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How to Disable and Hide Meta AI Across Facebook, Instagram, Messenger, and WhatsApp

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Meta is embedding AI into practically every app it owns. From Facebook and Instagram to WhatsApp and Messenger, Meta AI has quietly rolled out across search bars, chat windows, and countless other features designed to help you find information faster. The catch? Not everyone wants an AI assistant popping up every time they open an app.

Here's the tough truth: Meta won't let you completely disable Meta AI on any of its platforms. The company views AI as core infrastructure now, so it's not going anywhere. But don't worry—you can still hide conversations, silence notifications, and reduce AI's presence significantly to clean up your interface.

Let's walk through how to do this on each platform.

Can You Completely Turn Off Meta AI?

Short answer: No.

Meta considers Meta AI a fundamental part of its ecosystem. You cannot fully disable it across Facebook, Instagram, or WhatsApp. What you can do instead:

  • Hide your conversation with Meta AI.
  • Mute notifications from Meta AI.
  • Disable specific AI features, like comment summaries on Facebook.

While Meta AI technically remains in the app, these settings dramatically reduce how often you'll encounter it.

How to Hide Meta AI on Instagram

Even if you've never chatted with Meta AI on Instagram, you can preemptively hide the conversation so it doesn't clutter your inbox later. Here's how:

  1. Open Instagram.
  2. Tap the Messenger icon in the top right corner.
  3. Select the New Message icon.
  4. Find and select Meta AI from suggestions or search.
  5. Open the conversation with Meta AI.
  6. Tap the info icon (i) in the top right.
  7. Select Mute.
  8. Turn on Mute Messages and choose Until I change it.
  9. Optionally, enable Hide Message Previews to block message previews too.

Once done, Meta AI won't send annoying notifications on Instagram anymore.

How to Disable AI Features on Facebook

Beyond the search bar, Meta AI powers Facebook's comment summary feature—those AI-generated summaries that let you quickly grasp a discussion without reading hundreds of comments.

If you don't want this on your posts, do this:

  1. Open Facebook.
  2. Go to Settings & Privacy.
  3. Click Settings.
  4. Scroll to Audience and Visibility.
  5. Select Posts.
  6. Toggle off Allow comment summaries on your posts.

After this change, Facebook won't generate AI summaries for comments on your posts.

How to Hide Meta AI in Facebook Messenger

Want Meta AI gone from Messenger? Just mute the conversation:

  1. Open Facebook.
  2. Tap the Messenger icon.
  3. Tap the search box.
  4. Select the Meta AI conversation.
  5. Tap the info icon (i) in the top right.
  6. Select Mute.

What's interesting here is that this setting syncs across your web and mobile versions, so you only need to do it once.

How to Turn Off Meta AI Notifications on WhatsApp

WhatsApp is a bit different—you need to start a conversation with Meta AI before you can silence it.

Follow these steps:

  1. Open WhatsApp.
  2. Tap the Meta AI icon.
  3. Send any message to start the conversation.
  4. Go back to your chat list.
  5. Swipe left on the Meta AI conversation.
  6. Tap More.
  7. Select Mute.
  8. Choose Always to permanently silence notifications.

Done. Meta AI will stay quiet on WhatsApp.

You Can Turn Meta AI Back On Anytime

Change your mind later? Just unmute the conversation and everything returns to normal—no need to reconfigure anything.

The real concern here is that Meta is embedding AI deeper into its ecosystem with each update, and complete removal isn't an option. Every major tech company is doing this right now. But while you can't eliminate Meta AI entirely, you can minimize its presence by hiding conversations, muting notifications, and disabling unnecessary AI features.

If you prefer using Meta's apps the traditional way without constant AI interruptions, these steps will keep your interface cleaner and your experience more familiar.

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Building a Robust Knowledge Base for AI and Large Language Models

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The rise of large language models like ChatGPT, Claude, and Gemini is fundamentally reshaping how we organize and leverage information. Knowledge bases have evolved from simple document repositories into something far more powerful: a long-term memory system for AI. Instead of just storing files for manual lookup, a well-built knowledge base becomes the context that helps AI understand your entire operation, make smarter decisions, and deliver genuinely useful support.

A properly constructed knowledge base does more than preserve important information—it enables AI to automatically search through, synthesize, and apply that data without requiring manual intervention from humans.

In this guide, we'll walk through building an effective knowledge base specifically designed for LLMs, covering everything from data collection to implementation strategies that let AI actually leverage what you've stored.

Why Build a Knowledge Base?

At its core, a knowledge base is a centralized repository of everything a person or organization knows. This includes meeting notes, project documentation, emails, source code, technical specifications—essentially any data with lasting value.

Individuals can create personal knowledge bases to capture their own expertise and experience. Organizations, meanwhile, can build shared repositories that let every team member access the same unified information source.

The benefits are substantial. First, you'll make better decisions because you always have full context. Instead of trying to remember where you jotted something down or digging through dozens of separate apps, AI can instantly synthesize all relevant information.

Knowledge bases also create consistency across teams. Everyone draws from a single source of truth, eliminating misunderstandings and preventing the spread of outdated information.

Even before AI entered the picture, knowledge bases were genuinely useful. But here's what's interesting: LLMs multiply that value exponentially. Previously, finding information meant you had to remember where documents lived and manually search for them. Today, AI does that work automatically.

Through techniques like RAG (Retrieval-Augmented Generation), language models can autonomously locate relevant documents within your knowledge base, then use that data to answer questions or complete tasks. Users no longer need to be involved in the search process at all.

Data Collection: The Critical Foundation

A knowledge base only delivers real value when it's comprehensive. That's why your first step is identifying every data source your organization currently owns. Typically, these include:

  • Meeting transcripts and notes
  • Project management tools (Linear, Jira, Trello, etc.)
  • Work logs from AI coding agents (Claude Code, Codex, Cursor)
  • Email communications
  • Internal documentation
  • Source code repositories
  • In-person discussions and conversations
  • Technical documentation and internal wikis

Once you've cataloged all sources, the next objective becomes automated synchronization into your knowledge base.

This is absolutely critical. If you have to manually remember to copy meeting notes or upload documents every single day, something will eventually slip through. Miss even one piece of information, and your knowledge base loses value—AI can't see the complete picture anymore.

An effective knowledge base needs to operate almost entirely on autopilot. Consider setting up scheduled tasks (cron jobs) that run daily to:

  • Sync all meeting notes
  • Update changes from project management tools
  • Archive work history with AI coding agents
  • Ingest new documents and code changes

Automation ensures all data stays current without any manual effort.

What About Face-to-Face Conversations?

Real-world conversations are the trickiest part to automate. Two main approaches exist.

The first option is recording everything and using AI to transcribe it. But this requires everyone's consent and creates significant privacy concerns.

The second approach is documenting discussions after meetings. In practice, though, technical conversations often continue through other channels—developers then implement solutions using AI coding agents like Claude Code or Cursor.

Here's the insight: when engineers discuss a bug fix and then work through implementation with an AI agent, the actual knowledge gets captured in that agent's conversation history. You can absolutely synchronize that into your knowledge base automatically.

Putting Your Knowledge Base to Work

After syncing all your data, the next stage is actually using it. Two primary approaches exist.

AI Queries on Demand

This is the straightforward method. When you need answers, you simply ask your AI. The LLM searches your knowledge base and responds based on actual stored information rather than just relying on training data.

Examples might include:

  • What solution did the team decide on for this feature last week?
  • Why was this module designed the way it currently is?
  • How did Project A handle a similar error?

AI automatically locates the right documents and synthesizes answers.

AI Autonomously Consulting Knowledge Bases

Now this is the truly powerful approach. Rather than waiting for user questions, AI proactively consults your knowledge base whenever performing tasks like:

  • Writing code
  • Debugging issues
  • Designing new features
  • Creating documentation
  • Responding to messages

This means AI constantly operates with full contextual awareness instead of working from just the immediate conversation.

Helping AI Find the Right Information

Once AI has access to your knowledge base, you face another critical question: how does it locate the correct information among millions of documents? Two established methods exist.

Text-Based Indexing (Grep)

The first approach involves creating a comprehensive Markdown index file that acts like a table of contents for your entire knowledge base. This file describes what data exists, where it's stored, and how everything is organized. Every time the knowledge base updates, this index updates too.

The advantage is that AI can use text search tools like grep to quickly pinpoint needed information. The real concern is that as your knowledge base grows, so does this index file, increasing the token count per query.

Embeddings and RAG

This is what most modern AI systems prefer. Instead of keyword matching, documents get converted into vector embeddings—mathematical representations of semantic meaning.

When users ask questions, the system performs semantic search to find documents with the highest conceptual similarity, then feeds those excerpts to the LLM.

This process is what RAG (Retrieval-Augmented Generation) does. The advantage? AI only reads genuinely relevant sections instead of loading entire knowledge bases into its context window. This saves tokens, improves speed, and enhances scalability.

For most applications today, combining Embeddings + RAG remains the most effective approach for LLM-powered knowledge bases.

Final Thoughts

Knowledge bases are becoming essential infrastructure in modern AI systems. Their value isn't about storage—it's about enabling AI to remember and leverage your complete operational context.

Building an effective knowledge base doesn't require choosing the perfect technology. What matters most is ensuring every data source automatically syncs. When AI has access to your entire work history, documentation, conversations, and codebase, it transforms from a simple chatbot into a genuine project assistant that truly understands what you're building.

As AI agents become increasingly prevalent, knowledge bases will likely evolve into "long-term memory" systems that let AI work continuously, retain context across sessions, and support humans with genuine intelligence.

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n8n tutorial - Lesson 28: Build a RAG Chatbot with n8n Pinecone and Telegram

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n8n tutorial - Lesson 28: Build a RAG Chatbot with n8n Pinecone and Telegram

Hi everyone, in this post I'll show you how to build a RAG chatbot that answers questions over Telegram using n8n, Pinecone, and Claude Haiku — a practical n8n RAG pipeline tutorial from real session notes. This is part of the n8n Workflow Automation Tutorial series, and by the end you'll have a working Telegram bot that queries your Pinecone knowledge base on demand.

How to do:

Step 1 — Prepare the RAG Query Sub-Workflow

Before building the Telegram-facing workflow, you need to make your existing RAG query workflow callable by other workflows in n8n.
  1. Open your existing RAG query workflow — in this series it is named T8-B2-RAG-Query.
  2. Add a new trigger node: search for and select When Executed by Another Workflow.
  3. Inside that trigger node, define one input field:
    • Field name: query
    • Field type: String
  4. Save the workflow and set its status to Active.

Note — The Call n8n Workflow tool node only lists sub-workflows that have a When Executed by Another Workflow trigger. If your workflow uses a Chat Trigger or Manual Trigger only, it will not appear in the tool's dropdown. You must add this trigger and declare the input schema for the workflow to be usable as a tool.

Step 2 — Create the RAG Telegram Workflow

This step builds the main workflow — T8-B3-RAG-Telegram — that receives a Telegram message and routes it through an AI Agent with Pinecone-backed RAG.
  1. Create a new workflow and name it T8-B3-RAG-Telegram.
  2. Add a Telegram Trigger node as the entry point. Configure it with your Telegram bot token so it listens for incoming messages.
  3. Add an AI Agent node and connect it to the Telegram Trigger output.
    • Set the model to Claude Haiku 4.5 (or your preferred model credential).
    • Pass the user's message from the trigger as the agent's input prompt.
  4. Inside the AI Agent's Tools section, add a Call n8n Workflow tool.
  5. In the Call n8n Workflow tool, select T8-B2-RAG-Query from the workflow list and map the query field to the incoming user message.
  6. Add a Telegram node at the end, set the action to Send Message, and map the Chat ID and the AI Agent's output text as the message body.
  7. Save and set the workflow to Active.

Tip — When mapping the Chat ID for the reply, use the value from the Telegram Trigger output — typically $json.message.chat.id. This ensures the bot always replies to the correct conversation thread.

Step 3 — Test the End-to-End RAG Chatbot

With both workflows active, send a real message through Telegram to confirm the full pipeline works.
  1. Open Telegram and find your bot.
  2. Send a question that is covered by your Pinecone knowledge base.
  3. Confirm that:
    • The Telegram Trigger fires and passes the message to the AI Agent.
    • The AI Agent calls T8-B2-RAG-Query with the query field populated.
    • The RAG query retrieves relevant context from Pinecone and returns it to the agent.
    • The agent composes a response and the Telegram send node delivers it back to the chat.
  4. Check the execution logs in n8n for each workflow to verify no errors occurred.

Tip — If the bot replies but the answer is not grounded in your knowledge base, check that the T8-B2-RAG-Query workflow is returning results correctly by running it manually with a test query value first.

Step 4 — Set Up a Cloudflare Named Tunnel (for a Stable Webhook URL)

A named Cloudflare Tunnel gives your local n8n instance a permanent public HTTPS URL, so Telegram webhooks never break on restart — unlike Quick Tunnel which generates a new URL every time.
  1. Log in to your Cloudflare account and add your domain (e.g. dan14.vn) on the free plan.
  2. At your domain registrar, replace the existing nameservers with the two Cloudflare nameservers provided — for example:
    • brenna.ns.cloudflare.com
    • hasslo.ns.cloudflare.com
  3. Wait for DNS propagation — typically 1–2 hours but can take up to 24 hours. Cloudflare will send a confirmation email when the domain is active.
  4. Once you receive the "your domain is now active on Cloudflare" email, open a terminal and run:
    1. cloudflared tunnel login — a browser window opens; select your domain and click Authorize.
    2. cloudflared tunnel create n8n-tunnel — note the Tunnel ID that is returned.
    3. cloudflared tunnel route dns n8n-tunnel n8n.dan14.vn — this creates a CNAME record in your Cloudflare DNS automatically.
  5. Create the tunnel config file at C:\Users\<user>\.cloudflared\config.yml with the following content: 
    tunnel: <TUNNEL_ID>
credentials-file: C:\Users\<user>\.cloudflared\<TUNNEL_ID>.json
ingress:
  - hostname: n8n.dan14.vn
    service: http://localhost:5678
  - service: http_status:404
  6. Start the tunnel by running: cloudflared tunnel run n8n-tunnel

Note — The last ingress rule — service: http_status:404 — is required as a catch-all. Cloudflare will reject the config if no catch-all rule is present.

Step 5 — Update n8n Webhook URL and Restart

After the tunnel is live, update n8n's environment config so all webhooks use the new permanent domain.
  1. Open your Docker Compose file at D:\n8n\docker-compose.yml.
  2. Find the WEBHOOK_URL environment variable and set it to: https://n8n.dan14.vn
  3. Save the file and restart Docker:
    • Run docker compose down then docker compose up -d in the same directory.
  4. In n8n, deactivate and reactivate any webhook-based workflows — for example T7-B2-Telegram-Chatbot and T8-B3-RAG-Telegram — so they re-register with the new URL.

Production tip — Always deactivate then reactivate webhook workflows after changing WEBHOOK_URL. n8n caches the webhook registration URL at activation time, so existing active workflows will still point to the old address until you cycle them.

Key Lessons from This Session

  1. Sub-workflows must have a "When Executed by Another Workflow" trigger to appear as a callable tool. Chat Trigger and Manual Trigger are not recognized by the Call n8n Workflow tool node — you must add this specific trigger and declare the input schema.
  2. Named Tunnels require your domain to be managed inside Cloudflare DNS. Quick Tunnel works without a domain but the URL changes on every restart, breaking all registered webhooks.
  3. The Cloudflare tunnel setup order matters: add domain → change nameservers → wait for propagation → login with cloudflared → create tunnel → route DNS → write config → run tunnel.
  4. Reactivating workflows after a webhook URL change is mandatory. n8n does not automatically re-register webhooks when the environment variable changes.

Conclusion:

In this n8n RAG pipeline tutorial, you built a fully functional RAG chatbot on Telegram by chaining a Telegram Trigger, an AI Agent, and a callable RAG sub-workflow backed by Pinecone — a key milestone in this n8n workflow automation series. You also learned how to give your local n8n instance a stable public URL using a Cloudflare Named Tunnel, which is essential for reliable webhook-based workflows in production. Next session, the focus shifts to completing the Named Tunnel setup and verifying the permanent webhook URL end-to-end.

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

Tags: n8n RAG pipeline tutorial, n8n tutorial, n8n workflow automation, Telegram chatbot n8n, Pinecone n8n integration, Cloudflare tunnel n8n, AI agent n8n, n8n sub-workflow

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Integrating Google Calendar into n8n Using OAuth2: A Complete Setup Guide

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n8n is an open-source workflow automation platform that lets you connect hundreds of applications together without writing complicated code. Here's what's interesting: once you integrate Google Calendar into n8n, every change to your schedule—a new event, a cancellation, a time shift—can become the starting trigger for an automated workflow that handles everything downstream.

To make this work, n8n requires OAuth2 credentials that you create yourself on Google Cloud. The process sounds intimidating at first, but it really only takes 10-15 minutes if you follow the steps below carefully.

Connecting Google Calendar to n8n: Step-by-Step

Step 1: Add the Google Calendar Node to Your Workflow

Open n8n and create a new workflow. Click the Add first step button in the center of your screen. In the search bar that appears on the right, type calen to filter your options. You'll see suggestions including Calendly and Google Calendar—select Google Calendar.


Step 2: Choose Your Trigger or Action

After selecting Google Calendar, n8n displays all available features organized into two categories. The Triggers section contains 5 types of event-based activators: On event cancelled, On event created, On event ended, On event started, and On event updated. The Actions section includes 6 direct actions you can perform on your calendar. Pick the trigger that matches your workflow goal. For example, choose On event started if you want your workflow to run the moment an event begins.


Step 3: Open the Credential Settings

Your Google Calendar Trigger node appears with a configuration panel on the right. In the Credential field, you'll see "No credentials" with a red warning icon. Click the Set up credential button in the bottom right corner to begin authentication.

A Google Calendar account window opens, displaying your OAuth Redirect URL in the format http://localhost:5678/rest/oauth2-credential/callback. Copy this URL now—you'll need it in the next step. Click Click To Copy for a quick copy.


Step 4: Create a New Project on Google Cloud

Open your browser and navigate to console.cloud.google.com. Click your current project name in the top navigation bar to open the project selector. Click New project in the upper right corner.

On the new project creation page, enter a name in the Project name field—something like n8n-QTM-CLD works well. The Project ID generates automatically and cannot be changed afterward. Click Create to finish.


Step 5: Select Your New Project

Once your project is created, Google Cloud shows a success notification in the Notifications area (bottom right corner). Click Select Project in that notification to switch to your new project. The navigation bar title updates to reflect your new project name.


Step 6: Enable the Google Calendar API

In Google Cloud Console, go to APIs & Services and select Library. Alternatively, use the search bar next to your project name and search for Google Calendar API, then open its detail page. Click the Enable button to activate the API for your project. Once enabled, the page switches to the API management dashboard with tabs for Overview, Documentation, and Support.


Step 7: Configure the OAuth Consent Screen

Go to APIs & Services and select OAuth consent screen. You'll see a message saying "Google Auth Platform not configured yet." Click Get started to begin configuration.

The setup includes 4 short steps. In the App Information step, enter an app name in the App name field—for example, n8n-QTM-Calendar. Select a support email address in the User support email field. Click Next.

On the Audience step, keep the default setting as External since you're using a personal Google account. Click Next.

On the Contact Information step, enter an email address where Google can notify you of any project-related changes. Click Next.

On the Finish step, check the box to agree with Google API Services User Data Policy, then click Continue. Next, click Create OAuth client to complete the consent screen setup.


Step 8: Create Your OAuth Client ID

After your consent screen is ready, the OAuth Overview page displays a Metrics section indicating no OAuth client exists yet. Click Create OAuth client to begin.

On the Create OAuth client ID page, set Application type to Web application (item 1 in the screenshot). Enter a name in the Name field—for example, Web client 1 (item 2).

Scroll down to the Authorized redirect URIs section. Click Add URI and paste the callback URL you copied from n8n in Step 3—specifically, http://localhost:5678/rest/oauth2-credential/callback. Click Create.


Step 9: Copy Your Client ID and Client Secret

Google Cloud displays an "OAuth client created" dialog with complete details. Copy both your Client ID and Client Secret right now—after you close this dialog, you won't be able to view your Client Secret again. You can also download a JSON file for secure storage using the Download JSON button.


Step 10: Enter Your Credentials in n8n and Sign In

Return to n8n and paste your Client ID and Client Secret into the corresponding fields in the Google Calendar account window. Click Sign in with Google to complete the connection.

Troubleshooting the "Access blocked" Error

If you encounter an error message saying "Access blocked: [app name] has not completed the Google verification process" with code Error 403: access_denied, you've hit a common issue. This happens when your app is in Testing mode and you haven't added your account to the test user list.

Here's how to fix it: Return to Google Cloud Console and go to APIs & Services → OAuth consent screen. Select the Audience tab from the left menu (item 1 in image 20). Click Add users (item 2), enter your Gmail address in the email field (item 3), then click Save (item 4). Go back to n8n and try signing in with Google again.

Check the boxes to grant n8n access to your Google Calendar and click Continue.


What Can You Do With Google Calendar Connected to n8n?

Once the connection is established, Google Calendar becomes part of your n8n automation ecosystem. Here's what becomes possible.

Automate Workflows Based on Calendar Events

  • The five Google Calendar trigger types in n8n let you build workflows that activate automatically whenever your schedule changes. When a new event is created, n8n can automatically send confirmation emails to attendees via Gmail, create matching tasks in Notion or Trello, or post alerts to your Slack channel. When an event starts, n8n can send SMS reminders, open related documents, or launch a meeting bot. When an event ends, n8n can automatically save summary notes, update project statuses, or send feedback surveys to participants.

Manage and Sync Calendar Data Across Systems

  • Using the Actions group, n8n can actively read, create, update, or delete calendar events based on data from other applications. For instance, when a new order arrives from WooCommerce, n8n automatically creates a shipping event on your calendar. When a customer books through a Typeform form, n8n instantly creates the corresponding appointment and sends Google Meet links to both parties. Everything runs hands-free on your end.

Important Considerations Before Going Live

  • If you're running n8n on localhost instead of a real domain, Google OAuth won't permit authentication in production. For actual work, you'll need to deploy n8n on a server with a valid HTTPS address and update the Authorized redirect URI in Google Cloud accordingly. The real concern is that apps in Testing mode only support up to 100 test users, and tokens expire after 7 days. If you want stable long-term usage, plan to submit your app for Google verification.


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5 Best AI Coding Subscriptions for 2026: Finding Value Beyond 'Unlimited' Plans

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AI-powered coding tools are advancing at breakneck speed. In just a few years, they've evolved from novelty experiments into genuine productivity multipliers—helping developers write code faster, squash bugs quicker, refactor intelligently, and analyze projects with minimal friction.

There's a shift happening in how these platforms charge. Previously, many AI coding services lured users with "unlimited" packages. Pay a flat monthly fee, get nearly unrestricted access. Simple. Appealing. Unsustainable.

The reality is brutal: running cutting-edge AI models costs serious money. So providers are moving away from unlimited tiers toward token limits, credit systems, weekly caps, and hourly restrictions. Users still pay monthly subscriptions, but with explicit controls over how much they can actually consume. What's interesting here is that this isn't necessarily bad news. When designed thoughtfully, token and credit-based plans give developers better cost predictability and eliminate the false "unlimited" facade that actually tanks your speed or locks features when you cross some invisible threshold.

Below are five AI coding subscriptions that deliver genuine value right now—each suited to different workflows and development needs.

MiniMax Token Plan

MiniMax stands out for developers hunting for low-cost AI with reasonable limits. Around 20 USD per month buys access to MiniMax's coding models through web and desktop apps, plus integration with familiar tools: Claude Code, Cursor, Cline, Roo Code, Kilo Code, Codex CLI, and OpenCode.

Instead of hourly or weekly caps, MiniMax uses token-based metering. You control allocation directly. Prefer pay-as-you-go? Credit packs start at just $5. For coding, debugging, refactoring, or building AI agents, the cost-to-output ratio here is genuinely competitive.

MiMo Token Plan

MiMo is newer but gaining serious traction in developer circles. Fast processing speed, efficient token consumption, and genuinely impressive UI generation capabilities set it apart.

Like MiniMax, you subscribe monthly and receive credits usable across MiMo's AI model ecosystem. The real concern is whether the speed gains justify the cost—but for large projects, they often do.

Xiaomi's MiMo-V2.5-Pro supports context windows up to 1 million tokens, ideal for large codebases or tasks requiring the AI to retain massive amounts of code context. It integrates with OpenCode, Cline, OpenClaw, Kilo Code, and Blackbox—perfect if you're building custom workflows or deploying AI agents.

GLM Coding Plan

GLM Coding Plan used to own the "cheapest AI coding option" title. Lately, Z.ai adjusted pricing—likely to offset operating costs for their newer GLM-5.2 model and shore up tool integrations.

It's no longer the bargain basement pick, but it remains solid for developers wanting a dedicated coding assistant rather than a general-purpose chatbot. Supports Claude Code, Cline, Kilo Code, OpenCode, and OpenClaw. Focus is practical: writing, debugging, building features.

OpenAI Codex

If you're on ChatGPT already, OpenAI's Codex extension for Visual Studio Code is the path of least resistance. It integrates directly into the ChatGPT ecosystem—no separate AI coding subscription needed.

Codex understands your entire project structure. It generates code, fixes bugs, analyzes codebases, and modifies multiple files simultaneously. The catch? Daily or weekly usage limits apply. Heavy-duty developers burn through these fast. You can buy additional credits to keep going, but costs add up.

If you're already on ChatGPT Plus or higher, Codex deserves a trial run before exploring alternatives.

Kimi Code

Kimi Code skips token counting in favor of weekly quota resets. Track your weekly allowance, not cumulative token spend. Simpler mental model.

Works on web, Visual Studio Code, CLI, and developer-focused tools. The AI handles codebase analysis, file edits, terminal commands, refactoring, debugging, and feature development. Their Kimi K2.7 Code model has noticeably improved coding quality, making it a solid pick for Kimi ecosystem regulars.

Which AI Coding Plan Should You Choose?

There's no universal best. Your pick depends on tools you already use and daily workload intensity.

ChatGPT Plus users: Start with OpenAI Codex. It's pre-integrated, runs smoothly in VS Code, and understands projects well.

Need a backup service or plan to build AI agents? MiniMax Token Plan wins on budget and generous limits. GLM Coding Plan fits if you want a specialized ecosystem.

Large codebases or exploring different workflows? MiMo Token Plan impresses with speed and 1-million-token support.

Heavy Kimi users? Kimi Code balances weekly resets with solid real-world coding support.

The industry trend is clear: AI coding providers are abandoning "unlimited" theater in favor of transparent, resource-aware subscriptions. This helps you control costs while letting developers fund better, stronger models. Everyone wins.

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n8n tutorial - Lesson 27: Connect n8n to Telegram via Cloudflare Tunnel

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n8n tutorial - Lesson 27: Connect n8n to Telegram via Cloudflare Tunnel

Hi everyone, in this post we'll walk through how to connect n8n to Telegram using a Cloudflare Tunnel — making your n8n telegram chatbot reachable from the internet without a VPS. This is part of the n8n Workflow Automation Tutorial series and covers the exact setup used to activate the T7-B2-Telegram-Chatbot workflow in a real session.

How to do:

Step 1 — Install Cloudflare Tunnel (cloudflared)

You need cloudflared installed locally to create a public tunnel to your n8n instance running on localhost:5678.
  1. Open a terminal and run: winget install Cloudflare.cloudflared
  2. After installation, start a quick tunnel with: cloudflared tunnel --url http://localhost:5678
  3. Copy the generated public URL — it looks like https://random-name.trycloudflare.com. You will need this in the next step.

Note — This is a "quick tunnel." The URL changes every time you restart cloudflared. That means you must update WEBHOOK_URL and restart n8n each time. A Named Tunnel fixes this — covered at the end of this post.

Step 2 — Set WEBHOOK_URL in Docker Compose

n8n needs to know its own public URL so it can register the correct webhook address with Telegram.
  1. Open your docker-compose.yml file in a text editor.
  2. Under the environment section for your n8n service, add or update:
    • WEBHOOK_URL=https://your-tunnel-url.trycloudflare.com
  3. Save the file, then restart n8n by running: docker compose down && docker compose up -d

Tip — If you skip this step, n8n will still register a localhost address with Telegram, and the webhook will never receive messages. Always set WEBHOOK_URL before activating any Telegram workflow.

Step 3 — Build the Telegram Chatbot Workflow (T7-B2-Telegram-Chatbot)

This workflow receives a user message from Telegram, passes it to an AI Agent, and sends the reply back — the core n8n telegram chatbot pattern.
  1. In n8n, create a new workflow named T7-B2-Telegram-Chatbot.
  2. Add a Telegram Trigger node as the starting node. This registers a webhook with Telegram automatically when the workflow is activated.
  3. Add an AI Agent node (using Claude Haiku 4.5 or your preferred model) connected to the Telegram Trigger.
  4. In the AI Agent node, set Source for Prompt to Define below, then set the prompt expression to: {{ $json.message.text }}
    • This extracts the actual text the user typed in Telegram.
  5. Do not add a Simple Memory node — Telegram Trigger uses a session concept, but Simple Memory caused issues in testing and was removed.
  6. Add a Telegram node at the end to send the AI Agent's reply back to the user.

Tip — The prompt expression {{ $json.message.text }} is the correct path for Telegram Trigger output. If you use a generic {{ $json.text }} it will return empty and the agent will have no input to work with.

Step 4 — Activate the Workflow

Activating the workflow registers the webhook URL with Telegram — this is the step that makes everything live.
  1. Make sure cloudflared is still running and your n8n instance is up with the correct WEBHOOK_URL set.
  2. In the workflow editor, click the Inactive toggle in the top-right corner to set the workflow to Active.
  3. n8n will call the Telegram API to register the webhook at your Cloudflare Tunnel URL automatically.
  4. Open Telegram, find your bot, send a message, and verify the AI Agent replies.

Note — Every time the Cloudflare quick tunnel restarts and gives you a new URL, you must: update WEBHOOK_URL in docker-compose.yml, restart n8n, then toggle the workflow Inactive → Active again. This re-registers the new URL with Telegram.

Step 5 — Understand the Quick Tunnel Limitation and Next Steps

The quick tunnel works perfectly for testing your n8n workflow automation, but it has one real-world drawback you need to plan around.
  1. The problem: The quick tunnel URL (e.g. https://random-name.trycloudflare.com) changes on every restart of cloudflared.
  2. Each URL change requires three actions:
    • Update WEBHOOK_URL in docker-compose.yml
    • Restart n8n with docker compose down && docker compose up -d
    • Toggle the Telegram workflow Inactive then Active to re-register the webhook
  3. Long-term fix — Named Cloudflare Tunnel: A named tunnel gives you a fixed subdomain (e.g. https://n8n.yourdomain.com) that never changes. Setup requires a Cloudflare account with a domain attached.
  4. Alternative — VPS deployment: When you deploy n8n on a VPS, the Webhook node automatically gets a public URL. You no longer need cloudflared at all.
    • Note: Oracle Cloud free tier signup from some regions (e.g., Vietnam) can be difficult — factor this into your planning.

Production tip — For a stable n8n telegram chatbot in production, a Named Cloudflare Tunnel or a VPS is strongly recommended. The quick tunnel is only suitable for development and testing sessions.

Key Lessons from This Session

  1. Always set WEBHOOK_URL before activating a Telegram workflow. Without it, n8n registers a localhost address that Telegram cannot reach.
  2. Quick Cloudflare Tunnel URLs are temporary. The URL changes on every restart — you must update WEBHOOK_URL, restart n8n, and re-activate the workflow each time.
  3. Use {{ $json.message.text }} for the AI Agent prompt in a Telegram Trigger workflow. This is the correct data path from the Telegram Trigger output.
  4. Remove Simple Memory when using a Telegram Trigger. Simple Memory added complexity without benefit in this setup and caused issues during testing.
  5. The Webhook node in n8n does not create a public URL by itself. It only creates an endpoint — you still need cloudflared, ngrok, or a VPS to expose it publicly. On a VPS, the public URL is automatic.
  6. Named Cloudflare Tunnel or VPS is the correct production path. The quick tunnel is a development shortcut, not a stable deployment strategy.

Conclusion:

In this n8n tutorial, we connected a local n8n instance to Telegram using a Cloudflare Tunnel, activated the T7-B2-Telegram-Chatbot workflow, and got a real AI-powered chatbot responding to Telegram messages. The key takeaway for this n8n workflow automation pattern is that WEBHOOK_URL and tunnel stability are the two variables that determine whether your bot stays online. In the next post in this series, we'll merge the Telegram Chatbot with a RAG pipeline so the bot can answer questions from a knowledge base.

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

Tags: n8n telegram chatbot, n8n tutorial, n8n workflow automation, cloudflare tunnel n8n, n8n webhook setup, n8n docker webhook, n8n AI agent, n8n beginner tutorial

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Getting Started with Mapify AI: Turn Any Content Into Mind Maps in Minutes

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Mapify is an AI-powered tool that does something pretty clever: it transforms lengthy content into visual mind maps in just a few minutes. Instead of manually reading, analyzing, and organizing ideas yourself, you simply feed Mapify your text, documents, website links, or YouTube videos—and it automatically identifies key points and builds a structured, easy-to-follow mind map for you.

What's interesting here is how Mapify handles content summarization. The tool excels at extracting the most important information and presenting it as logical branches, making it ideal for students and professionals who need to absorb knowledge quickly. Beyond just auto-generating mind maps, Mapify lets you edit branches, add new information, and reorganize the structure however you like. Here's how to get started.

How to Create Mind Maps with Mapify

Step 1:

Head to the Mapify website using the link below and set up your account.

https://mapify.so/vi/app/new

Once you're in, you'll see an input field where you can enter your mind map request and upload documents to use as reference material if needed.

Step 2:

Below the input field, you'll find configuration options for how Mapify generates your mind map. Click through each setting to customize them based on your needs.

Different mind map styles work better for different types of content. Once you've adjusted everything to your liking, hit the Mapify button to generate your map.

Step 3:

Wait a moment and your mind map appears. Below the generated map, you'll see several options to refine the content if you want to make changes. For example, you can request to add illustrative images—though this feature is restricted to Mapify's paid plans.

Step 4:

Once you're happy with your mind map, look for the Share button in the top right corner.

The real concern here is that free Mapify accounts have limited export options. You can download as an XMind file or share via a link, so keep that in mind depending on what you need.


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Create Seamless Repeating Patterns with PatternedAI in Canva

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Repeating patterns are an excellent choice for background design across countless project types. Whether you're creating personal touches like custom invitations or polishing professional presentations, a well-designed repeating pattern can elevate your entire composition and make it visually cohesive. If you want to create repeating patterns without the design complexity, PatternedAI on Canva is your answer.

PatternedAI offers multiple approaches to pattern creation. You can browse pre-made templates, convert any image into a seamless repeating pattern, or let AI generate an entirely original pattern based on your text description. Let's walk through how to design repeating patterns using PatternedAI in Canva.

How to Design Repeating Patterns with PatternedAI on Canva

Step 1:

Open Canva and tap the three-dot menu icon, then select Apps.

Next, search for "Patterned" and click the PatternedAI app that appears in the results to integrate it into your Canva workflow.

Then select the design you want to use PatternedAI with for creating your repeating pattern.

Step 2:

You'll enter a new interface with the pattern design section on your left. First, scroll down and sign in to access PatternedAI. We recommend using your Google account to authenticate.

Step 3:

PatternedAI gives you two main options: describe your pattern with text or upload an image. With the text description approach, write detailed specifications for what you want to create. Here are some example prompts to get started.

1. Wildflower Pattern

A seamless pattern of delicate wildflowers, green leaves, soft pastel colors, watercolor style, elegant, clean background.

2. Tropical Leaf Pattern

A seamless tropical leaf pattern with monstera and palm leaves, fresh green tones, minimal, modern, flat illustration.

3. Geometric Pattern

A seamless geometric pattern with circles, triangles, and abstract lines, blue and white color palette, minimalist style.

4. Educational Pattern

A seamless pattern featuring books, pencils, rulers, notebooks, and light bulbs, colorful flat design, education theme.

5. AI and Technology Pattern

A seamless pattern with robots, AI icons, circuit lines, chat bubbles, glowing blue colors, futuristic flat design.

6. Historical Pattern

A seamless pattern inspired by ancient history, bronze drums, temples, scrolls, swords, traditional motifs, warm earth tones.

7. Kids Pattern

A seamless pattern with cute animals, stars, clouds, rainbows, soft pastel colors, cartoon style.

8. Food Pattern

A seamless pattern with coffee cups, croissants, donuts, cookies, warm brown and beige colors, hand-drawn style.

9. Summer Pattern

A seamless summer pattern with seashells, waves, palm trees, sunglasses, bright tropical colors.

10. Christmas Pattern

A seamless Christmas pattern with snowflakes, pine trees, gifts, candy canes, red, green, and gold colors.

If you prefer the image upload option, hit Create and the app will transform your photo into a seamless pattern. You can then tweak various settings and instantly preview how your repeating pattern will look.

If you choose the Gallery option, you'll have access to a collection of pre-designed patterns ready to use as backgrounds.

Step 4:

Choose your preferred repeating pattern style in PatternedAI. Finally, click "As a background" to apply the pattern as your design background and you're done.

Test how it looks in your design. If it doesn't feel right, simply create a new one.


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