Lal Chand
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Lal Chand

AI Workflow Automation Engineer

Automations that pay for themselves. Built in days, owned by you.

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Content / Social Media SaaS · 21 days (MVP)

Newsly — AI-Powered News & Trend Scanner with Auto-Posting

The founder of Newsly is a solo creator who posts on LinkedIn 5x/week. He spent 90 minutes a day reading TechCrunch, HackerNews, and 8 other sources, then 60 more minutes writing posts. That's 12.5 hours a week of content work for a 1-person business. He hired me to build "the system that does what I do, in 10 minutes a day instead of 2.5 hours."

Newsly — AI-Powered News & Trend Scanner with Auto-Posting

Client: Newsly (SaaS startup, paying user 1) Industry: Content / Social Media SaaS Timeline: 21 days (MVP) Stack: Next.js 15 · Supabase · Claude API · Buffer API · LinkedIn API · n8n · Vercel Slug: /case-studies/newsly-ai-content-platform

The problem

The founder of Newsly is a solo creator who posts on LinkedIn 5x/week. He spent 90 minutes a day reading TechCrunch, HackerNews, and 8 other sources, then 60 more minutes writing posts. That's 12.5 hours a week of content work for a 1-person business.

He hired me to build "the system that does what I do, in 10 minutes a day instead of 2.5 hours."

What I built

A three-layer system:

Layer 1: Scanner (n8n, runs every 4 hours)

  • Pulls from RSS: TechCrunch, The Verge, HackerNews, Product Hunt, Reddit r/startups
  • Stores raw articles in Supabase
  • Deduplicates by URL hash

Layer 2: Trend Analyzer (n8n + Claude, runs every 6 hours)

  • Pulls last 24 hours of articles
  • Sends to Claude with prompt: "Find 3 emerging themes that aren't yet over-saturated. Return JSON with theme, supporting articles, and confidence score."
  • Stores themes in Supabase with article references

Layer 3: Web App (Next.js)

  • Founder logs in, sees trending themes ranked by confidence
  • Clicks "Generate post" → Claude writes 3 variants in his voice (he uploaded 50 of his old posts, system prompt embeds his style)
  • Founder picks one, edits if needed, hits "Schedule"
  • Buffer API queues it for posting

Key engineering decisions

  1. Claude over GPT for content generation. Claude's voice control is dramatically better when you give it 50 sample posts as style reference. GPT-4 was too "LinkedIn-y."
  2. Stored themes separately from posts. A theme might be reused across 3 posts over a week. Decoupling them meant we could surface "themes you haven't covered yet."
  3. Supabase pgvector for similarity. When the founder asked for "trending themes that don't overlap with what I've already posted," I embedded his last 30 posts and filtered new themes by cosine distance.
  4. Buffer over native LinkedIn API. LinkedIn's API has aggressive rate limits and weird approval requirements. Buffer abstracts all of that and supports cross-posting to X.

The numbers

  • Founder's time: 12.5 hrs/week → 45 min/week (94% reduction)
  • Posting consistency: 3–4 posts/week → 5 posts/week, every week
  • Engagement: Founder reports ~40% higher avg engagement (better-timed, theme-driven)
  • MVP cost: $2,200 fixed price + $30/mo running cost (Claude + Supabase + Buffer)

What I learned

The AI doesn't replace the founder — it kills the research and the first-draft work. The founder still reviews, still picks, still edits. That 10% human-in-the-loop is what makes the output sound like him and not like every other AI-written LinkedIn post.