Amazon EventBridge is a serverless event bus that makes it easy to connect applications together using data from your own applications, Software-as-a-Service (SaaS) applications, and AWS services.

In this post, I will be building a simple solution to monitor CI/CD pipeline of React app using CircleCI, CloudWatch and EventBridge.

So combining everything, I do the following steps:

• Create an EventBridge Rule.
• Create a Lambda function to log the CircleCI events.
• Config CircleCI that sends job status to EventBus.

Prerequisites

The following must be done before following this guide:

• Setup an AWS account
• Install the AWS CLI
• Configure the AWS CLI with user credentials
• Setup an CircleCI account

Creating an AWS Lambda Function

Create a Lambda function to log the CircleCI events. Specify this function as target of EventBridge rule in next step.

This is what the lambda function should look like:

module.exports.circleciLog = async event => {
  console.log(event);
  return null;
};

For more details, check our official tutorial here.

Creating an EventBridge Rule with custom source

You can create rule from Amazon EventBridge console. In our case I am going to use AWS CLI:

The following command creates a rule with custom source circleci.myapp

$aws events put-rule --name "circleci.myapp" --event-pattern "{"source":["circleci.myapp"]}"

Next, get Arn of lambda function circleci-log-dev-circleci which was created in previous step,

$aws lambda get-function  --function-name circleci-log-dev-circleciLog --query Configuration.FunctionArn

Copy Arn, then add target to rule circleci.myapp.

$aws events put-targets --rule circleci.myapp --targets "Id"="1","Arn"="arn:aws:lambda:REGION:ACCOUNTID:function:circleci-log-dev-circleciLog"

Okay all done! Let’s create CircleCI config and push project to Github.

Configuring CircleCI

CircleCI configuration is stored in a single YAML file located at ~/.circleci/config.yml, for more details, check out here.

In the config, CircleCI runs following command to Send custom events to EventBridge so that they can be matched to rules:

aws events put-events --entries '[{ "Source": "circleci.myapp", "DetailType": "CircleCI state change", "Detail": "{ "app": "myapp", "type": "fail" }"}]'

Theconfig.yml should look like:

https://medium.com/media/e8a20d4adc761effc3d0cb7b11e7af92/href

All set to go, Try it out!

• Push a commits to a remote repository master branch.
• Open the Amazon CloudWatch console.
• In the navigation pane, choose Logs, filter/aws/lambda/circleci-log-dev-circleciLog .

Here is what logs look like:

Creating a custom CloudWatch metric to monitor Lambda logs

This is an optional step, I found it is really useful, because it allows me to monitor for specific strings in my Lambda logs and send an alert when found.

• Go to your CloudWatch console.
• Select the checkbox next to your lambda log group, Click “Create Metric Filter”.

• In filter pattern enter something like “fail”. Click “Assign Metric”.

• Enter a name for this metric (this name can be later on be used to setup an alarm).
• Click “Create filter”.
• Go to the “Alarms” section.
• Click “Create Alarm”.
• At the bottom of the list of metrics categories, find the “Custom Metrics” dropdown, and select “Log metrics”.
• Find and select your metric name, click “Next”.
• Select a reasonable period of time, then select “Sum” as the Statistic.
• Setup the alarm to be triggered when the metric is “> 0” in 1 period.

That’s about it! I hope you have found this article useful, You can find the complete project in my GitHub repo.

Monitoring CI/CD Pipelines with Amazon EventBridge was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

The post Monitoring CI/CD Pipelines with Amazon EventBridge appeared first on Crypto Investing Insider.

Every week, my team at Invector Labs publishes a newsletter to track the most recent developments in AI research and technology. You can find this week’s issue below. You can sign up for it below. Please do so, our guys worked really hard on this:

From the Editor: Where We See Shapes AI Sees Textures

Image analysis is one of the hottest areas of artificial intelligence research. In recent years, AI image classification algorithms have become famous both for its progress as well as its mistakes. We all have seen the news of AI models misclassifying images of dark-skinned humans or tricky objects. While vision systems powered by AI have been able to outperform humans on some image recognition tasks under fixed conditions, they also fail miserably with the introductions of the simplest distortions. Now a team of German AI researchers has an idea why.

In a recent study presented at the International Conference on Learning Representations in May, a team of researchers from the University of Tübingen in Germany highlighted the sharp contrast between how humans and machines “think,”. While humans are clearly more biased towards shapes when analyzing images, the current generation of AI techniques focuses more on textures which also introduces a lot of confusions. If this result is proven to be correct, it can help to improve the accuracy of image analysis systems to surpass humans under all sorts of conditions.

Now let’s take a look at the core developments in AI research and technology this week:

AI Research

AI researchers from education powerhouse Udacity, published a paper proposing a method to generate videos lessons based on audio narrations.

>Read more in this coverage from VentureBeat

In a shocking discovery, AI researchers from the University of Tübingen, Germany Proved that image recognition systems are typically bias towards textures, not shapes, which is the root caused of many misclassifications.

>Read more in this coverage from Quantas Magazine

In a somewhat surprising paper, AI researcher from the University of Tartu, Estonia proposed a test that shows that deep reinforcement learning agents can learn from the perspectives of other agents.

>See the complete research paper here

Cool AI Tech Releases

Facebook open sourced an implementation of a deep learning recommendation model that combines concepts of collaborative filtering and predictive analytics.

>Read more in this blog post from the Facebook AI Research team

AI researchers from the Massachusetts Institute of Technology open sourced Gen, a new probabilistic programming language for causal inference.

>Read more in this coverage from MIT News

TensorFlow Lite has been ported to the Arduino IOT operating system which means that soon we will see deep learning models running on Arduino micro-controllers.

>Read more in this blog post from Hackster.io

AI in the Real World

Self-driving startup Tier IV raised $100 million for an open source platform for autonomous vehicles.

>Read more in this coverage from Crunchbase News

IBM and MIT collaborated on launching GANPaint, a system that uses AI to help designers add, modify or remove objects to images without loosing the core context.

>Read more in this coverage from MIT News

Researchers from Harvard University use AI to try and figure out whether the authorship of some of the most disputed works in The Beatles’ back catalog can be attributed to John Lennon or Paul McCartney.

>Read more in this coverage from the Financial Times

Last Week in AI was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

The post Last Week in AI appeared first on Crypto Investing Insider.

Ilya Suzdalnitski recently published, “Object-Oriented Programming — The Trillion Dollar Disaster.” This screed goes to great lengths to point out OOP’s shortcomings. While I don’t necessarily agree with everything he says, I do agree with one thing:

OOP, as taught and practiced with languages like C++, Java, and C#, has been a disaster.

These languages treat OOP as a way to apply abstract data types, rather than the message-based conception espoused by Alan Kay.

There is another way to look at OOP: from the standpoint of Smalltalk, which was the first language to popularize OOP in the 1980s and 1990s. (Simula was the first language to introduce OOP concepts.)

To understand the essence of Smalltalk’s philosophy, watch this video clip of Alan Kay’s tribute to Ted Nelson (start at 2:17):

https://medium.com/media/cbda8c7512200e900518c7f2a3d2d839/href

Some more insight comes from Alan Kay’s “The Early History Of Smalltalk” (©1993 ACM):

Smalltalk is a recursion on the notion of computer itself. Instead of dividing “computer stuff” into things each less strong than the whole — like data structures, procedures, and functions which are the usual paraphernalia of programming languages — each Smalltalk object is a recursion on the entire possibilities of the computer. Thus its semantics are a bit like having thousands and thousands of computers all hooked together by a very fast network.

and

Smalltalk’s contribution is a new design paradigm — which I called object-oriented — for attacking large problems of the professional programmer, and making small ones possible for the novice user. Object-oriented design is a successful attempt to qualitatively improve the efficiency of modeling the ever more complex dynamic systems and user relationships made possible by the silicon explosion.

The big takeaway is this: All the complaints you’ve heard about object-oriented programming and inheritance and scalability issues have come from using C++, Java, C#, etc. The Abstract Data Type philosophy is detrimental to large-scale software development.

Alan Kay famously said:

Actually, I made up the term “object-oriented,” and I can tell you I did not have C++ in mind.

https://medium.com/media/f17fe1a63c537c21a8c157350f80d9b0/href

(Start at 10:00.)

Said Alan Kay: “The Internet was done so well that most people think of it as a natural resource like the Pacific Ocean, rather than something that was man-made. When was the last time a technology with a scale like that was so error-free?”

Alan Kay likened his object-oriented philosphy to how the Internet was built, or how biological organisms consisting of billions of cells are built. Smalltalk is fantastic for truly scalable solutions.

As much as Mr. Suzdalnitski admires Erlang, it is not an object-oriented language in the sense that Smalltalk is, in the sense that Alan Kay intended. I would argue that Smalltalk is the only true, pure object-oriented programming language in history. It doesn’t even have built-in syntax for conditional and iterative execution, both of which are procedural elements!

The Object-Oriented Schism was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

The post The Object-Oriented Schism appeared first on Crypto Investing Insider.

Lawmakers from both parties on Capitol Hill are looking to define “blockchain” and see how government could use the technology.

The United States Senate committee of commerce, science and transportation approved the Blockchain Promotion Act on Tuesday.

The US Congress is moving forward on key piece of legislation designed to lay the regulatory framework for blockchain technology and to see how the government could benefit from its use.

The Senate Commerce, Science and Transportation Committee on Tuesday approved the Blockchain Promotion Act, bipartisan legislation that directs the Department of Commerce to come up with a standard definition of “blockchain,” the accounting ledger technology that underlies bitcoin and other cryptocurrencies. Blockchain distributes copies of the ledger across multiple computers so everyone in a network is working off a single record of all transactions, which is resistant to tampering and easy to audit.

“Blockchain is an exciting new technology with great potential and promise,” Sen. Ed Markey, a Democrat from Massachusetts and co-sponsor of the bill, said in a statement. Markey noted that the technology is already being used in the private sector to expand access to renewable energy, enhance health care delivery systems and improve supply chain efficiency.

“This legislation will help further understand applications for this technology and explore opportunities for its use within the federal government,” he added.

The bill, which was reintroduced in February, has bipartisan sponsors in both the Senate and the House of Representatives. It’s meant to prevent a patchwork of blockchain definitions at the state level and to set the framework for future regulation of the technology. There are already several bills that have been introduced around blockchain technology.

Lawmakers also see opportunity to use the technology to make many governmental functions more efficient. The working group described in the legislation will help do that. Many experts consider blockchain to be a major innovation in double-ledger accounting that could be very useful to government agencies to help deliver services, prevent tax fraud, eliminate bureaucracy and reduce waste. For instance, it could be used to keep track of health care and Social Security benefits or improve document management and storage. Another potential use is helping agencies — for instance, the Federal Communications Commission in better allocating and managing wireless spectrum licenses.

“This bipartisan, bicameral bill will bring a broad group of stakeholders together to develop a common definition of blockchain, and, perhaps even more importantly, recommend opportunities to leverage the technology to promote new innovations,” Rep. Doris Matsui, a Democrat from California, said in a statement.

Specifically, the working group would provide recommendations to other government agencies, including the National Telecommunications and Information Administration and the FCC to study using the technology to help manage wireless spectrum and provide other efficiencies within the federal government.

Matsui is sponsoring an identical bill in the House along with Rep. Brett Guthrie, a Republican from Kentucky. The bill’s sponsors in the Senate are Sens. Markey and Todd Young, a Republican from Indiana.

US Senate Committee Approves the Blockchain Promotion Act was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Intelligence Born From a Virus

Chapter one began the series by looking at how scientists measure and compare intelligence. By examining through that lens, we are better able to understand just how intelligence works — at least, our understanding of how it works. Turns out, it’s not about brain size nor a ratio of brain:mass, but about the neurons and how the neurons interact. If you haven’t read part one and are interested in understanding how our brains work, you can read about it here:

The Origins & Future of Intelligence: Chapter 1

Now that we have a better understanding of how it works, the question remains about why it works like that; i.e., what, if anything, happened to humans that transformed our brains into a powerhouse, giving us the ability to not only survive but dominate our planet? Recently, a surprising new theory emerged regarding how organisms inherited such a power: viruses gave it to us. Chapter 2 takes a look at our viral ancestry, and how it gave rise to intelligence.

You Are Barely You

We like to imagine that life is simple. A single egg-and-sperm combination carries all of the genetic materials that create us. It is the foundation of who we are, whether we’re a single cell, a newborn baby or a fully grown adult.

But reality isn’t as simple. For example, you might know that bacteria inhabit our gut and help us with the process of digestion. Funny side effect: those bacteria affect your physical health (obesity, diabetes, etc.) as well as your mental health (autism, anxiety, depression). Also, those bacteria aren’t the only ones that inhabit us; in fact, microorganisms outnumber our own body’s cells 10 to 1.

Think about it this way: imagine all of the cells in your body (yours and your microorganism’s) were transformed into congruently sized gumballs — whatever flavor you want, it doesn’t matter. Those gumballs are put into a bucket. Then you could reach in and try to grab a gumball that was your cell, not a microorganism. Statistically, after reaching in ten times, you only pull out one of your own cellular gumballs.

With that perspective in mind, it shouldn’t be so hard to believe that not much of your DNA is yours, either. A huge chunk of your DNA comes from viruses. Approximately 5% to 8% of the human genome comes from ancient viruses. Viral-based genetic code affects the bodies in ways you might’ve never thought about. For example, viral-based genetic code regulates hormones in a female’s body that control birth timing. And yes, you’ve probably guessed where I’m headed — viral-based genetic code has recently been linked as a catalyst to intelligence. Specifically, it’s the ancestors of retroviruses, retrotransposons, that are the ones that found their way into an evolutionary symbiosis with our brains (as well as other aspects of us).

Retrovirus Ancestor Fossilized in our DNA

Scientists have a term for the genetic code of an organism that can be traced back to retroviral origin: endogenous retroviruses (ERVs). When talking specifically about humans, sometimes it’s referred to as human endogenous retroviruses (HERVs).

Why specifically retroviruses and not other types of viruses? Because retroviruses do things a bit differently. A normal virus finds a host cell, injects itself inside and hijacks the cell’s controls, like how pirates might jump aboard a ship to take it for themselves. But unlike a pirate, a virus uses those cell’s controls to make the cell create clones of the virus. Those clones then escape the cell (frequently by brute force, killing the infected cell) and find other cells to infect.

Retroviruses do things a little differently, and they do it with the help of an enzyme (think of it as Red Bull for cellular-level actions; enzymes are used to start certain actions within the cell) they have called Reverse Transcriptase. Reverse transcriptase is like watching a transcriptase video on rewind. Transcriptase is also an enzyme; it uses DNA to produce RNA. A retrovirus, though, has RNA inside of it, so it uses reverse transcriptase to turn its RNA into DNA. That DNA then finds its way into the nucleus, where it can attach itself to the cell’s existing DNA.

If that explanation was a bit dense, all you need to know is that retroviruses can turn their code into DNA, which can then be added to the a cell’s existing DNA.

This viral-injected DNA adds an extra line of code to the cell’s DNA, telling the cell to make more of the virus. The cell does it without second thought because it’s now brainwashed. The newly cloned viruses are formed along the edge of the cell at the membrane, and leave after the cell opens the door and waves goodbye to them with a brainwashed smile on its face. While this seems isolated to an individual human, imagine that the retrovirus’s genetics found its way into an egg or sperm cell. At that point, the literal building blocks of the next life will have retroviral DNA included.

Noah’s Arc

With all of that background information out of the way, we are finally getting to the most relevant part: piecing together ERVs and the brain. It starts at the synapse, which is the part of the nerve cell that transmits electrical or chemical signals to other nerve cells. After the synapse sends out a signal to another nerve, something magical happens. A gene called Arc activates and begins creating RNA. That RNA carries a message, which is transmitted to the other nerves. The RNA is encapsulated and sent out of the cell. If you recall, this is what a retrovirus does.

Effectively, Arc ‘infects’ other neurons with the raw materials of memory.

That’s because researchers believe that Arc is an ERV. At this point, we don’t know exactly what the message is that’s encoded in RNA and delivered, but we do know that Arc plays a very important role in the brain, especially with regards to storing information — over time, it tells the nerves how to organize themselves to store more information efficiently. If the Arc gene is not properly sending instructions, synapses of the nerves die out; the gene has been implicated in neurological disorders. When researchers removed the Arc gene from animals, those animals could still store short-term memories, but could no longer store long-term ones. In other words, Arc is kind of a big deal.

While we have been looking at intelligence from the lens of human intelligence, I want to point out that the Arc gene is not unique to humans; it is found in many organisms, and it behaves in a similar way. That being said, the researchers responsible for making this discovery independently of each other (Budnik’s group looked at Arc in flies, while Shepherd’s looked at Arc in rodents) think that the Arc genes “seem to have evolved from two distinct retroviruses that entered the species’ genomes at different times.”

It’s very different that what we’ve been taught in science class, but new discoveries such as the Arc gene’s origins require us to be open-minded and change how we understand the core of who we are, and how we became intelligent. So far, research has shown that around 8% of human DNA comes from retroviruses, but that might be just the tip of the iceberg. Regardless of what other strange origin stories lie beneath, we do know that Arc’s existence inside our brains is something we can be grateful for to our viral ancestors.

How information is transmitted to different neurons in the brain is a key piece to understanding intelligence, but it’s not the only one. One big factor that sets human brains apart from other organisms’ brains is the density of neurons in our cerebral cortex, as discussed in Chapter 1. Chapter 3 will examine this density anomaly and tackle how it came about.

Got Questions? Wanna Chat?

Ask Me Anything on Worthyt or Follow Me on Twitter.

The Origins and Future of Intelligence: Chapter 2 was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

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A retrospective is a time for our team to look back. Its function, however, is to improve the way in which we move forward. Let’s digest the original definition from Scrum:

The Sprint Retrospective is an opportunity for the Scrum Team to inspect itself and create a plan for improvements to be enacted during the next Sprint.

The first two components are activities, the third is a result. And while most teams have fruitful conversations and create action items to yield that result, few have systems in place to ensure that positive change is actually created.

1. Inspect
2. Create a plan 
3. Enact improvements

The problem starts with the notion of “improvement.” It’s both, vague and subjective, so even a plan of activities feels like a step in the right direction (Spoiler: it’s not). If you take measures to concretely define improvement, however, you can hold yourself and your team accountable to your action items. For that, we can use SMART goals.

SMART goals contextualize improvement

Research has shown that goals that are both specific and time-bound are considerably more likely to yield results than generic action items.

Putting a number and a date to each retrospective action item ensures:

1. The department understands and aligns on what constitutes success, and
2. Progress towards this goal is black and white — trending toward or away from the goal.

While there are plenty of systems that put a number and date to goals, but for the sake of this post, we’ll stick to one that’s tried-and-true: SMART (Specific, Measurable, Achievable, Relevant, Time-bound) goal-setting.

To best position your team to work with SMART goals, you’ll need to adjust all three components of the retro. You’ll be inspecting with more data, creating a plan using SMART goals, and enacting improvements by making progress transparent to everyone on the team.

Inspect: Use data to diagnose the biggest issues

Most teams decide on goals using only qualitative feedback. A team member raises what they perceived to be a large bottleneck, and the whole team immediately starts trying to mitigate that issue. This method gives precedence to what individuals remember and feel, not necessarily the largest and most pressing problems.

If you bring more data points into diagnosing the problem, however, you’re more likely to get a holistic understanding of each bottleneck. Quantitative data helps counteract recency bias and enables you to prioritize based on actual risk that the problems present to your team’s productivity.

Let’s say a given engineering team is trying to diagnose why they didn’t get to as many features as they anticipated this sprint. One engineer, Hannah, makes the following hypothesis:

I feel like there were more pull requests than usual that were open at any one given time. I think it’s because people were too busy to get to code reviews, so work piled up.

Several engineers nod their head. They also noticed that there were more open PRs than usual in GitHub.

Instead of immediately brainstorming action items, Hannah and her team investigate further. They start by looking at their Time to Review this past sprint, and realize it’s relatively low — just 6 hours. This is contradictory to Hannah’s assessment that the review process was slower than usual. From there, they see that their average number of Review Cycles is about 1.2, where most Pull Requests are approved after one review. Also, seems pretty good.

Finally, they found a red flag when they looked at their Time to Merge. They realize that many pull requests stay open for a long time after they’re reviewed as developers move on to new tracks of work.

The gut instinct of the team recognized the symptom — long-running pull requests — but not the cause. Without data, they couldn’t have uncovered and addressed a deeper systemic problem.

Other data points you may consider looking at:

• All recent activities, including Pull Requests, Code Reviews, and Tickets, to remind your team of what they worked on last sprint, and where they might have gotten stuck.

• The most important pull requests last sprint. Look at pull requests that had a big effect on the codebase, as well as pull requests that were larger or older than the rest.

• Process metrics including outcome metrics like Cycle Time and Pull Request Throughput, but also metrics that represent more specific areas of the software development process, like Time to Open, Time to Review, and Time to Merge.

Plan: Align with SMART goals

Once your team has fully diagnosed an issue using both qualitative and quantitative data, they’ll have to decide on one, specific metric that they can use as their SMART goal.

Specific

The success of hitting or missing your metric should be black or white, so you need a concrete number in your goal. “Improving our Time to Review” is vague, “Decreasing our Time to Review to under 4 hours” is specific.

Also, make sure the metric is narrow enough that the team knows which behaviors drive this metric up or down. Metrics that are too broad can obscure progress since they’re affected by many different kinds of unrelated data. Hannah’s team, for example, would want to choose a metric like Time to Review, rather than total Cycle Time, so the team can easily self-correct when they notice the metric trending in a negative direction.

Measurable

The way in which you measure your metric depends on your objective. If you’re measuring output, for example, a simple count can do the trick. If you’re looking to adhere to specific standards — such as keeping pull requests small, or keeping downtime minimal — you’ll want to decide between tracking the simple average and tracking it as a sort of Service Level Objective (SLO) based on a percentile.

Here are a few examples:

While averages are more commonly used in process metrics, SLOs enable your team to deviate from the goal in a few instances without hindering their ability to meet the target.

Assignable

Pick one person to own and track this goal. Research has shown that having exactly one team member check in at regular intervals drastically increases the chances that a goal will be hit. Apple championed the idea of a Directly Responsible Individual (DRI) for all initiatives, and teams at leading tech companies like Microsoft have applied the DRI model to all DevOps related functions.

Ownership will also help you secure buy-in for bringing data into retros. Consider asking the person who uncovered the problem in the first place to own the goal.

Realistic

Make sure your goal is reachable, so your team feels success if they’ve put a concerted effort into reaching the goal.

Execute: Increase visibility to keep goals front of mind

The true test of your action items come after the retro. How frequently will your team think about these metrics? Will success be known across the team? If your team is unsuccessful, will they be able to try a different adjustment?

To keep the goal front of mind, you need to make progress visible to everyone on the team. Many managers use information radars, either in shared spaces or in universally accessible dashboards.

Making progress transparent equips the team to bring the results-oriented discussions outside of their retros. Effective goals will be brought up during standups, 1:1s, and even pairing sessions. Repetition will secure focus and will further unify the team around success.

makes engineers

When Boaz Katz, the founder and CTO of Bizzabo, started setting concrete targets he found that sharing success motivated his team to find more ways to improve. He told us, “My team developed a winning attitude and were eager to ship even faster.”

When the whole team feels success each retro, the momentum creates a flywheel effect. Team members are eager to uncover more improvement opportunities creating a culture around enacting positive change to your processes.

Your retro action items aren’t working. Here’s why. was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

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WATCH Founding Hacker Noon, Independent Publications, and the Future of Tech with David Smooke

Was great to meet Amber in person and be a guest on the Cazzell Report. Have been reading her for awhile, and I suggest you do the same  hackernoon.com/@ambercazzell. In this video, we talk about independent tech publications and make predictions for the future of artificial intelligence, cryptocurrencies, and decentralized technologies.

Watch the Video

…Also don’t forget to check out and subscribe to Hacker Noon on Youtube. We’re releasing 1–2 interviews a week with top tech professionals.

About Tech Independence [VIDEO] was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Founder Interviews: Anurag Goel of Render

After leaving his position as Head of Risk at Stripe, Anurag Goel started Render, a cloud platform where developers and startups can host any application or website quickly and easily. Render now serves more than 80 million requests every week.

What’s your background, and what are you working on?

I am the founder of Render, a cloud platform where developers and startups can host any application or website quickly and easily. I was previously the Head of Risk at Stripe where I helped the company launch in 2011 and grow to $5B in valuation by the time I left in 2016. I also created Crestle, an AI infrastructure company which was acquired by doc.ai in 2018.

At Render we’re making it effortless to host applications in the cloud. Traditionally, developers have had to pick between large cloud providers like AWS which are complex and difficult to use, or Platform-as-a-Service solutions like Heroku which are prohibitively expensive especially as your app scales.

Render gives you the best of both worlds: instant setup and ease of use as well as the power and flexibility previously only available on large clouds. From simple React sites and Rails apps to complex applications like Elasticsearch and Kafka, Render is an easy and cost-effective platform to host everything from a weekend project with a single static page to a rapidly scaling startup with tens of containerized microservices.

Thousands of developers and startups have joined Render since we launched in April 2019, and we’re now serving more than 80 million requests every week.

What motivated you to get started with your company?

I was solving my own problem. After I left Stripe, I had time to figure out what I wanted to do next, and I dabbled in a few different domains like healthcare, real-time infrastructure and natural language processing. I created simple apps in each of these domains to better understand problems I’d be excited about, but hosting these apps on existing cloud platforms was always painful. I was forced to go through the same steps over and over again, and each step involved complex configuration in addition to tedious post-launch maintenance. I wasn’t alone in this either; my friends were going through similar issues with apps of their own and we were all complaining to each other about how running an app in production was still a miserable experience.

This was when I first considered building Render, but decided not to because the problem seemed too big to tackle and I figured ‘someone else would fix it’. Instead, I dug deeper into AI, and as I was taking an online deep learning course offered by fast.ai, I noticed how everyone was struggling with running Jupyter on AWS. I was still looking for problems to solve, so I decided to build Crestle, which lets anyone spin up a GPU-backed Jupyter notebook in the cloud with a single click. It was really well received, and for a while I considered building Crestle into a larger company. However, I soon realized that I wasn’t excited enough about the problem to work on it for the next ten years, which is how long it takes to build something truly useful and enduring.

But building Crestle gave me a lot of insight into Kubernetes and Docker (which are both spectacular advancements in application packaging and deployment), and it helped me connect the dots and finally conceptualize a cloud platform that would be both incredibly easy to use and extremely flexible and powerful. In the end, I think I just got tired of waiting for large cloud providers to get better at user experience, and decided to build the cloud I’d always wanted for my own apps.

What went into building the initial product?

I first started working on Render in July 2017, and the initial version was a serverless Python coding environment in the browser that interacted with an API backed by Kubernetes and Docker. It was great for building quick HTTP endpoints and getting instant feedback on your code, but after a few months it was clear that most apps wouldn’t be able to run on it without creating strong vendor lock-in (like with AWS Lambda). I realized that for Render to run most real-world applications I had to build out a more generic platform; so I switched tracks and built out the second iteration of Render in late 2017/early 2018. This prototype was built to host regular Python, Node, and Ruby web apps with instant, continuous deploys from GitHub. I shared it with my friends who were excited about it, and when they started using it for their own projects it was clear that Render was useful enough to poke at further.

This is when I decided to raise a seed round so I could build out a team and expand beyond the prototype. Steve Herrod from General Catalyst (also early investors in Stripe) led the round, and Ruchi Sanghvi at the South Park Commons Fund helped me kickstart the fundraising process and also served as a sounding board throughout. They’ve both continued to be very helpful.

But funding is only part of the equation; not long after I finished raising, Adrian Duong and I met through a mutual friend and I convinced him to join me on our ambitious journey. Ralph Landon and Maša Mlakar joined later in 2018, and together we built out the initial platform that launched earlier this year. We’re continuing to build our engineering and design teams in SF, and I feel very thankful for the amazing group of people I get to work with every day.

Render’s tech stack hasn’t changed significantly since early 2018. We picked Go for the backend because we interact closely with Kubernetes and Docker (which are both written in Go), and because it had a lot of momentum and prior art as an infrastructure language. It also helped that it was blazing fast and great for concurrent programming which we ended up doing a lot of even early on. Our frontend is a React dashboard which interacts with our Go API through GraphQL (using Apollo Client). We started out with a monolith and have since broken it up into a few different services to increase fault-tolerance and allow for independent scaling.

We’ve found our current stack to be reasonably productive, but Go can sometimes be more verbose that I’d like (insert obligatory rant about lack of generics). If I were building a webapp that didn’t involve building infrastructure, I’d probably pick Python or Elixir because it’s easier to be more productive with them. However, Go still remains the best option for building fast, scalable and reliable infrastructure, which is of course our bread and butter at Render.

How have you attracted users and grown your company?

We launched Render in invite-only access in the summer of 2018. Most early users were developers in our personal networks, but soon we started seeing users we didn’t know personally. This happened entirely through word of mouth, and also because Render ended up being the easiest option for fast.ai model deployment, which meant that most students taking the course used Render by default.

After a few months it was clear we were ready to launch formally, so we opened signups at the end of April this year. This was a major turning point; it got us a lot of attention from multiple outlets, which snowballed into thousands of new users many of whom became extremely vocal Render supporters. If you search for @getRender or render.com on Twitter you’ll see our users spontaneously telling their followers about us because they love the product, and word-of-mouth continues to be our primary growth channel.

We’ve also created guides to deploy a lot of different frameworks and applications on Render (available at https://render.com/docs) and they’re very helpful for our users, and are also becoming a material growth channel through Google searches for ‘how to deploy X’. Another avenue that’s worked well is links to Render in documentation for popular open source projects. Render is now an official deployment option for Gatsby, Create React App, Hugo, Vue and many other widely used open source frameworks, and we’re seeing an increasing number of users discovering us through these channels.

There’s much more we can and need to do for growth, but the best kind of growth has always come from improving our product in ways that expand its reach. Adding support for GitLab and enabling block storage for Render apps are two examples where we significantly expanded the potential use cases Render enables. We believe sustainable, cost-effective growth can only happen through word-of-mouth, which means really sweating the details and building something users love so much they want to share it with everyone they know.

What’s your business model, and how have you grown your revenue?

Our business model is quite simple: we charge for hosting. We have a free plan for static sites, but everything else is a paid service. When we launched Render’s private beta in early 2018 we spent a lot of time on pricing and also involved early users in these discussions, and we’ve continued to refine it since then. Shortly after we launched, we introduced new paid products and pricing plans which are all available at https://render.com/pricing. There are a couple of lessons I’ve learned about pricing: you have to experiment, and as a startup you have to fight the urge to underprice. Render is not the least expensive option to host an app, but it is the easiest and most cost-effective. We save our users a lot of time which translates to real money, especially if you don’t have to hire devops engineers who’re quite hard to find and recruit.

Somewhat unexpectedly, our revenue growth has far outpaced user growth, which seems to be a good sign. We think it’s because once our users discover how easy it is to spin up a service on Render, they end up transferring all their services over to us. We’ve seen this happen repeatedly for users previously on Heroku and AWS. Also, when you make something extremely easy to do, people will naturally do more of it, so we’re seeing users deploy apps they wouldn’t have put in production otherwise; this is quite gratifying because we’re empowering more people to share their creations with the world.

What are your goals for the future?

Our overarching goal is to continue to simplify deployment and hosting for all kinds of apps, to the point where Render is the default choice for anything you’d want to run online. We’re building Render for the very long term, and in the best case it’s a multi-decade endeavor so our goals are commensurately ambitious. We’re working to achieve them one day at a time by listening to our users and building Render with a relentless focus on user experience. There is a lot of work ahead of us, but the feedback we’ve gotten so far is very encouraging so we’ll continue to do our best for our users.

In the short term, we’re focused on enabling more use cases and applications on Render. We recently launched the ability to create and mount disks for any Render app, and this lets you run complex stateful applications like Elasticsearch, Kafka, and MongoDB. We’re also working on integrating best-in-class logging and monitoring services so our users can get better visibility into their workloads. Finally, a lot of developers are waiting for Render to offer hosting regions in Europe and Asia, so we’re working on making that happen.

What are the biggest challenges you’ve faced and obstacles you’ve overcome? If you had to start over, what would you do differently?

As I mentioned earlier, the first time I thought about building Render it felt too big to take on and I kept the idea on the back burner for almost a year. That was clearly a mistake. Ideas that seem too broad or ambitious can sometimes be the best ones because very few people are working on them, and even though they seem intractable at first, there’s often a different way of looking at the problem that helps you get started and make headway. Once we started building Render, it became obvious that we could make it much more usable than all the existing options, and that developers cared enough about UX to willingly migrate their apps from established providers like AWS and Heroku to a new startup like ours.

Have you found anything particularly helpful or advantageous?

It was helpful to see Stripe grow from a handful of people to a few hundred, because I saw firsthand what worked (and what didn’t) as the company scaled. It was also helpful to join the company pre-launch and to know that it was in private beta for nearly two years; in a world where startups are built and launched over a weekend, sometimes it does make sense to think long and hard about the product you want to build and take the time to get it right.

It was also very useful to be part of a technical community like the South Park Commons as I was building the first version of Render. Even though the product was invite-only, I invited (read coerced) everyone around me to use it and received invaluable feedback that continues to inform our product decisions today.

What’s your advice for entrepreneurs who are just starting out?

I’ve found intellectual rigor and discipline to be both invaluable and underrated: these are two of a very small number of things that are entirely under your control as a startup, and I wish more companies practiced them diligently. Thinking hard isn’t always fun (as humans we’re unfortunately hardwired for intellectual laziness), but it does lead to products that stand out and solutions that have been overlooked. Similarly, the discipline to focus only on things that need to be done (as opposed to things that seem vaguely useful but more fun) is crucial. There are always an infinite number of things to work on at a startup, but time is the most precious resource you’ll ever have, and ruthless prioritization is essential.

I’d also recommend taking to the time to find an idea or space you care deeply about before starting your company; founder-idea fit is at least as important as how big your market is or who you end up working with. In the best case, you’ll be working on your company for a decade, so you have to be truly excited about the space or risk burning out fairly quickly.

Finally, I’d question all entrepreneurial advice (including the above) because everyone’s talents, skills and circumstances are unique; this makes it very hard to give generic advice that’s truly useful, but very easy to ascribe successful outcomes to specific actions and to ignore the role luck plays in any startup’s evolution.

Where can we go to learn more?

You can learn more about Render at https://render.com and follow us on Twitter at @getRender. You can also ask me anything and I’d be happy to help if I can. I’m available in Render’s community chat at https://render.com/chat and on Twitter at @anuraggoel.

Founder Interviews: Anurag Goel of Render was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

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aBey E-commerce Blockchain | Q&A with the Creator and Partner.

aBey A Multi-Layered Programmable Blockchain Approach to Digital Currency for High-Volume Transactions in E-Commerce Systems

There is a new economy coming and it’s called the Digital Economy. A new form of finance, a new way of doing business and a new way for the online service industries.

The online service industry has grown exponentially with a 276.9% increase in worldwide e-commerce sales over the most-recently tracked period. Blockchain ecosystems are preparing for just this bringing amongst other cost-saving attributes and investment opportunities into a secure marketplace.

View Statistics here: https://www.shopify.com/enterprise/global-ecommerce-statistics

The new Digital Economy will provide improved financial vehicles in an improved online marketplace to better serve the supply chain from the merchant through the affiliate marketer to the customer in a fair ecosystem where smart contracts and miners ensure due diligence.

Improving areas such as transaction volumes where transactions are expected to be settled in real-time with borderless low-cost payment structures whilst ensuring a user-friendly customer experience, a proper rewarding system for the back-end processes and energy efficiency to support a carbon-neutral economy.

The advent of a Digital Currency on such a platform further enhances the ecosystem to drive the value of the Digital Asset and the investment vehicle behind it. Key components with Blockchain efficiency allow for the ecosystem to collect, store and analyze data in the most proficient way.

The new era of Blockchain Technology has given Mathematicians, Scientists, Engineers, Developers, and Researchers the gateway to building new systems that will enable software packages to improve the Online Supply Chain experience but most importantly improve the way business is conducted.

There are of course many interesting aspects on how these new systems are being built and what better way to find out than go directly to the source, the creator, active researcher and co-founder of the aBey Blockchain.
Allow me to introduce Ciprian Pungila (PhD)

aBey

Q&A

#Q1 Scalability: What is the difference between the “historical landmark” and Side Chains by design in this Blockchain model?

Sidechains are mechanisms that have the potential to enhance the capabilities of blockchains in general, by moving assets to other blockchains, and they are extrinsic mechanisms associated with blockchains. By comparison, historical landmarks are intrinsic blockchain mechanisms (tightly integrated into the existing blockchain) because they allow for faster download times, faster integration with the blockchain’s mainnet and significantly less storage when doing so. Why download hundreds of GB worth of blockchain data (and waste bandwidth and time) to join a blockchain’s mainnet, when you could do the same with just a few MB? That’s the problem historical landmarks propose to resolve.

#Q2 Customer Refunds: The Mediators mining model on your Blockchain is an exciting industry first. How will this introduction to the Blockchain be tested before the launch and how does it affect scalability?

We have worked on retrieving high-profile names in the e-commerce, marketing, and payment processing industries to support the efforts and join as Mediators in our upcoming blockchain. A number of these partners have already approved and confirmed their presence, and we are confident more will join, as our platform grows in popularity and virtual presence. By design, Mediators are meant to be acting through tightly-coupled roles into the blockchain — scalability is therefore achieved easier than with loosely-coupled elements. Plus, there is the potential of only a minimum number of Mediators to achieve consensus, which is outlined in the whitepaper to be 6 by default, just as in a real court of trial. As with any small-numbered consensus participants, especially in tightly-coupled systems, scalability should not become a concern. The tests we are performing involve simulating real-world fintech transactions, alongside refund requests (to a percentage which is commonly found in the real world of e-commerce), in our own testnet, for example starting with 6, 18 and 72 Mediators.

#Q3 The Name function: Is the name function similar to EOS use case and will the Blockchain provide for all E-commerce functionality B2B, B2C, C2C, C2B, B2G, G2B, G2G

The EOS Name function, as I understand it from the Cypherglass initiative, is meant to allow people to buy names in the blockchain — and it is not an intrinsic feature of the blockchain, it’s a paid service on top of the blockchain itself. By comparison, names in our ABEY blockchain are completely free, optional and open for everyone to use, and they are an intrinsic feature of the blockchain — their purpose is to allow users to locate their peers faster (should they, of course, want that).

#Q4 Compatibility: How compatible will the Blockchain be with Linux or WordPress? Will there be a plugin version?

The blockchain’s code is cross-platform compatible, so it could run on Windows, Linux and MacOS X. As for WordPress, because we understand the potential this could bring to e-commerce served throughout WordPress-based websites, we do plan to create in the near future at least one sample plugin for e-commerce integration of the blockchain functionality into WordPress-based websites.

#Q5 UI Front End: Interaction between the different parties is very important. Equally the visual aspect of the products on offer.
How is the designing of the front end going?

The designing process is going well and so far, is on track. We have some very talented in-house designers in charge of UI/UX, and we aim to create an ergonomic, friendly user-experience, where people feel right-at-home when opening their wallet, for example, and the experience is intuitive enough to allow even newcomers to adjust quickly to the platform. Basically, we’re aiming for a feeling similar to that of e-banking, but with much less fuss and hassles, focusing on the practical and essential aspects of functionality only.

#Q6 Trust & Privacy: How do you aim to gain the trust of E-commerce users from traditional websites and protect their online privacy?

We understand trust is the major factor playing an important role in any e-commerce business. That’s why we have already acquired a few major partners to support the initiative, where the blockchain will be playing a major role in the way business is handled, and how it may be used to acquire products, goods, and services.

Additionally, as the coin grows in exposure, it will also attract more investors, which is why we are aiming to expand our presence in the crypto-exchange industry as well. So far, we have been listed on one public exchange, and are already in the talks with others about a near-future integration.

#Q7 Affiliate Marketing: The lifestream of E-commerce, Affiliates. How will you ensure traceability, accountability and precise distribution of funds on all levels of % based commissions?

Affiliates work with different business models in modern e-commerce. Some of those involve direct sales, others involve multi-level marketing, while some involve even more complex models. Because of this extrinsic complexity of the business use-cases, we introduce the Trusted Payment Gateway concept, which acts as a buffer between the merchant providing the product or service, and the affiliates making the sale to the final customer. The TPG is the one responsible for the distribution of funds, traceability, and accountability, and is business-specific and focused on the marketing-model used for it. As with many approaches we take in our platform, it is up to the merchant to choose which business model fits best for him, and for which products. He could choose to work with multiple such models of course, for maximizing profits. TPGs will be open for intrinsic competition in the blockchain, to the benefit of the customers and affiliates as well, and we will provide at least one full-disclosure open-source TPG for businesses to use for implementing the model.

#Q8 Investment: A fascinating additional attraction to your Blockchain is the “Lending” Implementation for Investors. Can you explain to us how this will work?

There are numerous ICOs nowadays for ensuring lending models in the blockchain. Because each model attracts a different type of investor (e.g. lenders), we have decided to simply open the door to making this a possibility — but it is up to the lender to cross that door and step further into the type of business model he wishes to use. To leverage the use-cases and improve the speed of development, we will provide at least one full-disclosure open-source use-case for lending, with technical details covered in the whitepaper. This way, multiple lenders can create a blockchain-focused marketplace for lending crypto, with different business models and intrinsic competition — which is generally to the advantage of the end-user.

#Q9 Alpha, Beta, Launch: Share with us your Roadmap in short.

An accurate roadmap shall be disclosed soon, as we have long-term ambitious goals and there is still a lot of strong research going on in many aspects of core functionality — some of which is reflected in past or upcoming research papers. In rough estimates, we plan to have a beta version of the blockchain itself by the end of the year, alongside its own dedicated programming language to support development coming shortly thereafter. Every functionality layer will follow afterward.

Thank you Ciprian, these are privileged times to be creating Blockchains Technologies and more importantly Blockchains that will improve real business use cases.

Next up is my interview with Philipp Sauerborn who has seen the potential and partnered with the aBey E-Commerce Blockchain.

Q&A

#Q1 aBey is a Blockchain-powered solution to many inherent problems in the current blockchain technology. How do you see the aBey Blockchain streamline your business here at aPay?

Using a proprietary approach to a transaction-focused blockchain as the core of the aPay platform, an technological advantage which is also shared with the aBey blockchain in many ways, we can benefit from the technical edge that the highly-innovative aBey blockchain solution offers, while dramatically enhancing the capabilities for lightning-fast transaction processing, an essential and core aspect of e-commerce nowadays. aBey offers people unique, innovative and pragmatical use-cases for high-volume e-commerce which can be built on top of decentralized/public blockchains. aPay and aBey are interoperable, but they do remain different in core aspects of functional behavior at the same time: aPay bridges the physical world with the virtual world of e-commerce, while aBey enhances the digital experience of e-commerce through innovative blockchain-based use-cases for the emerging world of blockchain-focused fintech.

#Q2 Products are of utmost importance in your E-commerce business. How do you see a Blockchain platform improving the customer’s product experience and overall experience whilst shopping online?

The main focus of our blockchain-based research and ongoing projects remains bridging the virtual world to the physical world of fintech, from two different perspectives: the tangible aspect, through aPay and its associated card and interoperability with the crypto-markets, and the open/decentralized aspect, through aBey and its associated use-cases for true private banking use-cases in the blockchain. We aim much higher than many ICO’s because our use-cases are much stronger and cover a larger spectrum of functionality which is essential to e-commerce business aspects. As we transition into a future of blockchain-based interoperability, we are the first to build, manage and enhance a powerful all-in-one e-commerce blockchain-focused infrastructure and ecosystem that bring it all together seamlessly. To the end-user, that means more transparency, less waiting times, and for business users, higher potential for more consistent, reliable income streams.

#Q3 Are there any restrictions in B2B communication (supplier & merchant), between the Blockchain and Web2 models as not all suppliers are on Blockchain?

We strongly believe that any restrictions (whether technical, functional, or bureaucratic by nature) between a blockchain and Web2 models can be tackled through proper infrastructure design and modularity. Since aPay is a payment processor, it can be used just like Visa or MasterCard, or PayPal — with little to no impact on the UX overall.

#Q4 Will you move your entire aPay business onto the eBay Blockchain platform and what challenges do you foresee?

We do not foresee any changes to the way the aPay business is handled or structured now. aPay is a permissioned blockchain, which is a fundamentally different concept than the permissionless aBey blockchain. Although aPay and aBey remain interoperable, they are fundamentally different by nature. We will utilize however the full potential of aPay and the aPay protocol to the full extent.

#Q5 Even more important than your product listing is your relationship with your customers and their shopping experience on the new Blockchain. How do you aim to grow your customer base, basket size, and margin on the aBey Blockchain?

I think your question is aimed more to a retailer, or business that would use our platform. aPay is a Platform as a Service (PAAS) provider. The businesses that use our service will find that their customers will experience the potential that the aBey blockchain offers, alongside the aPay business although they may not even realize it. aBey has exploded into the crypto space at an unprecedented rate and is quickly establishing itself as the next generation of blockchain. So far there are more than 100,000 adopters of aBey, within just a few months after launch. That is an 8x times faster growth rate than Bitcoin in the early days, which makes us very optimistic about its future and adoption. That proves us that we are not only on the right path. The early aPay adopters believe in us and in the future of the aBey blockchain and technology.

#Q6 On the other side is your suppliers. How do you intend to streamline the Supply Chain accountability and traceability aspects of your E-commerce business?

For the businesses that use our service, depending on the actual use case, the efficiency benefits will vary. In a service-driven and service-oriented commercial world, now and in the future, that includes millions of freelancers, small businesses, home workers, part-time entrepreneurs, students and for the sake of the argument any service provider that is either required or willing to deploy technology for the deliverance or administration of its services: using aPay and /or aBey can greatly contribute to accountability, being a core feature of both. Traceability goes hand in hand with accountability in the provision of services, as you would typically charge receive an invoice from your supplier after certain/pre-agreed milestones have been reached. But where traceability is a wanted side effect, certainly credibility aspects play a vital role in any blockchain adoption. For one the main benefit of blockchain technology: trustlessness. On the other side, in the case of our protocols the core feature of “reverse transaction” will greatly contribute to the credibility of suppliers using our technology and increase the credibility of their service provision.

#Q7 Targeted audiences with SEO and Back-Linking is a very important aspect of building traffic and getting customers back onto your platform. How do you see a Blockchain solution for improving your conversation rate and retain customers?

For any modern business, the data-based decision model becomes more and more important. In compliance with all data and consumer data protection regulations incl. GDPR, one can not only target audiences more efficiently. One of the great revenue models of the future for any company relying on the provision of quality data is to reward audiences for providing data willingly and for interaction on a platform. Blockchain and the facilitation of token/coins have fundamentally enhanced, if not even triggered this function, with the most prominent cases being Facebook (libra) and Block1 (Voice, EOS). This, however, can be brought into virtually any use case of blockchain including the aBey and/or aPay models. Reward clients for providing data, for sharing, for doing surveys, using offers, refer clients, vote, compare, author, like, promote, test, use — the possibilities are endless. This binds customers to a great extent, creates trust and builds a relationship — all of which are key aspects for conversion and retention.

#Q8 Technology goes hand in hand with the team that works behind it to make it all happen. How do you go about choosing the right people to drive your E-commerce business especially now with Blockchain integration?

Our philosophy is that the right people are those that which are led by life-long principles, long-term beliefs, and goal-driven passion. We aim to attract the right people by proof of example and by the transparency of the work that we accomplish, but also by the promise of a better future — one that, just a few years ago, seemed utopian. We lead by example, and we are reshaping the future of fintech technology as we know it, through innovations that will change the world in so many ways.

Thank you, Philipp, it is fundamentally important for businesses to be able to go through this intermediate process of Blockchain integration. For businesses to grow and scale in the future, Technological advantages will separate them from their competitors and ultimately ensure exponential growth.

By Morne Olivier

Freelance Writer | Digital Content Marketing > Blockchain Technology.

#morneolivier

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aBey E-commerce Blockchain | Q&A with the Creator and Partner was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

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A beginner’s guide to blockchain cryptography

Cryptocurrency was the top ranked tech Google search of 2018. This should come as no surprise because Bitcoin, the first and most well-known cryptocurrency, took off around 2015. With its meteoric rise, the hype of blockchain technology followed suit.

Blockchain is the underlying concept and framework that Bitcoin is built upon. As opposed to being dependent on a centralized system, such as using banks, social media companies, and cloud storage, blockchain is a decentralized system that leverages a distributed ledger of record keeping. This 2-minute video produced by the U.S. Treasury gives a great overview of blockchain.

In short, the defining features of blockchain that make it an enabler for innovations like cryptocurrency are that it’s distributed, decentralized, append-only, consensus-based, and perhaps most critically, secured by cryptography.

If blockchain is cryptocurrency’s underlying skeletal system, then cryptography is its nervous system — it enables information and identity to be securely exchanged between actors throughout a broad network. Cryptography is a vast field of applied mathematics, and in order to distill how it works and connect its importance to blockchain applications within a few paragraphs, I’ll develop an analogy to a classic social game.

Imagine playing the broken telephone game…

There are a few problems associated with this:

1. The original message has changed
2. The receiver doesn’t know that the message has changed
3. The receiver doesn’t know if the message came from the expected sender

In order to solve these issues, blockchain cryptography utilizes a digital signature (like a seal on the package) to verify and authenticate the transactions on the network.

The digital signature achieves the following purpose:

1. Ensures that the message has not been tampered with
2. Provides verification that it is the correct person who signed the paper (transaction)
3. No one else has forged or copied the signature
4. Once signed, the signature cannot be repudiated

How does the digital signature work?

To apply cryptography in the scenario of the telephone game, if person A wants to send message “peas” to person B, he/she would use a private key to “lock” the message. Locking the message here means encrypting the message with asymmetric cryptography.

Asymmetric cryptography uses a pair of public and private keys that are arithmetically paired but not identical. The public key can be shared with everyone, but the private key in the pair is kept secret. One key from the pair is used to lock the message, and only the opposite key can be used to unlock the message.

The best analogy I’ve heard describing this process is like turning a chicken into chicken nuggets- it cannot be reversed to figure out the original message. Person A’s message then turns into a set of random letters, represented as the secret message in the above illustration because the random letters do not make literal sense.

As illustrated, signing the message with the person’s unique private key creates the digital signature.

Sending the message

When person A is ready to share the message, he/she will send the original message, the public key(the other pair to the private key) and the digital signature as a package to the targeted receiver, person B.

Receiving the message

When person B receives and opens the package, he/she would take the public message from person A and run it through the same cryptography function to get the secret message. Person B keeps this secret message for verification purpose later.

Then person B will take the public key and the digital signature from person A, and run those through a cryptography verification function to get the secret message that’s embedded within the digital signature. Since the private and public keys are arithmetically paired, anyone else’s public key will not be able to unlock the sender’s digital signature.

Now, the receiver, B has both secret messages- one encrypted from the cryptography function, and one unlocked from the digital signature. He/she can compare if they are the same.

Again, we are not comparing the original message here because the chicken nuggets cannot be transformed back to the chicken. If both secret messages are the same, then the receiver knows the message and the sender are verified as intended. Otherwise, the receiver knows somewhere along that process, something has been changed.

I hope my attempt to simplify the blockchain cryptography concept provides a helpful overview for understanding this technology.

Thanks for reading!

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