Experiment: I tried both options I was confused about—n8n and Claude Code—and found that both are valuable. If you prefer to avoid looking at code or Python scripts, Claude Code may not be the right choice for you. If you want to present workflows visually to clients, n8n is a better option. Ultimately, it depends on two factors: your clients and your own working style. Claude Code is generally more powerful in terms of reach, as code usually is. Learning: The traditional approach still matters—understanding pain points, businesses, and how to reach clients is the only real way to grow. Tools are just a means to solve problems. You can evaluate any tool by analyzing its pros and cons. With Claude, you can get guidance based on your situation. Provide context such as your budget, available time, and project details, and it can suggest which platform or tool is best for you. Note: Any project that you guys want me to do next. Project should be complex.

Recruiting agency. 140 job placements annually. 23 hours per opening just screening resumes. That's 3,220 hours annually. Two full-time people just reading resumes. THE SCREENING BOTTLENECK: Average job opening: - 180 applications received - First-pass screening: 8 minutes per resume - 180 × 8 minutes = 1,440 minutes = 24 hours But only 12 candidates invited to interview. In other words: 168 resumes (93%) rejected after 8 minutes of review each. That's 22.4 hours spent saying "no." THE WHAT-WE-LOOK-FOR LIST: Client had clear requirements: - Years of experience: minimum 3, maximum 10 - Required skills: list of 8-12 keywords - Required education: bachelor's minimum - Location: within 50 miles or willing to relocate - Industry experience: specific sectors 90% of resumes failed on these basic criteria. But recruiter still had to read entire resume to determine this. THE COST BREAKDOWN: 140 job openings × 23 hours = 3,220 hours annually At $45/hour recruiter cost: $144,900 in screening labor Value added: Identifying 12 good candidates per opening Cost per good candidate found: $144,900 / 1,680 good candidates = $86 per good candidate THE SOLUTION I BUILT: Resume screening automation: - Resume received (PDF, Word) - System extracts: education, experience, skills, location - Compares against job requirements - Scores 0-100 based on match - Flags top 15% for human review - Auto-rejects bottom 85% with personalized message Recruiter reviews only pre-qualified candidates. THE RESULTS: Before automation: - Time per job opening: 23 hours - Resumes manually reviewed: 180 - Good candidates identified: 12 - Annual screening time: 3,220 hours After automation: - Time per job opening: 3.5 hours - Resumes manually reviewed: 27 (top 15%) - Good candidates identified: 11 - Annual screening time: 490 hours TIME SAVED: 2,730 hours annually (85% reduction) THE QUALITY QUESTION: "But doesn't automation miss good candidates?" Measured: 140 job openings, compared results.

1. Found a company without website 2. Sent them a WhatsApp message 3. Look what I did for you 4. Costs $1200 but $500.00 5. Take it or leave it. 6. $200 to seal the deal and $300 after 7. They agreed. 8. I will upload the website tomorrow and show you what I did and how I did it. How much do you charge per website?

Most wildlife camera trap images still get reviewed manually — one by one. I built a system to change that. A deployment-focused Wildlife AI pipeline that detects animals, processes batches of images, and runs locally using Docker. Next step: making it explain what it sees using local AI (Ollama + RAG). Would love thoughts from everyone in the community. 👉 https://open.substack.com/pub/saratsarvepalli/p/building-a-wildlife-ai-system-for

Any advice on the best AI option to generate my website efficiently?