now automatically pull in data using tech tools like JIRA and GitHub and a company’s financial tools like QuickBooks, Gusto and (soon) ADP. It then uses its algorithms to cluster this data, figure out how much time employees spend on projects that would qualify for a tax credit and automate the tax filing process. Throughout the process — and to interact with the government if necessary — the company keeps humans in the loop.

“So all our [customer success] team is engineers,” Lobo noted. “Because if you don’t have engineers they can’t inform the decision-making process. They help figure out if there are any loose ends and then they deal with the audits, communicating with the government and whatnot. That’s how we’re able to effectively get SaaS-like margins or more.”

Ideally, a tool like Boast pays for itself and the company says it has secured more than $150 million in R&D tax credits since launch. Currently, it’s also doubling growth year over year, and that’s what made the founders decide to raise outside money for the first time. That funding will go toward increasing the sales team (which is currently only four people strong) and improving the platform, but Lobo was clear that he doesn’t want to be too aggressive. The goal, he said, is not to have to raise again until Boast can hit the $30 to $50 million revenue mark.

Once fully implemented, Boast also effectively becomes a system of record for all R&D and engineering data. And indeed, that’s the company’s overall vision, with the tax credits being somewhat of a Trojan horse to get to this point. By the middle of next year, the team plans to offer a new product around R&D-based financing, Lobo tells me.

Over the years, the Boast team also focused on not just growing its customer base but also the overall startup ecosystem in the markets in which it operates, with a special focus on Canada. The Boast team, for example, is also the team behind the popular annual Traction conference in Vancouver, Canada (Disclosure: I’ve moderated sessions at the event since its inception). A thriving startup ecosystem creates a larger client base for Boast, too, after all — and coincidently, the team met its investors at the event, too.

Enso Security, a Tel Aviv-based startup that is building a new application security posture management platform, today announced that it has raised a $6 million seed funding round led by YL Ventures, with participation from Jump Capital. Angel investors in this round include HackerOne co-founder and CTO Alex Rice; Sounil Yu, the former chief security scientist at Bank of America; Omkhar Arasaratnam, the former head of Data Protection Technology at JPMorgan Chase and toDay Ventures.

The company was founded by Roy Erlich (CEO), Chen Gour Arie (CPO) and Barak Tawily (CTO). As is so often the case with Israeli security startups, the founding team includes former members of the Israeli Intelligence Corps, but also a lot of hands-on commercial experience. Erlich, for example, was previously the head of application security at Wix, while Gour Arie worked as an application security consultant for numerous companies across Europe and Tawily has a background in pentesting and led a security team at Wix, too.

Image Credits: Enso Security / Getty Images

“It’s no secret that, today, the diversity of R&D allows [companies] to rapidly introduce new applications and push changes to existing ones,” Erlich explained. “But this great complexity for application security teams results in significant AppSec management challenges. These challenges include the difficulty of tracking applications across environments, measuring risks, prioritizing tasks and enforcing uniform Application Security strategies across all applications.”

But as companies push out code faster than ever, the application security teams aren’t able to keep up — and may not even know about every application being developed internally. The team argues that application security today is often a manual effort to identify owners and measure risk, for example — and the resources for application security teams are often limited, especially when compared the size of the overall development team in most companies. Indeed, the Enso team argues that most AppSec teams today spend most of their time creating relationships with developers and performing operational and product-related tasks — and not on application security.

Image Credits: Enso Security / Getty Images

“It’s a losing fight from the application security side because you have no chance to cover everything,” Erlich noted. “Having said that, […] it’s all about managing the risk. You need to make sure that you take data-driven decisions and that you have all the data that you need in one place.”

Enso Security then wants to give these teams a platform that gives them a single pane of glass to discover applications, identify owners, detect changes and capture their security posture. From there, teams can then prioritize and track their tasks and get real-time feedback on what is happening across their tools. The company’s tools currently pull in data from a wide variety of tools, including the likes of JIRA, Jenkins, GitLab, GitHub, Splunk, ServiceNow and the Envoy edge and service proxy. But as the team argues, even getting data from just a few sources already provides benefits for Enso’s users.

Looking ahead, the team plans to continue improving its product and staff up from its small group of seven employees to about 20 in the next year.

“Roy, Chen and Barak have come up with a very elegant solution to a notoriously complex problem space,” said Ofer Schreiber, partner at YL Ventures . “Because they cut straight to visibility — the true heart of this issue — cybersecurity professionals can finally see and manage all of the applications in their environments. This will have an extraordinary impact on the rate of application rollout and enterprise productivity.”

Grid AI, a startup founded by the inventor of the popular open-source PyTorch Lightning project, William Falcon, that aims to help machine learning engineers work more efficiently, today announced that it has raised an $18.6 million Series A funding round, which closed earlier this summer. The round was led by Index Ventures, with participation from Bain Capital Ventures and firstminute. 

Falcon co-founded the company with Luis Capelo, who was previously the head of machine learning at Glossier. Unsurprisingly, the idea here is to take PyTorch Lightning, which launched about a year ago, and turn that into the core of Grid’s service. The main idea behind Lightning is to decouple the data science from the engineering.

The time argues that a few years ago, when data scientists tried to get started with deep learning, they didn’t always have the right expertise and it was hard for them to get everything right.

“Now the industry has an unhealthy aversion to deep learning because of this,” Falcon noted. “Lightning and Grid embed all those tricks into the workflow so you no longer need to be a PhD in AI nor [have] the resources of the major AI companies to get these things to work. This makes the opportunity cost of putting a simple model against a sophisticated neural network a few hours’ worth of effort instead of the months it used to take. When you use Lightning and Grid it’s hard to make mistakes. It’s like if you take a bad photo with your phone but we are the phone and make that photo look super professional AND teach you how to get there on your own.”

As Falcon noted, Grid is meant to help data scientists and other ML professionals “scale to match the workloads required for enterprise use cases.” Lightning itself can get them partially there, but Grid is meant to provide all of the services its users need to scale up their models to solve real-world problems.

What exactly that looks like isn’t quite clear yet, though. “Imagine you can find any GitHub repository out there. You get a local copy on your laptop and without making any code changes you spin up 400 GPUs on AWS — all from your laptop using either a web app or command-line-interface. That’s the Lightning “magic” applied to training and building models at scale,” Falcon said. “It is what we are already known for and has proven to be such a successful paradigm shift that all the other frameworks like Keras or TensorFlow, and companies have taken notice and have started to modify what they do to try to match what we do.”

The service is now in private beta.

With this new funding, Grid, which currently has 25 employees, plans to expand its team and strengthen its corporate offering via both Grid AI and through the open-source project. Falcon tells me that he aims to build a diverse team, not in the least because he himself is an immigrant, born in Venezuela, and a U.S. military veteran.

“I have first-hand knowledge of the extent that unethical AI can have,” he said. “As a result, we have approached hiring our current 25 employees across many backgrounds and experiences. We might be the first AI company that is not all the same Silicon Valley prototype tech-bro.”

“Lightning’s open-source traction piqued my interest when I first learned about it a year ago,” Index Ventures’ Sarah Cannon told me. “So intrigued in fact I remember rushing into a closet in Helsinki while at a conference to have the privacy needed to hear exactly what Will and Luis had built. I promptly called my colleague Bryan Offutt who met Will and Luis in SF and was impressed by the ‘elegance’ of their code. We swiftly decided to participate in their seed round, days later. We feel very privileged to be part of Grid’s journey. After investing in seed, we spent a significant amount with the team, and the more time we spent with them the more conviction we developed. Less than a year later and pre-launch, we knew we wanted to lead their Series A.”

Adaptive Shield, a Tel Aviv-based security startup, is coming out of stealth today and announcing its $4 million seed round led by Vertex Ventures Israel. The company’s platform helps businesses protect their SaaS applications by regularly scanning their various setting for security issues.

The company’s co-founders met in the Israeli Defense Forces, where they were trained on cybersecurity, and then worked at a number of other security companies before starting their own venture. Adaptive Shield CEO Maor Bin, who previously led cloud research at Proofpoint, told me the team decided to look at SaaS security because they believe this is an urgent problem few other companies are addressing.

Pictured is a representative sample of nine apps being monitored by the Adaptive Shield platform, including the total score of each application, affected categories and affected security frameworks and standards. (Image Credits: Adaptive Shield)

“When you look at the problems that are out there — you want to solve something that is critical, that is urgent,” he said. “And what’s more critical than business applications? All the information is out there and every day, we see people moving their on-prem infrastructure into the cloud.”

Bin argues that as companies adopt a large variety of SaaS applications, all with their own security settings and user privileges, security teams are often either overwhelmed or simply not focused on these SaaS tools because they aren’t the system owners and may not even have access to them.

“Every enterprise today is heavily using SaaS services without addressing the associated and ever-changing security risks,” says Emanuel Timor, general partner at Vertex Ventures Israel . “We are impressed by the vision Adaptive Shield has to elegantly solve this complex problem and by the level of interest and fast adoption of its solution by customers.”

Onboarding is pretty easy, as Bin showed me, and typically involves setting up a user in the SaaS app and then logging into a given service through Adaptive Shield. Currently, the company supports most of the standard SaaS enterprise applications you would expect, including GitHub, Office 365, Salesforce, Slack, SuccessFactors and Zoom.

“I think that one of the most important differentiators for us is the amount of applications that we support,” Bin noted.

The company already has paying customers, including some Fortune 500 companies across a number of verticals, and it has already invested some of the new funding round, which closed before the global COVID-19 pandemic hit, into building out more integrations for these customers. Bin tells me that Adaptive Shield immediately started hiring once the round closed and is now also in the process of hiring its first employee in the U.S. to help with sales.