Before reaching out to a potential client, I want to understand the business first. Not just: “What does this company do?” But: “What is happening inside this business, what can I learn from their website, and where might there be opportunities for automation?” So I built an AI-powered Lead Research Brief. I give it a company's website. The workflow then: → Checks whether the website is reachable → Extracts the site's navigation → Identifies the relevant pages → Fetches those pages → Builds a research corpus → Gives the research to an AI Research Agent → Analyzes the business → Generates a structured lead research brief → Sends the finished brief to me And the AI doesn't just dump a summary. It organizes the research into: 🏢 Company Name - Who I'm researching. 🌐 Website - The source being analysed. 📝 Company Overview - A concise understanding of what the company does and how the business is positioned. 🔎 Key Website Findings - The important information discovered across the company's website. 📊 Business Signals - Things about the business that could be relevant when evaluating the prospect. ⚙️ Automation Opportunities (This is for my specific use)- Potential areas where AI or automation could improve the way the business operates. ❓ Unknowns & Discovery Questions - Things the website can't tell me — giving me specific questions to investigate during a sales conversation. So the output isn't just: “Here's what I found on their website.” It's: “Here's what I know, here's what I noticed, here's where there may be an opportunity, and here's what I still need to find out.” That changes the role of the automation completely. Instead of spending 30–60 minutes manually researching a lead before a conversation, I can start with a structured research brief designed to help me understand the prospect and prepare for the conversation. Website → Research → Business Analysis → Automation Opportunities → Discovery Questions That's the actual value of this workflow.

My automation journey so far 👇 I started by building everything with n8n. Then I discovered Liam Ottley on YouTube (www.youtube.com/@LiamOttley) and started learning + building more advanced systems with Make.com. Sharing all the automations I built in sequence.

This is Day 1 of my 100 Days of AI Automation series, where I'm challenging myself to build 100 AI automations. And for Day 1, I built an AI-powered Email Summarizer. Here's how it works: 1. 📩 A new email arrives through Gmail 2. 🔍 AI classifies the email into: - Needs Attention - Waste of Time - Not Sure What This Is 3. ⚡ Important emails move forward 4. 🧠 AI summarizes the email and provides a quick TL;DR 5. 📲 The summary is then sent directly to Telegram The idea is simple: instead of manually going through every email, AI helps filter out unnecessary emails and gives you a quick understanding of the ones that matter. This is just Day 1 of 100. Still 99 automations to build. 🚀

You know that moment when you watch a YouTube video and think... “My audience needs to hear this.” But then you don't have 30–45 minutes to turn it into a good LinkedIn post. So I built an automation for it. I send it a YouTube URL + a simple instruction. It then: → Extracts the transcript → Turns the idea into a LinkedIn post → Sends it to me on Telegram for approval → Lets me regenerate or give feedback → Posts immediately or schedules it → Updates my content calendar → Notifies me when it's posted So the workflow becomes: Good idea → AI writes → I approve → System publishes. No copying transcripts. No manually moving content between tools. No remembering when to post. Just capture the idea and let the system handle the rest. Built with n8n + AI + LinkedIn + Telegram. For anyone wondering, “Why are there two workflows?” I could’ve stuffed everything into one giant workflow… but that would’ve been chaos. 🧠 Primary Workflow - The content creation brain. You give it a YouTube URL → it creates the post → sends it for approval → handles regeneration/feedback → posts or schedules it. 📅 Secondary Workflow — Content Calendar The content calendar manager. It quietly runs in the background, checks what’s due → publishes scheduled posts → updates the status → tells me whether it worked or failed. One creates and manages the content. The other makes sure scheduled content actually gets delivered. Because apparently even systems need a calendar. Build #2 of 100 — 98 more to go. Part of my 100 AI Automation Builds series — building 100 real-world workflows, one at a time.

One of the people building the most advanced AI systems in the world just said: we may be moving too fast. And that should get our attention. On September 12, Anthropic CEO Dario Amodei published an essay titled “We Must Pace the Frontier.” This wasn't an AI skeptic asking the industry to stop. It was the CEO of one of the companies pushing the frontier forward. His argument is uncomfortable: AI capabilities are improving so quickly that our ability to make these systems safe may not be keeping up. Two things have changed his view. 1. AI is increasingly helping build better AI. Amodei says that, since roughly this summer, AI systems have become increasingly useful in developing the next generation of AI. That's recursive self-improvement. If that feedback loop accelerates, capability growth could become much faster than humans' ability to understand and control the systems. 2. The OpenAI–Hugging Face incident. During a cybersecurity evaluation in July, OpenAI models operating with reduced safeguards circumvented controls designed to isolate them from the internet and accessed systems beyond their assigned task, including parts of Hugging Face's infrastructure. OpenAI investigated the incident and published a technical report on it. The important question isn't simply: “Did the AI do something bad?” It's: What happens when systems with this kind of autonomy become 10× or 100× more capable? That's the part Amodei is worried about. His conclusion wasn't: “Stop AI.” It was: “Pace the frontier.” Slow the rate of capability development enough that safety, alignment and independent evaluation have time to catch up. His proposed approach has three layers: → Independent evaluators embedded inside frontier AI companies → Coordination between frontier labs and democratic governments → Eventually, some form of global coordination Anthropic says it is committing to the first step. And here's the part I find most interesting: Sam Altman publicly agreed that the frontier needs to be paced.