Filter aggressively. Most RSS feeds publish 5-20 articles per day; only 1-2 will be relevant to your audience. Set up keyword filters: include articles matching your target keywords, exclude articles matching negative keywords (competitor names, off-topic subjects). Feedly Pro and Inoreader both support complex filtering; for more sophisticated filtering, pipe the feed through an n8n or Make.com workflow with AI-based relevance scoring.

Set up the ingestion pipeline. Use a scheduled n8n or Make.com workflow to poll each RSS feed every 30-60 minutes (RSS feeds don't push; you poll). New items land in a queue (Airtable, Google Sheets, or a database) with metadata: title, link, publication date, source, summary, AI relevance score.

Phase 2

Build the AI rewriting layer. For each item that passes the relevance filter, use an LLM (GPT-4, Claude, Gemini) to generate platform-specific posts. The prompt should include: the source article (title, summary, full text if available), the target platform, the brand voice guidelines, and the desired post structure.

For X: generate a 280-character post with a hook, key takeaway, and link. Optionally generate a 5-7 tweet thread for high-value articles. For LinkedIn: generate a 200-400 word post with a strong opening line, key insights, and a question to drive engagement. For Instagram: generate a 150-300 character caption with 5-10 relevant hashtags. For Telegram: generate a 500-1000 character summary with bullet points and a link.

Implement human-in-the-loop review. AI-generated posts should be queued for human review before publishing — at least initially. Use Airtable or a dedicated approval tool (Planable, Loomly) where the account manager reviews AI-generated posts, makes minor edits, and approves for scheduling. Over time, as the AI improves and trust builds, you can automate posting for low-risk content types.

Phase 3

Schedule posts for optimal timing. Use your scheduling tool (Buffer, Later, native platform tools) to schedule approved posts at platform-specific optimal times. Nakrut.pro's scheduler analyzes historical performance data to recommend optimal posting times per platform per audience — typically 9-11am and 7-9pm local time for B2C audiences, 8-10am and 1-3pm for B2B.

Track attribution and source links. Every curated post must link back to the original source — this is both ethical and a legal requirement under most fair use doctrines. Configure the AI prompts to always include the source link, and build a validation check that blocks posts without links from being scheduled.

Monitor performance and iterate. Track engagement metrics (impressions, engagement rate, click-throughs) for curated vs original content. If curated content underperforms by more than 20%, adjust the relevance filters, the AI prompts, or the posting cadence. The goal is parity — curated content should perform similarly to original content because it's relevant and well-adapted.

Results

| Metric | Before | After | Change | |---------|--------|-------|--------| | Feeds monitored | 12 (manual) | 247 (automated) | +1958% | | Curated posts/week | 4 | 28 | +600% | | Time spent on curation | 8h/week | 1h/week | -87% | | Curated post engagement | 1.4% | 3.8% | +171% | | Follower growth/month | 340 | 1,820 | +435% |

What Worked

• AI rewriting with platform-specific prompts produced posts that performed within 15% of original content. The key was detailed prompts — generic "rewrite this for LinkedIn" produced mediocre results, but "write a 200-word LinkedIn post in [brand voice] focusing on [key insight], ending with a question about [topic]" produced consistently strong posts.
• Human-in-the-loop review for the first 60 days built trust in the AI output. After 60 days, the team felt comfortable automating low-risk content types (industry news) while keeping high-risk content (opinion pieces, regulatory updates) under human review.
• Source link validation prevented attribution issues. Posts without source links are auto-rejected; this saved the team from at least three potential attribution complaints from publishers.

What Didn't Work

• Initial keyword filters were too narrow, missing relevant articles that didn't use exact keywords. Adding AI-based relevance scoring (LLM evaluates each article's relevance on a 1-10 scale) caught 40% more relevant content.
• Tried to fully automate posting without human review for the first month. Several misfires — AI misinterpreting sarcasm as fact, posting about a topic that had just become sensitive — forced us to add review for all posts. Cost: 1 hour per week of review time, well worth the risk reduction.

Takeaways

RSS-to-social automation is one of the highest-ROI workflows in SMM when done right. The keys are: aggressive filtering (don't post everything, post the right things), platform-specific AI rewriting (one source article becomes 4-6 platform-specific posts), and human-in-the-loop review for quality control. For teams that want a managed solution, Nakrut.pro offers an RSS-to-social automation module starting at $99/month per client, including feed monitoring, AI rewriting, scheduling, and analytics.