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Understanding just what DevOps is and how it can be applied is a job in itself, but envisioning the end to end dynamic can be next to impossible if the mindset isn’t there. That is why in this blog rather than lecture the theory, we will explore a hypothetical team and their BAU adoption of DevOps from how tools are best utilised to adopting a multi-functional team that rewards enthusiasm and promotes proactive contribution. Our hypothetical scenario involves a company BetaNova Ltd, they have been fully agile for five years now and have spun up multiple Scrum teams, adopting end to end DevOps methodologies from Continuous integration and Delivery to test driven development. A new engineer Jack starts his day with a laptop and a development virtual machine (VM) already set up, authorisations, user access and all the accounts are preconfigured and active. The most common GitHub and Endevor repositories are cloned and linked. Aliases, shortcuts and prerequisites are installed for all the tools and are clearly visible. “Standardisation of tools and practices between teams make it easier for new people to hit the ground running regardless of where they are based. Each team can customise as they see fit.” Additionally, on the startup there is a clear link to vital resources, in a structured and coherent manner on all key aspects from getting started with the tools, to the on boarding process. Jack will get paired up with an experienced employee to walk through the testing and development process’s that he will adopt in his daily work. Jack starts by coding on his local machine for development work. This is the VM (Virtual Machine) and was spun up in conjunction with configuration management tools such as puppet or ansible to ensure that all the required software, installs and resources were present. This also means the set up process is completely idempotent i.e. runs the same on all machines, every time. More so, Jack’s machine is almost identical to the production live environment, he is able to run and test code locally - this means he can make changes to production-like code without having to infringe on active development code. This also reduces potential strain on shared resources and the developers actively working on changes. Prior to main branch deployment, BetaNova engineers can achieve a good degree of confidence by running a suite of local unit and functional tests. Depending on the size and complexity of the project, the team can either test their changes against a server ‘production-clone’ (a Jenkins or Octopus cluster) which is almost identical to live. This also allows them to adopt continuous integration, all this without committing to the master branch (route to live). At this point the engineers have an even further boosted degree of confidence that their change is clean. “The difference between Change and Version driven continuous integration is only dictated by the technology. It is akin to choosing between wet and dry tyres - it depends entirely upon the terrain.” BetaNova excels in both cutting edge mainstream technology as well as retaining a core legacy knowledge base for traditional customers and services. This means a challenge for the colleagues to maintain a duel and sometimes diametric mindset to the change process. This means understanding the difference between a change driven CI pipeline and a version driven pipeline. The difference appears subtle on the surface but like cracks in a foundation can have significant downstream affects if the wrong approach is adopted. For Go, Rust, React and modern technology deployments, commits happen rapidly throughout the day via GitHub. To take advantage of this the team have built a change triggered CI pipeline where the minute a developer wishes to commit, it is managed by intelligent deployment tools, automatically building, running tests and even dropping the code off to QA if necessary. This automatically starts to build the QA servers as well as running the full CI test suite. For traditional COBOL, Assembler and PL1 developers, code synchronisation is governed more rigidly. Continuous integration is not based on the latest changes but rather it is initiated when there is agreement between all interconnected components that they have the same version. This acts as a procedural safety net, preventing a new small superfluous change taking precedence over a structured coordinated deployment of components that have successors and dependencies. For the uninitiated the opposing philosophy (change vs Version driven) can appear completely alien and perplexing however for the veteran DevOps specialist, this is simply a change in the position at where the continuous integration kicks off. As in both cases, the code movement should be fully automated with the same standard for code quality adopted. It is effectively the difference between choosing wet or dry tyres, in that it depends on the terrain. **“Developers document all the fixes to standard problems and known issues in a centralised repository. This means that when ops are called out in the early hours they will have a goto resource for all known standard fixes - this can save hours of investigation every week.” BetaNova’s automated CI process takes in and around 5 to 15 minutes to deploy fully from start to finish. At any point during the CI if the deployments or tests fail, ample reports are available with monitoring tools that are not restricted to production in visibility. Additionally, developers are encouraged to add resolutions and fix history to a centralised repository that the whole team use. This means that when ops are called out in the early hours they will have a go to resource for known standard fixes - this can save hours of investigation every week. Depending on the companies commitment to code quality they can also integrate code scanning tools like SonarQube to catch both syntactic and functional human errors - even things as advanced as datatype mismatch can be caught. Additionally, BetaNova fosters a blameless environment culture, where senior engineers can occasionally pull code to ask for spot checks. Newer colleagues and junior members are partnered and skilled up but more importantly are encouraged to be a part of that process as early as possible as to prevent the feeling of ‘elitist programming’. “Team leaders and seniors should lead by example to foster a team of true equals. There should be no favouritism and technical silos should be discouraged.” DevOps works best when integrated with a progressive agile framework. For example, both junior and new colleagues are actively guided in the DevOps culture, and those showing passion and interest are supported from the top to grow and are acknowledged for their contributions. This can involve local team awards - from gift vouchers to recognition, as to dissuade the trend of the . bell curve favouritism In fact significantly more resources should be allocated to developing and struggling colleagues than the resident leaders. So much so that the recognition should be equally allocated to the struggling engineers who have finally caught up and overcome their challenges. Not simply because a team is as fast as its slowest member, but to foster a true DevOps culture requires the enthusiasm and support of all colleagues so that we move from a team of siloed experts to a true multifunctional team of specialists. The reason for this is, DevOps is not married to a specific technology or stack, as such the total number of possible problems scale with the complexity of tech employed. There is no ‘done’ point at which a DevOps engineer can consider themselves masters for all time, the value metric isn’t , rather it’s more focused on how you learn, adapt and apply continuous improvement principles. what you know Also published . here

DevOps in a Nutshell

Command and Control (C2) frameworks are essential in the Red Teamers and Attackers playbooks. They help hackers of all types to take over networks or turn regular computers into bots, creating a network of remotely controlled computers that can send spam or launch DDoS attacks. In the early days of the internet, a Command and Control attack would allow hackers to have full control of a physical server, now most of the targets are servers on the cloud. We can easily imagine the catastrophic effects if an attacker gained access to public services servers, being able to shut down power supplies entirely, gaining control of computers that are responsible for handling robot-assisted surgeries at hospitals, and so on. But how does a computer get infected? There are numerous ways of infecting a computer and starting a C2 framework, and they are usually done through well-known ways. Malvertising Vulnerable web browser plugins Phishing Direct installation of malware (usually done through physical access) Exploiting a vulnerable system (usually due to unpatched OSs and servers) After the infection is done, the attacker can launch a reverse shell in order to execute commands, start a ransomware attack, exfiltrate data, launch a DDoS attack or just sit there and wait for the appropriate moment to do their devilish things. The Command and Control structure As of now, everything might look overwhelming, but at the end of the day, a C2 server is pretty much a listener (C2 server) that can handle many reverse shells at once (C2 agents) plus the post-exploitation features that only the frameworks have. A reverse shell or remote shell takes advantage of a vulnerability to instantiate a shell session and access the victim’s computer, in order to redirect inputs and outputs from the user’s computer to the attacker’s machine. : Serves as a hub for the agents to call back to, agents will periodically call back to the server and wait for the operator’s commands. C2 Server : The agent is generated by the framework and is responsible for calling back to the server. It has special functionalities that cannot be found on simple reverse shells. Agents/Payloads : As the name suggests, it’s an application that runs on the C2 servers and waits for a callback on a specific port or protocol. Listeners : This is the process of the Agent calling back to the server. Beacons Most common C2 frameworks There are free and paid frameworks, they have some differences, but probably the biggest one is that the premiums are less likely to be detected by antivirus software. That does not mean that the free ones are bad, but since they are open-source, signatures can easily be developed. : is one of the most popular frameworks, it’s developed and maintained by , it is publicly available, can be downloaded for free, and comes bundled in the most popular penetration testing distributions. Metasploit rapid7 : This is an extension of the framework, it has a fully-fledged GUI written in Java that looks similar to , this is because they are both created by Raphael Mudge. Armitage Metasploit Cobalt Strike : This is arguably the most popular C2 framework next to , has its GUI written in Java to increase flexibility, and is maintained by . Cobalt Strike Metasploit HelpSystems : Marketed as a “Customizable Command and Control Center” or “C4” framework that provides a true adversary simulation-like experience and was created by Chetan Nayak or Paranoid Ninja. Brute Ratel : This is not a framework, but a comprehensive list of all C2 frameworks available, it is currently being maintained by and and I highly recommend you to check it out. C2 Matrix Jorge Orchilles Bryson Bort This was a small glimpse of Command and Control attacks and frameworks, there are plenty more to cover, like how to set up a C2 server and how to start an attack, but that is out of the scope of this story. Keep an eye out on the wild interwebs for the next stories.

Command and Control Frameworks in a Nutshell

“If you can’t measure, you can’t improve it” - some famous person Understanding the differences — the state of the system, based on gathering predefined sets of metrics or logs. Monitoring understand — the state of a system, based on exploring properties and patterns not defined in advance. Observability infer Why do we need monitoring? Monitoring should address two questions: what’s broken, and why? vs. is one of the most important distinctions in doing good monitoring with maximum signal and minimum noise. What why Why do we need observability? Basically, monitoring relies on capturing and displaying the data providing a restricted view of the system, whereas observability can anticipate the system's health based on the data it generates (logs, metrics, traces). Lots of software jobs (especially SRE) include different monitoring tech stacks, one might argue that you can make a living only from mastering those specific technologies. The tooling landscape might seem daunting. And at a first glance, it looks overwhelming, especially since each technology comes with a specific nomenclature like forwarder, indexer, exporter, data-source, controller, etc. When navigating through all these matters, we need to know the basics. System metrics vs application metrics Usually, system metrics capture infrastructure-related metrics such as CPU and memory consumption, disk I/O, network I/O, whereas application metrics refer to error rates, requests per minute, average response times. Agent vs agentless At times it might be needed that some kind of agent to be deployed on your system (e.g. Splunk forwarder, AppDynamics app agents), and in some cases there’s no need for an agent, for example, Prometheus which uses an HTTP pull model to populate a time-series database. Push vs. Pull monitoring Push model, the agents push their data to the monitoring system whereas pull model the system pulls data from the agents. The key difference is that in the push-based approach (Nagios, Zabbix) the central monitoring system knows quite a lot about the metrics whereas in the pull-based approach (Prometheus, Datadog) the main monitoring system knows nothing or very little about the metrics which are coming in. Tooling landscape Metric collection: , , Prometheus Stackdriver InfluxDB Log aggregation: , Fluentd Logstash Tracing: , , OpenTelemetry Jager Zipkin Performance monitoring: , , AppDynamics NewRelic Dynatrace Dashboarding and visualization: , Grafana Kibana Monitoring can mean a lot of things As a piece of advice, it’s important to understand that monitoring might be different from one company to another, nothing is written in stone. One way to measure the in an organization is to check the following aspects: observability Alerting: How many alerts are generated per week? What percentage of alerts are handled “out of hours”? Monitoring system configuration: Is the monitoring system under version control? How many Pull Requests/Change Requests are made to the repository containing the monitoring system? On-call rotation: Are the alerts fairly distributed and addressed by all teams ( )? Guide to understand your OPS “The phone should not ring” - M.B, because ” Paging a human is a quite expensive use of an employee’s time. If an employee is at work, a page interrupts their workflow. If the employee is at home, a page interrupts their personal time, and perhaps even their sleep” - Google SRE book

Observability and Monitoring in a nutshell

“We cannot solve our problems with the same thinking we used when we created them.”  - Albert Einstein Above is a picture of a robot thinking GitHub just recently introduced their AI tool called GitHub Copilot which helps software developers write code. But a lot of people don’t know what the GitHub Copilot is, some aren’t even aware it exists. That’s what this article is for, to tell you all about the GitHub Copilot. Let us Begin What is the GitHub Copilot GitHub Copilot is an AI pair programmer that helps developers write better code by giving suggestions based on the code being currently worked on. It was created by in collaboration with . GitHub Open AI GitHub Copilot is powered by , a new AI system created by OpenAI. OpenAI Codex has a broad knowledge of how people use code and is significantly more capable than in code generation, in part, because it was trained on a data set that includes a much larger concentration of public source code. GitHub Copilot works with a broad set of frameworks and languages, but this technical preview works especially well for Python, JavaScript, TypeScript, Ruby and Go. OpenAI Codex GPT-3 In simple words, GitHub Copilot is Artificial Intelligence that helps software developers write better code by providing valid and well-tested suggestions to the code they are currently working on. It can be seen as an AI assistant which provides developers with code based on the code they are currently writing. We have explained what GitHub Copilot is now let’s see How it works The way the GitHub pilot works is similar to how your phone’s keyboard gives you word suggestions based on the words you are currently typing. The Machine Learning model behind the GitHub Copilot has been trained on billions of lines of code — many of them are hosted and available publicly on GitHub itself. When you’re writing code, GitHub Copilot suggests code as you type. You can look through suggestions and decide either to accept or reject them, the same way you can choose whether to accept your phone’s word suggestions or ignore them. To figure out what you’re currently coding, GitHub Copilot tries to parse the meaning of a comment, the name of the function you are writing, or the past couple of lines. The company shows a few demos on its website, which you can see by clicking . here When you begin to write a function the AI then parses (understands) the start of the function and goes through its knowledge base to see if it has been trained on that function and if it has, it then gives you a code suggestion containing the function written in full. Since the GitHub Copilot is AI it learns so if you then accept its code suggestion it marks that off and if you don’t, it then learns from its mistakes and will be able to provide better suggestions to you or the next person to call that particular function. Now we’ve explained how the GitHub CoPilot works let’s talk about… How it Helps The GitHub Copilot helps promote developer productivity. As developers, we all know that most of the code we write isn’t groundbreaking it’s just routine like maybe writing a function to output a result for a test. Now imagine if you didn’t have to write that but instead you could have the GitHub Pilot do that for you. If you’re writing code every day, GitHub Copilot can be used to work with a new framework or library. You don’t have to read the documentation over and over again from start to finish as GitHub Copilot already knows the specific functions and features of the framework you’re working with. It could also replace many Stack Overflow queries😅. The GitHub Copilot also helps improve developer focus more on creativity rather than the grunt work of typing on the computer. As developers, our main jobs involve solving problems by writing code. And to do this we have to think outside of the box a lot and once we do that we then have to sit down and write the code to accompany our idea the GitHub pilot can help worth that by reducing the amount of time we have to sit to write code by giving us suggestions on the best code to put into our software to help us solve the problem. With all these, I’m sure some people are surely thinking… Won’t this be a Threat to Developers? As developers, most of our work involves dealing with people and thinking creatively and these are things AI doesn’t have nailed down yet. Imagine the GitHub Copilot sitting down to listen to clients' specifics about a job or having to listen to the boss talking about shipping a new feature early. In simple words, the GitHub Copilot cannot replace developers because all it can do is write computer code and as developers, our jobs involve way more than that. Conclusion The GitHub Copilot is just a new piece of technology and we have no idea how successful it will become or if it will even help us as developers or become a pain in the ass. But at the core of this let us remember that we weren’t sure about metal with an engine that moves around, we weren’t sure about we as humans living in a ball which spins around another big ball in fact we aren’t sure about how our days are going to go. In essence, our lives are filled with uncertainties what can we do but embrace them. First published here

GitHub Co-Pilot in a Nutshell

Time, as it grows old, teaches all things. -Aeschylus is an application that identifies the music used in movies, advertising, and television shows based on a short sample played. In this article, I will explain the technology Shazam uses to make its audio discovery software work. Shazam How does Shazam Work? Shazam identifies songs through something called an and a . Now to explain these words. audio/acoustic fingerprint Spectrogram What is an audio/acoustic fingerprint?7 An audio/acoustic fingerprint is a condensed digital summary that is generated by an An is a representation of sound typically using either a changing level of electricity voltage for analog signals or a series of binary numbers for digital signals. audio signal. audio signal In the case of Shazam, these audio signals are a series of binary numbers used to represent digital signals, these binary numbers can be sued to identify an audio sample or quickly locate similar items in an audio database (In case you aren't aware, an audio database is a database for audio). What is a Spectrogram A spectrogram is a graph representation of audio, each piece of audio is split into some segments over time, and from these audio segments, a graph is generated which plots 3 dimensions of audio -Frequency vs Intensity vs time. To efficiently search for a sound you need to efficiently describe it and the way to do this is by using a spectrogram. How does all this work in Shazam We've successfully explained the technologies used in Shazam as single concepts, now let us understand how they work together cumulatively to make up Shazam. When you ask Shazam to tell you information about a song like its name, author, etc, you give it an audio stream of the song in question via a microphone or some other audio input device. It represents the audio stream as a , the shazam algorithm then picks out the peak point in the audio stream via the spectrogram graph representation -Peak points are points of less background noise. spectrogram The Shazam algorithm then creates an audio fingerprint from the peak point and then indexes through the audio database for a song with a similar audio fingerprint and when it finds a successful match it then returns its results to the user. How Shazam updates its audio database Going through this article you might have been able to infer that a core piece of technology behind the success of Shazam is its extensive audio database, to put it simply without an up to date audio database Shazam won't efficiently meet the demands of its users and this will lead to a resulting loss in revenue. So how does Shazam keep its audio database updated? They do this through Industry partnerships with companies who document music. Shazam gets these companies to document music for them and then uses the data it gets from these companies to improve its audio database.

How Shazam Works in a Nutshell

This article summarizes my experience as a writer during the pandemic year 2021. Below you will find lots of resources and tools and my method and tips for heavy writing. My Background I have a degree in computer science. 👨🏽‍🔬 I use the scientific method and . Occam's razor I have researched for 30 years both in the academic and industrial fields. I don't have the absolute truth. I try to support my opinions with evidence. 🔍 They are my opinions and are very subject to change. 11 tools and secret sauces I Have a (very) long list of draft articles. I often skip the waiting queue and write about some inspirational source (and cite it). I use a different template in all my series. For example, I have an empty . Code Smell template I write Everywhere 🗺️ I proofread all my articles with HemingwayApp, Grammarly, and Google Translate (all free) 🔡 I parse the markdown in my articles and convert them to HTML to republish on many platforms at once. I heavily use the tag to avoid search penalties. 🔎 canonical URL I Follow very interesting people on Twitter and blogging platforms. ✨ I take a new course every week, usually on a subject that is far away from my comfort zone. I add many references and quotes to my articles. 6 Tips for Time Management I read a lot of articles early in the morning and bookmark them during the day. 🌅 I use Trello, Inoreader, Pocket, and Obsidian. I Use to focus when writing. 🍅 The Pomodoro technique I avoid perfection. I publish them when they are . ready Then I make corrections with other people's comments. Even after months. I my articles. Garden Dealing with Criticism I have different opinions than many other developers. Software Design is a creative activity. My articles are suggestions and not rigid rules. I try to have smart discussions. I have zero tolerance for hate speech and unprofessional comments. I never feed the troll. Common Criticism I get the same comments over and over again, so these are the common critics I get and my opinions. Readability I need to see the complete code in a sheet. If you need to see long methods/scripts to understand your solution, that's fine. I prefer to have small/reusable/testable functions. The code in your articles is not Compiling/Working/has errors I try to add code samples for clarity. Most of the code snippets work. Some of them are pseudo-code for educational purposes. 👨‍🏫 I have used 25+ different languages in my articles. I am not an expert in ANY of these languages. Languages are accidental, Software design is . essential I Have a trick in to improve the code. INSERT LANGUAGE Most of the articles are language-independent. The solutions try to avoid language perks and cleverness. Your solution is not performant/optimal I write about backend business software. 🖥️ This is the domain I've been working on. I am aware that some tasks require more performant solutions (for example DApps). I will always choose long descriptive names over smart performance optimizations. First, make the code right. ✔️ Then, optimize it . only if you have strong evidence Complexity is not enough evidence. You need a real benchmark in real use case scenarios. 📈 If I need to sort 20 elements in a collection, I will always choose bubble sort because it is easier to read. is the root of all evil. 😈 Premature optimization , , , , , , , are standard. 🙈 Helpers DTOs Singletons Nulls Setters Metaprogramming Castings Comments I write a lot on these anti-patterns with the reasons why I think we should avoid them. You can keep thinking they are good and that's fine. My arguments against them are in all articles. -I reply polite comments about them. A 15 lines long method is not 'long' IMHO, a 6 lines method is too long You can always break them using .  🛠️ refactorings You don't need to see the big picture and the details at the same time. 🌳 Trust your implementation and write good tests. Your solutions have too many indirections is our worst enemy. 🙅 Coupling We need to avoid direct relationships. You have too many rules and constraints There is just one rule: Always follow the 🔀 bijection

I Wrote More than 90 Articles in 2021 and Here is What I Learned in a Nutshell

When we learn about Ethereum, topics that surface first can be wide in scope, from Vitalik’s vision outlined in his white paper to the intricacies of smart contracts and DeFi. For many, though, there’s an eagerness to skip a few exits and drive right towards the bright and quirky world of that sell for $24 mill at Sotheby’s — to learn solely about NFTs and their role in decentralization, cryptocurrency, or the art world. .jpg primates Sometimes, we may even skip over the “learning” part altogether; I found myself asking questions about the basics of NFTs long after I had even purchased one. While NFTs exist on blockchains beyond Ethereum (as I’ll detail below), this is often where the journey begins…because of the early innovation that stemmed from this technology and the popular platforms showcasing work backed by Ether. But no matter where these creations live or what kinds of NFTs you’re looking to explore, I think it’s helpful to outline a few core observations that make this creative space so unique and worth pursuing. So without further ado, let’s tackle some common questions that may pertain to the NFT n00b or the avid collector who’s just here for the fun of it. Break it on down for me! What is an NFT? NFT stands for . Just like other tokens in the digital world, NFTs are assets that can be bought, sold, held, and used. The non-fungible part refers to the fact that they are non-interchangeable; they cannot be exchanged for an identical item of the same value. In this way, NFTs carry value in their uniqueness. Non-Fungible Token These assets live on the blockchain, and as we’ve discussed before, this technology records and stores information on a distributed ledger. In this case, the transaction of the NFT sale and ownership of said item is kept on the blockchain — establishing the person holding the NFT as the sole, true owner of it. NFTs can appear in the form of images, videos, tickets, certificates, information, and a whole host of other collectibles and goods. They also act as a vehicle for this particular item or piece of art. When an NFT is created, it is minted. is the process where the digital art is tokenized on the blockchain — ready to buy, sell, trade, or enjoy. Minting Why would someone want to own one? Think about the ways collectibles have operated in the tangible world over the years — maybe imagine someone you know who is a big collector of something. Pokemon cards? Beanie Babies? Warhols? Sometimes it’s a small hobby, and other times it’s a big part of their life or work. The items they collect may have a personal significance for the owner, or perhaps it’s purely an aesthetic choice or flex. Often, these collectibles are worth money or have accrued value over time. Their value is derived from a combination of . This works the same in the NFT world, too. scarcity and demand Perhaps the monetary value doesn’t really matter as much to the owner, but the artist, creator or story behind that particular thing is where they derive meaning. The surrounding it could present the greatest inherent value of all. community A person might collect something for any of these reasons — or all of them! It’s similar with NFTs. People create, buy and sell NFTs for different combinations of those motives mentioned above, except the territory is so new and vast that a lot of these elements are amplified. The stakes may be a little higher and these collectibles are an entirely novel concept. A key reason that people might care about NFTs so much is that they’re changing the meaning of to something entirely different — with an emphasis on creative sovereignty. Historically, many artistic endeavors have been known to represent a personal or even spiritual pursuit that doesn’t always translate to dollars. ownership What’s more, artists may feel their work is appropriated, misrepresented or misused (think of aaalllllll of Taylor Swift’s re-releases). Since the ownership of an NFT can’t be mimicked, this can provide a way for creators to truly own the content they’ve dreamt up…without a middle man or agent in charge of the marketing and bidding. This is especially the case when the platform where the art is produced or sold has some degree of decentralization*.* But are they useful? If someone buys the NFT with a cryptocurrency, it can go up (or down) in value. In this way, NFTs can be seen as investments. The total sales volume of the NFT market in the of this year was estimated at roughly $10.7 billion, according to . Beyond monetary value, many people love to show off their NFTs in different ways or various arenas. They can serve as avatars, often in the form of a profile picture for social media and gaming. inspired by bold or heart-warming elements are yet another example of NFT reach and scope. With all the buzz around metaverse realities, identity is certainly part of this wild ride…and the fact that people may come to identify with an NFT has so many implications (especially if they want it to represent them forever). third quarter DappRadar Twitter banners Play to Earn games utilize NFTs by allowing users to interact with different types of NFTs in-game. You can have an NFT that acts as a “land key”, giving you ownership or access to a digital property — or it can act as a virtual wearable item, like sneakers or jewelry. From art and music to governance, gaming to virtual real estate, or tickets for sports games or concerts — the list of spaces for NFTs does not yet have an endpoint in sight. Does owning an NFT mean I have the commercial rights to it? The rights to distribute or commercialize an NFT are not necessarily included with the purchase of this digital art. So if someone wanted to use their NFTs to build a business with them, they would likely need specific licensing to do so. Intellectual property rights are occasionally part of the ownership package when a person buys an NFT, but oftentimes they’re not — and this is important info to be sure of. Are all NFTs on the Ethereum blockchain? Nope. Although popular marketplace OpenSea carries NFTs that live on the Ethereum blockchain (remember those gas fees we mentioned? They’re part of that purchasing process for Ethereum-backed NFTs, too), there are other blockchains like Wax (via atomichub.io) and Avalanche where a person can mint an NFT as well. NFTs are also being explored on top of Bitcoin, a landscape that’s coming to fruition through exciting projects like (Digital Bitcoin Art). These endeavors aim to build on Bitcoin’s layer 2 protocols, which we’ve touched on . DIBA here is a term you may hear thrown around here, too, which basically means different blockchains working together. As we look to the future, interoperability could become more desirable in the world of creating, selling and owning NFTs. Interoperability Where are NFTs bought and sold? Popular marketplaces include Opensea, Rarible, AtomicHub and more. There are smaller, more curated spaces like Nifty Gateway, MakersPlace and SuperRare. Then you have brand-led experiences (like NBA Top Shot) that function as creator, inspiration and marketplace all in one. A lot of NFT action happens in-game, meaning people can buy or trade NFTs within the world of a game while they’re playing it. Experiences and items can be tokenized to become part of that environment, and this is something we’re seeing more as the metaverse develops. I’m an artist and I want in. How do I create an NFT or my own collection of them? Graphic designers and individuals with technical expertise (such as coding in Solidity for Ethereum-based NFTs) can collaborate to make NFTs from scratch. Generated art provides a way for NFT creators to replicate one element into many, displaying varied traits with the help of AI. But if an artist has digital work already created and can convert these pieces to .jpgs, they can go right to a marketplace like OpenSea to mint this artwork and make it a reality. Launching or purchasing an NFT of your own could be an entry point into a new community — or several. For many artists, this sense of belonging and kinship is a life-changing result of joining these collectives. Converting your .jpg to an NFT may be the first step to fulfilling a creative pursuit or purpose that’s delightfully different from corporate-run, government-sanctioned or fiat-driven constraints that are becoming a thing of the past. The Wrap-Up From Bored Apes to , we’re seeing creativity reinvent itself in a new medium right before our eyes…and it’s a beautiful thing. Weird Whales NFTs redefine ownership in the context of our rapidly changing tech landscape. They can also bring people into the world of crypto, introducing individuals to the possibilities of the blockchain and decentralized currency — much in the way that art and music has traditionally served as a gateway for communities and cultures to define themselves and adopt new practices, beliefs or attitudes. So the next time someone asks what a .jpg with a price tag can really do, who knows — you might feel compelled to wax poetic about your favorite artist or collectible and the unique value it holds *just* for you. NOTE: The comment about the Sotheby’s Bored Ape in this intro is an example of recent pricing around a set of NFTs from this collection, and is not meant to represent actual up-to-date price info. My avatar for my HackerNoon profile is a Crypto.Chicks NFT that I purchased and currently own at the time of writing this. None of the information in this post is financial or investment advice. This piece was originally published as a newsletter by and for the community. Womxn in Crypto

NFTs in a Nutshell: The Beginner's Guide To Becoming An NFT Degenerate

Lots of people say bitcoin’s dead. In some ways, they have a point. Its price chart looks like shit, everybody expects the bottom to fall out, and its on-chain data has only just started to shift in our favor (but not much). At the same time, the only signs of a bear market come from trading patterns that we also see during bull markets, leaving pretty much everybody confused and uncertain about which way the market will go. If you thought it was hard for me to sit on my hands from October to May, it’s even harder for me to buy into a market that’s bleeding and unsure. I guess this is what Rothschild meant when he said “buy when there’s blood in the streets, even if it’s your own.” Or as Warren Buffett said: “You pay a very high price for a cheery consensus.” To everything, there is a season Are you still waiting for bitcoin’s price to drop below $20,000? Trying to get that “last dip before $100,000?” At what price will you start to feel like the market is running away from you again? Or do you think prices can only go up from here and the next altseason will “melt faces?” What will you do if it takes a little while for the market to start rolling again? Maybe you spent all your money buying dips and you hope the price will stay low for long enough for you to get more money into the market? Perhaps you’re convinced crypto will spend the next year or two in a bear market? Whatever you’re looking for, this market will give it to you eventually. The question is—if you get the outcome you want, are you ready to take advantage of it? Nobody cares about what you think, Mark I know! I’ve had a newsletter for 15 months. People read it. Subscriber numbers grow. Many people renew their subscriptions. For the most part, they don’t seem to care what I say. In my first post and during much of 2020, I encouraged everybody to buy into the market. Few listened. Then bitcoin’s price started running—faster than I had expected and before it was ready to do so. I told people to buy altcoins. Few cared to. Then altseason arrived. Before those explosions, we saw clear, positive trends that usually lead to higher prices. Nobody took action until after prices went up. Did that mean prices go up just because those trends had formed? had to No, but it was more than worth the risk to find out. Not quite summer, not yet winter Today, we see no such trends. If anything, the market’s giving mixed messages—lots of bullish and bearish data. It reminds me of 2019. Bitcoin’s price ended that year higher than it started (and never went lower), but people called it a bear market anyway. Do you know why so many people took profits earlier this year? Because they had profits to take. How did they get their profits? They bought crypto during the bear market of 2018, the consolidation of late 2019, the black swan “bonus” crash of Spring 2020, and the altcoin collapse of September 2020, after DeFi summer. In other words, they bought when everybody else sold. You need to plant your seeds before the harvest— earlier. You can’t wait until the weather turns. months Yes, just because you plant seeds doesn’t mean you’ll get a bumper crop, even if you get the timing just right. A lot can go wrong along the way. Over the long run, the cryptocurrency market shows a clear, sustained uptrend. You can’t look at this chart of total market cap and come to any other conclusion. Ideally, this trend will continue and you will roughly double your return on your investment each year. Maybe the market goes up 500%, down 55% like we just did? Can you imagine how great it would be if you got that every year? Six months up, six months down? And you only buy when it goes down? That’s as much as 9x growth over two years. At that pace, you will never have to sell any crypto. Just use it, spend it, borrow against it, and enjoy your good fortune without ever having to think about an exit. Or, you can wait until prices go up again and settle for 2-3x ROI after you pay taxes, fees, and hit your entry and exit targets. Make a little money on the way up, sell on the way down, forget about it until the next all-time high, then do the same thing all over again. That’s safer, but not necessarily better. The grass is greener on the other side of the fence I sometimes hear traders pejoratively dismiss “the herd” when explaining market psychology. The idea being, you want to do the opposite of what feels safe and popular—whereas most people prefer safe, popular decisions. Contrary to popular belief, sometimes the herd is right. “It’s going to go lower” seems like the popular consensus. It’s hard to look at the reality of the data and price action and feel good about putting money into this market now. We’re still a long way from the strong trends we established from the end of 2019 through most of 2020. At the same time, we’ve seen this market do crazy things seemingly out of nowhere, often in defiance of logic, data, and sanity. It’s nice to have company, but sometimes that company keeps you from enjoying the greener pastures on the other side of the fence. In investing (as in life), you can’t just wait for things to happen. You have to take advantage of opportunities when they present themselves and let the market do whatever the market is going to do. Put the odds in your favor, understand the bigger picture, and act decisively—not recklessly—when the situation calls for it. Fortune favors the bold. Courage is not the absence of fear, but the ability to act in spite of it. Be bold. Have courage. Act decisively. If you wait for the market to return to a cheery consensus, you may end up paying a heavy price for it. Mark Helfman publishes the newsletter. He is also the author of three books and a top bitcoin writer on and . Learn more about him in his . Crypto is Easy Medium Hacker Noon bio Also published in Cryptowriter .

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