We’re building an agent (MK1) that does large-scale competitor analysis across dozens of newsletters automatically! Scraping → Structuring → Compressing → Multi-LLM analysis → aggregation → dashboards. If it were as simple as “drag a few n8n nodes,” trust me, we’d be doing that. Allow me to elaborate: 1. The data sources we pull from are NOT friendly to scrapers. - Requests get blocked instantly - HTML structure changes unpredictably - Anti-bot systems shut down your pipeline mid-run - Content loads dynamically - Layouts differ per issue - Rate limits kick in - Rendering methods break your parser When you have to keep the entire structure consistent for downstream LLM analysis, a single DOM change breaks the whole chain. No-code tools don't handle that kind of fragility well. 2. The content isn’t simple text, it requires meaningful structure. When you’re analyzing 30–100 newsletters at a time, you need: - Section extraction - Visual mapping - CTA identification - Ad block recognition - Tone markers - Intent patterns - Word & emoji stats - Structural compression (to cut token costs by ~70%) 3. Real orchestration > visual workflows People underestimate what happens when you’re: - Running 40+ analysis jobs in parallel - Retrying failed tasks - Re-queuing partial data - Handling timeouts - Managing token budgets - Caching compressed representations - Tracing every run end-to-end - Ensuring idempotency 4. Maintaining the scraper is half the battle When the website changes structure (which happens often), your scraper must: - adapt automatically or - be fixable with minimal downtime You cannot do that reliably in a visual builder. These aren’t static URLs. Each issue is rendered differently and sometimes changes backend structure.Our scraping approach has to evolve constantly. Even a small structure shift breaks an entire n8n chain.

I am looking for a select few of experienced developers that can take on jobs for my AI consulting agency. We have reached a volume of new jobs that I can no longer handle on my own. If you are an n8n specialist, no-code tool specialist, voice ai specialist, or anything of the sorts I would love to chat!

We’re working with a newsletter agency that wants their competitor research fully automated. Right now, their team has to manually: - Subscribe to dozens of newsletters - Read every new issue - Track patterns (hooks, formats, CTAs, ads, tone, sections, writing style) - Reverse-engineer audience + growth strategies We’re trying to take that entire workflow and turn it into a single “run analysis” action. High-level goal: - Efficiently scrape competitor newsletters - Structure them into a compressed format - Run parallel issue-level analyses - Aggregate insights across competitors - Produce analytics-style outputs - Track every request through the whole distributed system How the system works (current design): Step 1 – You trigger an analysis You give the niche. The system finds relevant competitors. Step 2 – Scraper fetches issues Our engine pulls their latest issues, cleans them, and prepares them for analysis. Step 3 – Convert each issue into a “structured compact format” Instead of sending messy HTML to the LLM, we: - extract sections, visuals, links, CTAs, and copy - convert them into a structured, compressed representationThis cuts token usage down heavily. Step 4 – LLM analyzes each issue We ask the model to: - detect tone - extract key insights - identify intent - spot promotional content - summarize sections Step 5 – System aggregates insights Across all issues from all competitors. Step 6 – Results surface in a dashboard / API layer So the team can actually use the insights, not just stare at prompts. Now I’m very curious: what tech would you use to build this, and how would you orchestrate it? P.S. We avoid n8n-style builders here — they’re fun until you need multi-step agents, custom token compression, caching, and real error handling across a distributed workload. At that point, “boring” Python + queues starts looking very attractive again.

I'm working with an Agency that has 3 potential prospects, 2 in HVAC and one in Dentistry, And they need to show a demo, I need an Voice Agent that sounds the most Human like, it shouldnt have that robotic Voice, and sound like how GPT's Voice Assistant sounds like, and their is some backend automation that goes with it. Its destined that out of those 3 meetings, 1 will be closed... and for that you will be looking at $400 in commission and $800/m role if you would be up for it.

Hello everyone! 👋 I'd like to share **ALGOR.IA** with you, a SaaS I developed that's helping businesses automate their customer service and sales on WhatsApp through artificial intelligence. ## What does ALGOR.IA do? ALGOR.IA is a platform that allows you to create **intelligent virtual agents** for WhatsApp. These agents can: - **Provide 24/7 automated customer service** intelligently and automatically - **Qualify and nurture leads** automatically - **Send personalized mass marketing campaigns** - **Schedule appointments** and manage tasks - **Provide complete analytics** on conversions and performance The idea is simple: while most tools help you **serve** customers, ALGOR.IA helps you **sell** more. ## How was it created? I developed ALGOR.IA using a modern and efficient approach: - **Interface (Frontend)**: Built using **Cursor**, an AI-assisted development tool that allowed me to create a modern and intuitive interface more quickly - **Automations**: All automation logic was developed in **N8N**, a visual workflow automation platform that allows creating complex flows without needing to write traditional code This combination allowed me to focus on user experience and functionality, instead of getting lost in technical programming details. ## What does it deliver? ALGOR.IA delivers practical results for businesses: ✅ **Intelligent automated customer service** - Your customers receive immediate responses, even outside business hours ✅ **Increased conversions** - Clients using the platform are reporting 40%+ increases in sales ✅ **Time and resource savings** - Reduces the need for large customer service teams ✅ **Targeted marketing** - Sends personalized messages to thousands of contacts in a segmented way ✅ **Complete analytics** - Tracks conversion metrics, response rates, ROI, and much more ## How does it deliver? Delivery is simple and straightforward: 1. **Quick setup**: In less than 10 minutes you connect your WhatsApp Business account