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Cheat Sheet / Updated 06-17-2024
Google Workspace offers a huge number of keyboard shortcuts that not only enable you to navigate the app interfaces quickly but also let you easily invoke many app features and settings. Here you see some of the more useful shortcut common to the Google Workspace apps, as well as some handy shortcuts you can use with Gmail and Calendar. Do you need to memorize them all? Don't be silly. But do read through the lists, as you'll probably find two or three that you'll find useful every day.
View Cheat SheetCheat Sheet / Updated 06-17-2024
Your MacBook keyboard puts efficiency at your fingertips. Startup keys, shortcut key combinations, and special function keys invite you to perform different tasks with a single touch — from turning up the sound volume to deleting selected text.
View Cheat SheetArticle / Updated 05-31-2024
At work as well as in your personal life, you’ve almost certainly been bombarded with talk about generative artificial intelligence (AI). It’s all over the mainstream media, in trade journals, in C-suite conversations, and on the front lines of whatever work your organization does. There’s no escaping it. The stories make AI sound so miraculous that, in fact, you could be forgiven for thinking it must be a bunch of hype. But the reality is, generative AI can truly be transformational for businesses. You can leave it for textbooks to fill in the details about what AI is and how it works. But in a nutshell, AI relies on building large language models (LLM) with the help of machine learning (ML). AI trains on vast amounts of data, immerses itself, and learns from the data in ways not unlike how humans learn (but a whole lot faster, and ingesting far, far more data). Notice that the title of this article refers to generative AI. This AI doesn’t just make recommendations — it actually creates new data or content, or generates insights by using the power of natural language processing (NLP) and ML. Tackling many tasks What can generative AI really do for your business? What business problems can it solve? For starters, it’s a fantastic headache remedy. Some of the business headaches generative can cure include Production bottlenecks: Got processes that are stuck and unable to keep up with the demands of customers? Generative AI breaks through bottlenecks by automating processes, improving efficiency, facilitating faster and better human decisions, increasing output, maximizing resources, and speeding up development cycles. Tedious tasks: Generative AI can tackle mundane and tedious tasks, freeing up human brainpower for real value-creating initiatives that your people will find more fulfilling. Inconsistencies and noncompliance: Generative AI creates consistency across your organization’s communications and enforces compliance with internal and external standards. It’s easy for discrepancies and errors to pop up and multiply — generative AI can identify these issues, offer insights and recommendations, and even automatically fix them. Training hurdles: Generative AI helps new hires onboard and get up-to-speed quickly by generating training materials and job simulations. Personalized instruction can fill knowledge gaps. Customer-service struggles: When equipped with information-retrieval solutions, the technology can answer questions quickly and can even handle some customer interactions entirely on its own. It also improves live human interactions by empowering agents and creating instant conversation summaries. Exploring the use cases What generative AI can do for your organization boils down to three primary areas: Creating: This is what it sounds like — using AI to come up with something new. It also may mean editing or revising something that has already been created, by a person or AI, perhaps by turning it into a different format. For your marketing team, a generative AI tool can write the first draft of an ebook about a new product, or create a press release or search engine optimization (SEO)-ready web content. It can come up with a knowledge base article on the latest product feature to help the support team, or a best-practices management article for learning and development. It can help the human resources (HR) team write a job description, making sure it’s doing so in inclusive language. The product development team will love how it ingests and crunches a list of features and bug tickets to come up with release notes. Analyzing: This means taking an in-depth look at content of some kind and generating insights. Generative AI can spot trends or reach conclusions of some sort, perhaps even analyze sentiment amid a batch of customer feedback. Marketing may ask the AI platform to process a webinar recording and summarize the key takeaways. The support team can have it scour customer support survey responses to come up with insights on areas of improvement to consider. Generative AI can help learning and development conjure up some FAQs by analyzing and categorizing what’s in an internal wiki. AI can listen to a recording of a job interview and create a summary for a recruiter. Product developers can have it study customer feedback to find insights for what new features to prioritize. Governing: The govern use case includes a focus on compliance, looking for language that runs afoul of legal and regulatory rules. It finds incorrect terminology and statements and works to prevent data loss and global compliance problems. This type of AI work also means checking for factual accuracy, detecting claims that are wrong and suggesting replacement wording. Marketers can use it to find errors and violations in advertising copy, and for HR, AI can flag non-inclusive language in employee communications, then make suggested revisions. The learning and development team may use it to ensure training materials are compliant with industry certification requirements and other vital standards. Making it happen Many generative AI tools are out there right now, and they’re ready for the masses. Countless people subscribe to platforms such as ChatGPT and Google’s Gemini, and Meta AI is now built right into social media platforms. For the use cases outlined in the preceding section, though, it’s essential to seek an enterprise-grade, full-stack generative AI platform rather than a consumer-targeted AI assistant. Your organization will want a platform that can be truly customized to your needs and integrated with your operations, trained on accurate data that’s relevant to your business and industry, and fully in line with your security and compliance requirements. So, do it yourself? That’s not such a great plan, either. Building your own AI stack can be slow and expensive. Look for a partner that can abstract the complexity so you can benefit from the AI-first workflows, not get bogged down building and maintaining infrastructure. When picking a platform, follow these tips: Keep pace with your organizational needs. Get a tool that can deploy custom AI apps in a snap for any use case, including digital assistants, content generation, summarization, and data analysis. Seek the right model. Palmyra LLMs from Writer, for example, are top-ranked on key benchmarks for model performance set by Stanford’s Holistic Evaluation of Language Models. Connect to your company knowledge. An LLM alone can’t deliver accurate answers about information that’s locked inside your business knowledge bases. For that, you need retrieval-augmented generation (RAG), which is basically a way to feed an LLM-based AI app company-specific information that can’t be found in its training data. Check out writer.com/product/graph-based-rag for more information. Be sure it’s fully customizable. You need consistent, high-quality outputs that meet your organization’s specific requirements, and a general consumer tool can’t do that. You also must have AI guardrails that enforce all your rules and standards. Integrate the tool. To fit into your flow, AI apps need to be in your people’s hands however they’re working. You need an enterprise application programming interface (API) and extensions that’ll build tools right into Microsoft Word and Outlook, Google Docs and Chrome, Figma, Contentful, or whatever else your people love to use. Deploy it your way. Look for options that include single-tenant or multi-tenant deployments. Get things done quickly. Look for a platform that can have you up and running in days, not months. Wouldn’t you rather spend your time adopting than tediously building? Keep it secure. Here’s an incredibly vital area where consumer tools can leave your enterprise at great risk. You need an LLM that’s secure, auditable, and never uses your sensitive data in model training. You’ll lose a lot of sleep if your tool doesn’t comply with the standards your organization must follow, whether that means SOC 2 Type II, HIPAA, PCI, GDPR, or CCPA. Find a tool that manages access with single-sign on (SSO), multifactor authentication, and role-based permissions. Writer is the full-stack generative AI platform for enterprises. It empowers your entire organization to accelerate growth, increase productivity, and ensure compliance. For more information on how to transform work with generative AI, download Generative AI For Dummies, Writer Special Edition.
View ArticleArticle / Updated 05-30-2024
For each web coding issue identified by a validator, you need to determine what course of action to take. Although some culprits that repeatedly crop up are easy to fix, such as missing alt text and <noscript> tags, you’re bound to find coding issues that completely baffle and stump you. For instance, if you get an error message that reads XML Parsing Error: Opening and ending tag mismatch: br line 52 and body, it might be difficult to figure out what that means, let alone why it was caused and how you should fix it. As a strategy then, try to fix the issues within the code from the top down, as they’re listed in the validation results, because sometimes fixing one issue resolves another. With the XML parsing error, that issue might disappear when you correct for an omitted closing element on a <br /> tag listed earlier in the error results. The best way to find out how to code better and make fewer mistakes before validation testing is to make lots of honest mistakes and figure out how to correct them on your own. Most often, you can fix noncompliant code by hand or with the help of a good HTML editor. To help you identify some of the more common coding mistakes, here several code issues along with suggestions about how to fix them. Problem Solution alt text attribute missing from <img> tag Add the alternative text attribute, either with or without a description, as in <img src="images/logo.gif" width="150" height="150" alt="Pete’s Pizza"> <img src="images/flourish.gif" width="200" height="150" alt=""> . <noscript> tags missing from code Add <noscript> tags below each instance when JavaScript is present in in-line JavaScript or at the end of the content before the closing body tag. Between the <noscript> tags, insert HTML content (text, graphics, media files, and so on) that describes the function of the JavaScript and, when appropriate, how visitors can access the information revealed by it, as shown here: <script language="JavaScript" src="bookmark.js" type="text/javascript"></script><noscript>The JavaScript used on this page provides a quick link that allows visitors to automatically bookmark this page. As an alternative, please use your browser’s Bookmark This Page feature.</noscript> Flashing or flickering element(s) detected, such as animated GIFs, Java applets, and other multimedia plug-ins Adjust the speed of any animations to avoid causing the screen to flicker with a frequency between 2 Hz and 55 Hz. Animations that exceed these two measures may cause seizures in visitors with photosensitive epilepsy. No DOCTYPE specified Add a valid DOCTYPE above the opening <head> tag. No HTTP charset parameter specified This special meta tag specifies the character set used in the HTML code. Some HTML editors include it automatically when generating new blank web pages. If validation finds that this tag is missing from your HTML or XHTML code, insert the following code by hand: <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> . For HTML5, insert <meta charset="utf-8"> . No <title> tag specified Add a unique title between <title> tags in the head area on each page. No <meta> tags specified Add meta keywords and meta description tags to the head of each page. These can be identical on every page on the site. If desired, you may also add additional meta tags as needed. No Robots tags specified Add the Robots <meta> tag in the head of the page to instruct web spiders and robots whether to index the page and follow any hyperlinks, such as <meta name="Robots" content="All"> . Deprecated <font> tags detected Move all the presentation markup of the HTML (page, fonts, tables, links, and so on) to an external CSS file and remove all <font> tags and HTML and inline formatting attributes. Deprecated table height attribute detected Control table cell heights, when necessary, with CSS styles. Style attributes detected in the opening <body> tag Move body attributes, like margin attributes and background page color, to a BODY tag redefine style in an external CSS file. type attribute not specified for JavaScript or CSS Add the type="text/css" attribute for <style> tags and the type="text/javascript" attribute for <script> tags: <style type="text/css" ><script type="text/javascript"> . Entity name used instead of entity number Change the entity name to an entity number, such as using $#169; instead of © to create the copyright symbol (c). No background color attribute was specified for a CSS style that specifies text color Provide each style that contains a text color attribute with an attending background color attribute. The background color should match, or closely match, the background color upon which the text will display on. When you’re finished identifying and adjusting all the noncompliant code identified by the validation tools, and have fixed everything that needed fixing, move on to the retesting and acceptable failure phase of the testing process.
View ArticleArticle / Updated 05-30-2024
The success of your DevOps initiative relies heavily on following the process, but it’s also important to use the right tools. Selecting a cloud service provider isn’t an easy choice, especially when DevOps is your driving motivation. GCP (Google Cloud Platform), AWS (Amazon Web Services), and Azure have more in common than they do apart. Often, your decision depends more on your DevOps team’s comfort level with a particular cloud provider or your current stack more than the cloud provider itself. After you’ve decided to move to the cloud, the next decision is to decide on a cloud provider that fits your DevOps needs. Here are some things to consider when evaluating cloud providers with DevOps principles in mind: Solid track record. The cloud you choose should have a history of responsible financial decisions and enough capital to operate and expand large datacenters over decades. Compliance and risk management. Formal structure and established compliance policies are vital to ensure that your data is safe and secure. Ideally, review audits before you sign contracts. Positive reputation. Customer trust is absolutely key. Do you trust that you can rely on this cloud provider to continue to grow and support your evolving DevOps needs? Service Level Agreements (SLAs). What level of service do you require? Typically cloud providers offer various levels of uptime reliability based on cost. For example, 99.9 percent uptime will be significantly cheaper than 99.999 percent uptime. Metrics and monitoring. What types of application insights, monitoring, and telemetry does the vendor supply? Be sure that you can gain an appropriate level of insight into your systems in as close to real-time as possible. Finally, ensure the cloud provider you choose has excellent technical capabilities that provide services that meet your specific DevOps needs. Generally, look for Compute capabilities Storage solutions Deployment features Logging and monitoring Friendly user interfaces You should also confirm the capability to implement a hybrid cloud solution in case you need to at some point, as well as to make HTTP calls to other APIs and services. The three major cloud providers are Google Cloud Platform (GCP), Microsoft Azure, and Amazon web Services (AWS). You can also find smaller cloud providers and certainly a number of private cloud providers, but the bulk of what you need to know comes from comparing the public cloud providers. Amazon Web Services (AWS) As do the other major public cloud providers, AWS provides on-demand computing through a pay-as-you-go subscription. Users of AWS can subscribe to any number of services and computing resources. Amazon is the current market leader among cloud providers, holding the majority of cloud subscribers. It offers a robust set of features and services in regions throughout the world. Two of the most well-known services are Amazon Elastic Compute Cloud (EC2) and Amazon Simple Storage Service (Amazon S3). As with other cloud providers, services are accessed and infrastructure is provisioned through APIs. Microsoft Azure Before Microsoft launched this cloud provider as Microsoft Azure, it was called Windows Azure. Microsoft designed it to do just what the name implies — serve as a cloud provider for traditionally Windows IT organizations. But as the market became more competitive and Microsoft started to better understand the engineering landscape, Azure adapted, grew, and evolved. Although still arguably less robust than AWS, Azure is a well-rounded cloud provider focused on user experience. Through various product launches and acquisitions — notably GitHub — Microsoft has invested heavily in Linux infrastructure, which has enabled it to provide more robust services to a wider audience. Google Cloud Platform (GCP) The Google Cloud Platform (GCP) has the least market share of the three major public cloud providers but offers a substantial set of cloud services throughout nearly two dozen geographic regions. Perhaps the most appealing aspect of GCP is that it offers users the same infrastructure Google uses internally. This infrastructure includes extremely powerful computing, storage, analytics, and machine learning services. Depending on your specific product, GCP may have specialized tools that are lacking (or less mature) in AWS and Azure. Finding DevOps tools and services in the cloud Literally hundreds of tools and services are at your disposal through the major cloud providers. Those tools and services are generally separated into the following categories: Compute Storage Networking Resource management Cloud Artificial Intelligence (AI) Identity Security Serverless IoT Following is a list of the most commonly used services across all three of the major cloud providers. These services include app deployment, virtual machine (VM) management, container orchestration, serverless functions, storage, and databases. Additional services are included, such as identity management, block storage, private cloud, secrets storage, and more. It’s far from an exhaustive list but can serve as a solid foundation for you as you begin to research your options and get a feel for what differentiates the cloud providers. App deployment: Platform as a Service (PaaS) solution for deploying applications in a variety of languages including Java, .NET, Python, Node.js, C#, Ruby, and Go Azure: Azure Cloud Services AWS: AWS Elastic Beanstalk GCP: Google App Engine Virtual machine (VM) management: Infrastructure as a Service (IaaS) option for running virtual machines (VMs) with Linux or Windows Azure: Azure Virtual Machines AWS: Amazon EC2 GCP: Google Compute Engine Managed Kubernetes: Enables better container management via the popular orchestrator Kubernetes Azure: Azure Kubernetes Service (AKS) AWS: Amazon Elastic Container Service (ECS) for Kubernetes GCP: Google Kubernetes Engine Serverless: Enables users to create logical workflows of serverless functions Azure: Azure Functions AWS: AWS Lambda GCP: Google Cloud Functions Cloud storage: Unstructured object storage with caching Azure: Azure Blob Storage AWS: Amazon S3 GCP: Google Cloud Storage Databases: SQL and NoSQL databases, on demand Azure: Azure Cosmos DB AWS: Amazon Relational Database Service (RDS) and Amazon DynamoDB (NoSQL) GCP: Google Cloud SQL and Google Cloud BigTable (NoSQL) As you explore the three major cloud providers, you notice a long list of services. You may feel overwhelmed by the hundreds of options at your disposal. If, by chance, you can’t find what you need, the marketplace will likely provide something similar. The marketplace is where independent developers offer services that plug into the cloud — hosted by Azure, AWS or GCP. The table below lists additional services provided by most, if not all, cloud providers. Common Cloud Services Service Category Functionality Block storage Data storage used in storage-area network (SAN) environments. Block storage is similar to storing data on a hard drive. Virtual Private Cloud (VPC) Logically isolated, shared computing resources. Firewall Network security that controls traffic. Content Delivery Network (CDN) Content delivery based on the location of the user. Typically utilizes caching, load balancing and analytics. Domain Name System (DNS) Translator of domain names to IP addresses for browsers. Single Sign-On (SSO) Access control to multiple systems or applications using the same credentials. If you’ve logged into an independent application with your Google, Twitter or GitHub credentials, you’ve used SSO. Identity and Access Management (IAM) Role-based user access management. Pre-determined roles have access to a set group of features; users are assigned roles. Telemetry, monitoring and logging Tools to provide application insights on performance, server load, memory consumption and more. Deployments Configuration, infrastructure and release pipeline management tools. Cloud shell Shell access from a command-line interface (CLI) within the browser. Secrets storage Secure storage of keys, tokens, passwords, certificates and other secrets. Message Queues Dynamically scaled message brokers. Machine Learning (ML) Deep learning frameworks and tools for data scientists. IoT Device connection and management.
View ArticleCheat Sheet / Updated 05-07-2024
Unlike traditional software, Salesforce is software-as-a-service (SaaS). You sign up for a subscription and log in through a browser, and the software is immediately available. You may need to make some adjustments to make all aspects apply to the details of your business. There’s no purchase, installation, or hardware setup required! With Salesforce, you have a full suite of services to manage the customer life cycle.
View Cheat SheetCheat Sheet / Updated 04-30-2024
As AI tools grow more complex, effectively communicating with them is becoming a necessary skill for most professions. Learning the art of crafting effective prompts unlocks creativity and enhances decision-making abilities. Whether you’re a developer building the latest AI application, a marketer leveraging chatbots, or a writer automating content creation, the skill of writing AI prompts is indispensable. Poorly worded prompts will never yield the results you’re looking for. The good news is, you can practice and improve your prompting skills and find opportunities to advance in your career.
View Cheat SheetCheat Sheet / Updated 04-30-2024
When it comes to Blender, you can save time in many ways. Memorizing common mouse actions and numeric keypad hotkeys in Blender or common keyboard hotkeys in Blender’s 3D View help you work more efficiently in Blender. If memorization isn’t your thing, you can even print lists of these mouse actions and hotkeys and refer to them whenever you need to.
View Cheat SheetCheat Sheet / Updated 04-12-2024
SQL is a popular and useful programming language. You can make SQL even more useful if you know the phases of SQL development, the criteria for normal forms, the data types used by SQL, a little bit about set and value functions, as well as some tips on how to filter tables with WHERE clauses.
View Cheat SheetCheat Sheet / Updated 04-12-2024
Python is a flexible programming language that has become increasingly popular in the past few years. This cheat sheet is designed to give you a handy resource for common Python data types, Python operators, and Python functions. It includes Python data types, operators, special characters, f-strings, and functions for working with robots.
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