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Tape It launches an AI-powered music recording app for iPhone
Earlier this year, Apple officially discontinued Music Memos, an iPhone app that allowed musicians to quickly record audio and develop new song ideas. Now, a new startup called Tape It is stepping in to fill the void with an app that improves audio recordings by offering a variety of features, including higher-quality sound, automatic instrument detection, support for markers, notes and images, and more.
The idea for Tape It comes from two friends and musicians, Thomas Walther and Jan Nash.
Walther had previously spent three and a half years at Spotify, following its 2017 acquisition of the audio detection startup Sonalytic, which he had co-founded. Nash, meanwhile, is a classically trained opera singer, who also plays bass and is an engineer.
They’re joined by designer and musician Christian Crusius, previously of the design consultancy Fjord, which was acquired by Accenture.
The founders, who had played in a band together for many years, were inspired to build Tape It because it was something they wanted for themselves, Walther says. After ending his stint at Spotify working in their new Soundtrap division (an online music startup Spotify also bought in 2017), he knew he wanted to work on a project that was more focused on the music-making side of things. But while Soundtrap worked for some, it wasn’t what either Walther or his friends had needed. Instead, they wanted a simple tool that would allow them to record their music with their phone — something that musicians often do today using Apple’s Voice Memos app and, briefly, Music Memos — until its demise.
Image Credits: Tape It
“Regardless of whether you’re an amateur or even like a touring professional…you will record your ideas with your phone, just because that’s what you have with you,” Walther explains. “It’s the exact same thing with cameras — the best camera is the one you have with you. And the best audio recording tool is the one you have with you.”
That is, when you want to record, the easiest thing to do is not to get out your laptop and connect a bunch of cables to it, then load up your studio software — it’s to hit the record button on your iPhone.
The Tape It app allows you to do just that, but adds other features that make it more competitive with its built-in competition, Voice Memos.
When you record using Tape It, the app leverages AI to automatically detect the instrument, then annotate the recording with a visual indication to make those recordings easier to find by looking for the colorful icon. Musicians can also add their own markers to the files right when they record them, then add notes and photos to remind themselves of other details. This can be useful when reviewing the recordings later on, Walther says.
Image Credits: Tape It
“If I have a nice guitar sound, I can just take a picture of the settings on my amplifier, and I have them. This is something musicians do all the time,” he notes. “It’s the easiest way to re-create that sound.”
Another novel, but simple, change in Tape It is it that breaks longer recordings into multiple lines, similar to a paragraph of text. The team calls this the “Time Paragraph,” and believes it will make listening to longer sessions easier than the default — which is typically a single, horizontally scrollable recording.
Image Credits: Tape It
The app has also been designed so it’s easier to go back to the right part of recordings, thanks to its smart waveforms, in addition to the optional markers and photos. And you can mark recordings as favorites so you can quickly pull up a list of your best ideas and sounds. The app offers full media center integration as well, so you can play back your music whenever you have time.
However, the standout feature is Tape It’s support for “Stereo HD” quality. Here, the app takes advantage of the two microphones on devices like the iPhone XS, XR, and other newer models, then improves the sound using AI technology and other noise reduction techniques, which it’s developed in-house. This feature is part of its $20 per year premium subscription.
Over time, Tape It intends to broaden its use of AI and other IP to improve the sound quality further. It also plans to introduce collaborative features and support for importing and exporting recordings into professional studio software. This could eventually place Tape It into the same market that SoundCloud had initially chased before it shifted its focus to becoming more of a consumer-facing service.
But first, Tape It wants to nail the single-user workflow before adding on more sharing features.
“We decided that it’s so important to make sure it’s useful, even just for you. The stuff that you can collaborate on — if you don’t like using it yourself, you’re not going to use it,” Walther says.
Tape It’s team of three is based in Stockholm and Berlin and is currently bootstrapping.
The app itself is a free download on iOS and will later support desktop users on Mac and Windows. An Android version is not planned.
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Companies betting on data must value people as much as AI
The Pareto principle, also known as the 80-20 rule, asserts that 80% of consequences come from 20% of causes, rendering the remainder way less impactful.
Those working with data may have heard a different rendition of the 80-20 rule: A data scientist spends 80% of their time at work cleaning up messy data as opposed to doing actual analysis or generating insights. Imagine a 30-minute drive expanded to two-and-a-half hours by traffic jams, and you’ll get the picture.
As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now.
While most data scientists spend more than 20% of their time at work on actual analysis, they still have to waste countless hours turning a trove of messy data into a tidy dataset ready for analysis. This process can include removing duplicate data, making sure all entries are formatted correctly and doing other preparatory work.
On average, this workflow stage takes up about 45% of the total time, a recent Anaconda survey found. An earlier poll by CrowdFlower put the estimate at 60%, and many other surveys cite figures in this range.
None of this is to say data preparation is not important. “Garbage in, garbage out” is a well-known rule in computer science circles, and it applies to data science, too. In the best-case scenario, the script will just return an error, warning that it cannot calculate the average spending per client, because the entry for customer #1527 is formatted as text, not as a numeral. In the worst case, the company will act on insights that have little to do with reality.
The real question to ask here is whether re-formatting the data for customer #1527 is really the best way to use the time of a well-paid expert. The average data scientist is paid between $95,000 and $120,000 per year, according to various estimates. Having the employee on such pay focus on mind-numbing, non-expert tasks is a waste both of their time and the company’s money. Besides, real-world data has a lifespan, and if a dataset for a time-sensitive project takes too long to collect and process, it can be outdated before any analysis is done.
What’s more, companies’ quests for data often include wasting the time of non-data-focused personnel, with employees asked to help fetch or produce data instead of working on their regular responsibilities. More than half of the data being collected by companies is often not used at all, suggesting that the time of everyone involved in the collection has been wasted to produce nothing but operational delay and the associated losses.
The data that has been collected, on the other hand, is often only used by a designated data science team that is too overworked to go through everything that is available.
All for data, and data for all
The issues outlined here all play into the fact that save for the data pioneers like Google and Facebook, companies are still wrapping their heads around how to re-imagine themselves for the data-driven era. Data is pulled into huge databases and data scientists are left with a lot of cleaning to do, while others, whose time was wasted on helping fetch the data, do not benefit from it too often.
The truth is, we are still early when it comes to data transformation. The success of tech giants that put data at the core of their business models set off a spark that is only starting to take off. And even though the results are mixed for now, this is a sign that companies have yet to master thinking with data.
Data holds much value, and businesses are very much aware of it, as showcased by the appetite for AI experts in non-tech companies. Companies just have to do it right, and one of the key tasks in this respect is to start focusing on people as much as we do on AIs.
Data can enhance the operations of virtually any component within the organizational structure of any business. As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now. The goal for any company looking to tap data today comes down to getting it from point A to point B. Point A is the part in the workflow where data is being collected, and point B is the person who needs this data for decision-making.
Importantly, point B does not have to be a data scientist. It could be a manager trying to figure out the optimal workflow design, an engineer looking for flaws in a manufacturing process or a UI designer doing A/B testing on a specific feature. All of these people must have the data they need at hand all the time, ready to be processed for insights.
People can thrive with data just as well as models, especially if the company invests in them and makes sure to equip them with basic analysis skills. In this approach, accessibility must be the name of the game.
Skeptics may claim that big data is nothing but an overused corporate buzzword, but advanced analytics capacities can enhance the bottom line for any company as long as it comes with a clear plan and appropriate expectations. The first step is to focus on making data accessible and easy to use and not on hauling in as much data as possible.
In other words, an all-around data culture is just as important for an enterprise as the data infrastructure.
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The best way to grow your tech career? Treat it like an app
Software developers and engineers have rarely been in higher demand. Organizations’ need for technical talent is skyrocketing, but the supply is quite limited. As a result, software professionals have the luxury of being very choosy about where they work and usually command big salaries.
In 2020, the U.S. had nearly 1.5 million full-time developers, who earned a median salary of around $110,000, according to the Bureau of Labor Statistics. Over the next 10 years, the federal agency estimates, developer jobs will grow by 22% to 316,000.
But what happens after a developer or engineer lands that sweet gig? Are they able to harness their skills and grow in interesting and challenging new directions? Do they understand what it takes to move up the ladder? Are they merely doing a job or cultivating a rewarding professional life?
To put it bluntly, many developers and engineers stink at managing their own careers.
These are the kinds of questions that have gnawed at me throughout my 25 years in the tech industry. I’ve long noticed that, to put it bluntly, many developers and engineers stink at managing their own careers.
It’s simply not a priority for some. By nature, developers delight in solving complex technical challenges and working hard toward their company’s digital objectives. Care for their own careers may feel unattractively self-promotional or political — even though it’s in fact neither. Charting a career path may feel awkward or they just don’t know how to go about it.
Companies owe it to developers and engineers, and to themselves, to give these key people the tools to understand what it takes to be the best they can be. How else can developers and engineers be assured of continually great experiences while constantly expanding their contributions to their organizations?
Developers delight in solving complex challenges and working hard toward their company’s objectives. Care for their own careers may feel unattractively self-promotional or political — even though it’s in fact neither.
Coaching and mentoring can help, but I think a more formal management system is necessary to get the wind behind the sails of a companywide commitment to making developers and engineers believe that, as the late Andy Grove said, “Your career is your business and you are its CEO.”
That’s why I created a career development model for developers and engineers when I was an Intel Fellow at Intel between 2003 and 2013. This framework has since been put into practice at the three subsequent companies I worked at — Google, VMWare, and, now, Juniper Networks — through training sessions and HR processes.
The model is based on a principle that every developer can relate to: Treat career advancement as you would a software project.
That’s right, by thinking of career development in stages like those used in app production, developers and engineers can gain a holistic view of where they are in their professional lives, where they want to go and the gaps they need to fill.
Step 1: Functional specification
In software development, a team can’t get started until it has a functional specification that describes the app’s requirements and how it is supposed to perform and behave.
Why should a career be any different? In my model, folks begin by assessing the “functionality” expected of someone at their next career level and how they’re demonstrating them (or not). Typically, a person gets promoted to a higher level only when they already demonstrate that they are operating at that level.
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Geothermal technology has enormous potential to power the planet and Fervo wants to tap it
Tapping the geothermal energy stored beneath the Earth’s surface as a way to generate renewable power is one of the new visions for the future that’s captured the attention of environmentalists and oil and gas engineers alike.
That’s because it’s not only a way to generate power that doesn’t rely on greenhouse gas emitting hydrocarbons, but because it uses the same skillsets and expertise that the oil and gas industry has been honing and refining for years.
At least that’s what drew the former completion engineer (it’s not what it sounds like) Tim Latimer to the industry and to launch Fervo Energy, the Houston-based geothermal tech developer that’s picked up funding from none other than Bill Gates’ Breakthrough Energy Ventures (that fund… is so busy) and former eBay executive, Jeff Skoll’s Capricorn Investment Group.
With the new $28 million cash in hand, Fervo’s planning on ramping up its projects, which Latimer said would “bring on hundreds of megawatts of power in the next few years.”
Latimer got his first exposure to the environmental impact of power generation as a kid growing up in a small town outside of Waco, Texas near the Sandy Creek coal power plant, one of the last coal-powered plants to be built in the U.S.
Like many Texas kids, Latimer came from an oil family, and got his first jobs in the oil and gas industry before realizing that the world was going to be switching to renewables and the oil industry — along with the friends and family he knew — could be left high and dry.
It’s one reason he started working on Fervo, the entrepreneur said.
“What’s most important, from my perspective, since I started my career in the oil and gas industry, is providing folks that are part of the energy transition on the fossil fuel side to work in the clean energy future,” Latimer said. “I’ve been able to go in and hire contractors and support folks that have been out of work or challenged because of the oil price crash… And I put them to work on our rigs.”
Fervo Energy chief executive, Tim Latimer, pictured in a hardhat at one of the company’s development sites. Image Credits: Fervo Energy
When the Biden administration talks about finding jobs for employees in the hydrocarbon industry as part of the energy transition, this is exactly what they’re talking about.
And geothermal power is no longer as constrained by geography, so there are a lot of abundant resources to tap and the potential for high-paying jobs in areas that are already dependent on geological services work, Latimer said (late last year, Vox published a good overview of the history and opportunity presented by the technology).
“A large percentage of the world’s population actually lives next to good geothermal resources,” Latimer said. “[There are] 25 countries today that have geothermal installed and producing and another 25 where geothermal is going to grow.”
Geothermal power production actually has a long history in the Western U.S. and in parts of Africa where naturally occurring geysers and steam jets pouring from the earth have been obvious indicators of good geothermal resources, Latimer said.
“Fervo’s technology unlocks a new class of geothermal resource that is ready for large-scale deployment. Fervo’s geothermal systems use novel techniques, including horizontal drilling, distributed fiber optic sensing and advanced computational modelling, to deliver more repeatable and cost effective geothermal electricity,” Latimer wrote in an email. “Fervo’s technology combines with the latest advancements in Organic Rankine Cycle generation systems to deliver flexible, 24/7 carbon-free electricity.”
Initially developed with a grant from the TomKat Center at Stanford University and a fellowship funded by Activate.org at the Lawrence Berkeley National Lab’s Cyclotron Road division, Fervo has gone on to score funding from the DOE’s Geothermal Technology Office and ARPA-E to continue work with partners like Schlumberger, Rice University and the Berkeley Lab.
The combination of new and old technology is opening vast geographies to the company to potentially develop new projects.
Other companies are also looking to tap geothermal power to drive a renewable power-generation development business. Those are startups like Eavor, which has the backing of energy majors like bp Ventures, Chevron Technology Ventures, Temasek, BDC Capital, Eversource and Vickers Venture Partners; and other players including GreenFire Energy and Sage Geosystems.
Demand for geothermal projects is skyrocketing, opening up big markets for startups that can nail the cost issue for geothermal development. As Latimer noted, from 2016 to 2019 there was only one major geothermal contract, but in 2020 there were 10 new major power purchase agreements signed by the industry.
For all of these projects, cost remains a factor. Contracts that are being signed for geothermal that are in the $65 to $75 per megawatt range, according to Latimer. By comparison, solar plants are now coming in somewhere between $35 and $55 per megawatt, as The Verge reported last year.
But Latimer said the stability and predictability of geothermal power made the cost differential palatable for utilities and businesses that need the assurance of uninterruptible power supplies. As a current Houston resident, the issue is something that Latimer has an intimate experience with from this year’s winter freeze, which left him without power for five days.
Indeed, geothermal’s ability to provide always-on clean power makes it an incredibly attractive option. In a recent Department of Energy study, geothermal could meet as much as 16% of the U.S. electricity demand, and other estimates put geothermal’s contribution at nearly 20% of a fully decarbonized grid.
“We’ve long been believers in geothermal energy but have waited until we’ve seen the right technology and team to drive innovation in the sector,” said Ion Yadigaroglu of Capricorn Investment Group, in a statement. “Fervo’s technology capabilities and the partnerships they’ve created with leading research organizations make them the clear leader in the new wave of geothermal.”
Fervo Energy drilling site. Image Credits: Fervo Energy
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Andela begins global expansion in 37 countries months after going remote across Africa
More than a year after the pandemic began, remote work shows no signs of going away. While it has its cons, it remains top of mind for potential employees around the world before joining a new company.
But while most people in Africa still go to physical offices despite the pandemic, a few companies have nevertheless embraced this concept. Andela, a New York-based startup that helps tech companies build remote engineering teams from Africa, was one of the first to publicly announce it was going remote on the continent.
Today, it is doubling down on this effort by announcing the global expansion of its engineering talent. Over the past six months, the company has seen a 750% increase in applicants outside Africa. More than 30% of Andela’s inbound engineer applications also came from outside the continent in March alone. Half this number came from Latin America while Africa saw a 500% increase in applications as well.
When Andela launched in 2014, it built hubs in Nigeria, Kenya, Rwanda and Uganda to source, vet and train engineers to be part of remote teams for international companies. It also tested satellite models in Egypt and Ghana as substitutes to physical hubs.
The company would issue a call for applications, select a few (less than 1%), pay them a salary for the first six months and provide them with housing and food. It also helped developers improve their skills via training and mentorship. Over 100,000 engineers have taken part in the company’s learning network and community, and, as of 2019, Andela had more than 1,500 engineers on its payroll.
However, after noticing that this model wasn’t sustainable, it began to make changes.
In September 2019, it let go of 420 junior engineers across Kenya, Uganda and Nigeria. Nine months later, citing the pandemic, it laid off 135 employees while introducing salary cuts for senior staff. But despite the layoffs, the pandemic provided some form of clarity to how Andela wanted to operate — which was remote, judging by the success of the satellite models.
“In the very beginning, a developer had to be in Lagos to work with Andela. Then it became living in Nigeria. Then Kenya. Then Uganda, Rwanda,” CEO Jeremy Johnson told TechCrunch. “Before the pandemic, Andela was opening applications in country after country. The pandemic came and changed that as we opened up to the entire continent.”
Shutting down its existing physical campuses and going remote also helped the company focus on getting engineers with more experience to meet its clients’ requirements. That experiment, which the company conducted in less than a year, is also part of its mission to be a global company.
“That went so well and we thought ‘what if we accelerated it now that we’re remote and just enable applicants from anywhere?’ because it was always the plan to become a global company. That was clear, but the timing was the question. We did that and it’s been an amazing experiment,” Johnson added.
Now with its global expansion, its clients can tap into regional expertise to support international growth.
According to a statement released by the firm, it currently has engineers from 37 countries across Africa, Asia, Latin America, North America and Europe.
Johnson didn’t go into details about how many of these engineers are getting jobs from Andela or even its total developer count. He’s more interested in helping its clients solve the diversity issues that have plagued many Western corporations.
Andela is currently working with eight companies that have hired its engineers in Latin America and Africa. In addition to the diversity play, the CEO says that means Andela engineers get to prove themselves on a global playing field in a way the company has “always wanted to see.”
Andela serves more than 200 customers, including GitHub, ViacomCBS, Pluralsight, Seismic, Cloudflare, Coursera and InVision. GitHub is one company that seems to be benefitting from Andela’s new offerings. The company’s VP of Engineering, Dana Lawson, in a statement said, “As a business in the developer tool space, a lot of us are trying to enter those areas of the world (Southeast Asia, Latin America and Africa) where the emergent developers are coming so we can better understand their needs. Having a local presence there with amazing talent is super valuable to building a global product.”
Image Credits: Andela
In its quest to become a global company, going up against competition is unavoidable for the seven-year-old company. But since most of these companies are horizontal marketplaces (providing a wide range of expertise), whereas Andela is vertical, Johnson believes there’s enough market share to be acquired by the company.
“We are focused on building digital products, and because of that, we’re able to do more, essentially, for our customers… That’s where our focus is — [building long-term relationships] and around building great digital products,” the CEO said.
The company was founded by Jeremy Johnson, Christina Sass, Nadayar Enegesi, Ian Carnevale, Brice Nkengsa and Iyinoluwa Aboyeji. It has raised more than $180 million (up to Series D) from firms like Chan Zuckerberg Initiative, Generation Investment Management, Google Ventures and Spark Capital, at a valuation of about $700 million.
While announcing the layoffs last year, Andela said it was on an annual revenue run rate of $50 million. But when asked how this number has changed over the past year, Johnson said the company is “growing at a healthier pace as we’ve ever had.”
The future of remote work is global and Johnson believes Andela provides the vital link to talent wherever it is found. The company’s head of talent operations, Martin Chikilian, echoed similar sentiments regarding the expansion.
“We’ve seen exponential growth and interest from engineers from across Africa who want to work with some of the world’s most exciting technology-focused companies,” he said. “Growing our network of talent from Africa to include more markets is a unique proposition and we continue to match talent with opportunity beyond geographical boundaries.”
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Startup founded by ‘Survivor’ champ debuts airless bike tires based on NASA rover tech
As NASA is quick to remind people, the investments it funnels toward space exploration often wind up improving life on Earth — and it’s now in the business of speeding up some of that work through startups. SMART, a startup founded in 2020, has a partnership with NASA through the Space Act Agreement and is part of the agency’s formal Startup Program that aims to commercialize some of its innovations. The young company today revealed its first product: An airless bicycle tire based on technology NASA engineers created to make future lunar and Martian rovers even more resilient.
SMART’s METL tire is the first fruit of the startup’s work with NASA’s Glenn Research Center, where NASA engineers Dr. Santo Padula and Colin Creager first developed their so-called “shape memory alloy” (SMA) technology. SMA allows for a tire constructed entirely of interconnected springs, which requires no inflation and is therefore immune to punctures, but which can still provide equivalent or better traction when compared to inflatable rubber tires, and even some built-in shock-absorbing capabilities.
Engineers at NASA’s Glenn Research Center assemble the new shape memory alloy rover tire prior to testing in the Simulated Lunar Operations Laboratory. Image Credits: NASA
Dr. Padula and Creager’s key development was creating an alloy that can return to their shape at the molecular level, meaning they can deform to adapt to uneven terrain, including obstacles like gravel and potholes, and return to their shape without losing structural integrity over time.
SMART, which is co-founded by “Survivor: Fiji” champion Earl Cole and engineer Brian Yennie, worked with Padula and Creager, along with former NASA intern Calvin Young, to apply the benefits of SMA to the consumer market. They’re targeting the cycling market first with their METL tire, which is set to become available to the general public by early next year. Following that, SMART intends to also pursue bringing SMA tires to the automotive and commercial vehicle industries, too.
Image Credits: SMART Tire Company
Already, SMART has a partnership in place with Ford-owned Spin, the bike and scooter-sharing company focused on novel micromobility models. SMART’s technology has the potential not only to make flat tires or under inflation a thing of the past, but could reduce cost and waste long-term by supplementing the need for rubber tires, which need frequent replacement and can be a danger to riders or drivers when used without proper pressure.
SMART is also using WeFunder to seek crowdsourced equity investment, with SAFEs currently available at an $8 million valuation cap.
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Stacklet raises $18M for its cloud governance platform
Stacklet, a startup that is commercializing the Cloud Custodian open-source cloud governance project, today announced that it has raised an $18 million Series A funding round. The round was led by Addition, with participation from Foundation Capital and new individual investor Liam Randall, who is joining the company as VP of business development. Addition and Foundation Capital also invested in Stacklet’s seed round, which the company announced last August. This new round brings the company’s total funding to $22 million.
Stacklet helps enterprises manage their data governance stance across different clouds, accounts, policies and regions, with a focus on security, cost optimization and regulatory compliance. The service offers its users a set of pre-defined policy packs that encode best practices for access to cloud resources, though users can obviously also specify their own rules. In addition, Stacklet offers a number of analytics functions around policy health and resource auditing, as well as a real-time inventory and change management logs for a company’s cloud assets.
The company was co-founded by Travis Stanfield (CEO) and Kapil Thangavelu (CTO). Both bring a lot of industry expertise to the table. Stanfield spent time as an engineer at Microsoft and leading DealerTrack Technologies, while Thangavelu worked at Canonical and most recently in Amazon’s AWSOpen team. Thangavelu is also one of the co-creators of the Cloud Custodian project, which was first incubated at Capital One, where the two co-founders met during their time there, and is now a sandbox project under the Cloud Native Computing Foundation’s umbrella.
“When I joined Capital One, they had made the executive decision to go all-in on cloud and close their data centers,” Thangavelu told me. “I got to join on the ground floor of that movement and Custodian was born as a side project, looking at some of the governance and security needs that large regulated enterprises have as they move into the cloud.”
As companies have sped up their move to the cloud during the pandemic, the need for products like Stacklets has also increased. The company isn’t naming most of its customers, but it has disclosed FICO a design partner. Stacklet isn’t purely focused on the enterprise, though. “Once the cloud infrastructure becomes — for a particular organization — large enough that it’s not knowable in a single person’s head, we can deliver value for you at that time and certainly, whether it’s through the open source or through Stacklet, we will have a story there.” The Cloud Custodian open-source project is already seeing serious use among large enterprises, though, and Stacklet obviously benefits from that as well.
“In just 8 months, Travis and Kapil have gone from an idea to a functioning team with 15 employees, signed early Fortune 2000 design partners and are well on their way to building the Stacklet commercial platform,” Foundation Capital’s Sid Trivedi said. “They’ve done all this while sheltered in place at home during a once-in-a-lifetime global pandemic. This is the type of velocity that investors look for from an early-stage company.”
Looking ahead, the team plans to use the new funding to continue to developed the product, which should be generally available later this year, expand both its engineering and its go-to-market teams and continue to grow the open-source community around Cloud Custodian.
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Use Git data to optimize your developers’ annual reviews
The end of the year is looming and with it one of your most important tasks as a manager. Summarizing the performance of 10, 20 or 50 developers over the past 12 months, offering personalized advice and having the facts to back it up — is no small task.
We believe that the only unbiased, accurate and insightful way to understand how your developers are working, progressing and — last but definitely not least — how they’re feeling, is with data. Data can provide more objective insights into employee activity than could ever be gathered by a human.
It’s still very hard for many managers to fully understand that all employees work at different paces and levels.
Consider this: Over two-thirds of employees say they would put more effort into their work if they felt more appreciated, and 90% want a manager who’s fair to all employees.
Let’s be honest. It’s hard to judge all of your employees fairly if you’re (1) unable to work physically side-by-side with them, meaning you’ll inevitably have more contact with the some over others (e.g., those you’re more friendly with); and (2) you’re relying on manual trackers to keep on top of everyone’s work, which can get lost and take a lot of effort to process and analyze; (3) you expect engineers to self-report their progress, which is far from objective.
It’s also unlikely, especially with the quieter ones, that on top of all that you’ll have identified areas for them to expand their talents by upskilling or reskilling. But it’s that kind of personal attention that will make employees feel appreciated and able to progress professionally with you. Absent that, they’re likely to take the next best job opportunity that shows up.
So here’s a run down of why you need data to set up a fair annual review process; if not this year, then you can kick-start it for 2021.
1. Use data to set next year’s goals
The best way to track your developers’ progress automatically is by using Git Analytics tools, which track the performance of individuals by aggregating historical Git data and then feeding that information back to managers in minute detail.
This data will clearly show you if one of your engineers is over capacity or underworked and the types of projects they excel in. If you’re assessing an engineering manager and the team members they’re responsible for have been taking longer to push their code to the shared repository, causing a backlog of tasks, it may mean that they’re not delegating tasks properly. An appropriate goal here would be to track and divide their team’s responsibilities more efficiently, which can be tracked using the same metrics, or cross-training members of other teams to assist with their tasks.
Another example is that of an engineer who is dipping their toe into multiple projects. Indicators of where they’ve performed best include churn (we’ll get to that later), coworkers repeatedly asking that same employee to assist them in new tasks and of course positive feedback for senior staff, which can easily be integrated into Git analytics tools. These are clear signs that next year, your engineer could be maximizing their talents in these alternative areas, and you could diversify their tasks accordingly.
Once you know what targets to set, you can use analytics tools to create automatic targets for each engineer. That means that after you’ve set it up, it will be updated regularly on the engineer’s progress using indicators directly from the code repository. It won’t need time-consuming input from either you or your employee, allowing you both to focus on more important tasks. As a manager you’ll receive full reports once the deadline of the task is reached and get notified whenever metrics start dropping or the goal has been met.
This is important — you’ll be able to keep on top of those goals yourself, without having to delegate that responsibility or depend on self-reporting by the engineer. It will keep employee monitoring honest and transparent.
2. Three Git metrics can help you understand true performance quality
The easiest way for managers to “conclude” how an engineer has performed is by looking at superficial output: the number of completed pull requests submitted per week, the number of commits per day, etc. Especially for nontechnical managers, this is a grave but common error. When something is done, it doesn’t mean it’s been done well or that it is even productive or usable.
Instead, look at these data points to determine the actual quality of your engineer’s work:
- Churn is your number-one red flag, telling you how many times someone has modified their code in the first 21 days after it has been checked in. The more churn, the less of an engineer’s code is actually productive, with good longevity. Churn is a natural and healthy part of the software development process, but we’ve identified that any churn level above the normal 15%-30% indicates that an engineer is struggling with assignments.
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Pixie Labs raises $9.15M Series A round for its Kubernetes observability platform
Pixie, a startup that provides developers with tools to get observability into their Kubernetes-native applications, today announced that it has raised a $9.15 million Series A round led by Benchmark, with participation from GV. In addition, the company also today said that its service is now available as a public beta.
The company was co-founded by Zain Asgar (CEO), a former Google engineer working on Google AI and adjunct professor at Stanford, and Ishan Mukherjee (CPO), who led Apple’s Siri Knowledge Graph product team and also previously worked on Amazon’s Robotics efforts. Asgar had originally joined Benchmark to work on developer tools for machine learning. Over time, the idea changed to using machine learning to power tools to help developers manage large-scale deployments instead.
“We saw data systems, this move to the edge, and we felt like this old cloud 1.0 model of manually collecting data and shipping it to databases in the cloud seems pretty inefficient,” Mukherjee explained. “And the other part was: I was on call. I got gray hair and all that stuff. We felt like we could build this new generation of developer tools and get to Michael Jordan’s vision of intelligent augmentation, which is giving creatives tools where they can be a lot more productive.”
Image Credits: Pixie
The team argues that most competing monitoring and observability systems focus on operators and IT teams — and often involve a long manual setup process. But Pixie wants to automate most of this manual process and build a tool that developers want to use.
Pixie runs inside a developer’s Kubernetes platform and developers get instant and automatic visibility into their production environments. With Pixie, which the team is making available as a freemium SaaS product, there is no instrumentation to install. Instead, the team uses relatively new Linux kernel techniques like eBPF to collect data right at the source.
“One of the really cool things about this is that we can deploy Pixie in about a minute and you’ll instantly get data,” said Asgar. “Our goal here is that this really helps you when there are cases where you don’t want your business logic to be full of monitoring code, especially if you forget something — when you have an outage.”
Image Credits: Pixie
At the core of the developer experience is what the company calls “Pixie scripts.” Using a Python-like language (PxL), developers can codify their debugging workflows. The company’s system already features a number of scripts written by the team itself and the community at large. But as Asgar noted, not every user will write scripts. “The way scripts work, it’s supposed to capture human knowledge in that problem. We don’t expect the average user — or even the way-above-average developer — ever to touch a script or write one. They’re just going to use it in a specific scenario,” he explained.
Looking ahead, the team plans to make these scripts and the scripting language more robust and usable to allow developers to go from passively monitoring their systems to building scripts that can actively take actions on their clusters based on the monitoring data the system collects.
“Zain and Ishan’s provocative idea was to move software monitoring to the source,” said Eric Vishria, general partner at Benchmark. “Pixie enables engineering teams to fundamentally rethink their monitoring strategy as it presents a vision of the future where we detect anomalous behavior and make operational decisions inside the infrastructure layer itself. This allows companies of all sizes to monitor their digital experiences in a more responsive, cost-effective and scalable manner.”
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WhyLabs brings more transparancy to ML ops
WhyLabs, a new machine learning startup that was spun out of the Allen Institute, is coming out of stealth today. Founded by a group of former Amazon machine learning engineers, Alessya Visnjic, Sam Gracie and Andy Dang, together with Madrona Venture Group principal Maria Karaivanova, WhyLabs’ focus is on ML operations after models have been trained — not on building those models from the ground up.
The team also today announced that it has raised a $4 million seed funding round from Madrona Venture Group, Bezos Expeditions, Defy Partners and Ascend VC.
Visnjic, the company’s CEO, used to work on Amazon’s demand forecasting model.
“The team was all research scientists, and I was the only engineer who had kind of tier-one operating experience,” she told me. “So I thought, “Okay, how bad could it be? I carried the pager for the retail website before. But it was one of the first AI deployments that we’d done at Amazon at scale. The pager duty was extra fun because there were no real tools. So when things would go wrong — like we’d order way too many black socks out of the blue — it was a lot of manual effort to figure out why issues were happening.”
Image Credits: WhyLabs
But while large companies like Amazon have built their own internal tools to help their data scientists and AI practitioners operate their AI systems, most enterprises continue to struggle with this — and a lot of AI projects simply fail and never make it into production. “We believe that one of the big reasons that happens is because of the operating process that remains super manual,” Visnjic said. “So at WhyLabs, we’re building the tools to address that — specifically to monitor and track data quality and alert — you can think of it as Datadog for AI applications.”
The team has brought ambitions, but to get started, it is focusing on observability. The team is building — and open-sourcing — a new tool for continuously logging what’s happening in the AI system, using a low-overhead agent. That platform-agnostic system, dubbed WhyLogs, is meant to help practitioners understand the data that moves through the AI/ML pipeline.
For a lot of businesses, Visnjic noted, the amount of data that flows through these systems is so large that it doesn’t make sense for them to keep “lots of big haystacks with possibly some needles in there for some investigation to come in the future.” So what they do instead is just discard all of this. With its data logging solution, WhyLabs aims to give these companies the tools to investigate their data and find issues right at the start of the pipeline.
Image Credits: WhyLabs
According to Karaivanova, the company doesn’t have paying customers yet, but it is working on a number of proofs of concepts. Among those users is Zulily, which is also a design partner for the company. The company is going after mid-size enterprises for the time being, but as Karaivanova noted, to hit the sweet spot for the company, a customer needs to have an established data science team with 10 to 15 ML practitioners. While the team is still figuring out its pricing model, it’ll likely be a volume-based approach, Karaivanova said.
“We love to invest in great founding teams who have built solutions at scale inside cutting-edge companies, who can then bring products to the broader market at the right time. The WhyLabs team are practitioners building for practitioners. They have intimate, first-hand knowledge of the challenges facing AI builders from their years at Amazon and are putting that experience and insight to work for their customers,” said Tim Porter, managing director at Madrona. “We couldn’t be more excited to invest in WhyLabs and partner with them to bring cross-platform model reliability and observability to this exploding category of MLOps.”
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