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How do you get TradingView alerts into Telegram in under 2 seconds, 24/7, without losing or doubling a signal? The pipeline is easy to describe and hard to keep alive. Four traps decide it.
The shape: TradingView fires a webhook, Make.com catches it, formats the message the way the channel wants it, and posts it. Capture to post in under 2 seconds, around the clock, no human in the loop. That is the scenario in the image, running for a registered research analyst since 2025.
1. The 3-second window. TradingView gives a webhook receiver about 3 seconds to respond. A slow receiver loses the alert and nothing anywhere tells you. Fix: acknowledge instantly, process afterwards, never inline.
2. Telegram's rate cap. A channel takes roughly 20 messages a minute. One volatile candle across several symbols can fire more than that, and the extras vanish silently. Fix: a per-channel queue that preserves order and releases under the cap.
3. Duplicates. Retries and repeated triggers post the same signal twice unless every alert carries a dedupe key. Two identical entries in a row is how a channel loses trust in one afternoon. Fix: a key built from symbol, signal and bar time, checked before every send.
4. Silent death. The worst failure is the pipeline stopping while you assume it is posting. Fix: every run logged, every failure alerting you on a separate channel within seconds, and a heartbeat so silence itself raises an alarm.
One more for Indian channels: if the channel belongs to a SEBI-registered adviser, promotional messages need the registration number leading the message and you need retrievable send records. That is a formatting and logging problem, so the pipeline handles it.
Branded alert images instead of plain text are the same pipeline with one extra step: an image engine lays the signal over a base card by URL, so every alert ships as a picture. That build is its own case study on my profile.
The full case study is on my profile, and the delivered version (I install it on your Make.com account, with your templates, queue, dedupe, alerting and a send log) is the TradingView product on my services page.
Prem Patel, Nex Automations. Make.com Level 5 certified, official Make partner, Zapier Certified Expert.
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I built my own lead generation machine because I moved to Denmark with zero network.
I moved to Denmark.
I didn't know anyone.
I had no network.
And I needed to find companies I could sell automation services to.
I could spend hours manually searching for companies, finding emails, cleaning data, and sending messages one by one.
Or I could automate the process.
So I built a complete lead generation pipeline:
→ 🐍 Python scraper connected to Denmark's public government CVR database
→ 📊 Google Sheets synchronization to normalize and deduplicate companies
→ ✉️ Automated cold email workflow with 10 sector-specific templates using n8n
→ 📱 Daily Telegram reports with full pipeline statistics
→ 💼 Assisted LinkedIn outreach: 10 leads per day + a ready-to-send message
And these are the numbers:
📈 19,334 leads in the database
📧 71% email coverage
🏭 10 sectors
🔢 34 industry codes
✉️ 25 automated emails per day
♾️ ~534 days of outreach without repeating a lead
💰 $5/month total cost — basically just the VPS.
The interesting part isn't the scraper.
It's not n8n.
It's not Telegram.
The interesting part is that a process that used to take hours of manual work can now run almost entirely on its own.
And that's exactly what I want to build for companies:
Find repetitive processes → design the system → automate them → measure the results.
In the video, I break down the entire pipeline, piece by piece.
If you're building something similar, let me know which part you'd like me to break down next. 👇
#Automation #AI #LeadGeneration #n8n #Python #ColdEmail #SalesAutomation #B2BAutomation #Denmark #BuildInPublic
Really impressive system. The part that caught my attention is the automated cold email workflow.
Once you start scaling the volume, keeping deliverability consistent across domains and inboxes becomes just as important as the automation itself. Great build!
Aditya Chemicals — Corporate Manufacturing Website
Designed and developed the corporate website for Aditya Chemicals, a chemical manufacturing company serving international markets.
The website was structured to communicate the company's manufacturing capabilities, product portfolio, certifications, global presence, and industry experience through a modern and professional digital experience.
Key Features
Corporate website architecture
Product portfolio and product discovery
Product categorization and navigation
Certifications and compliance presentation
Company and manufacturing information
Global presence and market positioning
Events and exhibition announcements
Brochure download functionality
Contact and enquiry flows
Responsive design across devices
Modern, performance-focused UI/UX
My Role
I handled the website development and implementation, translating the company's business requirements and brand identity into a structured, responsive, and professional corporate web experience.
Technology
Next.js · React · TypeScript · Tailwind CSS · Responsive Web Development
Focus: Corporate Website · Chemical Manufacturing · Product Showcase · B2B Website · Global Business · Lead Generation
Super clean typography and layout, Himanshu! Designing an impactful corporate site for international markets is executed really well here.
LEAKLENS - revenue-risk dashboard
What it does
LeakLens is a revenue-risk dashboard that turns raw billing and usage data into a live account health view, flagging which customers are quietly drifting toward churn before it shows up in MRR.
The problem it solves
According to MGI Research, 42 percent of businesses experience revenue leakage, and SMB-focused SaaS companies face annual churn as high as 31 to 58 percent, often because billing and usage signals sit in disconnected systems no one is watching in real time. LeakLens closes that gap by turning scattered data into one clear risk view, before the cancellation happens.
How I built it
Built entirely in Bubble, using Bubble AI to scaffold the initial page layout and design system, then hand-refined the data model, workflows, and logic. The core architecture mirrors patterns from tools like Stripe (billing event ingestion) and Amplitude (usage event tracking). Two workflows handle CSV upload and parsing, feed the data into a unified account record, and trigger the dashboard build once both sources are loaded.
How to use or test it
There are two ways to get a live dashboard running:
Demo mode: Click Create Dashboard, then click Load Demo Data. Once the confirmation reads "Demo Data Loaded," click Build Dashboard.
File data: Upload the billing CSV and usage CSV (Download CSV here), click Upload CSV. Once it reads "Billing and Usage Data Loaded," click Build Dashboard.
Click on the account in Dashboard to get more details and recommendation on it.
What's next
LeakLens is currently an MVP, built to prove the core risk-detection logic end to end. The roadmap includes:
Live integrations with Stripe and Amplitude, replacing manual CSV uploads with real-time billing and usage data feeds
An AI response layer that turns each risk flag into action: drafting a personalized outreach email, suggesting a check-in call, or pinging the account's CSM directly, based on what the risk signal actually indicates.
Thank You!
😍 #BuiltWithBubble #NoCode #RevOps #ChurnPrevention
Impressive! Works very well...Wish you the best.