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Electrification of road transport promises environmental and commercial gains. At the threshold of an evolution in transport, the environmental consequences are huge. So too are the commercial gains for the earliest and fastest movers. By Serge Colle, Randall Miller, Thierry Mortier, Marc Coltelli, and Andrew Horstead @ey.com.

We are at the threshold of an evolution in road transport that will benefit the environment and the way we live our lives. Right now, road transport accounts for almost one-quarter of Europe’s total greenhouse gas emissions.

The article then provides deep insights on:

• eMobility nears inflection point
• The need for speed in the electrification of transport
• Infrastructure lags behind EV rollout
• Why fleet must electrify first
• An emerging eMobility ecosystem

Fleet will make the biggest and fastest contribution to the decarbonization of road transport. We have established that the fleet sector, though relatively small at 63 million vehicles (20% of Europe’s total vehicle parc) is disproportionately damaging to the environment. It accounts for more than 40% of total kilometers traveled and for half of total emissions from road transport. It makes, therefore, the biggest and most impactful test case.

Carbon dioxide emissions standards are, according to many observers, the single biggest accelerant of the eMobility transition. They have forced automakers down a decarbonized path and will be the fundamental driver of change.

There is massive momentum, at a country, city, business and individual level, behind the electrification of transport. Environmental benefits are, of course, the biggest prize. But there are also significant commercial rewards for the first and fastest movers in the ecosystem that underpins eMobility. Excellent read full of detailed stats and informative charts!

Enterprise interest in artificial intelligence, fueled by machine learning, continues to expand. In its most recent survey on AI Adoption in the Enterprise, O’Reilly found that 85% of organizations are at least exploring the use of artificial intelligence. By James Kulich.

The bad news: Too many artificial intelligence projects fail. Currently, an estimated 78%-87% of artificial intelligence projects never make it into production.

Machine learning models are dynamic. The initial model development stage is often quite experimental, involving many iterations of candidate models. Doing this effectively at scale requires good version control. Data used to develop models must be validated and appropriately split into training and testing sets. Models must be validated, both in terms of their technical performance and in terms of their effectiveness in addressing the needs for which they were created.

The article also deals with:

• In the beginning there was DevOps
• Taking the next step to ML Ops
• ML Ops and value creation

Most recently, ML Ops has come onto the scene as an application of the DevOps approach to all aspects of machine learning projects. ML Ops is both a philosophy and a way of organizing human and technical resources.

While the story of Mark Zuckerberg and Facebook has undoubtedly inspired an entire generation of young entrepreneurs and reshaped their imaginations about what’s possible, people too easily forget that a big part of what makes the story compelling is that it’s so unusual. Mark Zuckerburg is not only an outlier — he’s an outlier among outliers. By Ian Hathaway.

Researchers have known for a while that peak age for general entrepreneurship occurs around mid-career (mid-30s to mid-40s), but an influential academic study published last year shows just how misguided the popular narrative is that twenty-something tech billionaires are the norm even for high-growth, high-tech entrepreneurship.

Economists Pierre Azoulay, Benjamin Jones, Daniel Kim, and Javier Miranda, analyzed administrative government data on the founders of all U.S. businesses that were started during a recent eight-year period (2007-2014). This restricted-use dataset at the U.S. Census Bureau allowed the researchers to get an accurate and comprehensive view of all business startup activity in America.

Source: https://startupsusa.org/the-myth-of-the-young-startup-founder/

The authors calculated the average founder age (at the time of founding) along key startup characteristics (industry, financing, patenting, location) and outcomes (hyper-growth, acquisition, or IPO). Below is the average founder age along these dimensions:

• All companies (with at least one employee): 42 years
• Fastest growing 0.1% of companies: 45 years
• High-tech industry: 43 years
• Venture-backed: 42 years
• Filed patents: 45 years
• Successfully exited (acquisition or IPO): 47 years
• Located in Silicon Valley: 42 years
• Located in an entrepreneurial hub: 41 years

Founders aged in their 20s and 30s are less likely to start high-growth companies compared with their share of total companies founded. Conversely, founders aged 40 years and above are more likely to start high-growth businesses relative to their contribution of total companies founded. Super interesting read!

Blockchain is a technology with countless applications and a great potential that is not yet fully utilized. It is an ordered list of blocks that are chained together, hence dubbed the name blockchain. By Mahmoud Fathy.

One crucial feature it possesses is that blocks of the blockchain are added via consensus of the nodes building that chain. One method to achieve this consensus is providing Proof of Work (PoW) as it takes place by requiring the nodes to undergo an exhaustive computation in which one node solely should not be able to finish.

This article walks you through the code for a blockchain DApp running over websocket. It is shown here how to use the underlying blockchain operations with javascript to be presented on a web page. In part 2 we go through the implementation of the blockchain itself.

The program is structured according to the figure below.

• Web: UI components and event callbacks provided for the user
• Wallet: Abstracting blockchain operations to very basic buy and reward operations
• Blockchain: Blockchain operations implemented here, it is worth noting that wallet belongs to Go package blockchain
• Chainfabric: The underlying network which binds nodes together during the mining operation

Hopefully, you are excited to add webassembly to your projects after skimming through this article and you get a feel of how blockchain looks like until delving into it together in the next part of this article. All code can be found in this GitHub repository. Good read!

REST, CQRS and messaging are not just buzzwords to you? But you want to know how to put them all together? Then let’s make a plan! By Michael Zangerle. While working on Symfony applications for the last few years I have had a lot of fun but also my fair share of headaches while working on and especially maintaining them.

Source: @fusonic.net https://www.fusonic.net/developers/2021/03/01/a-pragmatic-architecture/

What author wanted to show you in this article is not a revolutionary new concept, but how to put some good concepts and ideas together to build a Symfony application that is reliable, maintainable, extendable, testable and easy to grasp with as few as possible dependencies.

The article content:

• The general problems
• REST conformity
• API documentation
• Validation
• Unnecessary complexity
• The controller
• The service
• CQRS
• Messaging
• API documentation

The approach to architecture should be a pragmatic one according author. So the focus is to provide the maximum value for developers and the customer. Interesting read!

This is a long-form post breaking down the setup author uses to run a SaaS. From load balancing to cron job monitoring to payments and subscriptions. There’s a lot of ground to cover, so buckle up! By Anthony N. Simon.

In this article author talks about a low-stress, one-person company that he runs from his flat in Germany. It’s fully self-funded, and author likes to take things slow. It’s probably not what most people imagine when somebody says “tech startup”.

Source: https://anthonynsimon.com/blog/one-man-saas-architecture/

The article content:

• A bird’s eye view
• Automatic DNS, SSL, and Load Balancing
• Automated rollouts and rollbacks
• Let it crash
• Horizontal autoscaling
• Static assets cached by CDN
• Application data caching
• Per endpoint rate-limiting
• Running scheduled jobs
• Keeping secrets
• Kubernetes manifests for app deployments

…and much more. You will get plenty of charts and links to services author uses. Nice one!

Typescript is a superset of Javascript, which means that all Javascript is syntactically valid Typescript. However, Typescript’s power comes from its strict typing of objects. By Two Devs in a Pod.

Type checking often focuses on the shape of an object, ensuring that every object of a particular type adheres to a set of guidelines. The article then goes on explaining:

• Why are types important in typescript?
• Interfaces vs. Types
• Mapped Types
• Intersections & Unions
• Object-Oriented Typescript
• Bottom Line

Interfaces are basically a way of describing a data shape or defining a contract. Meanwhile, a type is usually used for composition of other types, like a union or intersection. That being said, they are often used interchangeably, and the most important thing to remember is to be consistent in how they are used within a codebase. You will also get links to further reading and online typescript playground. Sweet!

Welp … sometimes “stuff” happens … and you find yourself having a really bad day. We’d like to believe that every database is well configured from the start with optimal log rotation, correct alerting of high CPU consumption and cache hit ratio monitoring. By Elizabeth Christensen.

One frustrating thing that can happen to your database has nothing to do with performance and queries. Rather, it’s running out of disk space to store data. Today we’ll drill into some good practices you can put in place to help prevent such from happening. And if it does, show how to get you out of a bind as quickly as possible.

The advice here is aimed at someone in a production situation prioritizing minimizing data loss:

• Backups: The most important thing
• Full disk
• A brief overview of how Postgres WAL archiving works
• Broken Archives
• What NOT to do:
  • Never remove WAL
  • Don’t immediately overwrite the existing data directory with a restore from a backup
  • Don’t just resize in place
• What you SHOULD do:
  • Take a filesystem-level backup right now
  • Create a new instance (or at least a new volume) with sufficient space
  • Fix the underlying issues

Important thing is to remember that Postgres has tools to help you recover quickly and efficiently. Nice one!

Brendan Thompson published this article about deploying Kubernetes (k8s) to the major public clouds. There is a lot of drive with Cloud Native technology and consuming public clouds native PaaS offerings and I think we are losing some of the joy and certainly flexibility when it comes to consuming those.

As a solution to this author personally prefers to use k8s where ever possible. It does, however, raise some other problems wherein organizations occasionally will try and push really nasty old COTS applications into k8s, which for anyway who has spent any time doing this knows that seldom does that work out well.

The content of the article:

• Design
• Infrastructure as Code
  • Backend
  • Providers
  • Variables
  • Network
  • Main
• Deployment

Source: https://brendanthompson.com/posts/2021/k8s-in-space-azure

We liked author’s suggestion: If your organisation is running in Azure and you’re looking for to use kubernetes, I would say that it is well worth checking out AKS as long as you do not have the requirement for fully private clusters. Good read!

We’re paying too much to deal with obsolete electronic parts. Keeping aging systems on their feet is a daunting and resource-intensive task. By Peter Sandborn.

The electronics, in essence, were fine—they just couldn’t easily be fixed if even the slightest thing went wrong. Although mundane in its simplicity, the inevitable depletion of crucial components as systems age has sweeping, potentially life-threatening consequences. At the very least, the quest for an obsolete part can escalate into an unexpected, budget-busting expense.

Call it the dark side of Moore’s Law: poor planning causes companies to spend progressively more to deal with aging systems. The crux is that semiconductor manufacturers mainly answer the needs of the consumer electronics industry, whose products are rarely supported for more than four years.

The defining characteristic of an obsolete system is that its design must be changed or updated merely to keep the system in use. Qinetiq Technology Extension Corp., in Norco, Calif., a company that provides obsolescence-related resources, estimates that approximately 3 percent of the global pool of electronic components becomes obsolete each month.

The systems hit hardest by obsolescence are the ones that must perform nearly flawlessly. Technologies for mass transit, medicine, the military, air-traffic control, and power-grid management, to name a few, require long design and testing cycles, so they cannot go into operation soon after they are conceived.

The absence of crucial parts now fuels a multibillion-dollar industry of obsolescence forecasting, reverse-engineering outfits, foundries, and unfortunately, a thriving market of counterfeits. Without advance planning, only the most expensive or risky options for dealing with obsolescence tend to remain open.

This is long but excellent read with many helpful examples and insights!