2 posts tagged with "productivity"

I Automated My Social Media Posting — So I Can Actually Enjoy My Evening

Or: how I taught n8n to handle my content hustle like a virtual assistant on steroids.

Why I Did This#

If you're anything like me, managing social media feels like a full-time job you didn’t apply for.

I found myself copying captions from Google Docs, downloading images, opening apps, pasting everything, uploading, re-uploading, clicking around — for each platform. Every. Single. Time.

That’s when I thought: “Can I automate this mess and just control everything from a Google Sheet?”

Spoiler: Yes. You totally can.

If you're new to automation, n8n's getting started docs are a great place to begin.

What I Built#

Using n8n, I created a workflow that does the following — all by itself:

• Looks at a Google Sheet for scheduled posts
• Finds the image in Google Drive
• Posts the content to Instagram, LinkedIn, and X (formerly Twitter)
• Updates the Sheet so I know what’s been posted

And the best part? I don’t even have to be awake for it to run.

The Stack#

This is a no-code/low-code build. Here’s what I used:

• n8n for automation
• Google Sheets as my content planner
• Google Drive to store my media
• Facebook Graph API to post on Instagram
• Twitter API
• LinkedIn API

Looking to integrate more platforms? Check out n8n’s list of integrations — it supports hundreds of apps.

How It Works#

1. The Schedule Trigger#

It all starts at 7 PM. n8n checks if there’s any post with Status = Scheduled.

2. Pull from Google Sheets#

If there's something to post, it grabs:

• The filename of the image
• The caption (called “Links” in my sheet)
• The row number (to update later)

3. Search & Download the Image#

Using the filename, it finds the matching image in a shared Google Drive folder and downloads it.

4. Post It Everywhere#

Then, using different APIs:

• It tweets the caption on X
• Posts the image + caption to LinkedIn
• Uploads the image and publishes it on Instagram via the Facebook Graph API (yep, it’s a 2-step process)

5. Update the Sheet#

Once done, it changes the Status to Uploaded — so nothing gets posted twice.

My Sheet Looks Like This#

Things I Learned#

• Instagram’s API is wild. You’ll need a Facebook Business Page, a connected IG account, and a developer app. But once it's set up, it’s smooth.
• OAuth tokens will test your patience. Save them in n8n credentials and be kind to your future self.
• Debugging in n8n is a joy. You can click on any node, see the exact data flowing through, and fix stuff on the fly.

What’s Next#

• Add OpenAI to auto-generate captions (maybe even suggest hashtags)
• Log post metrics in Notion
• Make it support image carousels and videos

How to Get Started#

1. Sign up for n8n: It’s free to start, and you can self-host or use their cloud version.
2. Create a Google Sheet: Set up your content planner with columns for topics, file names, captions, and status.
3. Connect Google Drive: Store your images in a shared folder.
4. Set Up n8n Workflow: Use the Google Sheets, Google Drive, and social media nodes to build your automation.
5. Test It: Run the workflow manually first to make sure everything works as expected.
6. Schedule It: Set the trigger to run at your preferred time (like 7 PM) so it posts automatically.
7. Sit Back and Relax: Enjoy your evenings while n8n does the heavy lifting.
8. Iterate: Keep improving your workflow as you learn more about n8n and your social media needs.

Final Thoughts#

This isn’t just a time-saver — it’s a mindset shift. Automate the repetitive stuff, so you can focus on the fun, creative, human things.

Hope this inspires you to give your own daily hustle a virtual assistant. If you try it — let me know. I’d love to see what you build!

You can also explore tools like n8n on the Nife.io Marketplace to easily automate your cloud storage and workflow operations

For better team collaboration and project visibility, try Teamboard from Nife.io—a unified space to manage tasks, track progress, and work more efficiently.

Computer Vision at Edge is a growing subject with significant advancement in the new age of surveillance. Surveillance cameras can be primary or intelligent, but Intelligent cameras are expensive. Every country has some laws associated with Video Surveillance.

How do Video Analytics companies rightfully serve their customers, with high demand?

Nife helps with this.

Introduction#

The need for higher bandwidth and low latency processing has continued with the on-prem servers. While on-prem servers provide low latency, they do not allow flexibility.

Computer Vision can be used for various purposes such as Drone navigation, Wildlife monitoring, Brand value analytics, Productivity monitoring, or even Package delivery monitoring can be done with the help of these high-tech devices. The major challenge in computing on the cloud is data privacy, especially when images are analyzed and stored.

Another major challenge is spinning up the same algorithm or application in multiple locations, which means hardware needs to be deployed there. Hence scalability and flexibility are the key issues. Accordingly, Computing and Computed Analytics are hosted and stored in the cloud.

On the other hand, managing and maintaining the on-prem servers is always a challenge. The cost of the servers is high. Additionally, any device failure adds to the cost of the system integrator.

Thereby, scaling the application to host computer vision on the network edge significantly reduces the cost of the cloud while providing flexibility of the cloud.

Key Challenges and Drivers of Computer Vision at Edge#

• On-premise services
• Networking
• Flexibility
• High Bandwidth
• Low-Latency

Solution Overview#

Computer Vision requires high bandwidth and high processing, including GPUs. The Edge Cloud is critical in offering flexibility and a low price entry point of cloud hosting and, along with that, offering low latency necessary for compute-intensive applications.

Scaling the application to host on the network edge significantly reduces the camera's cost and minimizes the device capex. It can also help scale the business and comply with data privacy laws, e.g. HIPAA, GDPR, and PCI, requiring local access to the cloud.

How does Nife Help with Computer Vision at Edge?#

Use Nife to seamlessly deploy, monitor, and scale applications to as many global locations as possible in 3 simple steps. Nife works well with Computer Vision.

• Seamlessly deploy and manage navigation functionality (5 min to deploy, 3 min to scale)
  • No difference in application performance (70% improvement from Cloud)
  • Manage and Monitor all applications in a single pane of glass.
  • Update applications and know when an application is down using an interactive dashboard.
  • Reduce CapEx by using the existing infrastructure.

A Real-Life Example of the Edge Deployment of Computer Vision and the Results#

In the current practice, deploying the same application, which needs a low latency use case, is a challenge.

• It needs man-hours to deploy the application.
• It needs either on-prem server deployment or high-end servers on the cloud.

Nife servers are present across regions and can be used to deploy the same applications and new applications closer to the IoT cameras in Industrial Areas, Smart Cities, Schools, Offices, and in various locations. With this, you can monitor foot-fall, productivity, and other key performance metrics at lower costs and build productivity.

Conclusion#

Technology has revolutionized the world, and devices are used for almost all activities to monitor living forms. The network edge lowers latency, has reduced backhaul, and supports flexibility according to the user's choice and needs. We can attribute IoT cameras to scalability and flexibility, which are critical for the device. Hence, ensuring that mission-critical monitoring would be smarter, more accurate, and more reliable.

Want to know how you can save up on your cloud budgets? Read this blog.