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Nov '25 •
spent around 2 years working with ai tools and here are my thoughts so far
Good morning everyone! After spending 2 years testing various AI development tools, I wanted to share my experience to help you save time and money. Disclosure: Some links below are referral links. 🏆 Top Tools Tested: - Replit - Manus - Lovable - Cursor - Claude Code My Top Recommendations 1. Replit - 9/10 Replit impressed me the most. With a single prompt, I built a fully functional real estate platform featuring: - User registration and authentication - Property listing system - Profile management - Image uploads - Clean, professional UI/UX - Zero bugs in initial deployment The polish and functionality right out of the box were remarkable. 2. Manus - Highly Recommended Using just 3 prompts, I created a comprehensive self-improvement web app tracking: - Workout routines - Sleep schedules - Health metrics - Goal achievement progress While the UI isn't as polished as Replit's output, the functionality is solid and works flawlessly. web app demo: https://rebourneapp-kk4y2c3m.manus.space/Referral link for manus: https://manus.im/invitation/UDSSKCGJRZTZQ6M 3. Lovable - Strong Contender Lovable has improved significantly and now offers competitive features. I've built my largest projects here, and it uses Supabase for the backend, which I've grown to appreciate. One caveat: You'll need Supabase MCP integration to work on Lovable projects in other tools like Cursor or Claude Code. However, once set up, these tools excel at adding features and debugging. Referral link: https://lovable.dev/invite/AD45UMY What's your experience with AI development tools? Any questions about these platforms? what is your favourite tool so far ?
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1d •
Fixing a Silent API Failure in an n8n Workflow
Today I helped fix a small but blocking issue in an n8n workflow that looked simple on the surface but kept failing at the API step. The flow was straightforward Webhook to HTTP request to Google Sheets and then an email notification The webhook was receiving data correctly but the workflow kept stopping at the HTTP request node. After digging in I found the issue was a mix of API authentication and how the JSON response was being handled. I debugged the HTTP request node checked headers tokens and payload structure then fixed the authentication logic. I also cleaned up the JSON parsing so the data mapped correctly into Google Sheets without breaking the execution. After that I ran multiple end to end tests to make sure the workflow completed successfully every time and triggered the email notification as expected. This is a good reminder that most automation failures are not about complexity. They usually come down to small details in API calls and data structure. Once those are right n8n workflows become very stable and predictable.
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2d •
Built a Simple AI Lead Qualification Workflow in Make.com
Today I worked on a small proof of concept for a B2B lead generation setup called NexaGrowth. The goal was to see how AI could quickly qualify incoming leads without adding complexity. The workflow starts with new leads coming in through a Google Form and webhook trigger. Once a submission comes in, the data is sent to OpenAI where the lead is analyzed to classify the industry, estimate company size based on the description, and assign a lead score from 1 to 10. That output is then structured cleanly and pushed into Airtable so the data is easy to review and filter later. If the lead score comes back as 8 or higher, the automation instantly sends a Slack notification so the team can act fast on high intent leads. Nothing fancy, just clean logic, clear prompts, proper data mapping, and basic error handling to keep the flow stable. This kind of setup is a great example of how AI can support lead qualification without replacing existing systems or overengineering the process. Simple automations like this often deliver the quickest wins.
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3d •
Building an AI Automation That Finds and Verifies Business Owner Emails
I recently worked on an automation where the input was simple but the logic was not. A Google Sheet from Outscraper containing business names and addresses. Sometimes a website. Sometimes a phone number. Emails were often wrong or missing. The goal was to enrich every single row without skipping anything. The workflow runs row by row in n8n. Even if an email already exists, the automation still rechecks the business. No assumptions. First step is controlled HTTP scraping. If a website exists, the automation checks key pages like homepage contact about and team. Text is capped and cleaned immediately to avoid token overload. Next comes AI reasoning. Instead of free text responses, the model is forced to return strict JSON only. Owner name email source confidence and notes. If the owner cannot be found, it returns blanks with a reason. No hallucinations. Email extraction happens from both scraped pages and AI inference but nothing is trusted yet. Every email then goes through Reoon verification with daily rate limits enforced. Batch control makes sure we never exceed the quota. Failed verifications are logged not retried endlessly. The sheet is updated in place. Correct columns only. No overwriting without intent. Idempotency checks prevent duplicates and infinite loops. Failures do not crash the workflow. HTTP errors timeouts missing websites all fall into a handled path with logs. End result is a clean verified CSV that can actually be used. Outscraper sheet in. Owner name and best email out. Verified. Logged. Reliable. This type of automation is where AI is useful only when the guardrails are tight.
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4d •
How I Think About Phone and SMS Automation for Real Businesses
One thing I’ve learned working on n8n projects across different teams is that phone calls and text messages are usually where automation breaks first. When a lead calls or sends a text about a home sale, timing and accuracy matter more than anything. So the first thing I focus on is capture. Every call or SMS needs to land in one place with context attached. Who contacted us. Where they came from. What they asked. From there the logic matters. Leads are routed based on rules that make sense for the business not just pushed blindly into a CRM. Some go to sales. Some trigger follow ups. Some need a callback task created immediately. CRM automation ties it all together. Updates happen automatically. Notes are added. Status changes are logged. No manual copying. No missed steps. Reliability is the part most people ignore. Webhooks fail. Phone systems timeout. APIs return bad responses. In n8n I always build retries logging and safeguards so one failed step does not break the entire flow or create duplicates. When done right the team does not think about the automation at all. Calls come in. Messages are handled. Leads move forward quietly. That is usually the sign the system is doing its job.
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