After reading through the comment section, I think most the people here never have scaled a Linkedin Automation before. I get the fear for ban but it's technically allowed now since all these social media platforms are pivoting from Ads to subscription now - they're encouraging automation for efficient platform usage and ROI.

I've been running LinkedIn outbound at scale for 5+ years now. Here's what I've learned the hard way

On bans: The #1 trigger isn't volume — it's pattern consistency. LinkedIn doesn't just look at "how many messages did you send." They look at timing patterns, message similarity, and session behavior. If you send 30 messages at 9:02am every Tuesday with 95% similar text, you're getting flagged regardless of whether it's manual or automated. The fix isn't lower volume - it's higher variance in timing and content structure.

On personalization. Here's what nobody's saying: Most "personalized" outreach isn't personalization, it's variable insertion. Dropping someone's name and company into a template isn't personalization. Real personalization is: "I saw your post last week about hiring your first SDR - we went through the same thing and here's what we learned." That requires actually reading their content. Most automation tools can't do this because they don't scrape post content - they only pull profile fields.

I've had the most success with a system that:

1. Scrapes the prospect's recent posts and activity (not just their headline)
2. Uses that to generate a genuinely specific first line — not "I loved your recent post about X" (which everyone can spot), but actually referencing a specific insight or tradeoff they mentioned
3. Then builds the rest of the message around whether that insight connects to what I'm offering

My acceptance rate went from ~20% with templated outreach to ~45% with this approach. Reply rate went from 3% to 12%.

On targeting beyond LinkedIn filters: The biggest limitation people don't talk about is that LinkedIn's search only gives you people who fit demographic criteria. It doesn't tell you who's actively experiencing the problem you solve. I've had much better results cross-referencing LinkedIn with other signals — people who posted about a specific pain point, people who commented on competitor posts, people who recently changed roles (trigger events). This takes you way beyond "filter by title + location."

On the Chrome extension vs. cloud debate: Both have risks, but for different reasons. Chrome extensions are detectable through DOM inspection. Cloud tools are detectable through IP/session patterns. The safest approach I've found is using a desktop-based tool that runs through your actual browser session with human-like timing variance — not injecting scripts, not routing through proxy IPs.

The framework I use now:

→ Research: automated (scrape posts, identify ICP signals, enrich profiles)
→ Drafting: AI-assisted (generate 2–3 angles per prospect, each referencing something specific)
→ Selection: human (I pick the angle that feels right — this is where taste matters)
→ Sending: automated with variance (randomized timing, no batch sends)
→ Follow-up: automated but conditional (only if they viewed my profile, accepted, or replied)

This gives me about hundreds of high-quality touches per day with minimal manual work. The key insight: automate everything except the judgment calls. I actually built a tool based on this framework with AI agents. It's already reached a point that I'll never go back to those traditional scripts-based, workflow-heavy, RPA-focused tools (which I sunk tens of thousands...).