n8n Creators Leaderboard Stats Workflow: Automated Creator Intelligence for n8n Libraries

The n8n Creators Leaderboard Stats Workflow is an automation blueprint for aggregating community performance data and transforming it into AI-generated Markdown reports. It pulls JSON statistics from a GitHub repository, correlates creator and workflow metrics, and produces structured insights that highlight the most impactful contributors and automations. This workflow is particularly suited for community managers, platform operators, and automation professionals who require repeatable, data-driven reporting.

Strategic value for community-led platforms

As marketplaces and workflow libraries scale, the volume of available automations grows faster than manual analysis can keep up. Systematic insight into which creators and workflows drive engagement is critical for:

• Recognizing top contributors and showcasing exemplary workflows to the broader community.
• Monitoring adoption trends using metrics such as unique visitors and inserters across time windows.
• Automating reporting for internal reviews, community updates, newsletters, and dashboards.

By embedding these analytics into an n8n workflow, you obtain a repeatable and auditable process instead of ad hoc, manual data pulls.

Workflow overview and architecture

The workflow implements a linear but extensible pipeline, designed around best practices for data ingestion, transformation, and AI-assisted reporting. At a high level, it performs the following stages:

• Data ingestion – Fetch aggregated JSON metrics from a GitHub repository via HTTP.
• Normalization – Extract and standardize creator and workflow records into consistent arrays.
• Ranking – Sort by key performance indicators and limit to top creators and workflows.
• Enrichment – Merge creator-level and workflow-level data on username.
• Targeted filtering – Optionally narrow results to a specific creator when required.
• AI-driven reporting – Use an LLM to generate a Markdown report with tables and qualitative analysis.
• File output – Persist the Markdown report as a timestamped file to a designated location.

This modular structure makes it straightforward to adapt the workflow to different data sources, metrics, or reporting formats while retaining a clear operational flow.

Key building blocks in n8n

1. Data retrieval layer

The workflow begins with two HTTP Request nodes that access JSON files hosted in a GitHub repository:

• One JSON file contains aggregated creator statistics.
• The other contains workflow-level metrics.

A Global Variables node stores the base GitHub path, which allows you to redirect the workflow to a different repository, branch, or analytics source without modifying multiple nodes. This is a recommended best practice for maintainability and environment-specific configuration.

2. Parsing and preprocessing

Once the JSON documents are retrieved, the workflow uses a combination of Set and SplitOut nodes to:

• Extract the data arrays that hold the creators and workflows.
• Normalize field names and structures so that subsequent nodes can operate consistently.

To keep the processing scope manageable and focused on the most relevant entries, Sort and Limit nodes are applied. For example:

• Limit to the top 25 creators based on the chosen metric.
• Limit to the top 300 workflows for detailed analysis.

Sorting can be tuned to prioritize particular KPIs such as weekly unique visitors or inserters, depending on your reporting goals.

3. Merging and optional filtering

The workflow then correlates creator and workflow datasets using a Merge node. The merge operation:

• Matches records on the username field.
• Enriches each workflow with its associated creator data, providing a unified view of performance.

A subsequent Filter node can be used to restrict the output to a single creator. This is particularly useful when the workflow is triggered interactively. For example, a chat-based interaction such as show me stats for username joe can be translated into a JSON payload that selects only that creator’s data for the report.

4. AI-powered Markdown report generation

The consolidated dataset is passed to an AI Agent node configured with your preferred LLM (for example, OpenAI). The agent is typically set with a relatively low temperature to favor consistency and accuracy over creativity.

The prompt is structured to instruct the model to produce a comprehensive Markdown report that includes:

• A concise but detailed summary of the creator and their overall impact.
• A Markdown table listing workflows with metrics such as:
  • Unique weekly visitors
  • Unique monthly visitors
  • Unique weekly inserters
  • Unique monthly inserters
• Community analysis describing why certain workflows perform well.
• Additional insights such as emerging trends and recommended next steps.

By codifying the report structure in the prompt, you can maintain consistent output across runs and make downstream consumption easier for both humans and systems.

5. Output and file handling

After the AI agent returns the Markdown content, the workflow converts it into a file and writes it to the configured local filesystem. The filename includes a timestamp, which simplifies versioning, auditability, and integration with downstream processes such as newsletters or dashboards.

Deployment and execution: quick start guide

1. Prepare the analytics source
   Clone the repository that contains the aggregated creator and workflow JSON files, or host your own JSON data in a similar structure.
2. Configure the GitHub base path
   Update the Global Variables node in n8n with the base GitHub URL that points to your JSON files.
3. Set up LLM credentials
   Ensure your OpenAI or other LLM credentials are correctly configured for the AI Agent node.
4. Activate the workflow
   Enable the workflow in your n8n instance.
5. Trigger the workflow
   Use either:
   • A chat trigger for conversational queries.
   • An execute-workflow trigger that receives JSON input, for example: {"username": "joe"}.
6. Review the generated report
   Locate the Markdown file in the configured output directory. Confirm the timestamped filename and validate that the content matches your expectations.

Primary use cases and stakeholders

• Community managers
  Generate weekly or monthly creator leaderboards, highlight trending workflows, and share insights in community updates or newsletters.
• Individual creators
  Track which workflows gain traction, refine documentation, and plan content or feature updates based on user behavior.
• Platform and product owners
  Use aggregated metrics to prioritize improvements, select workflows for featured placement, and inform roadmap decisions.

Customization and extension strategies

The workflow is intentionally designed to be adaptable. Common customization patterns include:

• Alternative analytics sources
  Adjust the GitHub base path variable to point to your own metrics repository or analytics pipeline. As long as the JSON structure preserves the expected data arrays, minimal changes are required.
• Different ranking criteria
  Change the Sort node configuration to emphasize different KPIs, such as:
  • Unique weekly inserters for adoption intensity.
  • Monthly visitors for long-term visibility.
• Enhanced AI prompts
  Extend the AI agent prompt to add new sections, for example:
  • Technical deep dives into workflow design.
  • Sample usage scenarios or code snippets.
  • Interview-style commentary for featured creators.
• Alternative storage backends
  Instead of writing to the local filesystem, switch to a cloud-based target such as Amazon S3 or Google Cloud Storage. This is useful for CI/CD pipelines, multi-node deployments, or centralized reporting.

Troubleshooting and operational considerations

Missing or incomplete JSON data

If the workflow fails to retrieve data or fields appear empty, verify the following:

• The Global Variables base path matches the actual GitHub repository and branch.
• The filenames used in the HTTP Request nodes are correct.
• The JSON documents contain the expected data arrays for both creators and workflows.

Suboptimal AI report quality

If the AI-generated Markdown is inconsistent or lacks structure:

• Reduce the temperature setting to encourage more deterministic responses.
• Refine the system prompt and include explicit examples of the desired table format and sections.
• Clarify which metrics must always be present in the output.

File write or permission errors

When the workflow cannot save the Markdown file:

• Confirm that the n8n process has write permissions on the target directory.
• Consider writing to an n8n workspace or a managed storage service if local permissions are constrained.

Security and privacy best practices

The reference implementation reads metrics from public GitHub JSON files. Even in this scenario, you should:

• Avoid embedding sensitive information in public JSON documents.
• If you rely on private metrics, store JSON files in a private repository and configure secure credentials for n8n.
• Review AI-generated reports for any personally identifiable information (PII) and apply anonymization or redaction where necessary.

Adhering to these practices ensures that automation does not inadvertently expose confidential data.

Expected report output

The final Markdown file produced by the workflow typically contains:

• A narrative summary of the creator’s performance and contribution to the ecosystem.
• A structured Markdown table listing each workflow with its key metrics, including weekly and monthly visitors and inserters.
• Community-oriented analysis that explains why certain workflows resonate with users.
• Forward-looking insights such as trends, opportunities for optimization, and recommended next steps for the creator or platform team.

This format is well suited for direct publication in documentation sites, internal reports, or community announcements.

Getting started with the template

To adopt the n8n Creators Leaderboard Stats Workflow in your environment:

• Clone the project that contains the workflow and analytics JSON files.
• Configure the Global Variables node to point to your GitHub metrics source.
• Set up your LLM credentials and validate the AI Agent configuration.
• Activate and trigger the workflow in n8n, then review the generated Markdown output.

Call to action: Visit the GitHub repository at https://github.com/teds-tech-talks/n8n-community-leaderboard to obtain the workflow files, run them locally, and contribute enhancements. For assistance with prompt engineering, output customization, or integration patterns, reach out to the author or join the n8n community chat.

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Pro tip: Add a scheduled trigger or cron-based node to generate reports at fixed intervals. You can then pipe the Markdown files into your newsletter workflow or internal analytics dashboard for fully automated community reporting.

Automate Shift Handover Summaries with n8n

Capturing clear and consistent shift handovers is critical for keeping teams aligned and avoiding information gaps. In busy environments, it is easy for important details to get lost between shifts.

This guide teaches you how to use an n8n workflow template to:

• Ingest raw shift notes through a webhook
• Split and embed notes for semantic search
• Store embeddings in a Supabase vector store
• Use an agent to generate a concise, structured handover summary
• Append the final summary to a Google Sheets log

By the end, you will understand each part of the workflow and how to adapt it to your own operations, NOC, customer support, or field service teams.

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Learning goals

As you follow this tutorial, you will learn how to:

• Configure an n8n webhook to receive shift handover data
• Split long notes into chunks that work well with embedding models
• Generate embeddings with a HuggingFace model and store them in Supabase
• Build an agent that retrieves relevant context and produces a structured summary
• Append the final output to a Google Sheet for long-term logging and reporting
• Apply best practices for chunking, metadata, prompts, and security

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Why automate shift handovers?

Manual shift handovers often suffer from:

• Inconsistent detail and structure
• Missed critical issues or follow-up actions
• Notes that are hard to search later

Automating the process with n8n helps you:

• Standardize the format of every handover
• Make past shifts searchable via embeddings and vector search
• Quickly surface related incidents and context for the next shift
• Maintain a central, structured log in Google Sheets

This workflow is especially useful for operations teams, network operations centers (NOC), customer support teams, and field services where accurate handovers directly impact reliability and customer experience.

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Concept overview: How the workflow works

Before we go step by step, it helps to understand the main building blocks of the n8n template.

High-level flow

The template workflow processes a shift handover like this:

1. A POST request hits an n8n Webhook with raw shift notes.
2. A Splitter node breaks the notes into smaller text chunks.
3. A HuggingFace Embeddings node converts each chunk into a vector.
4. An Insert node stores these vectors in a Supabase vector store.
5. A Query + Tool setup lets the agent retrieve relevant past context.
6. A Memory node keeps recent conversational context for the agent.
7. A Chat/Agent node generates a structured summary and action items.
8. A Google Sheets node appends the final record to your Log sheet.

In short, you send raw notes in, and the workflow produces:

• A semantic representation of your notes stored in Supabase
• A clear, structured, and consistent shift handover entry in Google Sheets

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Step-by-step: Building and understanding the n8n workflow

Step 1 – Webhook: Entry point for shift notes

The Webhook node is where your workflow starts. Configure it to listen for POST requests at the path:

shift_handover_summary

Any tool that can send HTTP requests (forms, internal tools, scripts, ticketing systems) can submit shift data to this endpoint.

A typical payload might look like this:

{  "shift_id": "2025-09-01-A",  "team": "NOC",  "shift_lead": "Alex",  "notes": "Servers A and B experienced intermittent CPU spikes. Restarted service X. Customer ticket #1245 open. No data loss. Follow-up: investigate memory leak."
}

In the workflow, these fields are passed along to downstream nodes for processing, embedding, and summarization.

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Step 2 – Splitter: Chunking long handover notes

Long text can be difficult for embedding models to handle effectively. To address this, the workflow uses a character text splitter node to break the notes field into smaller pieces.

Recommended configuration:

• chunkSize: 400
• chunkOverlap: 40

Why chunking matters:

• Improves embedding quality by focusing on smaller, coherent segments
• Helps keep inputs within model token limits
• Preserves local context through a small overlap so sentences that cross boundaries still make sense

You can adjust these values based on the typical length and structure of your shift notes, but 400/40 is a solid starting point.

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Step 3 – Embeddings: Converting chunks to vectors with HuggingFace

Next, the workflow passes each text chunk to a HuggingFace Embeddings node. This node:

• Uses your HuggingFace API key
• Applies a configured embedding model to each text chunk
• Outputs vector representations that can be stored and searched

The template uses the default model parameter, but you can switch to a different model if you need:

• Higher semantic accuracy for domain-specific language
• Better performance or lower cost

Keep your HuggingFace API key secure using n8n credentials, not hardcoded values.

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Step 4 – Insert: Storing embeddings in Supabase vector store

Once the embeddings are generated, the Insert node writes them into a Supabase vector store. The template uses an index named:

shift_handover_summary

Each stored record typically includes:

• The original text chunk
• Its embedding vector
• Metadata such as:
  • shift_id
  • team
  • timestamp
  • author or shift_lead

Metadata is very important. It lets you filter searches later, for example:

• Find only incidents for a specific team
• Limit context to the last 30 days
• Retrieve all notes related to a given shift ID

Make sure your Supabase credentials are correctly configured in n8n and that the shift_handover_summary index exists or is created as needed.

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Step 5 – Query + Tool: Enabling context retrieval for the agent

To generate useful summaries, the agent needs access to relevant historical context. The workflow uses two pieces for this:

1. Query node that searches the Supabase vector store using the current notes as the query.
2. Tool node that wraps the query as a callable tool for the agent.

When the agent runs, it can:

• Call this tool to retrieve similar past incidents or related shift notes
• Use that context to produce better summaries and action items

This is especially valuable when you want the summary to reference prior related issues, recurring incidents, or ongoing tickets.

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Step 6 – Memory: Keeping recent conversational context

The Memory node (often configured as a buffer window) stores recent interactions and context. This is useful when:

• The agent needs to handle multi-step reasoning
• There is a back-and-forth with another system or user
• You want the agent to remember what it just summarized when generating follow-up clarifications

By maintaining a short history, the agent can produce more coherent and consistent outputs across multiple steps in the workflow.

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Step 7 – Chat & Agent: Generating the structured shift summary

The core intelligence of the workflow lives in two nodes:

• Chat node: Uses a HuggingFace chat model to generate language outputs.
• Agent node: Orchestrates tools, memory, and prompts to produce the final summary.

Key configuration details:

• The Agent node is set with promptType of define.
• The incoming JSON payload from the webhook (shift_id, team, notes, etc.) is used as the basis for the prompt.
• The agent can:
  • Call the vector store tool to retrieve relevant context
  • Use the memory buffer for recent history
  • Produce a structured output that includes summary and actions

With the right prompt design, you can instruct the agent to output:

• A concise summary of the shift
• Critical issues detected
• Follow-up actions and owners

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Step 8 – Google Sheets: Appending the final handover log

The final step is to persist the structured summary in a log. The workflow uses a Google Sheets node with the operation set to append.

Configuration highlights:

• Select the correct spreadsheet
• Use the Log sheet as the target tab
• Map fields from the agent output into sheet columns

A recommended column layout is:

• Timestamp
• Shift ID
• Team
• Shift Lead
• Summary
• Critical Issues
• Action Items
• Owner

This structure makes it easy to filter, sort, and report on shift history, incidents, and follow-up work.

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Example: Sending a test payload to the webhook

To test your setup, send a POST request to your n8n webhook URL with JSON like this:

{  "shift_id": "2025-09-01-A",  "team": "NOC",  "shift_lead": "Alex",  "notes": "Servers A and B experienced intermittent CPU spikes. Restarted service X. Customer ticket #1245 open. No data loss. Follow-up: investigate memory leak."
}

Once the workflow runs, you should see:

• New embeddings stored in your Supabase vector index shift_handover_summary
• A new row appended in your Google Sheets Log tab containing the summarized shift handover

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Best practices for configuration and quality

Chunk size and overlap

Starting values of chunkSize 400 and chunkOverlap 40 work well for many use cases:

• Smaller chunks can lose context across sentences.
• Larger chunks risk exceeding token limits and may dilute focus.

Monitor performance and adjust based on the average length and complexity of your notes.

Using metadata effectively

Always include useful metadata with each chunk in the vector store, such as:

• shift_id
• team
• timestamp
• shift_lead

Metadata makes it easier to:

• Filter searches to specific teams or time ranges
• Generate targeted summaries for a particular shift
• Support future dashboards and analytics

Choosing an embedding model

Start with the default HuggingFace embedding model for simplicity. If you notice that your domain language is not captured well, consider:

• Switching to a larger or more specialized embedding model
• Using a fine-tuned model if you work with very specific terminology

Balance accuracy, latency, and cost based on your volume and requirements.

Maintaining the Supabase vector store

Over time, your vector store will grow. Plan a retention strategy:

• Decide how long to keep historical shift data
• Use Supabase policies or scheduled jobs to archive or delete older entries
• Consider separate indexes for different teams or environments if needed

Prompt design for the agent

Careful prompt design has a big impact on the quality of summaries. In the Agent node:

• Use promptType define to control the structure
• Pass the raw JSON payload so the agent has full context
• Explicitly request:
  • A short summary of the shift
  • Bullet list of critical issues
  • Clear action items with owners if possible

Iterate on your prompt until the output format consistently matches what you want to store in Google Sheets.

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Troubleshooting common issues

• Embeddings fail:
  • Verify your HuggingFace API key is valid.
  • Check that the selected model is compatible with the embeddings node.
• Insert errors in Supabase:
  • Confirm Supabase credentials in n8n.
  • Ensure the shift_handover_summary index or table exists and has the right schema.
• Agent cannot retrieve relevant context:
  • Check that documents were actually inserted into the vector store.
  • Make sure metadata filters are not too strict.
  • Test the query node separately with a known sample.
• Google Sheets append fails:
  • Verify Google Sheets OAuth credentials in n8n.
  • Double-check the spreadsheet ID and sheet name (Log).
  • Confirm the mapping between fields and columns is correct.

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Security and compliance considerations

Shift notes often contain sensitive operational details. Treat this data carefully:

• Apply access controls on the webhook endpoint, for example by using authentication or IP restrictions.
• Store all secrets (HuggingFace, Supabase, Google) as encrypted credentials in n8n.
• Limit Supabase and Google Sheets access to dedicated service accounts with minimal permissions.
• Consider redacting or detecting PII before generating embeddings or storing content.

These steps help you stay aligned with internal security policies and regulatory requirements.

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Extensions and next steps

Once the core workflow is running, you can extend it in several useful ways:

• Automated alerts: Trigger Slack or Microsoft Teams notifications when the summary includes critical issues or high-priority incidents.
• Search interface: Build a simple web UI or internal tool that queries Supabase to find
  

IoT Device Firmware Update Planner with n8n

Keeping IoT firmware up to date can feel like a constant battle. Devices are scattered across locations, releases ship faster than ever, and every missed update can turn into a security or reliability risk. Yet behind that complexity is a huge opportunity: if you automate the planning work, you free yourself to focus on strategy, innovation, and growth instead of chasing version numbers.

The IoT Device Firmware Update Planner built with n8n is designed to be that turning point. It transforms scattered release notes, device metadata, and tribal knowledge into a structured, intelligent workflow that plans firmware rollouts for you. Under the hood it combines webhooks, text splitting, embeddings, a Pinecone vector store, an agent-driven decision layer, and a simple Google Sheets log. On the surface, it gives you clarity, control, and time back.

This article walks you through that journey: from the pain of manual firmware planning, to a new mindset about automation, and finally to a step-by-step look at how this n8n template works and how you can adapt it for your own fleet.

From chaotic updates to confident rollouts

Firmware updates are not optional. They are the foundation for:

• Security patches that protect your devices and data
• Performance improvements that keep your fleet efficient
• New features that unlock customer and business value

Handling all of this manually across hundreds or thousands of devices is risky and exhausting. Human-driven planning often leads to:

• Misconfigurations and inconsistent rollout policies
• Unnecessary downtime and support incidents
• Missed compliance requirements or incomplete audit trails

Automating the firmware update planning process with n8n changes the game. Instead of reacting to issues, you build a repeatable system that:

• Collects and indexes device metadata and firmware release notes automatically
• Uses semantic search to surface relevant history and compatibility constraints
• Lets an agent orchestrate rollout decisions, canary stages, and blocking conditions
• Logs every decision for transparent audit and operational tracking

That is more than a workflow. It is a foundation for scaling your IoT operations without burning out your team.

Adopting an automation-first mindset with n8n

Before diving into the template, it helps to shift the way you think about firmware planning. Instead of asking, “How do I push this update?” start asking, “Which parts of this decision can be automated, and how can I guide that automation safely?”

With n8n you are not replacing human judgment. You are:

• Capturing your best practices in a repeatable, visible workflow
• Letting the system do the heavy lifting of data collection and analysis
• Using agents and LLMs as assistants that propose plans you can review and refine

This template is a practical example of that mindset. It gives you a starting point you can extend, experiment with, and improve over time. The goal is not perfection on day one. The goal is to build an evolving automation system that learns with you and supports your growth.

Inside the IoT Firmware Update Planner template

On the n8n canvas, the template looks compact and approachable, yet it connects several powerful building blocks. Here is what it includes at a high level:

1. Webhook – receives POST events such as device heartbeats, new firmware releases, or admin-triggered planning requests
2. Splitter – breaks long texts like release notes or device logs into smaller chunks
3. Embeddings (Hugging Face) – converts those chunks into dense vector embeddings for semantic search
4. Insert (Pinecone) – stores embeddings in a Pinecone index named iot_device_firmware_update_planner
5. Query + Tool (Pinecone wrapper) – runs similarity search and exposes results as a tool for the agent
6. Memory – keeps a buffer of conversational context for the agent
7. Chat (OpenAI model or alternative LLM) – provides the language model interface for reasoning
8. Agent – coordinates tools, memory, and prompts to craft a firmware update plan
9. Google Sheets – appends decision logs to maintain an auditable history

Each of these nodes is configurable and replaceable. Together they form a repeatable pattern you can reuse in other automation projects: trigger, enrich, store, reason, and log.

How the workflow runs, step by step

1. Events trigger the workflow

The journey begins with the Webhook node. It listens for POST requests such as:

• A new firmware release with detailed release notes
• Device telemetry that signals outdated or vulnerable firmware
• An administrator request to generate a rollout plan for a specific device group

Each event becomes an opportunity for the system to respond intelligently instead of waiting for manual intervention.

2. Text is prepared for semantic search

Firmware release notes and device logs can be long and dense. The Splitter node breaks this content into manageable chunks, using a chunk size and overlap tuned for better recall during search. These fragments are then passed to the Hugging Face embeddings node, which converts them into vector embeddings.

This step turns unstructured text into structured, searchable knowledge that your agent can use later.

3. Knowledge is stored in Pinecone

Each embedding is inserted into a Pinecone index named iot_device_firmware_update_planner. Over time this index grows into a powerful knowledge base that can include:

• Firmware release notes and change logs
• Device capabilities and constraints
• Historical incidents and rollout outcomes
• Compatibility mappings and upgrade paths

Instead of relying on memory or scattered documents, you gain a centralized vector store that your agent can query in seconds.

4. The agent plans the rollout

When a planning request arrives, the Agent node becomes the brain of the operation. It uses:

• The Query node to perform vector similarity search in Pinecone
• The Tool wrapper to feed search results into the agent
• Memory to preserve context across turns
• The Chat (OpenAI or other LLM) node to reason about the information

Based on the semantic context and your policy prompts, the agent produces a structured firmware update plan, which may include:

• Rollout percentages and phases
• Canary groups and test cohorts
• Blocking conditions and safety checks
• Rollback steps and verification actions

This is where your operational knowledge starts to scale. The agent applies the same level of care every time, without fatigue.

5. Decisions are logged for audit and learning

Finally, the workflow appends each decision to Google Sheets or another sink of your choice. Typical log fields include:

• Timestamps and request identifiers
• Device groups or segments affected
• Summary of the recommended rollout plan
• Operator notes or overrides

These logs provide a clear audit trail and a feedback loop. You can review what the agent recommended, compare it to real outcomes, and refine your prompts or policies over time.

What you need to get started

Setting up this n8n template is straightforward. Use this checklist as your starting point:

• n8n account, either self-hosted or on n8n Cloud
• OpenAI API key for the chat agent, or another supported LLM provider
• Hugging Face API key for embeddings, or a local embedding model endpoint
• Pinecone account with an index named iot_device_firmware_update_planner
• Google Sheets credentials with permission to append rows
• Secure webhook endpoints with authentication, for example HMAC signatures or tokens

Once these are in place, you can import the template, connect your credentials, and start experimenting with a small test set of devices.

Best practices for a reliable automation journey

Secure your webhooks

Webhooks are your entry point, so protect them carefully. Validate payloads using signatures or tokens, and avoid exposing unauthenticated endpoints. This reduces the risk of accidental or malicious triggers that could disrupt your planning process.

Use version control and staging

Keep firmware metadata in a versioned datastore so you always know which release is in play. Combine that with staging groups and canary rollout strategies to limit the impact of any unexpected behavior. The agent can incorporate these patterns into its recommendations.

Limit exposure of sensitive data

When you send data to external LLM or embedding services, sanitize user or device identifiers where possible. For highly sensitive environments, consider running embeddings or language models inside your own infrastructure and updating the template to point to those endpoints.

Monitor, observe, and iterate

Automation is not “set and forget.” Track success rates, failure counts, and rollback frequency. You can:

• Use the Google Sheets log for quick visibility
• Forward logs into your observability stack such as Datadog or ELK
• Set alerts when error thresholds or rollback counts exceed expectations

These signals help you continuously refine prompts, policies, and thresholds in the workflow.

Example use cases that unlock real value

Once the template is running, you can apply it to several high-impact scenarios:

• Automated canary rollout recommendations based on device telemetry and historical incidents stored in the vector index
• Compatibility checking that flags devices requiring intermediate firmware versions before a safe upgrade
• Release note summarization that highlights relevant changes for specific device models or features
• Post-update anomaly triage by querying similar historical incidents and recommended mitigations

Each of these use cases saves time, reduces risk, and builds confidence in your automation strategy.

Security considerations for high-stakes updates

Firmware updates are powerful and potentially dangerous if mishandled. Before you let automation make or suggest rollout decisions, ensure you have strong safeguards in place:

• Use cryptographic signing for firmware artifacts and verify signatures on devices
• Segment devices by criticality and apply stricter rollout policies to sensitive groups
• Document and test rollback plans, and include those steps in the agent prompt
• Encrypt sensitive logs and tightly control access to the vector store index

These measures let you enjoy the benefits of automation without compromising safety.

Scaling your fleet and controlling costs

As your IoT fleet expands, so do your data and compute needs. The template is designed to scale, and you can keep costs under control by:

• Batching small, frequent updates into grouped embedding requests where possible
• Retaining only high-value historical documents in the Pinecone index and archiving older or low-impact content
• Using more affordable embedding models for routine or low-risk queries, and reserving premium models for critical decisions

With these practices, your automation can grow alongside your business without runaway expenses.

Testing the n8n firmware planner safely

Before you trust any automated system with production devices, validate it in a controlled environment. A simple test path looks like this:

1. Use a small set of non-critical devices or a simulator as your testbed
2. Post a sample firmware release note payload to the webhook
3. Confirm that embeddings are generated and visible in the Pinecone index
4. Ask the agent to create a rollout plan for a test device group
5. Verify that the resulting decision log appears correctly in Google Sheets with all required metadata

Once you are comfortable with the results, you can gradually expand to larger groups and more complex scenarios.

Customizing and extending the template

The real power of n8n lies in how easily you can adapt workflows to your environment. This template is intentionally modular so you can evolve it step by step. Some ideas:

• Swap Hugging Face embeddings for a local or custom model endpoint
• Replace Pinecone with another vector database such as Milvus or Weaviate
• Add Slack or Microsoft Teams notifications to request human approval before a rollout proceeds
• Integrate device management platforms like Mender, Balena, or AWS IoT to trigger actual OTA jobs after the plan is approved

Each customization moves you closer to a fully integrated, end-to-end firmware management system tailored to your stack.

From template to transformation

The IoT Device Firmware Update Planner n8n template is more than a collection of nodes. It is a blueprint for how you can run safer, smarter, and more scalable firmware operations.

By combining semantic search, agent-driven decision making, and a simple yet effective audit log, you gain a system that:

• Learns from past incidents and outcomes
• Reduces operational risk and manual toil
• Frees your team to focus on innovation and higher value work

As you refine prompts, add new data sources, and connect more tools, this workflow can become a central pillar of your IoT automation strategy.

Take the next step with n8n automation

You do not need a massive project to get started. Begin small, prove the value, and grow from there.

To try the template:

• Import it into your n8n instance
• Connect your API keys for Hugging Face, Pinecone, OpenAI (or your chosen LLM), and Google Sheets
• Run it against a small, low-risk device group or simulator
• Review the agent’s plans, adjust prompts, and iterate

If you want help tailoring the workflow to your fleet or integrating it with your OTA provider, you can reach out to your internal platform team or automation specialists, or consult a step-by-step setup guide to walk through each configuration detail.

Build a Commodity Price Tracker with n8n

On a gray Tuesday morning, Maya stared at the dozen browser tabs open across her screen. Each one showed a different commodity dashboard – oil benchmarks, grain futures, metals indices. As the lead market analyst at a fast-growing trading firm, she was supposed to have answers in minutes.

Instead, she was copying prices into spreadsheets, hunting through email notes for context, and trying to remember why last month’s copper spike had looked suspicious. By the time she had something useful to say, the market had already moved.

That was the day she decided something had to change.

The problem: markets move faster than manual workflows

Maya’s job was not just to collect commodity prices. Her team needed to:

• Track prices automatically across multiple sources
• Preserve historical context and notes, not just numbers
• Ask intelligent questions like “What changed and why?” instead of scrolling through raw data
• Log decisions and anomalies in a structured way for reporting

Her current workflow was a patchwork of CSV exports, ad hoc scripts, and manual copy-paste into Google Sheets. Every new commodity or data source meant more maintenance. Every new question from the trading desk meant another scramble.

When a colleague mentioned an n8n workflow template for a commodity price tracker, Maya was skeptical. She was not a full-time developer. But she knew enough automation to be dangerous, and the idea of a no-code or low-code setup that could handle prices, context, and reasoning in one place felt like a lifeline.

The discovery: a production-ready n8n workflow template

Maya found a template that promised exactly what she needed:

Track commodity prices automatically, index historical data for intelligent queries, and log results to Google Sheets using a single n8n workflow.

Under the hood, the architecture used:

• n8n as the orchestrator
• LangChain components to structure the AI workflow
• Cohere embeddings to convert data into vectors
• Redis vector store for fast semantic search
• Anthropic chat for reasoning and natural language answers
• Google Sheets for logging and reporting

It was not just a script. It was a small, production-ready architecture designed for real-time commodity monitoring and historical analysis.

From chaos to structure: how the architecture works

Before Maya touched a single node, she wanted to understand the flow. The template diagram showed a compact but complete pipeline that mirrored her daily work:

• Webhook to receive incoming commodity price data in formats like CSV, JSON, or HTTP POST payloads
• Text splitter to break large payloads into manageable chunks for embedding
• Cohere embeddings to convert each chunk into a dense vector representation
• Redis vector store to insert and later query those embeddings with metadata
• Query + Tool so an AI agent could retrieve related records using vector search
• Memory (window buffer) to maintain short-term conversational context
• Anthropic chat to reason over retrieved data and user questions
• Agent to orchestrate tools, memory, and model outputs and decide what to do next
• Google Sheets to log structured results for dashboards and audits

In other words, the workflow would not just store prices. It would remember context, understand questions, and write clean, structured logs.

Meet the cast: the key components in Maya’s workflow

n8n as the conductor

For Maya, n8n became the control room. Using its visual editor, she wired together a Webhook node at the entry point to accept data from external scrapers, APIs, or cron jobs. From there, each node handled a specific part of the pipeline, with n8n orchestrating the entire flow without her needing to write a full application.

Text splitter: making big payloads manageable

Some data sources sent large payloads with multiple commodities and notes. To make them usable for embeddings, she added a Text Splitter node configured to:

• Split by character
• Use a chunk size of 400
• Include an overlap of 40 characters

This way, each chunk kept enough neighboring context so that embeddings stayed meaningful, while still being efficient for API calls.

Cohere embeddings: turning prices and notes into vectors

Next, she wired a Cohere embeddings node directly after the splitter. With her Cohere API key stored securely in n8n credentials, each chunk of text or hybrid numeric-text data was transformed into a dense vector. Semantically similar records would live near each other in vector space, which later made “show me similar events” queries possible.

Redis vector store: where history becomes searchable

Maya configured a Vector Store node to connect to Redis and operate in insert mode. She created an index called commodity_price_tracker and mapped key metadata fields:

• symbol
• timestamp
• price
• source

This metadata would let her filter by time range, symbol, or source later, instead of sifting through raw vectors.

Anthropic chat and the agent: reasoning over the data

On top of the vector store, Maya used Anthropic chat models to interpret results. The LLM would:

• Read retrieved context from Redis
• Understand her natural language questions
• Compute things like percent change or trend summaries
• Decide what needed to be logged

To coordinate all this, she set up an Agent node that combined:

• The Tool node for vector search over Redis
• A Memory node (window buffer) to keep recent interactions
• The Anthropic Chat node for reasoning and synthesis

In the agent’s prompt template, she added explicit instructions like “compute percent change when asked about price differences” and “log anomaly details to Sheets when thresholds are exceeded.”

Google Sheets: the single source of truth for the team

Finally, every meaningful decision or processed price update passed through a Google Sheets node. Maya designed a simple schema:

• Date
• Symbol
• Price
• Percent Change
• Source
• Notes

This gave her analysts a clean table to build charts, pivot tables, and dashboards without touching the underlying automation.

The turning point: building the workflow step by step

Setting it up felt intimidating at first, but once Maya broke it into steps inside n8n, the picture became clear.

Step 1 – Creating the webhook entry point

She started with a Webhook node:

• Method: POST
• Path: /commodity_price_tracker

Her existing scrapers and APIs were updated to send price updates directly to this URL. What used to be a scattered set of CSV dumps now flowed into a single automated pipeline.

Step 2 – Splitting incoming content

Next, she added a Text Splitter node right after the webhook. Configured with:

• Split by: character
• Chunk size: 400
• Overlap: 40

This ensured each chunk maintained context across boundaries while staying efficient for embedding calls.

Step 3 – Generating embeddings with Cohere

She then dropped in a Cohere embeddings node and connected it to the splitter’s output. With her API key stored via n8n credentials, every chunk was turned into a vector representation ready for semantic search.

Step 4 – Inserting into the Redis vector store

The Vector Store node came next, configured to:

• Operate in insert mode
• Use index name commodity_price_tracker
• Attach metadata including symbol, timestamp, price, and source

With this, every new price update was not just stored, but indexed with rich context for later retrieval.

Step 5 – Building query and tool nodes for interactive search

To make the system interactive, Maya added a Query node configured to run similarity search on the same Redis index. She then wrapped this query capability inside a Tool node so that the agent could call vector search whenever it needed historical context for a question or anomaly check.

Step 6 – Adding memory and chat for conversations

She connected a Memory node (buffer window) to keep track of recent user queries and important context. Then she wired in an Anthropic Chat node to handle natural language tasks like:

• Summarizing price movements over a period
• Explaining anomalies in plain language
• Parsing complex instructions from analysts

Step 7 – Orchestrating everything with an agent

The Agent node became the brain of the system. Maya configured it to:

• Call the vector search tool when past data was needed
• Use memory to keep the conversation coherent
• Follow a prompt template that explained how to handle numeric queries, compute percent changes, and decide when to log events

After a few prompt iterations, the agent started responding like a smart assistant familiar with her commodity universe, not just a generic chatbot.

Step 8 – Logging everything to Google Sheets

Finally, she connected a Google Sheets node at the end of the workflow. For each decision or processed price update, the node appended a new row with fields like:

• Date
• Symbol
• Price
• Percent Change
• Source
• Notes

That single sheet became the trusted record for her team’s dashboards and reports.

What changed for Maya: real-world use cases

Within a week, the way Maya’s team worked had shifted.

• Daily ingestion, zero manual effort
  Major commodity prices like oil, gold, and wheat flowed in automatically every day. The vector store accumulated not just numbers but context-rich embeddings and notes.
• Smarter questions, smarter answers
  Instead of scrolling through charts, Maya could ask the agent: “Show ports with the largest month-over-month oil price increase.” The system pulled relevant records from Redis, computed changes, and responded with a synthesized answer that referenced specific dates and values.
• Anomaly detection with a paper trail
  When a price delta exceeded a threshold, the agent flagged it. It logged the event to Google Sheets with notes explaining the anomaly, and could optionally trigger alerts to Slack or email. The trading desk now had both real-time warnings and an auditable history.

Best practices Maya learned along the way

As the workflow matured, a few practices proved essential:

• Normalize data before embeddings
  She standardized timestamps, currency formats, and symbol naming so similar records were truly comparable in the vector space.
• Be selective about what goes into the vector store
  Rather than stuffing every raw field into embeddings, she kept only what mattered and used metadata for details. Older raw data could live in object storage if needed.
• Monitor vector store growth
  She set up periodic checks to prune or shard the Redis index as it grew, keeping performance and cost under control.
• Lock down access
  Role-based API keys, IP allowlists for webhooks, and restricted access to the n8n instance and Redis helped protect sensitive financial data.
• Test prompts and edge cases
  By feeding the system malformed data, missing fields, and outlier prices, she refined prompts and logging to keep decision-making transparent and auditable.

Scaling the workflow as the firm grew

As more commodities and data sources were added, Maya had to think about scale. The template already supported good patterns, and she extended them:

• Batching webhook events during peak periods to avoid overload
• Increasing Redis resources or planning a move to a managed vector database when throughput demands rose
• Inserting message queues like Redis Streams or Kafka between ingestion and embedding to buffer and parallelize processing

The architecture held up, even as the volume of price updates multiplied.

Security, compliance, and peace of mind

With financial data in play, Maya worked with her security team to:

• Encrypt data at rest where possible
• Restrict access to the n8n instance and Redis
• Rotate API keys on a regular schedule
• Review how personally identifiable information was handled to stay aligned with regulations

The result was a workflow that was not just powerful, but also responsible and compliant.

Testing, validation, and troubleshooting

Before rolling it out fully, Maya ran a careful test suite:

• Sample payloads covering typical updates, malformed data, and extreme values
• Checks that the splitter and embedding pipeline preserved enough context
• Validation that metadata like symbol and timestamp were stored correctly and could be used for retrieval filters

When issues came up, a few patterns helped her debug quickly:

• Irrelevant embeddings? She verified input text normalization and experimented with different chunk sizes and overlaps.
• Noisy similarity results? She added metadata-based filters in Redis queries, such as limiting matches by time window or commodity symbol.
• Unexpected agent outputs? She tightened prompt instructions, clarified numeric behavior, and adjusted memory settings so relevant context stayed available longer.

The resolution: from reactive to proactive

A month after deploying the n8n commodity price tracker, Maya’s workday looked completely different.

Instead of juggling CSVs and screenshots, she watched a live Google Sheet update with clean, structured logs. Instead of fielding panicked questions from traders, she invited them to ask the agent directly for trends, anomalies, and explanations. Instead of reacting to the market, her team started anticipating it.

The core of that transformation was simple but powerful: an n8n-based workflow that automated ingestion, enabled semantic search over historical data, and logged every decision for downstream reporting.

Start your own story with the n8n template

If you see yourself in Maya’s situation, you do not need to start from scratch. You can:

• Clone the existing n8n workflow template
• Plug in your own API keys for Cohere, Redis, Anthropic, and Google Sheets
• Point your data sources to /commodity_price_tracker
• Adapt the prompt and metadata to match your commodity set and business logic

From there, you can extend the workflow with alerting channels, BI dashboards,

Send Embedded Images with n8n & Gmail API (Without Losing Your Mind)

Ever copied the same email, pasted the same image link, and crossed your fingers that it would not break for the hundredth time? If you are tired of wrestling with email clients or manually adding images that mysteriously disappear, this n8n workflow template is here to save your sanity.

In this guide, you will learn how to use n8n plus the Gmail HTTP API to send emails with embedded (CID) images. No more “image blocked” icons, no more broken URLs. The workflow grabs a public image, converts it to base64, wraps it in a multipart/related MIME message, and sends it through Gmail – all on autopilot.

Why bother with embedded images in emails?

Embedded images are perfect for:

• Transactional emails that need charts, receipts, or screenshots inline
• Marketing campaigns where the design should look the same everywhere
• Any email where “image missing” is not the vibe you are going for

Instead of relying on external image URLs that email clients might block or strip, CID images are bundled directly into the email MIME structure. That means the image travels with the message.

Why use the Gmail HTTP API instead of the n8n Gmail node?

n8n already has a Gmail node, and it is great for simple emails. But when you want fine-grained control over the MIME structure, especially for inline images using Content-ID, you need access to the raw message.

That is where the Gmail HTTP API comes in. It lets you send a fully handcrafted MIME message to:

POST https://www.googleapis.com/gmail/v1/users/me/messages/send

With this approach you can:

• Download an image from a URL
• Convert that image to base64 in n8n
• Build a multipart/related MIME message with a CID image
• Send it using the Gmail API endpoint users/me/messages/send

In short, the Gmail HTTP API gives you full control, which is exactly what you need for embedded images.

What this n8n workflow template actually does

The template wires everything together so you can go from “plain text email” to “HTML email with embedded image” without hand-writing MIME every time. Here is the high-level flow:

1. Trigger the workflow manually while you are building and testing
2. Define who the email is from, who it goes to, the subject, and the HTML body
3. Fetch an image from a URL using an HTTP Request node
4. Convert the binary image into a base64 string using an ExtractFromFile node
5. Assemble a multipart/related MIME message with the HTML and image parts
6. Send that MIME message through the Gmail HTTP API with proper encoding

Under the hood, n8n handles the data passing between nodes so you can focus on what the email should say, not how to glue bytes together.

Step-by-step: building the workflow in n8n

1. Start with a Manual Trigger

In development you probably do not want this firing every 5 minutes. Use a Manual Trigger node so you can click “Test workflow” whenever you are ready.

Later, once everything works, you can replace the Manual Trigger with:

• A Schedule node (for regular reports)
• A Webhook or other trigger (for event-based emails)

2. Set your message details (Set node)

Next, use a Set node to define the basic email metadata and the HTML content. Typical fields are:

• from: sender@example.com
• to: recipient@example.com
• subject: Email with embedded image
• body_html: <p>This email contains an embedded image:</p> <p><img src='cid:image1'></p>

The important part is the HTML image tag:

<img src='cid:image1'>

The cid:image1 value must match the Content-ID of the image part in your MIME message. If you rename it, remember to update it in both places or the image will not show up.

3. Grab the image with an HTTP Request node

Now it is time to fetch the image you want to embed.

Add an HTTP Request node and point it to the image URL you want to use. In the template a sample URL is already included so you can test right away.

Make sure the node is configured to return binary data. Check the node settings to confirm that the response is stored as binary (usually under binary.data or similar).

4. Convert the image to base64 (ExtractFromFile node)

Gmail expects the image data in base64 inside the MIME message, so we need to convert the binary file.

Use an ExtractFromFile node with the operation “Move File to Base64 String”. This node:

• Takes the binary image from the HTTP Request node
• Converts it into a base64 string
• Stores the result in a property, for example chart1

In the template, the destinationKey is set to chart1. That property will later be injected into the MIME body.

5. Compose the raw MIME message (Set node)

This is where the magic and the slightly nerdy part happens. Use another Set node to build the complete raw MIME message as a single string.

The template uses an expression that pulls values from previous nodes and assembles something like this:

From: {{$node["Message settings"].json.from}}
To: {{$node["Message settings"].json.to}}
Subject: {{$node["Message settings"].json.subject}}
MIME-Version: 1.0
Content-Type: multipart/related; boundary=boundary1

--boundary1
Content-Type: text/html; charset=UTF-8

<html>
<body>
{{$node["Message settings"].json.body_html}}
</body>
</html>

--boundary1
Content-Type: {{$node["Get image"].item.binary.data.mimeType}}
Content-Transfer-Encoding: base64
Content-Disposition: inline
Content-ID: <image1>

{{$json.chart1}}

--boundary1--

Keep an eye on these details:

• Boundary consistency: The same boundary value (boundary1) must be used everywhere in the MIME body.
• Content-ID: Set to <image1> with no quotes. This must match cid:image1 in your HTML.
• Base64 content: Insert the property from the ExtractFromFile node, for example {{$json.chart1}}.
• HTML charset: The HTML part uses charset=UTF-8 to avoid weird character encoding issues.

At this point you have a complete MIME email with an HTML part and an inline image part, ready to ship.

6. Send the email using the Gmail HTTP API

Finally, add another HTTP Request node to call the Gmail API endpoint:

POST https://www.googleapis.com/gmail/v1/users/me/messages/send

The body must be JSON with a raw field that contains the MIME message, base64url-encoded. The template uses an expression like this:

{ "raw": "{{ $json.raw.base64Encode() }}" }

Key points here:

• Encoding: Gmail expects base64url. n8n’s base64Encode() handles this correctly so you do not have to manually tweak characters.
• Authentication: Use an OAuth2 credential with at least the https://www.googleapis.com/auth/gmail.send scope.
• Credential type: In the template, the nodeCredentialType is gmailOAuth2. Replace it with your own Gmail OAuth credentials configured in n8n.

Once this node is configured correctly, hitting “Test workflow” should send a shiny HTML email with an embedded image straight from your Gmail account.

Troubleshooting and pro tips

Automation is great until one tiny field name breaks everything. Here are some common gotchas and how to avoid them.

• Image binary field name: The ExtractFromFile node stores the base64 in a property defined by destinationKey (for example chart1). If you rename this, make sure to update the reference in the “Compose message” Set node, otherwise your email will have an empty image part.
• Base64 vs base64url: Gmail wants base64url in the API payload. n8n’s base64Encode() handles the conversion correctly. If you ever manipulate the string manually, remember that base64url replaces + and / and trims padding.
• Character encoding: Keep charset=UTF-8 in your HTML Content-Type header. It avoids random hieroglyphics appearing in your email body.
• Large images: Huge images can push your message over Gmail’s size limits. Compress or resize images before embedding if you are sending detailed charts or high resolution assets.
• Gmail sending limits: If you plan to send a lot of emails, keep Gmail API quotas and per-account daily sending limits in mind. Automation is fun until you hit a quota wall.

Security and compliance basics

Even if this is “just a workflow,” you are still sending real email on behalf of real accounts, so treat it like production infrastructure.

• Store OAuth2 credentials securely using n8n’s credential system, not in plain Set nodes.
• Use verified sender addresses where required by your provider or organization.
• Follow email best practices like DKIM, SPF, and handling unsubscribe requests if you are sending newsletters or marketing campaigns.

Alternative ways to send images in email

If this approach feels a bit too “MIME-lab” for some use cases, you have options.

• Use the built-in Gmail node Perfect for simple messages where you do not need custom MIME or CID images. Easier to configure, but less flexible for inline embedding.
• Host images and use absolute URLs Just drop a standard <img src="https://..."> in your HTML. This is simpler, but images are fetched externally and can be blocked or require the recipient to “click to load images.”
• Use a transactional email API Providers like SendGrid or Mailgun have their own APIs and SDKs for attachments and inline images. If you prefer provider-managed sending, you can integrate those with n8n instead of Gmail.

Where to go from here

Once you have this template working, you can:

• Swap in your own images and HTML templates
• Embed multiple CID images in one email
• Connect this workflow to other systems in n8n for dynamic reports, invoices, or dashboards

Embedding images with n8n and the Gmail HTTP API gives you full control over the MIME structure and how inline images are delivered. No more manual hacks, no more guessing why the image disappeared in transit.

Call to action: Download the template, plug in your Gmail OAuth credential, set your sender and recipient, and hit “Test workflow.” If this helped you escape repetitive email tasks, subscribe for more n8n automation guides and troubleshooting tips.