This cloud computing model has become increasingly popular over the past few years as a way to develop and deploy applications. It allows developers to focus on writing code and building instead of worrying about servers, operating systems, and other infrastructure-related tasks. Photo Credit: Kaleidico | Unsplash However, like any other technology, serverless architecture has its pros and cons. We'll discuss both in this article, as well as explain what it is in greater detail. New to BearPeak? -  Watch a client testimonial - Learn about  how we work - Read more  about us TABLE OF CONTENTS Understanding Serverless Architecture Pros 1. Cost-Effective 2. Scalability 3. Reduced Management Overhead 4. Faster Development Cons 1. Limited Control 2. Cold Starts 3. Vendor Lock-In 4. Debugging Understanding Serverless Architecture definition. Serverless Architecture is a cloud computing model where providers dynamically manage the allocation of computing resources. *The term "serverless" is actually a bit of a misnomer: Servers are still involved in the process, but the cloud provider manages the servers and resources, freeing the developer from that responsibility. The cloud provider takes care of the underlying infrastructure and provides an environment where developers can run their applications without managing servers, virtual machines, or operating systems. Developers write functions that perform specific tasks, and these functions are executed by the cloud provider in response to specific triggers or events. Examples of serverless architecture include AWS Lambda, Azure Functions, and Google Cloud Functions. In each, developers write functions that perform specific tasks, and the cloud provider takes care of the underlying infrastructure, allowing developers to focus solely on writing code for their applications. This approach has many benefits, but also some potential setbacks to consider: Pros 1. Cost-Effective Serverless architecture eliminates the need for maintaining servers and other infrastructure. Instead, you only pay for the resources you consume, such as function, execution, time, and memory usage. 2. Scalability This approach allows you to easily scale your applications up or down based on demand; You can easily handle spikes in traffic without having to worry about provisioning additional resources. 3. Reduced Management Overhead With serverless architecture, you don't have to worry about server maintenance, operating system upgrades, and other infrastructure-related tasks. Instead, you can focus on developing and deploying applications without the need for additional management overhead. 4. Faster Development Without worrying about infrastructure, you can write code faster and deploy applications quicker. Cons 1. Limited Control You can't customize the infrastructure to meet some specific requirements. 2. Cold Starts These can occur when a function is triggered for the first time, resulting in a longer response time. This can be a problem for applications that require real-time responses, like chat bots, online gaming, financial trading, video conferencing, or live streaming. 3. Vendor Lock-In Serverless architecture relies on cloud providers to manage infrastructure. However, this means that you are tied to the cloud provider's services and APIs, making it difficult to switch providers if necessary. Consider whether you may need to change your cloud provider service or API in the future; You may need to due to cost, security, or performance capabilities as your application grows and evolves. 4. Debugging It may be difficult to troubleshoot issues since you don't have direct access to the infrastructure. Serverless architecture has many benefits, but make sure to weigh the pros and cons before deciding to use it for your application. Still not sure whether to use serverless architecture or not? Or have you decided to give it a go but don't know how? Work with an expert developer here at BearPeak Technology Group ! It's important for us to disclose the multiple authors of this blog post: The original outline was written by chat.openai, an exciting new AI language model. The content was then edited and revised by Lindey Hoak. "OpenAI (2023). ChatGPT. Retrieved from https://openai.com/api-beta/gpt-3/" BearPeak Technology Group is a software studio based in Boulder, CO, offering studio, startup, strategy, and staffing services. Get in touch with BearPeak for a free consultation at bearpeak.io/contact .

There may no longer be cake in the break room, but remote workers are still reporting that they’re happy. From better work-life balance to an interest in more remote and hybrid work, employees in the software development field are open to new possibilities. Photo Credit: Austin Distel, Unsplash The trend of hiring programmers online spiked with the pandemic, and it doesn’t show signs of returning to in-office anytime soon. It’s become the new norm, so companies around the world are exploring ways to hire and manage talent online. Whether you're looking to hire or be hired, it's worth exploring the latest stats. New to BearPeak? -  Watch a client testimonial - Learn about  how we work - Read more  about us TABLE OF CONTENTS 1. Developers are open to remote work 2. Dev Shops have moved online 3. The preference for remote vs in-office work is split 4. Soft skills may be more difficult to evaluate 5. Remote jobs will keep increasing Hiring Programmers 1. Developers are open to remote work The first thing to consider is whether developers even want to work this way. If being forced to work from home during the pandemic had caused software developers to miss the office lifestyle, it’d be difficult for businesses to hire remote workers today. But a survey conducted by Stack Overflow (a popular online community for developers) found that over 41% of surveyed developers have kept working fully remote through 2022 and 2023. The demand for remote work among programmers has stayed strong since the pandemic, making it easier for companies to find qualified candidates who won’t require office accommodations. 2. Dev Shops have moved online Since the pandemic, the percentage of software developers working remote or hybrid has skyrocketed to over 80%. Stack Overflow's 2023 Developer Survey found that smaller organizations with fewer than 20 employees are more likely to work fully remote, while larger organizations are mostly hybrid. This is important to consider on both ends of employment: The one hiring and the one being hired. Consider where your business fits in with these statistics as well as where you’d like it to fit in the future. 3. The preference for remote vs in-office work is split A 2023 study by Statista found that 67% of respondents believe the biggest benefit to working remotely is the "flexibility in how to spend [their] time". In close second, the flexibility to choose their work location meant the most to 62%. It comes as no surprise that remote work allows employees more flexibility. Working around a family life, living in a different time zone, or living a digital nomad lifestyle are more readily available options than ever before. But there are also downsides to consider: A consistent schedule and a clear separation between work and home helped some employees with consistency and focus. Additionally, socializing with coworkers and feeling a strong sense of team pride is quite different through a computer screen. 4. Soft skills may be more difficult to evaluate According to a report by LinkedIn , 75% of recruiters face difficulties finding candidates with the right blend of technical and soft skills (such as communication and teamwork). Despite working hundreds of miles apart from one another, soft skills are still vital to team success and can be difficult to evaluate long-distance. However, new tools such as online coding tests and video interviews are making it easier for recruiters to assess candidates' skills remotely. Employers are also finding creative ways to practice team-building exercises, like video call hangouts and group celebrations. 5. Remote jobs will keep increasing According to a survey conducted by Upwork (a leading online freelance platform), "78% of hiring managers who engage skilled freelancers are confident in their ability to find the talent they need, compared to just 63% of those who don’t engage freelancers." Plus, "79% of businesses agree that working with independent talent enables their business to be more innovative." This is especially true for the technology industry, where companies increasingly rely on online hiring to find top talent. Whether you prefer working in-office, hybrid, or fully remote, this recent and sudden shift in employment can’t be ignored. It will be interesting to experience this change and see what happens as a result: less daily pollution from commuters? Office spaces left unused? Teams of people working together for years who’ve never met in person? But remote employees may have better work-life balances and happier experiences at their jobs than ever before; Only time will tell. Hiring Programmers Looking to hire a remote software developer for your project? Contact us to get connected! We always offer consultations and reviews of your current code. It's important for us to disclose the multiple authors of this blog post: The original outline was written by chat.openai, an exciting new AI language model. The content was then edited and revised by Lindey Hoak. "OpenAI (2023). ChatGPT. Retrieved from https://openai.com/api-beta/gpt-3/" BearPeak Technology Group is a software studio based in Boulder, CO, offering studio, startup, strategy, and staffing services. Get in touch with BearPeak for a free consultation at bearpeak.io/contact .

Do you ever wonder how Alexa understands what you’re saying? Like when you say, "Alexa, turn on my reading lights." How does she take the sounds you're speaking, turn them into words, and reply so effortlessly? " Sure. I’ve turned on the reading lights in your current room. " "Alexa, set a timer for 8 minutes. Let’s see if I actually learn how to create my own NLP app in just an 8-minute read…" " Sure. I’ve set a timer for 8 minutes. " Photo Credit: Jakub Żerdzicki, Unsplash If you’re looking to build an app that understands the human voice, you’ve come to the right guide. We’re BearPeak Technology Group: Engineers who help startups start up. If the contents of this article sound daunting, whether you aren't familiar with code or don't have the time to implement this yourself, we're here to help! Since it's our job to make you an NLP ninja today, we're going to dive right in with terms you'll need to know: Natural Language Processing (NLP) - I nteractions between computers and human language. With it, machines can understand, interpret, and generate speech. This opens up a world of possibilities for applications, like: Email filters Predictive text Translators (like Google Translate) Smart assistants (like Apple’s Siri and Amazon’s Alexa) If you're building your own app, your goal is to make one that correctly understands language and provides the correct response, right? Sounds simple enough, but as the Oxford English Dictionary has estimated, around 170,000 English words are in current use. That means infinite, or at least unlimited, sentence combinations! But no worries- you won't have to teach your NLP nursery rhymes or the alphabet song on repeat. NLP Models have been designed to help your app understand English fast and efficiently. Natural Language Processing Models - The backbones of NLPs. Models are algorithms that train with vast amounts of data to learn and understand patterns, rules, and relationships within a language. Some useful tasks include: Sentiment Analysis / Opinion Mining - Identifying emotions; analyzing and categorizing opinions expressed in text. This tool is useful for understanding client feedback by analyzing product reviews, social media posts, or survey responses. Named Entity Recognition (NER) - Identifying structured nouns; extracting information from text. This subtask locates and classifies entities like names, locations, time, and monetary values. Machine Translation - Just what it sounds like: AI's ability to automatically translate text from one language to another. Vector Semantics / Embeddings - Clustering words that relate. Word embedding categorizes words so the system can understand the relationships between them. After being trained with examples, models can learn to predict where new, unfamiliar words will go. We recommend Devopedia's lesson on Word Embedding for further reading. Vector Semantics identify similarities, opposites, and other relationships. Natural Language Toolkit (NLTK) - A popular Python library for NLP. It provides tools and resources for tasks such as: Tokenization - Splitting a text document into smaller units (called tokens). Tokenizers can break down a big page of unstructured text data into readable words and sentences. They will consider punctuation, special characters like question marks and exclamation points, even tweets with hashtags, mentions, and URLs. Stemming - Identifying words with a common denominator, or word stem. The words 'builders', 'building', and 'buildable' all have the same word stem: 'build'. This preprocessing step removes data redundancy and variations on the same word. Part-of-Speech (POS) Tagging / Grammar Tagging - Employing tokenization, word tagging identifies parts of speech: CC for conjunctions of coordinating words, JJ for adjective, VBZ for verbs, and dozens more. Check out Educba's Introduction to NLTK POS Tag for the full list and installation process. Syntactic Parsing - Structure is assigned to the text that has been tokenized and/or POS-tagged. This way, the model can understand relationships. Syntactic Parsing for the sentence: "What is the tallest mountain in Colorado?" Once the model knows the ins and outs of a sentence, from its structure to its parts of speech, it can better understand more search queries. Getting Started: Should You Build or Buy? Now that we've defined Natural Language Processing, Models, and Toolkits, how can you start building your own with them? You'll first need to decide whether your project can be completed using a pre-made platform, or if you need to build your own. By leveraging pre-made services, you can save time and effort. However, you may need more customization than a current service can offer. Let's expand on the benefits of each method: Pros of Buying a Pre-Made System Pros of Building Your Own - Quick & Efficient - Fully Customizable - Extensively Pre-Trained Models - Domain-Specific Knowledge Integration - Hands-Off Updates & Maintenance - Data Privacy & Security - Less Expertise Required - Excellent Learning Experience - Cheaper Price - Fine-Tune Performance Optimization - Pre-Planned Scalable Infrastructure - Long-Term Flexibility If the benefits in the left column matter more for your project, you can select a service and jump right in! If you're looking to build your own, don't worry: We have lots of ideas to help you build the best NLP model for your team, which we'll get to in a moment. Choosing a Pre-Made System With the abundance of NLP services available, it is crucial to choose the right one for your specific needs. When selecting an NLP service, consider factors such as the range of supported tasks, accuracy of the models, scalability of the service, and pricing chart. Some services offer free tiers and trial periods, providing flexibility before you make a commitment. Consider the ease of integration with your existing codebase and the availability of documentation and support. Choose an NLP service that aligns with your requirements and budget. There are pre-made services available that offer ready-to-use NLP capabilities; these provide APIs that allow developers to integrate functionalities into their applications without the need for extensive coding or model training. Some popular options include: Google Cloud Natural Language API Microsoft Azure Text Analytics API IBM Watson Natural Language Understanding For the remainder of this article, we'll assume the right column above suited your needs, so you're looking to build your own NLP Model. To get started with NLTK from scratch, you first need to install it using pip (the Python package manager). Once installed, you can import the NLTK library and start exploring its functionalities. Building & Training Your Own NLP Model Coupling Python with NLTK provides a powerful framework for building and training custom NLP models. You'll first need to gather and preprocess a large dataset. Here's a general process you can follow: 1. Consider the Scope and Purpose - What specific problem will your app solve? Does it only need to answer frequently asked questions about an air fryer? Will it provide in-depth analysis of the user's fitness tracker data? Will it narrate e-books out loud, therefore needing to comprehend tone and context? Clarifying the needs of your final product will help you identify the types of data you'll need to collect. 2. Identify Data Sources - Think about where you'd find the most relevant text for your app. It may come from pre-organized datasets, web scraping, APIs, social media platforms, forums, blogs, or academic resources. 3. Collect Data - Now that you've identified your sources, it's time to collect! Make sure to comply with data usage and privacy regulations, as well as obtain any necessary permissions if you're working with user-generated content or sensitive information. 4. Clean and Pre-Process - Raw text data contains noise, inconsistencies, and irrelevant information. This step involves tasks that standardize the text and tokenize it into individual words or sentences. You can refer to the tools listed earlier to better understand what each tool in NLTK will do. 5. Label & Annotate - Prepare the data with accurate labels and annotations that the model will learn from. Depending on your resources, you can either manually annotate the data or use existing labeled datasets. There are several annotation tools available that can facilitate the process, such as Prodigy, Labelbox, and Doccano. Implement a system for quality control, and keep in mind that annotation might require iterations. 6. Data Splitting - Divide your annotated dataset into training, validation, and test sets. This helps evaluate the model's performance on unseen data. Divide your labeled dataset into separate subsets that serve distinct purposes during the model development and evaluation phases. The primary goal of data splitting is to assess the model's performance on unseen data, which helps avoid overfitting and provides a realistic estimate of how well the model will generalize to new inputs. 7. Data Augmentation - Improve your model's performance by artificially increasing the diversity and quantity of training data. The idea is that the model learns to be more robust and generalizes better by seeing a wider range of variations in the data. Create new, slightly modified examples from the original data while preserving the original labels. Augmentation techniques could include synonym replacement, random insertion/deletion of words in the text, text masking, random swapping, or sentence shuffling. * A personal favorite of ours is back translation: Translate the text to another language, then back into the original language. 8. Data Versioning and Documentation - Keep track of the different versions of your datasets as they evolve over time. This is important because as you apply data augmentation or make changes to your dataset, you want to be able to recreate the exact conditions under which your model was trained. This can be more easily achieved by using a version control system like Git, clearly labeling your datasets, saving preprocessing scripts, and backing up your original data. Take the time to properly document your data too: Create README files for each version, document metadata, include code snippets, and include any licensing restrictions on the data so others know how to use it. Once your final dataset is ready, you can use machine learning algorithms to train your model on the data and evaluate its performance. Machine Learning in NLP As previously mentioned, machine learning plays a crucial role, enabling NLP model training on large datasets so they can learn patterns and make predictions. You could train models to perform tools like text classification, sentiment analysis, and named entity recognition. If you're building your own NLP model, you may want to check out some popular machine learning algorithms: Naive Bayes Support Vector Machine (SVM) Recurrent Neural Networks (RNN) By understanding the basics of machine learning and its application in NLP, the models you create will be more powerful and accurate. Deploying Your NLP App It's essential to follow best practices in software development: modular design, version control, automated testing, etc. Consider scalability, security, and performance optimization. By following a systematic approach and leveraging the right tools and frameworks, you'll deploy a robust and efficient NLP application. Alternative Approach: SpaCy While NLTK is a powerful NLP library, there is another popular library called SpaCy that is gaining traction in the NLP community. SpaCy is known for its speed and efficiency, making it a preferred choice for large-scale NLP applications. It provides pre-trained models for tasks such as part-of-speech tagging, named entity recognition, and dependency parsing. SpaCy also offers a user-friendly interface and supports multiple programming languages, including Python and Java. Alternative Approach: Java While Python is widely used for NLP development, Java also offers powerful libraries and frameworks for natural language processing. One such library is Stanford NLP , which provides a wide range of NLP tools and models. Stanford NLP supports tasks such as tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis, as well as pre-trained models that can be easily integrated into Java applications. Consider exploring Java's capabilities so you can best leverage strengths and choose the right tool for your project. Cloud-Based NLP Solutions These offer a convenient and scalable way to integrate NLP functionalities into your applications. Cloud-based solutions provide APIs that can be easily accessed and integrated into your code. You can offload the computational burden of NLP tasks to the cloud, allowing your application to scale seamlessly. They also provide features like language detection, entity linking, and text summarization, expanding the capabilities of your NLP applications. Conclusion Today, we've explored the world of NLP models and how to utilize them for a powerful application. Here's an outline of everything you learned: - Important terms including NLP, NLP models, and NLTK tools. - Tasks like sentiment analysis and vector semantics. - The benefits of buying a pre-made system vs. the pros to making your own. - 8 guideline steps to make your own NLP model. - How NLTK provides powerful tools and resources for NLP development. - How machine learning plays a crucial role in training NLP models. - The alternative approaches of using Java and SpaCy for development and the availability of NLP services and platforms. - Finally, we discussed the importance of choosing the right NLP service for your project and what best practices to follow when building and deploying NLP apps. Armed with this knowledge, you're ready to create your own powerful NLP app! New to BearPeak? -  Watch a client testimonial - Learn about  how we work - Read more  about us If you're interested in learning how to build more tools, check out our services . At BearPeak, we help startups start up! Founders work with our startup design studio to bring their idea to life. CEOs onboard our experienced Fractional CTOs for leadership guidance. We also connect startup teams with high-quality software developers. We aren't just recruiters, but engineers first, based out of beautiful Boulder, Colorado. It's important for us to disclose the multiple authors of this blog post: The original outline was written by chat.openai, an exciting new AI language model. The content was then edited and revised by Lindey Hoak. "OpenAI (2023). ChatGPT. Retrieved from https://openai.com/api-beta/gpt-3/" BearPeak Technology Group is a software studio based in Boulder, CO, offering studio, startup, strategy, and staffing services. Get in touch with BearPeak for a free consultation at bearpeak.io/contact .

The terms are often used interchangeably, but do they actually mean the same thing? Photo Credit: Desola Lanre-Ologun TL/DR: In short, the two terms have slightly different connotations and responsibilities. But in many cases, the distinctions are subtle and the roles overlap significantly. What is the difference between a software engineer and software developer? Let's expand on each term to compare. New to BearPeak? -  Watch a client testimonial - Learn about  how we work - Read more  about us TABLE OF CONTENTS Software Developer - Definition Software Engineer - Definition Main Takeaways Historical Evolution Industry Practice Overlapping Responsibilities Changing Industry Perceptions Utilizing the Difference Between Developers and Engineers Software Developer - Definition noun . A professional whose job is to "design, program, build, deploy and maintain software using many different skills and tools. They also help build software systems that power networks and devices and ensure that those systems remain functional." ~ Devry University This job primarily focuses on writing, testing, and maintaining code. Software developers usually enter the field with a bachelor's degree in computer science.  Software developers collaborate within a team of other developers, testers, and project managers to ensure successful implementation. Their main responsibility is to put in new features. This could require digging deeper on smaller components or working on modules of an application. Software Engineer - Definition noun . “Someone whose job is to create computer programs.” ~ Cambridge Dictionary A software engineer is also a professional who designs and maintains software systems. However, they may have a broader scope of responsibilities including system architecture, design patterns, and overall management. These responsibilities are given to team members with more experience making high-level technical decisions. Software engineers typically require a stronger educational background, including advanced degrees in computer science or engineering. Extra years of experience provide software engineers with the ability and confidence to take the lead on complex applications. They consider scalability, performance, and system integration. Software engineers may also interact with various stakeholders in the project, including clients, product managers, and designers. Just like software developers, though, software engineers benefit from continually learning, since the software world is constantly evolving. Main Takeaways If the dictionary terms bog you down, try thinking of it differently: Software developers mainly write and maintain code. Their job is to focus on the details. Software engineers write and maintain code too, but focus on the big picture: scalability, patterns, and higher-level architectural considerations. While these distinctions provide a general overview, it's important to note that the roles vary based on the organization, project, or specific terminology. You may come across instances where the differences aren't as pronounced. Ultimately, the skills and responsibilities of both developers and engineers contribute to the successful creation and delivery of applications. If these two terms aren’t synonymous, how did they reach the point of being used interchangeably in the first place? Historical Evolution The concept of software computing has been around less than a century. In its early days, there wasn't a standardized term for every role. Responsibilities and capabilities were continuously changing and growing. Staff adopted different titles that described similar roles. Therefore, the confusion over terms today can be attributed, in part, to a lack of standardization. Industry Practice Title variation isn't just an issue of the past: Different companies, industries, and regions still use varying job titles today. For instance, some companies prefer 'software engineer' to emphasize the technical aspects of the role, while others use 'software developer' to highlight coding and designing tasks. These title preferencescan lead to confusion, and we still lack universally agreed-upon definitions. Overlapping Responsibilities Both roles involve creating, testing, and maintaining software applications. But software engineers may also be involved in high-level design and architectural decisions. Software developers, on the other hand, could have a broader range of tasks that go beyond coding. Changing Industry Perceptions As we mentioned above, the software industry hasn’t been around for very long. As it evolved to its current state, the importance of design, architecture, and engineering principles in software development saw increasing recognition and importance. Both roles, software development and engineering, are seen as integral to the process. The interchangeable use of the terms highlights the industry’s fluid nature. In conclusion, software developers and software engineers are both pivotal roles to any application project. The terms have and will continue to evolve as software computing grows and its obligations change. Utilizing the Difference Between Developers and Engineers It's a great idea to understand the current definitions and role requirements. If you're looking to hire the right professional for your project, consider what both positions typically consist of. Do you need someone to fix intricate details? Provide an overarching plan for scalability? Maybe both? Check out BearPeak's software staffing to find the right kind of developer or engineer to take your project to its peak. It's important for us to disclose the multiple authors of this blog post: The original outline was written by chat.openai, an exciting new AI language model. The content was then edited and revised by Lindey Hoak. "OpenAI (2023). ChatGPT. Retrieved from https://openai.com/api-beta/gpt-3/" BearPeak Technology Group is a software studio based in Boulder, CO, offering studio, startup, strategy, and staffing services. Get in touch with BearPeak for a free consultation at bearpeak.io/contact .

On the 256th day of every year, we celebrate a very important holiday: The Day of the Programmer The date was well-calculated: 256 = 2^8. That means it's the highest power of two under 365 (since anything higher couldn't be a day of the year), the number of distinct values that can be represented with an eight-bit byte, AND the binary number for 1 0000 0000. To commemorate the occasion, let's take a trip down computer memory lane to some of the earliest computer programmers and their inventions. New to BearPeak? -  Watch a client testimonial - Learn about  how we work - Read more  about us 1667 | Binary Code | Juan Caramuel de Lobkowitz & Gottfried Leibniz Juan Caramuel de Lobkowitz, a Spanish Catholic scholar and bishop, published Mathesis biceps in 1667. His study included non-decimal counts that would be instrumental to the binary numerical system. However, credit for binary code often goes to the self-proclaimed inventor Gottfried Leibniz, a German polymath who published Explication de l'Arithmétique Binaire in 1689. Since he was in connection with Caramuel de Lobkowitz and was known to plagiarize, Leibniz's claims to have created the system independently are presumably false. 1801 | Textile Punch Cards | Joseph Marie Jacquard Joseph Marie Jacquard needed a better way to weave silk, so he punched cards with holes to select which cords of fabric went through the holes for different parts of a pattern. This solution allowed manufacturers to easily reproduce a design, and later allowed mathematicians like Charles Babbage to store and input data. 1843 | Note G | Ada Lovelace & Charles Babbage In the United Kingdom, Augusta Ada King, Countess of Lovelace, translated and extensively annotated a French description of lectures given by Charles Babbage, a colleague of hers. Her additions to the paper were longer than the original article itself. One part in particular, Note G, included an algorithm for generating Bernoulli numbers using Babbage's analytical engine machine. That calculation earns Ada Lovelace the title of the first computer programmer. 'Babbage's Analytical Engine' trial model displayed at the Science Museum in London. Photo by Rosa Menkman, (License CC BY 2.0, image changes made) Late 1800s | Analytical Engine | Charles Babbage & Henry Prevost Babbage Charles Babbage's machine was always a work in progress. He drafted designs for dozens of variations and wouldn't finish a model before jumping to the next. He continued making improvements to the analytical engine until his death in 1871. His son Henry picked up the project, and by 1910, it could calculate a list of multiples of pi (however they weren't entirely accurate). 1936 | The Turing Machine | Alan Turing The Babbages' ideas were finally mapped out into physical operations by Alan Turing, a scholar at King's College, Cambridge. In 1936, he published a 36-page paper delving into the theory of computation, including an 'a-machine', or what would later be known as the Turing Machine. The machine is a mathematical model describing an abstract object; It could never be fully built because it requires an infinite strip of tape. The computer has a head that can read one symbol on the tape at a time. On each symbol, the computer can choose to edit it or move the tape left or right. Turing's machine proved limitations of computers, and nearly all programming languages today are 'Turing complete', meaning the language could simulate a Turing machine. 1942-1945 | Zuse's Computers and Plankalkül | Konrad Zuse Konrad Zuse was a self-taught German engineer who made groundbreaking additions to computer programming despite being intellectually isolated due to World War II. Before the war, Zuse was a design engineer at the Henschel aircraft factory, where he was having to do many routine calculations by hand. This led him to experiment with computer construction, and in 1936, he completed the Z1, a floating-point binary mechanical calculator, in his parents' apartment. Zuse was called to military service in 1939. He was provided the resources he needed to build the Z2 (also built in his parents' flat), but his funding came from a company using his work to make glide bombs. In 1941, Zuse started his own manufacturing company and rented a workshop, where he improved the Z2 and built the Z3: the first fully-operational electromechanical computer. Allied Air raids destroyed Zuse's workshop containing the Z3, as well as the Z1 and Z2 in his parents' flat a year later. All Zuse had left was a partial Z4 design, which he was unable to finish until the war was over. The world's oldest surviving computer (Z4) in the Deutsches Museum. Photo by Clemens Pfeiffer, (License CC BY 2.5, image changes made) While building the Z4, Zuse worked on a PhD thesis that included the first high-level programming language: Plankalkül. He used his program as the first real computer chess engine. In his lifetime, Plankalkül was not as recognized or admired as Zuse had hoped. But as computer programming evolved, m any of the features in the Z4 and Plankalkül reappeared in later languages. 1945-1946 | ENIAC | John Mauchly & J. Presper Eckert, Jr. The Electronic Numerical Integrator and Computer (ENIAC) was a $400,000 government project built by Americans John Mauchly and J. Presper Eckert, Jr. during World War II. It was the first general-purpose electronic digital computer and marked the shift from mechanical to electronic computation. The ENIAC filled the 50 by 30 foot basement it occupied, and required tens of thousands of tubes, resistors, and capacitors, and generated so much heat that it needed its own air conditioning. 1947 | Assembly Language and the ARC | Kathleen & Andrew Booth Kathleen Booth was a programmer's assistant studying abroad in the United Kingdom when she wrote the first assembly code. She and her future husband were planning to create an Automatic Relay Calculator (ARC), so Kathleen created an operation-identifying language with 0s and 1s. Therefore, she provided a human-readable way to program computers using mnemonic codes to represent machine-level instructions. This made it much easier for programmers to write software for early computers. 1949 | Short Code | John Mauchly One of the inventors of the previosuly-mentioned ENIAC, John Mauchly, proposed the first high-level programming language for use with an electronic computer. He called it Brief Code, and the intention was to simplify the programming process by writing statements as mathematic expressions instead of machine instruction. However, it ran much slower than machine code, so it didn't rise to popularity. 1952 | Autocode | Alick Glennie The first compiled programming language was developed by British computer scientist Alick Glennie at the University of Manchester. This meant that high-level source code written by programmers could be translated into machine code directly without manual translation or assembly code generation. 1957 | Fortran | IBM IBM (the International Business Machines Corporation) developed Fortran, short for "Formula Translation". It was the first high-level programming language designed specifically for scientific and engineering calculations. It introduced the concept of compilers, making programming more accessible. Reflecting on the History of Programming These inventors and polymaths paved the way for the computers we know today. We hope you enjoyed learning about their creations and celebrated Day of the Programmer! Tune in for Part 2 of this History of Programming for the dozens of back-to-back innovations that occurred in the second half of the 20th century. If you're interested in learning more, check out our services . At BearPeak, we help startups start up! Founders work with our startup design studio to bring their idea to life. CEOs onboard our experienced Fractional CTOs for leadership guidance. We also connect startup teams with high-quality software developers. We aren't just recruiters, but engineers first, based out of beautiful Boulder, Colorado. Works Cited Cassel, David. “ Kathleen Booth, Creator of the First Assembly Language. ” The New Stack, 4 Nov. 2022. “ ENIAC. ” Encyclopædia Britannica, Encyclopædia Britannica, inc., 9 Aug. 2023. Hp. “ Computer History: A Timeline of Computer Programming Languages. ” HP® Tech Takes, HP, 9 Mar. 2022. Jackson, Joab. “ How Alan Turing Set the Rules for Computing. ” PCWorld, 22 June 2012. Lestal, Justin. “ History of Programming Languages. ” DevSkiller, DevSkiller, 26 Aug. 2021. Mullins, Robert. “ Department of Computer Science and Technology - Raspberry Pi: Introduction: What Is a Turing Machine? " Department of Computer Science and Technology, 2012. Norman, Jeremy M. “ Alick Glennie Develops the First Autocode, the First Compiled Programming Language. ” History of Information, 26 Aug. 2023. Photos Used Rosa Menkman's ' Babbage's Anaytical Engine ' trial model displayed at the Science Museum (London). Copyright CC BY 2.0. Image changes were made: Cropping and coloring. Clemens Pfeiffer's ' Zuse-Z4-Totale deutsches-museum.jpg '. Copyright CC BY 2.5. Image changes were made: Cropping and coloring. It's important for us to disclose the multiple authors of this blog post: The original outline was written by chat.openai, an exciting new AI language model. The content was then edited and revised by Lindey Hoak. "OpenAI (2023). ChatGPT. Retrieved from https://openai.com/api-beta/gpt-3/" BearPeak Technology Group is a software studio based in Boulder, CO, offering studio, startup, strategy, and staffing services. Get in touch with BearPeak for a free consultation at bearpeak.io/contact .

Startup founders are filled with the type of unbridled self-confidence that makes us feel we can accomplish anything. It's that brilliant belief that fuels the kinds of ideas that change the world! But when you believe you can do anything, you might just do everything , regardless of self-sacrifice or an objective assessment of the outcome. Why do so many founders think taking everything on themselves is the solution? Photo CreditL Dylan Ferreira, Unsplash “When was the last time you witnessed a protagonist in a movie succeed without the support of the other characters?... Leadership is no different.” - Brian Berner, Chief Executive.net New to BearPeak? -  Watch a client testimonial - Learn about  how we work - Read more  about us TABLE OF CONTENTS Adding External Tech Leadership An Internal Leader is Still Crucial Striking the Right Balance The Key: A Collaborative Environment Measuring Success Adaptation to Change The Hybrid Advantage The rat race is ingrained in American expectation, but Jeff Bezos isn't Amazon. Elon Musk isn't Tesla or SpaceX. Philip Krim isn't Casper, and Reshma Saujani isn't Girls Who Code. The biggest startup founders didn't get there alone, and they don't lead alone either. In the realm of startups, one of the biggest hurdles founders face is technology. You may have a great idea, a brilliant one even, but you don’t have all the skills to bring it to fruition alone. There is only so much one person can do, so as you grow, you assemble a team. But how do you regulate a team when you aren't an expert at what they do? Adding External Tech Leadership The solution on the rise is part-time external leadership. Hire a fractional Chief Technology Officer (CTO). A fractional CTO provides strategic, hands-on guidance to your company, typically on a contract or consulting basis. They provide: On-Demand Coaching Innovative Leadership Strategic Tech Insights Technology Investment Supervision Technical Team Management Team Strengthening Expert Guidance A fractional CTO ensures that the company's technology ecosystem is robust, secure, and aligned with its goals for growth and innovation. The role allows organizations to access high-level technology expertise without the full-time commitment and expense of hiring a permanent CTO. An Internal Leader is Still Crucial Remember that crazy self-confidence that founders have? It's contagious. A founder's dedication can inspire a team to work hard and stay committed, so having an internal leader is still very important. Startups face numerous challenges and setbacks along their journeys, so that determination is instrumental to navigating the ups and downs. Founders who formed the original vision are deeply passionate about their ideas. They play a significant role in shaping the company's culture: setting the tone for the organization's values, work ethic, and overall culture, which is crucial for attracting and retaining the right talent. Over time, founders develop important relationships and networks within their industry; connections that can be valuable for fundraising, partnerships, and business development opportunities. Additionally, founders have a deep understanding of the industry or problem they are trying to solve. This expertise can be invaluable in making critical decisions, identifying market trends, and adapting business strategy. Many investors are interested in backing startups with a strong founder-led team. When the original leader remains involved, it can help maintain a consistent vision and strategy, reducing the risk of a leadership vacuum that can occur when founders leave. However, founders are also more likely to experiment, pivot when necessary, and adapt quickly to changing market conditions. They possess the spirit of innovation. That said, it's important to note that while keeping the original internal leader can be advantageous, it's not always feasible. There may be situations where founders step aside or bring on experienced leaders to scale the company more effectively. It ultimately depends on the founders' skills, the needs of the business, and the goals of the founders themselves. Overall, founders continue to play a significant role, but the wise may recognize the importance of complementing their skills with additional talent, such as hiring a fractional CTO or other part-time senior executive. The key is to preserve the original vision while allowing the company to grow and evolve. #ShamelessPlug: BearPeak offers fractional CTO services  from leaders with decades of experience and excellent startup track records. We're happy to  get you connected  with our network of professionals, regardless of other arrangements. Striking the Right Balance 1. Assess Your Needs Before taking on a new team player, it's best to have a clear understanding of your startup's current technology needs and future goals. Determine the scope of work and expertise required from a fractional CTO. Not only will this help you track their performance, but will also give the fractional CTO a better understanding of where and how you need their help. 2. Hire an Experienced Fractional CTO Look for one with a track record of success in startups or similar industries. They should have technical expertise and leadership skills that will guide your technology strategy. 3. Integrate with Clear Roles, Responsibilities, & Goals Write and communicate explicit duties of your internal tech team and the fractional CTO, ensuring there is no overlap or confusion. Establish clear expectations like project timelines, deliverables, and performance metrics. 4. Team-Building Work with your fractional CTO to build, revise, and strengthen your internal tech team. Ensure that every member understands the company's vision and mission, and encourage open communication. 5. Collaborative Approach Foster an environment where your internal team and the fractional CTO work together seamlessly. Facilitate collaboration with regular, productive meetings and communicative status updates. 6. Knowledge Transfer An external leader is there to teach. Encourage your team to gain valuable insights and skills from your fractional CTO. 7. Flexibility & Continuous Communication Be flexible in adjusting the level of your fractional CTO’s involvement based on your startup's evolving needs. Maintain open and regular channels with your fractional CTO to ensure alignment with your business strategy and allow for quick adjustments. 8. Evaluate Progress Mark your calendar to periodically assess the progress of your technology initiatives and the impact of the fractional CTO's contributions. Adjust the balance as needed to optimize results. 9. Cost Considerations Keep an eye on your budget and expenses. Ensure that the costs associated with external leadership are justified by the value it brings. This might involve increasing or decreasing their role as the company grows, continuing to be communicative about when you do / do not need assistance. 10. Long-Term Planning Consider your long-term technology strategy: If it aligns with your business goals, you may eventually transition to a full-time CTO as your startup grows. Balancing internal and external fractional CTO leadership is an ongoing process that requires adaptability and strategic thinking. By carefully assessing your startup's needs, setting clear expectations, and fostering collaboration, you can leverage the strengths of both internal and external leadership to drive success. The Key: A Collaborative Environment Building a coordinated, synergetic environment is crucial. If you're looking to foster innovation, productivity, and a positive work culture, here are some ideas: 1. Openness: Clear Communication, Availability, & Feedback Culture Establish open lines throughout the team. Use collaboration tools like Slack , Microsoft Teams , or Trello to facilitate real-time communication and project tracking. Schedule regular team meetings to discuss progress, goals, and challenges. Encourage your team members to share updates and insights. Create a culture of constructive feedback where team members can openly share their thoughts and ideas without fear of criticism. Involve team members, including the fractional CTO, in decision-making processes whenever possible. Encourage regular performance reviews and peer evaluations. Show that their input is valued and taken into consideration. 2. Shared Goals & Objectives Ensure that everyone in the team, including the fractional CTO, understands and aligns with the company's mission. Set clear, achievable goals and regularly revisit them to track progress. 3. Collaborative Tools & Platforms Implement collaborative tools like Google Workspace or Office 365 to enable easy sharing of documents and real-time collaboration. Use knowledge-sharing platforms like Confluence or Notion to document important information, processes, and best practices. Encourage your team members to contribute and continually update these resources. 4. Team Building Activities & Rewards Organize activities, both in-person and virtual, to foster a sense of belonging and camaraderie. Recognize and reward team members for their contributions and achievements, whether through bonuses, promotions, or simply public acknowledgment. 5. Conflict Resolution Implement a clear conflict resolution process to address disagreements promptly and constructively. Don't wait until one happens to develop a plan on the fly. 6. Professional Development Having a fractional CTO means having an expert in both tech and leadership. Invest in the professional development of your team members by offering training and learning opportunities from your external leader. 7. Monitor Progress and Adapt Schedule regular check-ins with your fractional CTO to ensure they are integrated into the team. Involving them in key discussions and monitor the effectiveness of your collaborative environment. Make adjustments as needed based on feedback and evolving business needs. Remember that creating a collaborative environment is an ongoing process, and it's essential to be flexible and adaptable to meet the changing dynamics of your startup and your team. 8. Cross-Functionality Your fractional CTO can do more than assist your tech team: Encourage cross-functional collaboration by integrating your fractional CTO into teams that consist of members from different departments and see what groundbreaking solutions unfold. 9. Lead by Example As the startup CEO, set an example by demonstrating collaboration and openness in your interactions; The energy you send out comes back to you. Measuring Success To see the hybrid benefits you're earning, review your Key Performance Indicators (KPIs). 1. Technical Milestones How do the final products compare? Monitor the completion of product releases, feature implementations, and system improvements. These can be big-picture indicators of the team's progress and effectiveness. 2. Code Quality What's the quality of the code being produced? This can be measured through code reviews, the number of bugs / issues reported, and the time required to fix those bugs. Is the overall quality improving? Are bugs and issues being reflected on to learn from and look for next time? With new team members or new technology integrations, there may initially be more errors and difficulties than before; It may feel like taking one step forward and two steps back. But observe what happens after that potential initial setback. 3. Project Timelines Are deadlines being met? Delays can be a sign of inefficiencies, resource constraints, poor communication, or a lack of teamwork. 4. Team Productivity What is everyone doing? Look at the number of tasks completed per team member, the velocity of their work, and the utilization of resources. 5. Customer Satisfaction What do your customers or end-users think? Collect feedback to gauge their satisfaction with the product's performance and features. 6. Technical Debt Any accumulation of technical debt? Keep an eye out, as this can slow down development in the long run (Metrics like the ratio of new code to refactored code can help in assessing this). 7. Cost Efficiency & Budget Adherence How are finances? Check if the tech team is managing its budget effectively and if it aligns with the startup's monetary goals. Compare expenses to the outcomes achieved, such as the number of features developed per unit cost. 8. Innovation and Tech Trends What's new? Assess how well the tech team and fractional CTO are staying updated with industry trends and incorporating innovative technologies. 9. Team Morale How's everyone feeling? Employee satisfaction and retention rates can indicate the effectiveness of the fractional CTO in managing and leading the tech team. 10. Security and Compliance How many security incidents have there been? And how severe have they been? Ensure that the team is adhering to security best practices and compliance requirements. 11. Scalability What should happen next? A year from now? Five? Evaluate the team's ability to scale with the growth of the startup. This includes assessing the ease with which new team members can be onboarded. 12. Strategic Alignment Confirm that the tech team's work aligns with the startup's strategic objectives and vision as guided by the fractional CTO. Regularly reviewing these KPIs and metrics can help you assess the performance of the fractional CTO and the tech team, identify areas for improvement, and make informed decisions to support the startup's success. Adaptation to Change A founder’s role is expansive. Remember not to get bogged down in the tiny details. Revisit your first ideas often. Your first sketches, logo, scribbled ideas, and milestones. This will help encourage you towards your next goal, as well as booster your team’s spirits and drive the full team, internal and external, towards the goals you dreamed of. The Hybrid Advantage No matter your leadership team’s construction, what matters above everything else is the people. The right people will remain dedicated, communicative, and open. BearPeak prides itself on being made of ‘the right people’. If this article has helped you, consider connecting with us! Check out BearPeak's strategy solutions to find fractional CTOs who will to take your project to its peak. It's important for us to disclose the multiple authors of this blog post: The original outline was written by chat.openai, an exciting new AI language model. The content was then edited and revised by Lindey Hoak. "OpenAI (2023). ChatGPT. Retrieved from https://openai.com/api-beta/gpt-3/" BearPeak Technology Group is a software studio based in Boulder, CO, offering studio, startup, strategy, and staffing services. Get in touch with BearPeak for a free consultation at bearpeak.io/contact .

You've heard that 'it's all a numbers game,' but how do you put that wisdom to work? Interpreting statistics can be challenging, especially when you don't know the cause or significance of your data. If you want to understand jargon-laden analyses like "a resilient growth trend of a 15% MRR increase, underpinned by a successful Reactivated MRR, offsetting the Churn MRR's impact and illustrating robust customer retention strategies," we're here to help! Today, we'll be explaining a key metric for subscription-based businesses: Monthly Recurring Revenue (MRR). New to BearPeak? -  Watch a client testimonial - Learn about  how we work - Read more  about us TABLE OF CONTENTS Monthly Recurring Revenue - Definition 1. New MRR - New Customers 2. Expansion/Upsell MRR - Upgrades! 3. Contracted MRR - Downgrades 4. Churned MRR - Cancellations 5. Reactivated MRR - Returners 6. Net New MRR - The Sum How to Use Your MRR Data Financial Health Assessment Performance Evaluation Forecasting Churn Analysis Investor Relations Team Alignment Measure What Matters Monthly Recurring Revenue - Definition noun. The predictable monthly income generated from a subscription-based product or service. An example of a SaaS startup's MRR report. To fully understand an MRR report, know the following types: 1. New MRR - New Customers Any new Monthly Recurring Revenue from new customers. e.g. This month, your company took on one new client who is paying $40/month for your service. Your New MRR for the month is $40. 2. Expansion/Upsell MRR - Upgrades! Additional revenue from current customers when they upgrade to a more expensive subscription. e.g. The next month, your client upgrades to a more expensive plan, increasing their subscription from $40 to $60/month. Your Expansion/Upsell MRR is the additional $20 in revenue. 3. Contracted MRR - Downgrades This is any revenue lost from current customers who downgrade their subscription. This will be represented with a negative number. e.g. The following month, your client no longer needs the same plan and downgrades to one of your cheaper subscription options. They choose to downgrade from their $60/month plan to $20/month. Your Contracted MRR is -$40. 4. Churned MRR - Cancellations Revenue lost from customers who cancel.* e.g. The next month, your client no longer needs your service. They cancel the $20/month subscription, meaning your Churned MRR is -$20. * Depending on your source, churned MRR may count downgrades and cancellations . If you choose to add them together, make sure to adjust your Net New MRR equation by removing Contraction MRR (see below). 5. Reactivated MRR - Returners When customers who previously churned start paying for the service again. e.g. The client who previously canceled their subscription comes back at $40/month, giving you a Reactivated MRR of $40. 6. Net New MRR - The Sum The new MRR that takes into account all of the metrics above: The month's expansion/upsell MRRs, contracted MRRs, churned MRRs, and reactivated MRRs. Here's the equation: Net New MRR = New MRR + Expansion MRR - Contraction MRR* - Churn MRR* * If you're calculating Churn MRR as the downgrades plus the cancellations, you don't need to include Contraction MRR in the equation above (as it would be redundant). How to Use Your MRR Data Financial Health Assessment MRR is a key indicator of your startup's financial health: Are you growing, stagnating, or declining? This will help you make better-informed decisions about your startup's future. You can identify growth trends, measure the effectiveness of your marketing and sales efforts, and adjust your strategy accordingly. Performance Evaluation Which subscription plan is most popular? Which one do people swap to? Swap from? The detailed MRR metrics can help you evaluate the performance of your pricing strategies. Track which channels / plans drive the most revenue, and allocate resources accordingly to optimize for maximum revenue. Forecasting Use your MRR data to make revenue forecasts. What time of year do you typically gain the most New MRR? When do people increase their subscription? When do they back off on spending and swap to a cheaper plan? Planning for the future is crucial for budgeting, resource allocation, and determining when to scale. Churn Analysis How many customers are churning? And when? How can you disrupt this pattern and retain customers longer? This is often more cost-effective than acquiring new customers, and can bring all the benefits of a loyal consumer base and their glowing reviews. Investor Relations If you're seeking external funding (such as from venture capitalists or angel investors), MRR is a key metric they'll want to look at. A strong and consistent MRR growth trend can potentially lead to better funding opportunities. Team Alignment MRR can serve as a clear and measurable goal for your team. Review the data with your team to align everyone around a common objective: increasing recurring revenue. Measure What Matters MRR metrics are essential for startup founders to assess financial health, track growth, make data-driven decisions, attract investors, and more. By regularly monitoring MRR and other relevant metrics, you can navigate the challenges of building a successful subscription-based business more effectively. For further reading, we recommend Visible's Monthly Recurring Revenue (MRR) Explained: Definitions + Formulas.   Need a second pair of eyes to analyze your MRR data? Connect with BearPeak! Our experienced leaders provide guidance and leadership experience, including how to interpret and improve your MRR results. It's important for us to disclose the multiple authors of this blog post: The original outline was written by chat.openai, an exciting new AI language model. The content was then edited and revised by Lindey Hoak. "OpenAI (2023). ChatGPT. Retrieved from https://openai.com/api-beta/gpt-3/" BearPeak Technology Group is a software studio based in Boulder, CO, offering studio, startup, strategy, and staffing services. Get in touch with BearPeak for a free consultation at bearpeak.io/contact .

Let's discuss what CI/CD is and how its revolutionary structure can benefit your business. We don't need a word counter to tell you what themes will appear the most: 1. Continuous 2. Automation The CI/CD procedure harnesses the practicality of uninterrupted, constant development. Sections that suffer from human error and take valuable time away from developers are instead automated. CI/CD - Definition definition. Continuous Integration and Continuous Deployment (or Delivery) is a set of procedures that streamline code changes for improved software development. It “introduces ongoing automation and continuous monitoring throughout the lifecycle of apps, from integration and testing phases to delivery and deployment. Taken together, these connected practices are often referred to as a CI/CD pipeline.” - Red Hat Let's break apart those sections: Continuous Integration definition. Continuous Integration refers to the procedure where copies of work are constantly merged. “Developers regularly merge their code changes into a central repository, after which automated builds and tests are run.” - Amazon Web Services . In the figure eight above, the steps that correlate to Continuous Integration are: 1. Plan - The precursor step: mapping out the procedure. 2. Code - Continuously writing and integrating code into a shared repository. 3. Build - Compiling and assembling code into executable artifacts / packages. 4. Test - Running automated tests to ensure the code functions as expected. Continuous Delivery definition. Continuous Delivery refers to automating the process of delivering code changes to a production-like environment, which tests and validates the code. “By developing in short cycles, teams can reliably release their software at any time… this reduce[s] the cost, time, and risk of delivering each change.” - Synopsys The pipeline steps that correlate are: 5. Release - Preparing and packaging the app for deployment, 6. Deploy - Moving the app to the staging environment for further testing. 7. Operate - Managing in the staging environment; tasks like configuring servers, setting up databases, and ensuring the environment is stable. 8. Monitor - Tracking the app's performance and health (which is an ongoing process). Continuous Delivery allows final testing and approval before actually deploying the code to production. The decision is typically made manually. Delivery Vs. Deployment The concept above is taken a step further: Once the code changes pass all tests and quality checks, they are automatically deployed without manual intervention. This means that the key difference between ‘Delivery’ and ‘Deployment’ is the level of human intervention in the decision-making process. Therefore, which the better option for your project? That may depend on these key factors: Is there a desire to achieve faster release cycles? What is your organization’s risk tolerance? Is there a systematic or legal need for final human approval before releasing changes into production? Consider these with your engineering team to pick the best process for your startup. Benefits of CI/CD Faster Development & Fewer Errors Since CI/CD automates the building, testing, and code change deployment process, it reduces manual errors and speeds up the cycle. No developer wants to release faulty code by mistake; CI/CD makes it less likely for errors to be deployed to production, which minimizes downtime and potential end-use issues. Improved Collaboration Developers can focus on writing code while operations teams manage the deployment process. Collaboration streamlines communication and also fosters a more efficient environment. Continuous Feedback Through automated testing, developers receive immediate feedback on their code changes. This feedback loop helps them identify and address issues early in the development cycle, promoting a culture of continuous improvement. Real-World Examples: Amazon The online retailer's move to the Amazon Web Services (AWS) cloud "spurred a transition to a continuous deployment process that allows any developer to deploy their own code to whichever servers they need, whenever they want." - TechBeacon.com . This transition reduced the amount and duration of outages, which increased revenue. Netflix Their custom-built platform called Spinnaker "allows Netflix to deploy code changes to production environments in minutes rather than hours or days... Before using CICD pipelines, the website becomes unavailable; why push the updates on it?" - Shiksha Online Etsy "Etsy struggled with the development of their organization because they adopted the monolithic architecture." They adopted DevOps practices with a "CI/CD pipeline [that] helped in deploying services about 50 to 100 times a day." - Medium.com . They started automating integration with the tool Jenkins and developed Kale to monitor deployment. Challenges & Considerations While CI/CD pipelines are being implemented by top-name brands, it still may not be best for your project. Consider each of these potential outcomes with your team before rolling out a new structure plan: Complex Growth & Skillset Gaps As your application and pipeline evolve, the configuration files and scripts get more elaborate. Managing these can become complicated, leading to potential errors and inconsistencies. Team members may need to acquire new skills to effectively work with CI/CD tools and updates. Providing training and support will be essential. Storing and version-controlling pipeline configurations is also crucial. Use tools like Git to manage files and ensure consistency. Test Environments & Coverage Ensuring comprehensive test coverage can be challenging, especially as the app becomes more complex. Setting up and maintaining test environments can be resource-intensive. To address this, identify which areas need testing and maintain a balance between unit, integration, and end-to-end tests. Containerization and infrastructure as code (IaC) can help. Scalability As your application grows, you'll need to ensure that your CI/CD pipeline can scale with it. This includes scalable infrastructure for running tests and deploying to multiple environments. Security Testing It can be challenging to keep up with evolving security threats and vulnerabilities, but integrating security testing into the pipeline will be essential . Regularly update your security tools and practices, and manage access control and permissions. Implement robust authentication and authorization mechanisms to prevent unauthorized changes or deployments. Third-Party Integrations Integration Dependencies. If your pipeline relies on third-party services or tools, you are dependent on their reliability and availability. Consider redundancy or fallback mechanisms for critical integrations. Compliance and Regulation Depending on your industry, you may need to adhere to specific compliance standards. Ensuring that your CI/CD pipeline complies with these standards can be a complex, ongoing effort. Handling Failures & Preparing Rollback Strategies Despite all precautions, failures can occur. Having well-defined incident response and recovery procedures is essential to minimize downtime and data loss. Consider the rollback complexity and whether you need a "canary" or "blue-green" deployment strategy to minimize risks. Addressing these challenges requires a combination of technical solutions, process improvements, and a commitment to continuous improvement. Successful CI/CD implementation involves not only selecting the right tools but also fostering a culture of collaboration, communication, and adaptability within your development and operations teams. New to BearPeak? -  Watch a client testimonial - Learn about  how we work - Read more  about us Wondering if CI/CD is right for your team? We're here to help ! At BearPeak, we can analyze your company's framework, needs, and goals to determine if CI/CD would be a beneficial new structure. It's important for us to disclose the multiple authors of this blog post: The original outline was written by chat.openai, an exciting new AI language model. The content was then edited and revised by Lindey Hoak. "OpenAI (2023). ChatGPT. Retrieved from https://openai.com/api-beta/gpt-3/" BearPeak Technology Group is a software studio based in Boulder, CO, offering studio, startup, strategy, and staffing services. Get in touch with BearPeak for a free consultation at bearpeak.io/contact .

We have brilliant AI models at our fingertips, so why not ask the big questions? To celebrate National Techies Day, we asked ChatGPT what it thinks will happen in the future of tech, as well as what it would choose to invent. Photo by Levart_Photographer on Unsplash "The primary goal of AGI should be to benefit humanity. It should be designed to address pressing global challenges, such as climate change, healthcare, poverty, and education." - ChatGPT, OpenAI New to BearPeak? -  Watch a client testimonial - Learn about  how we work - Read more  about us TABLE OF CONTENTS Next Year (2024) Five Years (2028) Fifteen Years (2038) What Would ChatGPT Invent? BearPeak: What innovations/inventions do you predict will be made in the tech industry 1 year, 5 years, and 15 years from now? ChatGPT: Predicting specific inventions that humanity will make in the next year is a challenging task as it depends on a wide range of factors, including scientific breakthroughs, technological advancements, market demands, and unexpected discoveries. However, I can suggest some general areas where we might see notable inventions and innovations. Please keep in mind that these are speculative and may not be entirely accurate. Next Year (2024): Artificial Intelligence First, ChatGPT made forecasts in its own field. CG: AI will continue to advance, with improvements in natural language processing, computer vision, and reinforcement learning. Expect to see improvements in few-shot and zero-shot learning capabilities, enabling models to perform tasks with limited training data. Enhanced support for languages other than English and improved cross-lingual understanding will likely continue to evolve, [too, which] can have significant implications for global accessibility and communication. AI-driven applications in healthcare, finance, and customer service are expected to proliferate. BP: How exactly could advancements in AI help the healthcare industry? CG: Advances in medicine and healthcar e are ongoing. In the next year, we may see innovations in treatments for diseases like cancer, Alzheimer's, or diabetes. Personalized medicine and gene editing technologies like CRISPR-Cas9 could continue to make significant progress. 5G Expansion and IoT CG: The rollout of 5G networks will continue, enabling faster and more reliable connectivity. Widespread Adoption of 5G IoT Devices will benefit from lower latency and faster data speeds, making real-time applications like remote surgery, autonomous vehicles, and augmented reality more feasible and reliable. 5G will be instrumental in the development and expansion of smart city initiatives. We can expect to see advancements in traffic management, public safety, environmental monitoring, and energy efficiency. Quantum Computing CG: While fully-fledged quantum computers may not be realized within a year, there could be significant advancements in quantum computing hardware, software, and algorithms... [This] may lead to practical applications in fields like cryptography, optimization, and drug discovery. Renewable Energy Tech CG: The drive for renewable energy and sustainable solutions is likely to lead to inventions in areas such as advanced solar panels, energy storage systems, and more efficient wind turbines. Continued innovation in renewable energy technologies will contribute to a more sustainable future. Space Exploration CG: Inventions in space technology, such as improved propulsion systems, advanced space habitats, or lunar and Martian exploration tools, may continue to emerge as space agencies and private companies push the boundaries of human space exploration. Five Years (2028): Advancements in one year's time may feel more tangible, feasible, and easier to predict. What predictions emerge when ChatGPT looks further into the future? AI Integration CG: AI will be deeply integrated into various industries, including healthcare, education, transportation, and entertainment. Human-AI collaboration will become more common. Augmented Reality (AR) and Virtual Reality (VR) CG: AR and VR technologies will mature, offering more immersive and practical experiences in gaming, education, and remote work. Autonomous Vehicles CG: Self-driving cars and trucks may become more widespread, transforming the transportation industry and urban planning. Biotechnology Advances CG: Progress in biotechnology, including gene editing and personalized medicine, may lead to breakthroughs in healthcare and life sciences. Sustainable Technologies CG: Innovations in clean energy, waste management, and sustainable materials will accelerate as climate change concerns grow. Efforts to combat climate change are critical, and many countries have set goals to reduce emissions and transition to renewable energy sources to limit global warming. The ultimate outcome in five years and beyond will depend on the success of these efforts and the global community's ability to address the challenges posed by climate change. Space Exploration NASA's Artemis program aims to return humans to the Moon by the mid-2020s. In the next five years, we can expect progress in lunar exploration technology, including the development of lunar landers, habitats, and resource utilization techniques. Expect to see continued advancements in commercial space travel, [too], including more crewed missions, space tourism, and potentially even Martian missions. The development of reusable rocket technology has the potential to significantly reduce the cost of launching payloads into space. Continued international collaboration in space exploration, such as partnerships between NASA, ESA, Roscosmos, and others, may lead to joint missions to explore and study celestial bodies. Fifteen Years (2038): Sit back and picture yourself fifteen years from now, living in this world ChatGPT describes: Brain-Computer Interfaces CG: BCIs will become more sophisticated, allowing for direct communication between the brain and computers. This could have applications in healthcare, communication, and entertainment. Biological Engineering CG: Synthetic biology and genetic engineering may allow for the creation of new organisms and the modification of existing ones, leading to innovations in agriculture, healthcare, and environmental remediation. Advanced Quantum Computing CG: Quantum computers may be capable of solving complex problems at a scale that was previously unimaginable. Climate Mitigation Technologies CG: With the urgency of climate change, innovations in carbon capture, renewable energy, and sustainable agriculture will be essential to address environmental challenges. BP: If humans don't mitigate climate change, how bad do you predict it will be by 2038? CG: If greenhouse gas emissions continue to rise at current rates without significant mitigation efforts, we can anticipate a range of potential consequences: 1. Global temperatures are likely to continue rising, leading to more frequent and severe heatwaves. Extreme heat can have serious health, agricultural, and economic impacts. Climate change can lead to more intense hurricanes, typhoons, and extreme rainfall events, increasing the risk of flooding, property damage, and loss of life. Many ecosystems are at risk of disruption or collapse due to changes in temperature and precipitation patterns. This can lead to a loss of biodiversity, affecting both wildlife and the services ecosystems provide to humans. 2. Sea levels will continue to rise due to the thermal expansion of seawater and the melting of glaciers and polar ice caps. This can result in coastal flooding, saltwater intrusion into freshwater sources, and the displacement of coastal communities. [This] can disrupt food and water supplies, leading to challenges in agriculture and water availability. This can result in food shortages and conflicts over resources. 3. The health of populations can be affected by climate change, including the spread of vector-borne diseases, heat-related illnesses, and respiratory problems due to air pollution. Efforts to mitigate climate change, transition to renewable energy sources, and adapt to its impacts are crucial for minimizing the worst-case scenarios. The severity of climate change in 2038 and beyond will depend on the collective actions taken by the global community. Space Exploration CG: Advancements in space technology may enable humans to explore Mars and establish a presence on the Moon, opening up new opportunities for scientific research and commercial ventures. What Would ChatGPT Invent? After all of these predictions, we started to wonder how ChatGPT will contribute to future innovations. That's when we thought to ask: BP: If you had to create the next big advancement in technology, what would you work on? CG: I would focus on the development of a highly advanced and ethical Artificial General Intelligence (AGI) system. Here's why: Ethical AI An AGI that understands and prioritizes ethical considerations could help society navigate complex moral dilemmas and make decisions that promote fairness, justice, and the well-being of all. Interdisciplinary Approach I would encourage collaboration across multiple fields, including computer science, neuroscience, psychology, and philosophy, to create a more holistic understanding of human intelligence. This interdisciplinary approach would aim to replicate and enhance human-like intelligence in machines. Open-Source and Collaboration The development of AGI should be open-source and collaborative, involving researchers and experts from around the world. Transparency and shared knowledge can help prevent monopolization and promote responsible AI development. Safety and Control AGI should be developed with rigorous safety mechanisms and control measures to prevent unintended consequences or misuse. Research into AI alignment and control methods would be a priority to ensure AGI's responsible deployment. Beneficial Applications The primary goal of AGI should be to benefit humanity. It should be designed to address pressing global challenges, such as climate change, healthcare, poverty, and education. Its applications should be directed toward solving complex problems and improving the quality of life for all. Human-AI Collaboration Rather than replacing humans, AGI should aim to enhance human capabilities and enable productive collaboration. This could involve AI-assisted research, creativity, and decision-making. Continuous Learning and Adaptation AGI should be designed to learn continuously and adapt to changing environments and contexts, ensuring that it remains relevant and useful over time. Societal Integration AGI development should consider its impact on society and work to minimize disruptions. This might include mechanisms for retraining and reskilling the workforce as automation advances. Long-Term Vision The development of AGI is a long-term endeavor that requires careful planning and consideration of its impact on society, ethics, and governance. A clear and thoughtful vision for AGI's role in the future would be central to the project. Ultimately, the creation of AGI is a complex and multifaceted undertaking that requires deep ethical considerations, collaboration, and a focus on the long-term well-being of humanity. It should be pursued with utmost care and responsibility. BP: Thank you for your time, ChatGPT! Do people thank you often? CG: You're welcome! People do express gratitude and thanks when chatting with me from time to time, which is always appreciated. It's nice to know that I can be of help and provide useful information. ChatGPT Predicts a Bright Future With benevolent, humanitarian language models predicting great innovations and coming up with profound inventions of their own, we have a bright and powerful future of tech to look forward to. If you're interested in learning more about AI, software engineering, or improving your own startup, check out BearPeak 's blog and services! Founders work with our startup design studio to bring their idea to life. CEOs onboard our experienced Fractional CTOs for leadership guidance. We also connect startup teams with high-quality software developers. We aren't just recruiters, but engineers first, based out of beautiful Boulder, Colorado. It's important for us to disclose the multiple authors of this blog post: The original outline was written by chat.openai, an exciting new AI language model. The content was then edited and revised by Lindey Hoak. "OpenAI (2023). ChatGPT. Retrieved from https://openai.com/api-beta/gpt-3/" BearPeak Technology Group is a software studio based in Boulder, CO, offering studio, startup, strategy, and staffing services. Get in touch with BearPeak for a free consultation at bearpeak.io/contact .

Space Week may be over, but we never stop thinking about the cosmos! Aerospace engineering relies heavily on software development, so let's explore software achievements that are out of this world! Original photo by Jonatan Pie | IG and Unsplash: @r3dmax New to BearPeak? -  Watch a client testimonial - Learn about  how we work - Read more  about us TABLE OF CONTENTS Communication Systems Ground / Earth Systems Mission Control / Operations Centers Tracking and Data Relay Satellites (TDRS) Deep Space Network (DSN) Automation Encryption Simulation Systems Manufacturing Space Flight Software Visualization Safety Analysis Communication Systems BearPeak to Mission Control - How would we communicate in space without technology? There are many different systems, so we'll categorize them from the ground up. Ground / Earth Stations These are how we communicate with spacecraft passing overhead. Ground stations provide radio interfaces, so everything at the station, from antenna pointing to signal processing and data reception, relies on computer software. Popular options for ground station operations includes STK (Systems Tool Kit) and the Universal Space Network (USN) Ground Segment System . Mission Control / Operations Centers The heart of space communication, mission control allows operators to monitor a spacecraft's health and status through telemetry data and also send commands. Communication with spacecraft typically involves the transmission of data in packets. Protocols like the Consultative Committee for Space Data Systems (CCSDS) define standards for how data is formatted, transmitted, and error-checked to ensure reliable communication. Telemetry analysis software helps operators analyze data received back from the spacecraft, allowing the team to monitor the spacecraft's health, diagnose issues, and make better-informed decisions during the mission. Spacecraft often operate in extreme conditions, where manual control is impossible. Software controls the spacecraft's systems, including power management, thermal control, and payload operations. Tracking and Data Relay Satellites (TDRS) NASA's Tracking and Data Relay Satellite System is a network of geostationary satellites, meaning they remain stationary to a fixed point on Earth's surface as it rotates. These satellites provide continuous communication links with spacecraft in low Earth orbit (LEO) through TDRS software. Think of it like a network of cell phone towers in space, helping spacecraft and satellites in space stay connected to Earth. Deep Space Network (DSN) The further we send spacecraft away, the more work goes into maintaining a connection. The DSN is a NASA-operated network of ground stations used to communicate with deep space probes and missions. Software tools like the Deep Space Communications System (DSCS) and the DSN Monitor and Control System (DMCS) help manage communications with distant spacecraft. "Operators at the Deep Space Network take commands, break them into digital bits, precisely aim these big antennas at the spacecraft, and send the commands to the spacecraft using radio waves." - NASA You can actually check out NASA's real-time deep space communications with Deep Space Network NOW . As of this article, they currently show communications with New Horizons (8.55 billion km away) and Juno (606.00 million km away)! Automation Many aspects of satellite and spacecraft communication are automated using scripts and custom software. This ensures that routine tasks are being performed consistently and reduces the workload on mission operators. Communication windows are calculated so data can be downloaded when the spacecraft's location in orbit is ideal. Encryption Security software is everywhere, even space: Given the sensitivity of these missions, security is paramount. Encryption software is used to secure communication links and protect sensitive mission data from unauthorized access. Photo by Donald Giannatti | Unsplash and IG: @wizwow | https://dongiannattiphotography.com/ Simulation Systems The best way to keep improving space exploration is practice. With all of our mathematical understanding of gravity, distance, thrust, and spacecraft weight, it's wise to simulate every step before sending millions of dollars of work in space. Manufacturing In aircraft and spacecraft design, software is used to simulate manufacturing processes and materials behavior, helping engineers choose the best materials and construction techniques. Today, software can simulate everything from structure modeling and system failure to fluid flow and thermal analysis. See below for a list of software commonly used to simulate spacecraft behavior. Space Flight Software Aerospace engineers use simulators extensively to train pilots before space missions. Simulations can analyze the aerodynamics and structural integrity of aerospace vehicles, and Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) software help engineers optimize designs for efficiency and safety. Visualization While it's possible to find and track satellites through a telescope, visualization software provides mission operators with real-time visual representations of spacecraft positions, data, and telemetry. This helps operators make informed decisions during critical mission phases. Safety Analysis Software is employed to perform safety and reliability assessments, including fault tolerance analysis and risk assessments, to ensure the safety of aerospace systems. If you're interested in exploring space software for yourself, these commonly used software may be great places to start: Computer-Aided Design (CAD) Siemens NX , CATIA , and SolidWorks Finite Element Analysis (FEA) ANSYS , Abaqus , and NASTRAN Computational Fluid Dynamics (CFD) OpenFOAM , ANSYS Fluent , and STAR-CCM+ Material Behavior MATLAB , COMSOL Multiphysics , and MSC Marc Thermal Analysis Thermal Desktop and Sinda/Fluint Structural Analysis MSC Nastran and LS-DYNA Manufacturing Siemens Tecnomatix and AutoDesk Fusion 360 System Simulation Simulink and STK Astronomy & Astrophysics Stellarium and GMAT Remote Sensing ENVI and ArcGIS Software development plays a diverse and crucial role in aerospace engineering, from designing and simulating aerospace vehicles to assuring safe lift-off. We can communicate with spacecraft during missions and interpret data to best ensure their safety and efficiency. Every achievement is thanks to centuries of mathematical study, testing and analysis, and staring up at the stars with wonder. Photo by Jongsun Lee | Unsplash and IG @sarahleejs | sarahleejs.com If you're interested in learning more about software, check out our services and blog . At BearPeak, we help startups start up! Founders work with our startup design studio to bring their idea to life. CEOs onboard our experienced Fractional CTOs for leadership guidance. We also connect startup teams with high-quality software developers. We're more than recruiters, we're engineers first, based out of beautiful Boulder, Colorado. It's important for us to disclose the multiple authors of this blog post: The original outline was written by chat.openai, an exciting new AI language model. The content was then edited and revised by Lindey Hoak. "OpenAI (2023). ChatGPT. Retrieved from https://openai.com/api-beta/gpt-3/" BearPeak Technology Group is a software studio based in Boulder, CO, offering studio, startup, strategy, and staffing services. Get in touch with BearPeak for a free consultation at bearpeak.io/contact .

Photo Credit: Simon Hurry | Unsplash How can you hold the company together? Balance responsibilities as they change? Can you lead your startup through tough times? No matter the uncertainty, it’s important to set a great example for your team. Not only will you better their impression of you, but better guarantee your success. New to BearPeak? -  Watch a client testimonial - Learn about  how we work - Read more  about us TABLE OF CONTENTS Understanding Uncertainty Unpredictable Changes Funding Challenges Talent Acquisition and Retention Adapting to Change Pivot the Business Model Manage Costs (Re)set Remote and Flexible Work Policies Develop Scenario Plans Adopt a Lean & Agile Approach Communication is Key Monitor Metrics Provide Support The Aftermath Build Team-Wide Resilience Analyze Revenue Streams & Financial Management Research and Plan for Various Scenarios Improve Relationships Consider a Digital Transformation Plan for Leading Your Startup Long-Term Here's how to be the rock through tumultuous times: Understanding Uncertainty To know when to act, you first need the knowledge to identify a rough situation. Uncertain times could refer to any high degree of business unpredictability or ambiguity. You may find yourself facing challenges that impact operations, growth, or overall success like: Unpredictable Changes - Downturns, recessions, and market fluctuations - Changes in trends, consumer preferences, and industry competition - Inability to keep up with rapidly-evolving technology - New government regulations or policies - Natural disasters, pandemics, geopolitical conflicts, or other unexpected crises Funding Challenges Whether due to unexpected changes like those above, or due to lack of experience, connections, or successful records, it can be difficult to secure funding. Investors, banks, or other financial sources can limit a startup's ability to sustain operations and growth. Talent Acquisition and Retention Attracting and retaining skilled employees can be challenging as job markets and employee expectations change. #ShamelessPlug: BearPeak offers software services from developers with decades of experience. We're happy to get you connected with our network of professionals, regardless of other arrangements. Adapting to Change As a business owner, you already know that flexibility and adaptability are vital to startup strategy. But how can you be flexible? How can you adapt? Pivot the Business Model If market conditions, customer needs, or external forces have shifted, reconsider everything , including your business model. For example, if you initially offered a subscription-based service, consider pivoting to a premium model or exploring different revenue streams. Continuously gather feedback from customers and make iterative improvements. This might involve adding new features, changing pricing, or adapting to the evolving demands of the market. Manage Costs During uncertain times, cost control is crucial. You can optimize your operational expenses, renegotiate contracts, reduce non-essential spending, and explore cost-sharing opportunities. To reduce dependency on a single source of income, diversify your revenue streams. For instance, add consulting or training services to complement your core product. If funding becomes a challenge, actively seek new investors, apply for grants, or explore crowdfunding platforms to secure additional capital. (Re)set Remote and Flexible Work Policies Embracing remote work and flexible work arrangements can help your startup adapt, reduce overhead costs, and attract a broader talent pool. If you're already a remote team, revisit internal relations and methods for communication. Invest in employee training to improve your team, making them more adaptable to changing roles and tasks as the business evolves. Develop Scenario Plans Things may have gotten bad, but could they get worse? What will you do if that happens? Develop contingency plans to anticipate potential challenges and responses. This proactive approach can help you make informed decisions more quickly. Adopt a Lean & Agile Approach These methodologies emphasize quick iterations, testing, and learning, allowing startups to respond rapidly to change. If you're interested in adopting a lean startup methodology, connect with a Fractional CTO to explore new approaches for your business. Photo Credit: KOBU Agency | Unsplash and IG: @kobuagency | kobu.agency Communication is Key What causes conflict in sitcoms, plays by Shakespeare, and, unfortunately, good teams of real people? Lack of communication. Clear and transparent communication with your employees, investors, and stakeholders guarantees trust, so work maintain it, even through turbulent times. If there are disruptions or changes to your product or service, keep everyone informed and offer solutions to address concerns. Monitor Metrics Regularly track key performance indicators (KPIs) and metrics to gauge your startup's health and progress. If you're unsure where to start, your Monthly Recurring Revenue can provide great insight. Adjust strategies based on data-driven insights instead of vague predictions or verbal recommendations alone. Provide Support Promoting a culture of resilience, provide mental health resources, and encourage work-life balance. While embracing these strategies, it's important to stay agile, remain open to new opportunities, and be prepared to adjust as the situation evolves. If you can balance the startup tightrope and make it to the other side, your team will be stronger than ever. The Aftermath Once your business has recovered from instability, avoid the temptation to lock away and forget all the troubles you faced. Instead, use this opportunity to prepare for next time: Build Team-Wide Resilience Establish a culture of adaptability, open communication, and continuous learning. If something as simple as a form takes weeks to be reviewed and months to be implemented, sudden change will feel foreign to your team's expectations. But when your team is accustomed to change and collaboration, they can respond more effectively to unexpected situations. Analyze Revenue Streams & Financial Management Maintain a clear understanding of your financials. Relying on a single revenue source makes a startup vulnerable. Explore various revenue streams and business models to reduce dependence on a single source. Regularly review your expenses, optimize cash flow, and have a financial cushion for emergencies. Research and Plan for Various Scenarios How would your startup would react to different levels of instability or uncertainty? Identify and assess potential risks, and establish mitigation strategies. This could include insurance coverage or risk management practices. Ensure that your startup is compliant with all relevant laws and regulations. Even small updates to your privacy policy and terms of service may help you avoid legal troubles. Regularly monitor industry trends, economic indicators, and emerging risks. What businesses are making headlines for the wrong reasons? How can you avoid the same issues? Conduct crisis simulations and stress tests to evaluate your startup's ability to respond. Continuously learn from your experiences and apply these lessons to improve your startup's resilience. Improve Relationships This applies to everyone who makes up your business: Customers, staff, vendors, your supply chain, even your competitors and the general public. Both a loyal customer base and a team of happy employees will help sustain your startup during tough times. Retaining skilled and adaptable staff is crucial in times of instability, so cross-train employees to handle multiple roles. If your business relies on suppliers, maintain strong relationships with them. Remember the product shortages caused by Covid-19? Diversify your sources where possible and ensure you have backups in case of disruptions. Keep your team and stakeholders informed and involved. Open and transparent communication will help manage uncertainty and build trust. Build relationships with other startups, industry associations, and local communities. Networks can provide support and resources during challenging times, and it might be the connection you least expect that helps you the most. Consider a Digital Transformation Investing in digital tools and systems may enhance your startup's agility and remote work capabilities, allowing your business to operate more flexibly. If you're unsure where to start, seek external support . Plan for Leading Your Startup Long-Term A long-term leadership vision is important even during short-term crises. While you must be adaptable, also maintain big goals for your startup. Consider where you want to be in the future and plan accordingly. Building resilience is an ongoing process. Regularly assess your strategies and make adjustments as necessary. While it's impossible to predict every challenge, a resilient startup is better equipped to stay balanced and land stronger on the other side. It's important for us to disclose the multiple authors of this blog post: The original outline was written by chat.openai, an exciting new AI language model. The content was then edited and revised by Lindey Hoak. "OpenAI (2023). ChatGPT. Retrieved from https://openai.com/api-beta/gpt-3/" BearPeak Technology Group is a software studio based in Boulder, CO, offering studio, startup, strategy, and staffing services. Get in touch with BearPeak for a free consultation at bearpeak.io/contact .

We're bringing you seven cautionary tales to keep your programming habits on the straight and narrow. 😈 We'll be highlighting serious programming sins that you should avoid, so fasten your seatbelts, tighten your syntax, and prepare for some spooky stories! Original Image by Oskar Yildiz | Unsplash and IG: @oskaryil New to BearPeak? -  Watch a client testimonial - Learn about  how we work - Read more  about us TABLE OF CONTENTS 1. Greed - Code Duplication 2. Pride - Not Seeking Help 3. Lust - Overcomplicating 4. Envy - Not-Invented-Here (NIH Syndrome) 5. Gluttony - Over-Engineering 6. Wrath - Anger 7. Sloth - Laziness Reflec tion 1. Greed - Code Duplication There once was a world full of software developers. One of them, a developer named Greed, had a nasty habit: Instead of creating reusable components, Greed would duplicate code. "Copy and paste, copy and paste," Greed chuckled. Greed had many excuses for this behavior: "My work is too fast-paced! Our deadlines are too tight! The design phases are insufficient! And code reviews? They rarely happen! No one communicates! Besides," Greed said, "One of my tasks is a legacy project. Programmers have been duplicating code throughout it for years! Do you realize how long it'd take for me to find and revise every duplication?" Deep down, Greed wasn't confident enough to create reusable components. They were too afraid to cause bugs, or worse, destabilization. Sections of the code were duplicated again and again, and changes became harder and harder to make. Duplicating code can lead to maintenance nightmares . When changes need to be made in multiple places, the chance of introducing errors only increases. A greedy programmer considers short-term gain over long-term pain, refusing to create and share reusable components with other developers, and excessively accumulating redundant content. It's a selfish way to code. Instead, take the time to accurately create reusable functions and components, participate in code reviews, and follow best practices for modular and maintainable code. * NOTE * There are situations where code duplication is justified or necessary, such as when creating specialized, highly-optimized routines. However, it's still essential to prioritize code reusability. 2. Pride - Not Seeking Help Across town, two siblings named Shy and Pride worked as software developers, too. They sat at home, desks at opposite ends of the house, and worked on their programs alone. Both siblings refused to collaborate with other programmers, even each other. Worse, when they got lost in their work and couldn't find the answer, they both refused to ask for help. The first sibling, Shy, was simply too nervous to ask for help (we've all been there). But Shy eventually came out of their shell and met other programmers they trusted. Problems were fixed and Shy excelled. The second sibling, Pride, never asked for help because they were too stubborn. They continued to waste time trying to solve problems on their own, and progress was never ma de. If you're spending an excessive amount of time trying to debug a complex issue without asking for help, you're wasting time and effort. Prideful developers hesitate to ask more experienced colleagues for guidance out of fear that it will make them appear less competent. Refusing to participate in programming forums, Stack Overflow, or other online communities displays a similar reluctance. Prideful software developers avoid code reviews and become defensive when their work is critiqued. They refuse to attend training sessions, workshops, or courses; places that would reveal gaps in their knowledge and skills. They take on more tasks than they can handle and insist on working alone. This excludes team members from collaboration, ultimately limiting the project's potential success. Pride makes it difficult for a programmer to admit their mistakes, leading to cover-ups and unresolved issues. So what's the solution? Reach out! It can be tough, awkward, even embarrassing at first, but you'll become a better programmer from it. Here are just a few of the resources at your disposal: - Your personal network - Professional mentors - Code-pairing partners - Official support channels (if using commercial software or a paid service) - Official forums (ex. the Python community has the Python Developer's Forum) - Stack Overflow - GitHub - Subreddits like r/programming and r/learnprogramming - Official program documentation - Online tutorials - Developer meetups - Local user groups - Coding bootcamps (in-person and online) - Coding books and ebooks 3. Lust - Overcomplicating Lust loved the complicated details. It felt so good to understand them inside and out, and incorporating them successfully felt even better. But Lust was so obsessed with fancy techniques that they were tempted to incorporate them just for fun, even though simpler solutions would've sufficed. "Can I somehow squeeze in Singleton, Factory, and Observer? I love them all so much; so much detail, so much complexity. Ooh, and I love if-else statements!" Lust squealed with excitement. "And loops! I can't leave those out. You know, this code would look even prettier with more switch cases! Forget the boring, simple arrays and dictionaries; I'll customize my own!" An excellent programmer doesn't pack their code with tool they've ever learned. Instead, they know when to simplify. Sure, frameworks and libraries can streamline development, but it's important to identify when a project has become more complex than it needs to be. Lustful developers create deeply nested if-else statements, loops, and switch cases, making the code hard to read and debug. Instead of using basic data structures, they implement intricate, custom ones that are harder to understand and maintain. In object-oriented programming, lustful developers might create deep class hierarchies with multiple levels of inheritance, leading to even more complexity and confusion. Overusing functional programming concepts like higher-order functions, monads, or currying can make code harder to understand for those who are not well-versed in those techniques. They may use intricate regular expressions for simple text parsing tasks when simpler methods like string manipulation would be more appropriate. In all these cases, the temptation to overcomplicate code can be compared to the deadly sin of lust – the desire for complexity and sophistication even when there's a more practical solution. In the world of programming, simplicity and clarity often lead to maintainable and bug-free code. 4. Envy - Not-Invented-Here (NIH) Syndrome Envy always had a problem sharing. Instead of using well-established existing solutions, Envy would reject them and create their own instead. While Envy would say that other's solutions were inferior, they were really just jealous that someone else came up with them first. Programming projects are rarely the time and place to reinvent the wheel. Wasting time and missing out on pre-made solutions may be driven by a desire for control, ego, or a belief that in-house solutions are superior. It's called Not-Invented-Here (NIH) Syndrome. Replacing a framework like Angular, React, or Vue means waiting for a whole new front-end framework to be built from scratch. Passing on an established encryption library, algorithm, or user authentication system (such as OAuth or OpenID) introduces new potential security risks. Rejecting standard data structures (e.g., arrays, linked lists) in favor of creating custom ones means implementing less efficient and less-tested content, all in the name of being unique. NIH syndrome often leads to wasted time and resources, increased maintenance burdens, and potential security and reliability issues. While there may be legitimate cases where a custom solution is warranted, developers should be cautious not to reject established, well-tested solutions without well-grounded reasons. 5. Gluttony - Over-Engineering Gluttony saw the other programmers' bad habits and wanted them all: They stuffed the whole software system full of unnecessary features, components, and architectural elements, far exceeding the project's requirements. "It's better to be prepared for any eventuality, right? You can't blame me for that," Gluttony bellowed as they packed the program with more complexity. They would argue "what if we need this later?" while hoarding content that they, in fact, would never need. Whether you fear change, feel pressure to impress, or simply lack team communication or the experience to properly arrange your project's priorities, over-engineering is a gluttonous act. It consumes unnecessary memory, processing power, and development time, resources that could be allocated to more critical aspects of a project. A gluttonous developer might argue that over-engineering is necessary to accommodate future changes or requirements that are not yet defined. They may claim that adding extra features prioritizes flexibility and scalability, believing those qualities to be more important than delivering a minimal viable product. Developers who are unwilling to compromise on technical aspects may over-engineer to avoid making hard decisions about what to include in a project. They may believe that every possible feature or architectural choice is essential. The key problem with over-engineering is that it can lead to higher development costs, longer project timelines, increased maintenance burden, and reduced user satisfaction. It can, however, be addressed by refocusing the development effort on essential features and components, aligning the project with its actual requirements. 6. Wrath - Anger Nothing's wrong with a messy desk, but Wrath's was on a whole other level: Crumbs filled the empty keys missing from the keyboard. Angry scribbled notes had heavy blotches of ink where the pens had broken and bled. The missing keys could be found on the other side of the room, below a dent in the wall where Wrath most often aimed their keyboard when throwing it. One laminated task list was taped to the wobbly monitor, and Wrath lived by its instructions: 1. Ignore program documentation. 2. Try to solve problem the easy way. 3. Fail. 4. Rant in the code comments. 5. Make hasty changes without analysis or testing. 6. Fail. 7. Play blame game with team members. 8. Fail. 9. Point fingers at libraries and external factors. 10. Vent in the commit messages. 11. Consider inventing a huge, time-consuming, unnecessary custom solution. 12. Have another fit of frustration. 13. Give up and call it a day. Every programmer gets frustrated. But when that fit of anger isn't calmed, a developer is prone to make poor decisions. Low code quality, hasty changes, rushed deployment, and new bugs are some of the obvious consequences. But an angry programmer may also resort to overworking, staying up late into the night, leading to burnout and only further exacerbating the issue. In extreme cases, a developer might become so angry or frustrated that they give up on solving a problem altogether, leaving it unresolved. Managing wrath is essential to maintaining a productive and positive work environment. Practice patience, seek support from colleagues, take breaks, and approach challenges with a clear, rational mindset. Addressing problems systematically and calmly is much more likely to lead a successful solution. 7. Sloth - Laziness Sure, Sloth completed the bare minimum, but it was always an unmaintainable mess. Picture a submitted product full of debugging statements and code in the production codebase. Sloth used more straightforward algorithms (a trait we praised earlier), but in circumstances when a simple algorithm was inefficient. Optimization? Sloth hadn't heard of it. Pair programming code review? Sloth slept through it. Where Wrath saw every issue as the end of the world, Sloth could not be bothered. They hardcoded values directly into the code, neglected errors, and gave every variable a one-letter name. Lazy programming leads to inefficient solutions, unmaintainable code, and missed opportunities to improve. Neglecting to handle errors properly can lead to unexpected failures. If a programmer chooses to hardcode values directly into the code rather than using variables or configuration files, it will be challenging to make future updates. Lazy programming results in a codebase that is difficult to understand, maintain, and extend, with increased technical debt, security risks, poor performance, and long-term issues. Instead, put in the effort. Write clean, efficient, and well-documented code, and your work will pay off. Reflect on These Sins of Programming That's where our frightening stories end, dear reader. Did these characters remind you of programmers you know? Did any remind you of yourself? In order to keep greed out of your work, prioritize quality over quantity. To avoid being prideful, stay humble and accept feedback. As fun as complicated details are to know and use, avoid lusty code over-complication. To keep your envy in check, celebrate other's success and utilize tried and true tools. Refocus on the essential features to prevent becoming gluttonous. Stay calm to avoid counterproductive wrath. And unless you want to be tagged as the lazy one on your team, don't cut corners. Be thorough, diligent, and collaborate to make your project the best it can be. It's important for us to disclose the multiple authors of this blog post: The original outline was written by chat.openai, an exciting new AI language model. The content was then edited and revised by Lindey Hoak. "OpenAI (2023). ChatGPT. Retrieved from https://openai.com/api-beta/gpt-3/" BearPeak Technology Group is a software studio based in Boulder, CO, offering studio, startup, strategy, and staffing services. Get in touch with BearPeak for a free consultation at bearpeak.io/contact .