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Over the weekend I had a ten mile walk into the countryside, and a four hour brainstorm with my business partner. All good stuff.

Today I go back to N8N, the workflow solution. Am following along the official tutorial for the moment. It’s clear to me that all companies that embrace automation properly from the ground-up will save a huge amount of time, freeing up their staff to focus on the important stuff. Whilst not technically AI, automation is clearly part of the new paradigm change that will begin happening across enterprises, especially the SME sector which hasn’t really normalised it yet.

N8N Scheduling

If you are going to build an automated network of actions for your company, then having scheduled triggers are a must. Of course you can use CRON or jobs in frameworks to do this, but having it in a nice easy UI is just way better.

In this example, you can see that you can easily setup these triggers with a lot of flexibility. They can be triggered every second or just once a month. For each time period, there are different types of options. So just having these triggers can give you some food for thought in what you might want to start automating.

Off the top of my head:

• At the start of every week I might want a script that goes out and captures all the relevant news for me from several RSS feeds. Those can then be put into a workflow.
• Or I might want to send out an email to the managers of departments to ask for a progress report.
• Or, could be an email shot, a sales report generation, a slack message, a LinkedIn message or it could check whether tasks have been done last week, and to alert whether there is anything top priority outstanding.

Anyway, point is, that time triggers give you a huge amount of flexibility. In the above image you can see that these triggers begin flows (currently empty!) that start at the beginning and end of the work week.

The NASA Node

Whatever you might think about NASA and the space industry in general, it’s pretty cool that you can just access NASA information from N8N. It takes one minute to get an API key from them, you throw it in N8N and you can now get Solar Flare reports from the last week …

You can also Test the step and see the output:

N8N Conditionals

You can then take the output from any node. In our case the NASA feed. What I like about this, is that N8N will show you the output of any API calls, and allow you to drag any field easily into the conditional statement. So here I have dragged ‘classType’ from the left and put it into the conditional, and searched for the string ‘X’ which is a class of solar flare.

When you test, you get a lovely UI showing the true or false.

The next step was outputting to postbin, which wasnt working as expected. I will continue with this tommorrow.

Automation Software Cuts Costs

I don’t know why I haven’t played with automation software more over the years. Probably because I was focused on coding. N8N abstracts away so much code to make this work, and unless you have a working foundation that can call any new API robustly and output it all easily … you would have to start from scratch and it would take a while. I just love how automation software takes away this hassle, and you can still integrate it with your internal systems if you want to.

Licencing

N8N has an enterprise licence agreement so if you ever want to self-host commercially in production, you still need to pay them a licence agreement. I totally get why they do this, but it’s annoying since I wanted to use this open source software freely. I think it means you can use it internally for free, but as soon as you want to build a product with it, the enterprise licence requirement kicks in.

Had a call with the guys yesterday at HeyGen.com

https://www.heygen.com

So these guys generate avatars for use in influencer marketing. Some key notes are:

• They have an API you can use
• Multilingual
• Pre-generated ones can basically say whatever you want
• You can speak to one of these avatars and they will talk back to you. Not pre-generated.

During the meeting they were also able to take a 4 second clip of me in the video, and basically render it and make me say anything. The mouth movements and voice are not super great, but for a four second sample it was good.

It’s clear to see we will just be interacting with these agents on websites, and as you talk to them, they will present the data necessary to complete your goal next to them.

I imagine OperatingSystems will just have these inbuilt. Back to ‘Clippy’!

High quality human avatars driven in real time by LLMs that you can talk to, will be the future of e-commerce.

It was always going to happen, and it’s been tried before but now it looks like the tech is going to converge.

Websites will just have a shopping assistant now that talks to you, suggests products, and you wont have to do as much searching, or even typing (which might be a good thing for those of us who’ve coded for years) …

Psychologically humans will connect more with another human looking avatar helping them to complete their goals.

You will talk to it, the LLM runs off using RAG to the objects available for sale, and then will come back the solution … and present it to the user. If it’s clothing, the avatar may just change clothing to demonstrate it.

Potentially further into the future, the avatar will interact with the product, like holding a laptop, etc … unsure exactly how, but I imagine eventually all products will be showcased in 3D so the avatar will be able to work with that somehow.

Interesting times.

Every day I see something that makes me both sad and excited, depending on what perception I want to take!

Vercel AI Chatbot

Carrying on from yesterday, I managed to switch the local Chatbot to OpenAI and so now I have got the local UI up and running and hooked up to OpenAIs API. And I can change the colour scheme now.

Ok, that’s it for today’s R&D update.

Will also have some news on the DXP project soon.

We’ll also go through N8N workflows from WordPress to the socials.

A quick detour away from N8N today in my R&D time…

The time has come where the app I’m working on really needs a traditional ChatGPT interface.

I did have a developer spasm and think about building the UI from scratch for fun, but …

Fortunately Vercel do some amazing templates out of the box for different purposes:

https://vercel.com/templates/

Vercel do an official ChatBot one and there’s a guy on Github who does a more feature full one albeit hasnt been updated for a while

https://github.com/mckaywrigley/chatbot-ui

The official Vercel one is updated more regularly, so I’m just going to use that. Below is how it looks on the demo. I’m currently trying to configure some bugs in it, but it will be great to get this up and running locally so I can start testing it to see if I really do need to start from scratch. It’s already integrated with Auth.js which I dont really want.

The other one is based on Supabase, which is an incredible offering. But will stick with this for now.

Yesterday took a good step forward on a stealth AI project I’ve been involved with since January. It’s operating in a crowded market, but we’ve recently identified a better way to pivot. Will be able to announce it soon, once final alpha tests have been done.

Also this week we have potentially found a new addition to our management team – a proven AI lead generation specialist. But more on that another time.

Today I began taking another look at N8N, I’d already tried a first time and was impressed. I was able to quickly create a workflow that read a webpage, summarised it, and then pumped it to Slack as a message.

Honestly, these automation tools WILL be part of every single company going forward. They will replace a lot of internal software platforms.

My first aim is to go from a WordPress post to publish on:

• X
• LinkedIn
• Facebook
• Instagram

There is already a template setup, which is what’s so powerful about this N8N stuff – most of what you want to automate has already been done.

• Installing N8N with Docker is super straightforward
• Look at the automation templates: https://n8n.io/workflows/
• For local use it’s super easy to create a webhook tunnel

https://n8n.io/workflows/3086-publish-wordpress-posts-to-social-media-x-facebook-linkedin-instagram-with-ai

Today’s video analysis is of the guy who made Windsurf – which I haven’t used yet, but am going to give a proper try. Might prefer it to Cursor!

Main takeaway is:

• Sales team combines AE and forward deployed technicals

In other news, this is a real time object identification locally that I want to try out

https://github.com/ngxson/smolvlm-realtime-webcam

For me Structured Outputs are essential when talking to an LLM. In most cases, an application needs to use data in a certain way so you want OpenAI to send back data in a specific structure, rather than having to format yourself.

Structured Outputs (SOs) ensure an OpenAI response is structured in the way you would like.

Prior to SOs you would use a specific prompt to get the response in the right structure.

Parsing a Rental Property Webpage

Let’s set a schema that will take a blurb from a Cottage Rental website and output an object …

import { NextResponse } from 'next/server';
import OpenAI from 'openai';
import { zodTextFormat } from "openai/helpers/zod";
import { z } from "zod";

export async function POST(request) {
  try {
    const client = new OpenAI({
      apiKey: process.env.OPENAI_API_KEY,
      organization: process.env.OPENAI_ORG_ID,
    });

    const requestData = await request.json();
    const { model, input } = requestData;

    const PropertyListing = z.object({
      cottageName: z.string(),
      numberOfGuests: z.number(),
      numberOfBedrooms: z.number(),
      numberOfBathrooms: z.number(),
      allowsPets: z.boolean(),
      keyFeatures: z.array(z.string()),
      marketingCopy: z.string()
    });

    const response = await client.responses.parse({
      model: model || "gpt-4o-2024-08-06",
      input: input,
      text: {
        format: zodTextFormat(PropertyListing, "property"),
      },
    });

    return NextResponse.json({ 
      success: true, 
      event: response.output_parsed 
    });
  } catch (error) {
    console.error('OpenAI API Error:', error);
    return NextResponse.json(
      { success: false, error: error.message },
      { status: 500 }
    );
  }
} 

I can call this API crudely with something like:

    try {
      const response = await fetch('/api/openai/parse-cottage', {
        method: 'POST',
        headers: {
          'Content-Type': 'application/json',
        },
        body: JSON.stringify({
          model: "gpt-4o-2024-08-06",
          input: [
            { role: "system", content: "Extract the cottage information." },
            {
              role: "user",
              content: `webpage html here`
            },
          ],
        }),
      });

      const data = await response.json();
      
      if (!data.success) {
        throw new Error(data.error || 'Failed to parse cottage');
      }
      
      setEvent(data.event);
    } catch (err) {
      console.error('Error parsing event:', err);
      setError(err.message || 'Failed to parse cottage');
    } finally {
      setLoading(false);
    }
  };

The response I get is

{
  "cottageName": "Point Break",
  "numberOfGuests": 8,
  "numberOfBedrooms": 4,
  "numberOfBathrooms": 3,
  "allowsPets": true,
  "keyFeatures": [
    "Beachside location",
    "Contemporary interiors",
    "Part of The Dunes development",
    "Traffic-free location",
    "Open plan living",
    "Floor to ceiling windows",
    "Balcony with ocean views"
  ],
  "marketingCopy": "A beautifully finished beachside house over three floors with contemporary interiors to the highest standard. Part of The Dunes development located directly in front of the beach. Tucked away in an almost traffic-free location. Open plan living, dining and kitchen space on the 2nd floor, taking advantage of the breath-taking views over the golden sand dunes, beach and Atlantic Ocean. The spacious living area comprises of two large sofas, TV, DVD player, coffee table, floor to ceiling windows, patio doors opening onto the balcony."
}

So, what that’s done is convert some HTML into a JSON object that summarises the HTML. Useful!