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A read-only editor is useful when users should view a template without changing its main structure. But sometimes, selected areas still need to remain editable—for example, a table where users enter prices, dates, or project details.

TinyMCE can support this approach by combining HTML’s contenteditable attribute with predefined CSS classes.

The main idea

Keep the editor content protected, but assign a special class such as editablecontent to tables that users are allowed to modify. Template authors can select this class directly from the TinyMCE table properties dialog.

The table_class_list option defines which table classes appear in that dialog:

tinymce.init({
  selector: "textarea",
  plugins: "table",
  menubar: "table",
  toolbar: "table",

  table_class_list: [
    { title: "None", value: "" },
    { title: "Editable Table", value: "editablecontent" },
    { title: "Other Table Type", value: "other_table_class" }
  ]
});

When the template author chooses Editable Table, TinyMCE adds the following class to the table:

<table class="editablecontent">

Your application can then detect this class and make only that table editable.

Why use predefined classes?

• Template authors do not need to edit HTML.
• Editable areas are clearly controlled.
• The same class can be reused across many templates.
• Other table options, such as border styles, can be added to the same list.

For larger configurations, TinyMCE also supports nested class menus. This helps organize editable states, visual styles, and other table types into separate groups.

Moving from an AI prototype to a production-ready application requires much more than calling an LLM. This course provides a practical introduction to Microsoft Agent Framework (MAF), Microsoft's open-source framework for building structured, scalable, and maintainable AI agents. Using a hands-on dentist appointment booking system, it demonstrates how to design agents that interact with external tools, maintain conversation history, remember user preferences, and execute complex business processes.

The course covers:

• Agent fundamentals: understanding the agentic loop, prompt design, tools, sessions, memory, context providers, and middleware.
• Building real applications: creating agents that search, retrieve, update, and persist data while supporting multi-turn conversations.
• Workflows and orchestration: when to use agents versus workflows, multi-agent architectures, handoff patterns, human-in-the-loop approvals, checkpointing, and durable execution.
• Production practices: choosing appropriate LLMs, monitoring with Application Insights, testing with DevUI, managing configuration, and deploying agents as Azure AI Foundry Hosted Agents.

A recurring theme throughout the course is that an AI agent is essentially a language model equipped with tools to accomplish a goal. Rather than focusing on a single demo, the instructor teaches reusable design patterns that can be applied to customer support, booking systems, research assistants, and many other enterprise AI solutions. By the end, you'll understand not only how to build intelligent agents, but also how to make them reliable, observable, secure, and ready for production.

Course: Building Agents with Microsoft Agent Framework – Pluralsight

Even if an AI understands your request, it may not present the answer in the format you expect. One of the simplest ways to improve the result is to clearly describe the expected output. Instead of letting the AI decide how to present the information, tell it exactly what you want.

Expected output instructions define the structure, length, and presentation of the response. They help make answers more consistent and reduce the amount of editing afterward.

Useful instructions include:

• Give me five bullet points.
• Keep the answer under 100 words.
• Return the information as a table.
• Provide only a checklist.
• Write a step-by-step guide.
• Do not include an introduction.
• End with a short summary.
• Give only the final answer.
• List the advantages and disadvantages.

You can also combine multiple instructions into one prompt.

Example Prompt

Compare three ways to save money on groceries. Present the result as a table with the columns Method, Benefits, Drawbacks, and Best For. Keep each table cell under 20 words. After the table, add three practical tips for getting started. Do not include a general introduction.

By describing the expected output, you give the AI a clear target. This makes the response easier to read, easier to reuse, and much closer to what you need without additional editing.

With several Microsoft Copilot experiences available, it can be difficult to know which one to use. Each is designed for a specific purpose, so choosing the right one helps you work faster, find better information, and get more accurate results.

A simple way to choose is to think about your goal:

• Need to find something? Use Copilot Search.
• Need a quick answer or help writing? Use Copilot Chat.
• Want AI to help complete your current task? Use Copilot Cowork.
• Need AI to manage a larger task in its own workspace? Use Copilot Scout.
• Researching a topic across many sources? Use Copilot Researcher.
• Working with numbers or business data? Use Copilot Analyst.

Understanding these roles makes it easier to select the right Copilot for the job. Instead of relying on a single AI experience for everything, you can choose the one that best matches your task and get more focused, efficient, and reliable results.

You are ready to publish. The article reads well, the presentation is clear, and your message is complete. But instead of finishing, you keep changing small details. Sound familiar?

This is a common productivity trap: endless polishing. After a certain point, extra edits often make very little difference. They take time and energy without noticeably improving the final result.

A better approach is to know when to stop refining and start sharing your work.

How AI can help

If you use an AI writing assistant such as Microsoft Copilot, let it perform the final review instead of repeatedly reading the document yourself. Ask it to:

• Check for clarity
• Suggest small improvements
• Find grammar or spelling issues
• Highlight sentences that could be easier to understand

This gives you a fresh perspective while helping you avoid unnecessary editing.

Remember the goal

The goal is not a perfect document—it is a document that communicates its message clearly. Once the important ideas are easy to understand and obvious mistakes are fixed, additional changes often provide only small benefits.

Learning to recognize when your work is "good enough" helps you save time, reduce mental fatigue, and spend more energy on creating your next great piece of work instead of endlessly refining the last one.

Have you ever received an AI answer that wasn't quite what you wanted? Often, the problem is not the AI—it is that the prompt did not include enough information. Adding a little more context helps the AI understand your request and produce a more useful response.

Before asking your question, provide the key details the AI needs:

• Goal – What do you want to achieve?
• Audience – Who is the content for?
• Format – Article, email, checklist, table, or another format.
• Tone – Friendly, professional, formal, or simple.
• Source material – Include any notes, text, or information the AI should use.

The more relevant information you provide, the fewer assumptions the AI has to make. This usually leads to more accurate answers and reduces the need for follow-up prompts.

Example Prompt

Write a short article about healthy breakfasts for busy parents. Use simple English and a friendly tone. Organize the article with an introduction, three practical tips, and a short conclusion. Keep it between 200 and 250 words. Use only the information provided below.

This prompt clearly defines the goal, audience, tone, format, length, and source material. Because the expectations are clear from the start, the AI is much more likely to produce the desired result on the first try.

Thinking about leaving Google without giving up convenience? Proton offers a privacy-focused ecosystem that covers email, cloud storage, passwords, calendars, VPN, documents, and more—all under one account with end-to-end encryption where possible.

A smooth migration works best in three stages:

• Start with Mail, Calendar & VPN: Import your emails, contacts, and calendar, then secure your internet connection with Proton VPN.
• Move Passwords & Files: Import passwords into Proton Pass, migrate files and photos to Proton Drive, and enable automatic photo backups.
• Finish with Docs & Sheets: Basic document editing and collaboration are available, although some advanced features and bulk imports are still limited.

Helpful features include:

• Email aliases to reduce spam and improve security.
• Built-in password management with two-factor authentication support.
• Secure file sharing with password protection and expiration dates.
• End-to-end encrypted video meetings, cloud storage, and document collaboration.

Before migrating, configure your account recovery options carefully. Because Proton uses strong encryption, losing both your password and recovery methods can permanently lock you out of your data. Overall, Proton provides a strong privacy-focused alternative, although some productivity tools are still catching up with established platforms.

Original video: I Rebuilt My Entire Digital Life on Proton: Full Walkthrough (en / 41:34) - Cloudwards (YouTube)

GitHub Copilot becomes much more effective when it is customized for your project. Rather than relying on one-off prompts, you can combine several reusable building blocks that provide context, standardize outputs, automate repetitive work, and create focused AI interactions.

A well-organized customization strategy typically includes the building blocks:

• Instructions: Define project-wide coding standards, architecture, documentation, and development guidelines.
• Custom Instructions: Add folder- or task-specific rules that extend the general instructions for a particular part of the repository.
• Templates: Provide reusable file structures with placeholders for creating consistent documents and source files.
• Prompts: Automate repetitive, multi-step workflows such as creating new features, documentation, or assignments.
• Chat Modes: Create specialized AI conversations for brainstorming, planning, reviews, or other focused tasks.

How They Work Together

A typical workflow might look like this:

1. Instructions provide the overall project context.
2. Custom Instructions add rules for a specific area of the repository.
3. Templates define the structure of the files to generate.
4. Prompts execute the workflow and create or update the required files.
5. Chat Modes support focused discussions before or during implementation.

Using these building blocks together makes Copilot more predictable, produces more consistent results, and reduces the need to repeatedly explain your project's standards and workflows.

Instead of helping you one prompt at a time, GitHub Copilot Coding Agent works like an autonomous developer. You assign it a GitHub issue, and it independently implements the requested changes while you continue working on other tasks.

How does it work?

1. Assign a GitHub issue to Copilot.
2. The Coding Agent creates its own branch and starts a secure GitHub Actions environment.
3. It analyzes the repository, writes the required code, runs tests and validation, and commits its progress.
4. When finished, it opens a draft pull request containing the proposed solution, implementation details, and a summary of the changes.
5. You review the code, provide feedback if needed, and decide whether to merge the pull request.

What can it do?

The Coding Agent is well suited for:

• Implementing new features
• Fixing bugs
• Refactoring existing code
• Adding or updating tests
• Improving documentation
• Performing routine maintenance tasks

Why use it?

Unlike Agent Mode, which works interactively inside your IDE, the Coding Agent runs asynchronously on GitHub. It can continue working in the background while you focus on other development tasks. Because every change is delivered through a standard pull request, your existing review process, branch protections, and approval workflow remain unchanged. When combined with Model Context Protocol (MCP), the Coding Agent can also use project-specific tools and external data sources to produce more accurate, context-aware solutions.