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n8n tutorial - Lesson 20: Error Handling in n8n: Build a Production Error Workflow

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n8n tutorial - Lesson 20: Error Handling in n8n: Build a Production Error Workflow

Hi everyone, in this session of the n8n Workflow Automation Tutorial series, we cover n8n error handling — specifically how to build a centralized Error Handler workflow that sends automatic Telegram alerts whenever any of your production workflows fail. This is a must-have pattern before you scale beyond a handful of active workflows.

How to do:

Step 1 — Create the Error Handler Workflow

Build a new workflow named T6-B1-Error-Handler with 3 nodes connected in sequence.
  1. Create a new workflow and name it T6-B1-Error-Handler.
  2. Add the Error Trigger node — this is an n8n built-in node that automatically receives the execution context whenever a linked workflow fails.
  3. The Error Trigger provides four key fields from the failed execution:
    • workflow.name — name of the failed workflow
    • execution.lastNodeExecuted — the node where execution stopped
    • execution.error.message — the error message text
    • execution.url — direct link to the failed execution in your n8n instance

Step 2 — Add a Code Node to Format the Error Message

Add a Code node after the Error Trigger to build a clean, readable Markdown message for Telegram.
  1. Add a Code node and name it Format Error Message.
  2. Extract the four fields from the error context: workflow.name, execution.lastNodeExecuted, execution.error.message, and execution.url.
  3. Truncate the error message if it exceeds 400 characters — long error strings break Telegram message formatting.
  4. Build the output as a Markdown legacy string with this structure:
    • A header line (e.g., ⚠️ Workflow Failed)
    • Workflow name
    • Last node that failed
    • Timestamp
    • Truncated error message
    • Clickable execution URL link

Tip — Use Markdown legacy mode in Telegram (not MarkdownV2). MarkdownV2 requires escaping many special characters that commonly appear in error messages, which causes send failures in production.

Step 3 — Add the Telegram Send Node

Add a Telegram node as the final step to deliver the alert.
  1. Add a Telegram node connected after Format Error Message.
  2. Set Resource to Message and Operation to Send Message.
  3. Set the Text field to reference the formatted message output from the Code node.
  4. Set Parse Mode to Markdown (legacy format).
  5. Ensure Append Attribution is turned OFF — attribution adds extra text to the message that pollutes alert readability.
  6. Make sure the workflow is set to Active — the Error Handler must be active to receive triggered events from other workflows.

Step 4 — Apply the Error Workflow Setting to Your Production Workflows

Link each of your production workflows to T6-B1-Error-Handler through their individual settings.
  1. Open each workflow you want to protect (in this session: 8 workflows from Week 5 — T5-B2, T5-B2b, T5-B3, T5-B3b, T5-B4, T5-B5, T5-B6, T5-B7).
  2. Click the Settings ⚙️ icon inside the workflow editor.
  3. Find the Error Workflow dropdown and select T6-B1-Error-Handler.
  4. Click Save.
  5. Repeat for all 8 workflows — this setting works for both Active workflows and Manual workflows.

Tip — You only need one shared Error Handler workflow for all your workflows. You do not need a separate error handler per workflow. This centralized pattern keeps alert management simple and consistent.

Step 5 — Test the Error Workflow with a Production Execution

Testing the Error Workflow requires a production execution — the standard test UI will not trigger it.
  1. Create a temporary test workflow named T6-B1-Test-Trigger-Error.
  2. Do NOT use a Manual Trigger for this test — manually running a workflow via the Execute Workflow button (the flask 🧪 icon) runs in test mode, which does not trigger the Error Workflow.
  3. Instead, use a Schedule Trigger set to run every 1 minute.
  4. Deliberately build the workflow so it will fail (e.g., reference a node that does not exist or misconfigure a required field).
  5. Set the test workflow to Active.
  6. Wait up to 1 minute for the Schedule Trigger to fire a production execution.
  7. Confirm the failure appears in Executions without the flask 🧪 icon — no flask means it was a production execution.
  8. Verify the Telegram alert arrives with the correct workflow name, failed node, error message, and execution URL.
  9. After the test passes, toggle the test workflow Active OFF or delete it to prevent repeated alerts every minute.

Production tip — In the n8n Executions list, executions with a flask 🧪 icon are test executions and will never trigger the Error Workflow. Executions without that icon are production executions and will trigger it. Always check for the flask icon when debugging why your Error Workflow is not firing.

Key Lessons from This Session

  1. Error Workflow only fires on production executions. Test mode (flask 🧪 icon, triggered via the Execute Workflow UI button) does not trigger the Error Workflow — only Schedule Triggers, Webhooks, or other external production triggers do.
  2. One Error Handler covers all workflows. You apply the same T6-B1-Error-Handler to as many source workflows as needed via Settings ⚙️ → Error Workflow — no duplication required.
  3. Truncate long error messages in code. Cap error message text at 400 characters before sending to Telegram — raw error messages can be very long and break message formatting.
  4. Use Markdown legacy, not MarkdownV2, for Telegram alerts. Error messages contain special characters that MarkdownV2 requires escaping, which causes frequent send failures.
  5. The Error Handler workflow must be Active. If the Error Handler workflow is inactive, it will not receive any triggered events regardless of how many source workflows are linked to it.
  6. Clean up test workflows after verification. A Schedule-triggered test workflow set to every 1 minute will keep firing and generating alerts — always toggle it off or delete it immediately after the test passes.
  7. The Error Trigger node provides all execution context automatically. You do not need to pass data manually — workflow.name, execution.url, execution.error.message, and execution.lastNodeExecuted are all available directly in the node output.

Conclusion:

In this n8n tutorial, we built a production-grade error handling pattern using a 3-node Error Handler workflow — Error Trigger, Code formatter, and Telegram alert — and applied it to 8 active production workflows through the Settings → Error Workflow option. The most important insight from this session is the distinction between test executions and production executions: the Error Workflow will only fire in production, so always validate it with a Schedule Trigger rather than the manual Execute button. This error handling setup is now the safety net for the entire n8n workflow automation system built across Week 5, and the next session moves on to the Sub-workflow pattern using the Execute Workflow node.

If you have any questions, feel free to leave a comment below. Thank you!

Tags: n8n error handling, n8n tutorial, n8n workflow automation, n8n error trigger, n8n telegram alert, n8n production workflow, n8n error workflow setup, workflow monitoring n8n

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n8n tutorial - Lesson 19: n8n Weekly Digest: Aggregate All Your Data in One Report

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n8n tutorial - Lesson 19: n8n Weekly Digest: Aggregate All Your Data in One Report

Hi everyone, in this session of the n8n Workflow Automation Tutorial series, we're building an n8n weekly digest workflow that pulls data from four Google Sheets, aggregates everything with a Code node, generates an AI summary using Claude, and delivers it to Telegram every Sunday at 7 PM. This is the capstone workflow for the YouTube Assistant project — and it's a great real-world example of parallel branching, cross-node aggregation, and LLM-powered reporting in a single automated pipeline.

How to do:

Step 1 — Create the Workflow and Set the Schedule Trigger

Create a new workflow named T5-B7-Weekly-Digest and configure a cron-based schedule to fire every Sunday at 19:00.
  1. In n8n, click + New Workflow and name it T5-B7-Weekly-Digest.
  2. Add a Schedule Trigger node and set the mode to Cron.
  3. Enter the cron expression 0 19 * * 0 — this means every Sunday at 19:00.
  4. Save the node. This single trigger will fan out into four parallel branches in the next step.

Tip — The cron expression 0 19 * * 0 breaks down as: minute=0, hour=19, any day-of-month, any month, weekday=0 (Sunday). Double-check your server timezone in n8n settings so the trigger fires at the correct local time.

Step 2 — Add Four Parallel Google Sheets Branches

Connect four separate Google Sheets nodes directly to the Schedule Trigger, one for each metrics sheet, so all four reads execute in parallel.
  1. Add a Google Sheets node, name it Get Comments Stats, and connect it to the Schedule Trigger.
  2. Configure it:
    • Credential: Google Sheets Personal (shared across all four nodes)
    • Spreadsheet: select T5-Comments-Queue
    • Operation: Get Rows
    • Always Output Data: ON
  3. Duplicate the node three times and rename them:
    • Get Title Stats → Sheet: T5-Title-Suggestions
    • Get Performance Stats → Sheet: T5-Performance-Snapshots
    • Get Metadata Stats → Sheet: T5-Video-Metadata
  4. Connect all four nodes directly from the Schedule Trigger output — they run in parallel, not in sequence.

Note — Always Output Data = ON is the correct setting here because these nodes act as reference lookups — you want the workflow to continue even if a sheet has no rows yet. This is different from trigger-style nodes where you want AOD = OFF to avoid empty runs.

Step 3 — Merge All Four Branches

Add a Merge node to collect all four streams into a single flow before aggregation.
  1. Add a Merge node and name it Merge All Sheets.
  2. Set Mode to Append — this stacks all rows from all four sheets into one item list.
  3. Connect all four Google Sheets nodes (Get Comments Stats, Get Title Stats, Get Performance Stats, Get Metadata Stats) as inputs to this Merge node.
  4. Verify in test mode that the merged output shows approximately 34 items (the combined row count from all four sheets).

Tip — The Append merge mode is the right choice when you don't need to join rows by a key — you just want all items stacked. The next Code node will use cross-node references to read each sheet's data individually, so the merged list here is mainly a trigger to pass execution forward.

Step 4 — Aggregate Stats with a Code Node Using Cross-Node References

Add a Code node that reads each sheet independently via cross-node references and builds a single structured summary object.
  1. Add a Code node named Aggregate Stats and connect it to Merge All Sheets.
  2. Set Mode to Run Once for All Items.
  3. In the code editor, reference each sheet's data using the cross-node pattern:
    • const comments = $('Get Comments Stats').all();
    • const titles = $('Get Title Stats').all();
    • const performance = $('Get Performance Stats').all();
    • const metadata = $('Get Metadata Stats').all();
  4. Build a nested summary object with these four sections:
    • comments: total, by_category, by_status
    • titles: total, by_status
    • performance: video_count, total_view, total_like, videos (list)
    • metadata: total, by_status
  5. For the performance section, sort rows to get the latest snapshot per video before summing totals — use a reduce or sort + dedup pattern in JavaScript.
  6. Add a week_label field formatted as DD/MM/YYYY using JavaScript's Date object to label the digest by week.
  7. Return return [{ json: summary }]; — a single item containing the full aggregated object.

Production tip — Always use $('Node Name').all() cross-node references inside a Code node running "once for all items" when you need to aggregate across multiple upstream branches. Never use .find() in expression fields for lookups — build an O(1) lookup map in Code instead.

Step 5 — Generate the AI Weekly Digest with Claude

Add a Basic LLM Chain node connected to the Anthropic API to turn the aggregated stats object into a formatted Telegram message.
  1. Add a Basic LLM Chain node named Weekly Digest and connect it to Aggregate Stats.
  2. Set the model to Claude Haiku 4.5 (consistent with the rest of the project).
  3. Set Temperature to 0.3 and Max Tokens to 2500.
  4. Write the system/user prompt using an XML 5-block structure:
    • Block 1 — Role: define the assistant as a YouTube analytics reporter
    • Block 2 — Data: inject the aggregated stats via {{ $json.summary }} or the relevant field
    • Block 3 — Format rules: specify telegram_markdown_legacy — single asterisk *bold*, no V2 syntax
    • Block 4 — Digest structure: list the 7 required sections (e.g., overview, comments, titles, performance, metadata, highlights, next week)
    • Block 5 — Constraints: language, tone, emoji use
  5. Test the node. If you get an authentication error, check your Anthropic API balance — a $0 balance silently stops all active workflows using Haiku.

Note — If the Claude API returns an error due to insufficient credit, top up your Anthropic account ($5–10 is enough to cover several weeks of digest runs) and re-run the workflow. This is an external dependency, not a design flaw — but it means you should monitor your Anthropic console balance regularly and consider enabling auto-recharge if available.

Step 6 — Send the Digest to Telegram

Add a Telegram node to deliver the AI-generated digest message every Sunday evening.
  1. Add a Telegram node named Send Weekly Digest and connect it to Weekly Digest.
  2. Select your existing Telegram Bot credential (reused from earlier workflows in this series).
  3. Set Chat ID to your target Telegram group or personal chat ID.
  4. Set Parse Mode to Markdown (legacy — single asterisk format, not MarkdownV2).
  5. In the Message field, reference the LLM output: {{ $json.text }} or the field your LLM Chain outputs.
  6. Set Append Attribution to OFF to keep the message clean.

Tip — Always use Telegram Markdown legacy (single * for bold, single _ for italic) when your LLM is prompted to produce Telegram-formatted text. MarkdownV2 requires escaping many special characters, which makes AI output unreliable without a post-processing step.

Step 7 — Activate the Workflow

With all 9 nodes connected and tested, activate the workflow so it runs automatically every Sunday at 19:00.
  1. Click the Inactive toggle in the top-right corner of the workflow editor to set it to Active.
  2. Verify the full node chain is connected:
    • Schedule Trigger → 4× Google Sheets nodes (parallel)
    • 4× Google Sheets → Merge All Sheets
    • Merge All Sheets → Aggregate Stats (Code)
    • Aggregate Stats → Weekly Digest (LLM Chain)
    • Weekly Digest → Send Weekly Digest (Telegram)
  3. Check Executions the following Monday morning to confirm the Sunday run completed without red errors.
  4. If the workflow ran during an Anthropic credit outage, top up the balance and manually trigger a test run to confirm recovery.

Production tip — After activating any workflow that depends on a paid external API (Anthropic, OpenAI, etc.), bookmark the provider's billing console and check it at least once a week. A $0 balance will silently stop all active workflows using that provider — there is no built-in n8n alert for third-party API credit exhaustion.

Key Lessons from This Session

  1. Parallel branching from a single trigger. Connect multiple nodes directly to one trigger node to read from several data sources simultaneously — this is faster and cleaner than chaining them sequentially.
  2. Use Merge (Append) to reunite parallel branches. Append mode stacks all items from all inputs without needing a join key — ideal when you plan to aggregate in the next Code node anyway.
  3. Cross-node references in Code nodes unlock multi-source aggregation. Use $('Node Name').all() inside a "Run Once" Code node to read each source independently and build a structured summary object.
  4. Always Output Data = ON for lookup/reference nodes. Set AOD=ON on any Google Sheets node acting as a data source so the workflow continues even when a sheet is temporarily empty.
  5. Sort and dedup snapshot data before summing. For lifetime stats stored as snapshots, always pick the latest snapshot per video before calculating totals — otherwise you double-count historical rows.
  6. Telegram Markdown legacy: single asterisk only. Prompt your LLM to use *bold* (one asterisk), not **bold** — Telegram legacy parse mode does not support double asterisks.
  7. Monitor third-party API credit balances regularly. A depleted Anthropic (or OpenAI) balance silently kills every active workflow using that credential — check balances on a set schedule and enable auto-recharge if available.
  8. Vietnamese (and other CJK-adjacent) tokens are ~3× heavier than English. If your AI output language is not English, set Max Tokens high enough to avoid truncated messages — 2500 is a safe floor for a 7-section digest.

Conclusion:

In this n8n tutorial, we built a complete n8n weekly digest workflow that uses parallel Google Sheets branches, a cross-node Code aggregator, a Claude LLM chain, and a Telegram sender to deliver a structured weekly report every Sunday — all fully automated. This pattern of branching + merging + Code aggregation is one of the most reusable patterns in n8n workflow automation, applicable any time you need to consolidate data from multiple sources into a single report. The next session opens Week 6: a multi-channel Content Factory that connects Blog, YouTube, Email, and Word output from a single idea using sub-workflows and error handling.

If you have any questions, feel free to leave a comment below. Thank you!

Tags: n8n weekly digest workflow, n8n tutorial, n8n workflow automation, n8n Google Sheets aggregate, n8n Telegram bot, n8n Code node cross-node reference, n8n LLM chain Claude, n8n parallel branches merge

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How to Generate Professional Documents, Presentations, and Spreadsheets on Perplexity AI

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Spending hours each week gathering data, writing reports, designing presentations, or wrestling with complex Excel formulas? Welcome to 2026—Perplexity AI has evolved far beyond a simple search engine or Q&A tool. What's interesting here is how it's transformed into a genuine work assistant capable of generating complete, professional-grade office files directly—including documents, slideshows, spreadsheets, and even lightweight web applications. Better yet, every output comes with built-in source citations, eliminating the "AI hallucination" problem that keeps many businesses up at night.

The platform now bridges the gap between research and production. Instead of copy-pasting information from chat windows into Microsoft Office, you get properly formatted, citation-verified files ready for download.

A Four-Step Process for Creating Documents, Slides, and Spreadsheets on Perplexity

To unlock Perplexity's full potential and receive well-structured, professional-grade files, follow this optimized four-step framework:

Step 1: Specify Your Desired File Format Upfront

The biggest mistake users make? Vague commands like "write me something about..." The AI system demands precision from the start. You must explicitly state the file type you want—whether that's a Word document, presentation deck, or spreadsheet—right at the beginning of your prompt. While Perplexity does support exporting to PDF, DOCX, and other formats afterward, framing your request with the correct file type from the outset produces noticeably better results and more accurate document structure.

  • Use phrases like: "Create a Word document (DOCX)", "Design a presentation deck (PPTX)", "Build an Excel spreadsheet (XLSX)", or "Generate a PDF report".

Step 2: Describe Your Structure, Sections, and Key Content in Detail

Give the AI a solid framework. Set page counts, define your goals, and list mandatory sections or topics. The more context you provide, the closer the output gets to perfection. Vague requests yield vague results—specific briefs yield polished files.

  • Standard prompt template: "Design a 10-slide presentation on our Q4 marketing strategy. It must include separate sections for: budgeted spend, target distribution channels, and KPI measurement metrics."

Step 3: Refine with Follow-Up Prompts

Your first draft rarely hits 100% accuracy—and that's okay. Perplexity excels at maintaining conversation context across multiple requests. Use short follow-up commands to ask the AI to add sections, restructure content, or adjust tone. This iterative approach is where the magic happens.

  • Refinement examples: "Add a conclusion slide with a call-to-action at the end" or "Condense the project summary on page 2 to be more concise"."

Step 4: Verify Sources and Export Your File

Before hitting download, hover over the citation numbers to spot-check where the data originated. Once you're satisfied, click the export option to grab your file in DOCX, PDF, PPTX, XLSX, or whichever format suits your needs. This verification step takes seconds and prevents embarrassing data errors from reaching stakeholders.

Sample Prompt Templates by File Type

Here are real-world examples showing the right way and the wrong way to structure your requests:

1. Creating Text Reports (Word / PDF)

  • Do This ✅: "Generate a professional PDF report on the current state of renewable energy in the US. Include data visualizations and cite all sources thoroughly."
  • Avoid This ❌: "Write something about renewable energy." (Too generic—lacks format, scope, and specific objectives.)

2. Building Comparison Tables and Analysis (Excel / Spreadsheets)

  • Do This ✅: "Build an Excel spreadsheet comparing the top 5 project management tools based on: pricing, core features, and real user ratings."
  • Avoid This ❌: "List some project management software." (The AI will return bullet points instead of a structured, interconnected data table.)

Perplexity's Automated File Generation: Next-Generation Features Explained

What sets Perplexity apart from other large language models is its data verification engine. When you request a file, the system runs deep research across trusted sources, synthesizes the findings, and embeds live source links directly into your document. The real concern is that AI "hallucinations" still plague many competitors—Perplexity has essentially solved this through built-in citations and real-time source tracking.

The core benefits include:

  • Trustworthy documents: Every statistic, market figure, or competitive analysis carries proper citations and sourcing. This boosts credibility when sharing with partners, investors, or leadership.
  • Eliminates manual export: No more copy-pasting paragraphs into Word or PowerPoint. Download publication-ready files in standard office formats instantly.
  • Automated structure: Instead of manually organizing a 10-slide deck, the AI intelligently distributes content based on industry best practices. Your job becomes editing and refining, not building from scratch.

Strengths and Limitations of Perplexity's File Generation

While this feature represents a significant leap forward in 2026, it does have technical boundaries worth understanding:

1. Key Strengths

  • Superior data reliability: Built-in citations make files suitable for legal documents, market research, academic papers, and other high-stakes use cases.
  • Labor savings: Cuts up to 80% of time spent hunting for raw data and building initial layouts.
  • Extended capabilities: Goes beyond basic office files—can generate simple web applications (HTML/CSS/JavaScript) to prototype and showcase ideas.

2. Areas for Improvement

  • Design stays minimal: Prioritizing accuracy over aesthetics means generated slides and documents have a bare-bones look. You'll likely need to adjust colors, fonts, and imagery to match your brand guidelines.
  • Depends on clear input: If you can't articulate structure clearly, outputs remain surface-level. Specialized, deep-dive analysis requires equally specialized prompts.

The Takeaway

Perplexity's automated document, presentation, and spreadsheet generation—grounded in real-time research and source verification—is reshaping how knowledge workers operate in 2026. By marrying accurate data retrieval with flexible file packaging, it's become an essential tool for managers, analysts, and academics alike. Start applying the prompt engineering techniques outlined above today, and watch your productivity soar.

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How to Find and Use AI Prompts for Image and Video Generation on PromptHero

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PromptHero is a dedicated platform for discovering, sharing, and searching AI prompts tailored for image and video generation tools. Instead of crafting prompts from scratch, you can browse millions of AI-generated images alongside the exact prompts used to create them. What's interesting here is that this resource works equally well for beginners exploring AI for the first time and experienced users looking to streamline their workflow and achieve better results faster.

The platform resonates particularly well with designers, content creators, marketers, educators, and anyone wanting to leverage AI for visual content. Thanks to its intuitive interface, extensive prompt library, and active user community, PromptHero has become a go-to destination for learning prompt engineering, finding creative inspiration, and producing high-quality AI outputs without spending hours experimenting.

How to Retrieve Prompts from PromptHero

Step 1:

Head to PromptHero using the link below.

https://prompthero.com/

You can browse PromptHero without logging in. However, creating an account is recommended if you want to save your favorite prompts for later.

Step 2:

On the homepage, you'll find a search bar surrounded by numerous AI-generated images shared by the community. Enter any topic you're interested in—or even the name of a specific image generation model.

You'll immediately see hundreds of results matching your keyword.

To narrow down results, use the filtering options at the top of the page.

Step 3:

Click on any image to view detailed information, including dimensions, the complete prompt text, the AI model used, negative prompts (if applicable), and other generation parameters like Steps, CFG Scale, and Seed values.

Copy the entire prompt text to use with your own AI model. You can always modify the prompt from PromptHero to suit your specific needs.

Here's an original image from PromptHero created with Midjourney.

Using the same prompt in ChatGPT produces a noticeably different image—which highlights how different models interpret the same instructions.

Tips for Getting the Most Out of PromptHero

  • Search using English keywords to unlock the broadest range of results.
  • Study multiple prompts rather than relying on a single template.
  • Mix and match elements from different prompts to develop your own unique style.
  • Edit prompts before using them to align with your specific goals.
  • Filter by the exact AI model you're working with to maximize output quality.

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Edit Images Directly in Gemini Using Canva Integration

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Gemini has evolved beyond just AI-powered search and content creation—it now integrates directly with Canva, letting you edit images on the fly without switching between apps. What's interesting here is how this integration streamlines your workflow, letting you generate, refine, and polish visual content all in one place.

With Canva built right into Gemini, you can create and modify designs including ads, social media posts, banners, posters, and educational materials using simple text prompts. This guide walks you through the entire process of editing images with Canva directly within Gemini.

Important note: You must switch Gemini to English to access this feature.

Start by logging into your Google account, then click on Personal Info in the left sidebar.

Look for the Language section on the right side and click it.

In the language settings screen, click the pencil icon next to your current language to switch to English.

Find English in the list and select it to change your Google account language.

How to Edit Images with Canva in Gemini

Step 1:

Open Gemini and click on Personal Intelligence.

Navigate to the new screen and select Connected Apps to manage your app integrations with Gemini.

Step 2:

In the apps list, click on Canva.

A permission prompt will appear. Click Allow to grant Gemini access to your Canva account.

Click Continue to proceed.

Click Agree and Continue to confirm the action.

Step 3:

You'll need to sign in to your Canva account to continue.

Next, allow the Canva AI Connector to request necessary permissions by clicking Allow.

Success—Gemini is now connected to Canva.

Step 4:

Now describe the image you want to create in Gemini as you normally would.

Once you have an image, type @ and select Canva from the menu that appears.


Type @Canva edit picture to start editing the image in Canva.

Step 5:

Gemini will display a notification with a link to open your image in Canva for editing. Click the link to access Canva.

You're now in Canva's editor, ready to polish your design.


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Understanding Open Source AI: What Actually Counts as Open?

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Here's something that might surprise you: OpenAI doesn't build open AI models. Their GPT and DALL·E offerings? Completely proprietary and closed-source. So what about Meta's Llama? Despite what Mark Zuckerberg says, it's not truly open source either—though it's more open than OpenAI's models, which is saying something.

When we talk about AI models, there are really three main categories:

  • Proprietary
  • Open source
  • Open

These distinctions apply to both large language models (LLMs) and text-to-image generators. The landscape is still forming, and the Open Source Initiative is currently hammering out a strict definition of what qualifies as truly open source AI. Let's break down where things stand right now.

What Does Open Source Actually Mean?

Before diving into open source AI, let's step back and define open source itself. It's not just trendy jargon—the Open Source Initiative maintains a formal definition that outlines the philosophy and core requirements. It's published under the Creative Commons Attribution 4.0 International License, but here's the essence.

Open source doesn't simply mean you can download or access code freely. True open source must be available to anyone who wants to use it, modify it however they see fit, and use it for any purpose. Open source licenses cannot restrict any "field of use"—and this is exactly where many supposedly open source AI models fall short.

The OSI maintains an approved license list, with major ones including Apache 2.0, MIT License, and GNU Public License.

What Are Proprietary AI Models?

Proprietary AI models represent some of the most powerful and widely-used systems available today. Private companies develop and operate them, keeping the source code, training methods, model weights, and even parameter counts under lock and key. You can only access proprietary models through official channels—chatbots, APIs, or applications built on top of these APIs.

Take OpenAI's GPT-4o. We don't know what training data went into it or how many parameters it has. The only way to use it is through ChatGPT, OpenAI's API, or third-party apps like Perplexity or Zapier Chatbots.

And yes, OpenAI charges for access. Want to use GPT-4o—arguably one of the best AI models out there? You're looking at $20 monthly for ChatGPT Plus, API fees, or integrating a paid service. You can't just download GPT-4o and run it on your own servers.

The same applies to virtually every other proprietary AI model:

  • GPT-4o mini and DALL·E 3 from OpenAI
  • Claude 3 and Claude 3.5 from Anthropic
  • Gemini and Imagen 3 from Google
  • Command R and R+ from Cohere
  • Midjourney

What Is Open Source AI?

Open source AI consists of models released under open source licenses. But here's where it gets complicated: researchers have discovered that many models claiming to be open source actually aren't. This deceptive practice is called "open-washing," and it's created serious confusion—even among AI writers.

Chart showing the "openness" spectrum of various AI models

Currently, the Open Source Initiative is working to define truly open source AI because existing licenses don't fully address the technical realities of modern AI models. Meeting genuine open source standards requires more than just sharing code—you need to provide training data, training code, model parameters, and much more. Code should be shared under open source licenses, while training data and documentation should use Creative Commons or similar open licenses.

Here's the thing about open source licenses: the strictest ones actually require you to publicly release everything you build with them and credit the original developers. That's the baseline.

What Are Open AI Models?

Open AI models occupy the middle ground between locked-down proprietary systems and the idealized vision of truly open source AI. (Until OSI releases their formal definition, OLMo 7B comes closest to that ideal.)

Simply put, open AI models are made available for free under certain conditions. Usually you can download them from Hugging Face and similar platforms, then run them locally after accepting whatever license terms come with them. You typically can fine-tune them on your own data to build custom versions, create chatbots, and develop applications. In most cases, you can examine model weights and system architecture to understand how they work (to a reasonable degree).

Open licenses still permit broad usage, but they include restrictions you won't find in true open source licenses. For example, Llama 3's license allows commercial use up to 700 million monthly active users and blocks certain use cases. You or I could build something with it, but Apple and Google couldn't. Similarly, Gemma 2's acceptable use policy prohibits "facilitating or encouraging users to engage in any form of criminal activity." That makes sense—Google doesn't want unethical Gemma-powered chatbots flooding social media.

These restrictions, while understandable, contradict open source philosophy. That's why this whole space has become contentious. Many researchers are developing classification systems to clarify exactly how open different models actually are. If any of these gain traction, we'll definitely keep you posted.

The Best Open and Open Source AI Models Today

Here's a rundown of the most noteworthy open and open source models available now. Where they fall on the open source-to-open spectrum is still being debated until we get a solid definition.

AI Model Developer Model Type License Parameters Notes
Llama 3.1 Meta LLM Custom 8B, 70B, 405B Usage restrictions and user cap limits
Gemma 2 Google LLM Custom 2B, 9B, 27B Restricted user categories
Phi-3 Microsoft LLM MIT 3.8B, 7B, 14B  
Mixtral 8x7B Mistral LLM Apache 2.0 8x7B  
Mistral 7B Mistral LLM Apache 2.0 7B  
DBRX Databricks/Mosaic LLM Custom Equivalent to 36B Mixture of Experts—parameter counting is complex
OLMo 7B Allen Institute for AI LLM Apache 2.0 7B Closest you'll get to truly open source AI right now
FLUX.1 [schnell] Black Forest Labs Image Generator Custom N/A Non-commercial use only
FLUX.1 [dev] Black Forest Labs Image Generator Apache 2.0 N/A  
Stable Diffusion Stability AI Image Generator Custom N/A Earlier versions including 1.5, 2.1, and SDXL available under open licenses

Should You Use Open or Open Source AI?

While truly open source AI models aren't as plentiful as we'd like, the best open models are surprisingly competitive with proprietary alternatives. Llama 3 405B and FLUX.1 can go toe-to-toe with GPT-4o and DALL·E 3. What's interesting here is: if you have the technical chops to work with open source models, you can achieve similar results for significantly less money with substantially more freedom.


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How to Create Mind Map Infographics Using NotebookLM

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We all face the same challenge: drowning in content. Between textbooks, research papers, and dense reports, retaining key information gets harder by the day. The real issue isn't having access to knowledge—it's organizing it in a way your brain can actually process. That's where mind maps come in. NotebookLM lets you generate structured mind maps automatically, turning chaotic information into visual knowledge you can actually remember and build on.

Creating a mind map in NotebookLM does more than save time—it fundamentally changes how you learn. Your ideas get arranged in a clear hierarchy that's easy to follow, remember, and expand upon. Here's how to build one from scratch.

1. Creating a Mind Map in NotebookLM

Step 1:

Start by gathering your source material. This could be a YouTube video, a PDF document, or any learning resource—for instance, a video about Vietnamese history for grade 12 students. Head to NotebookLM, create a new notebook, and paste your source material in.

Step 2:

Once NotebookLM processes your content, look for the arrow icon next to "Mind Map" in the sidebar and click it to get started.

Now type in your instructions for how you want the mind map structured, then hit Create.

Step 3:

Wait while NotebookLM analyzes your material and generates the mind map. When it's ready, click the map's title to view it.

Click the expand icon to see the complete mind map in full view.

Step 4:

Each branch has its own expand icon—click any branch to reveal the detailed knowledge and information nested inside.

When you click a specific item in the mind map, NotebookLM displays detailed information in the sidebar—concise but complete, giving you exactly what you need to remember the material. You can also add follow-up prompts here if you want to dig deeper.

To download your mind map, hit the save icon and export it as a PNG file.

2. Converting Your Mind Map Into an Infographic

Here's where it gets interesting. Once you've downloaded your NotebookLM mind map as a PNG, you can level it up by transforming it into a professional infographic using ChatGPT—complete with icons, illustrations, and polished design.

Upload your mind map image to ChatGPT, then paste the following prompt to have it redesign your map into a visually stunning infographic:

I'm uploading a mind map image. Please read all the content and transform it into a modern, professional infographic.\
\
OBJECTIVES\
\
- Do not recreate the mind map structure.\
- Redesign it into a visually appealing, beautiful, and easy-to-read infographic while preserving 100% of the content and hierarchical structure.\
\
REQUIREMENTS\
\
- Keep all content intact.\
- Do not omit, add, or modify any points.\
- Maintain the original branch order.\
- Number only main branches (1, 2, 3, 4...), not sub-branches.\
- Convert sub-points within the same group into bullet lists to save space.\
- Do not create individual boxes for each small point like in a mind map.\
- Group related content into clear information cards.\
\
DESIGN\
\
- Modern editorial infographic style.\
- Balanced layout with generous whitespace.\
- Main topic highlighted in the center.\
- Primary branches distributed evenly around the main topic.\
- Soft, curved connecting lines.\
- Each main branch gets its own color.\
- Sub-branches use lighter shades of the same color.\
- Rounded content boxes with subtle shadows.\
- Clear typography with strong visual hierarchy.\
\
ILLUSTRATIONS\
\
- Combine relevant illustrations and icons.\
- Each main branch includes one large illustration directly related to its content.\
- Each content group can include a small supporting icon.\
- Add small illustrations or thumbnails next to important information sections where appropriate.\
- Avoid icon overload.\
- Do not replace all illustrations with icons.\
\
ILLUSTRATION STYLE\
\
- Flat vector illustration\
- Editorial illustration\
- Semi-flat\
- Modern\
- Cohesive\
- Harmonious color palette\
\
QUALITY\
\
- Publication-ready quality.\
- Clean and polished.\
- Professional.\
- Readable at a glance.\
- Visual depth.\
- 16:9 aspect ratio.\
- High resolution.\
- Suitable for presentations, Canva, websites, or A3 printing.

What's interesting here is how quickly ChatGPT transforms the map. You'll immediately see an infographic that builds on your original mind map structure. Here's an example mind map created in NotebookLM:

And here's the polished infographic generated from that same map:


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Everything iPhone Users Need to Know About Apple's New AI-Powered Siri

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Apple just unveiled a completely reimagined Siri at WWDC 2026, built on the new Apple Intelligence platform. This is the biggest overhaul to Siri in years, transforming it from a basic voice assistant into something smarter—one that actually understands context and can help you accomplish real tasks without constant app-switching.

The old Siri handled simple requests: set a timer, send a text, make a call, check the weather. The new AI-powered Siri tackles complex tasks by tapping into your personal data, reading what's displayed on screen, and connecting across multiple apps—meaning fewer taps to get things done.

Beyond Voice Commands: How Siri AI Actually Works Differently

The biggest shift isn't better voice recognition. It's contextual understanding.

Think about your typical day. Need to confirm dinner plans? You're bouncing between Messages to read the invite, Maps for the location, Calendar to check availability, and Notes to save the details. It's fragmented and exhausting.

Apple designed Siri AI to eliminate this friction by combining three core capabilities: personal context awareness, on-screen content recognition, and the ability to search the web for answers.

Instead of launching app after app, you simply make a request and let Siri handle the legwork in the background.

If Apple's claims hold up in real use, this could streamline routine tasks noticeably.

Understanding Your Personal Context: Siri Knows Your Data

Apple calls this Personal Context—and it's powerful. Siri AI can search through information already stored across your Apple devices and surface the exact answer you need.

Examples of what you can ask:

  • "What's my hotel confirmation code?"
  • "Find photos from our beach trip with the family."
  • "Where did my friend send that restaurant address?"

Rather than you manually digging through Mail, Messages, and Photos, Siri connects your request to relevant data and delivers an answer.

What's interesting here is it feels more natural. You don't need to remember whether information lives in Mail or iMessages—you just describe what you're looking for.

That said, the feature's effectiveness depends on data quality. Messy email folders or vague message text mean Siri still might need your confirmation before returning results.

Siri Reads What's on Your Screen

Another major capability is on-screen content recognition. Siri can now understand what's being displayed and take immediate action.

See an address in a text message? Ask Siri to navigate there. Got a meeting mentioned in a note? Siri can add it to your calendar. Reading important details? Siri can convert them into reminders.

These aren't groundbreaking features individually, but they're small actions you perform dozens of times daily.

Apple says this screen-awareness will work across Apple Intelligence and integrate with apps like Photos, Safari, Messages, Reminders, and Music.

Important distinction: Siri AI is just one piece of Apple's broader Apple Intelligence ecosystem, not the whole AI platform by itself.

Visual Intelligence: Siri Now Sees Through Your Camera

One of the most exciting additions is Visual Intelligence, letting Siri analyze what your camera is pointing at.

This means Siri understands more than text and voice—it can process images and live camera feeds.

Apple demonstrated scenarios like:

  • Identifying objects.
  • Looking up nutritional information.
  • Answering questions about what the camera sees.
  • Analyzing content displayed on iPad, Mac, or Apple Vision Pro screens.

In practice, point your camera at something and ask Siri what it is or request related details. Simple but useful.

That said, Apple cautions that health, nutrition, financial, and safety information should only be treated as initial reference material. Always verify with trusted expert sources before making important decisions.

Writing Assistance Powered by AI

Content creation is one area expected to benefit most from Apple Intelligence.

The Writing Tools suite includes:

Feature

What It Does

Proofreading

Catches spelling and grammar mistakes

Rewriting

Generates multiple versions of the same passage

Summarizing

Condenses lengthy content into key points

Composition Help

Suggests improvements while drafting emails, texts, or notes

These tools work across most text-input fields, including third-party apps and websites.

Practically speaking, Siri AI can draft emails, edit passages, improve phrasing, or create a first draft for you to refine.

Apple emphasizes that you should carefully review important content before sending. Proper names, dates, addresses, prices, medical information, and legal language should never be accepted without verification.

Real-World Applications: Work and Daily Life

At work, Siri AI helps retrieve old information, summarize lengthy content, draft replies, or convert emails and messages into actionable reminders.

In daily life, it manages schedules, finds directions, locates photos, tracks to-do items, and answers quick questions.

Here's what makes it compelling: most of us store data everywhere. Texts in Messages, emails in Mail, photos in Photos, notes in Notes. It's fragmented across apps.

Siri AI aims to act as a connector between these data silos, helping you search faster and act quicker without manually opening every app.

The real concern is this: Siri AI should only support your workflow. You still own the decision of what matters, what gets saved, and what action to take.

How Does Apple Handle Privacy?

As Siri grows smarter about your life, privacy concerns naturally emerge.

Apple says Apple Intelligence processes requests directly on your device whenever possible.

For more complex tasks, it uses Private Cloud Compute technology.

According to Apple, only data needed for your specific request is processed, nothing is stored, and Apple can't access the content. The company notes that independent security experts can audit Private Cloud Compute to ensure transparency.

Still, you should proactively review privacy settings, check which AI features are enabled, and monitor Apple Intelligence activity on your device.

Opening Siri to Third-Party Apps

Apple also announced that developers can integrate their apps with Siri AI through the App Intents platform.

This opens doors to Siri interacting with services beyond Apple's ecosystem.

This is critical because your daily life doesn't happen only in Apple's default apps. You use banking apps, airline booking apps, task managers, fitness trackers, and countless services.

If developers properly use App Intents, Siri AI could become a unified control hub for diverse activities across your iPhone.

Apple's announcements sound impressive, but real-world experience will determine Siri AI's success.

Watch how well it handles everyday requests:

  • Does it find the correct message?
  • Does it confuse people with similar names?
  • Does Siri explain where answers come from?
  • Does it take action without confirmation?
  • Does the writing tone feel natural or robotic?

These small details decide whether Siri AI becomes a daily tool or just an interesting novelty.

Siri AI represents Apple's biggest stride toward creating a genuinely useful personal assistant. Instead of just answering questions, it bridges the gap from question to answer to action—with fewer steps along the way.

For users of iPhone, iPad, Mac, Apple Watch, AirPods, CarPlay, or Apple Vision Pro, Siri AI could soon feel like a natural part of your routine.

The smartest approach remains balancing AI convenience with human judgment. Siri AI can significantly reduce repetitive tasks, but critical decisions should still be verified and confirmed by you.


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