---
id: ins_here-top-deployed-workflows-https
operator: Manthan Patel
operator_role: 'I teach AI Agents and Lead Gen | Lead Gen Man(than) | 100K+ students'
source_url: https://www.linkedin.com/feed/update/urn:li:activity:7368132590295281665/
source_type: thread
source_title: 'I tested 4 automation platforms: Make, n8n, LangGraph, and Lindy'
source_date: 2026-04-10
captured_date: 2026-05-02
domain: [content, marketing, growth]
lifecycle: [content, measurement-experimentation]
maturity: frontier
artifact_class: workflow
score: { originality: 3, specificity: 4, evidence: 2, transferability: 4, source: 3 }
tier: B
related: []
raw_ref: raw/linkedin/reactions/linkedin-reactions-2026-04-10.md
---

# Here are my top 3 deployed workflows: https://resources.leadgenman.com/lindyai

## Claim
I tested 4 automation platforms: Make, n8n, LangGraph, and Lindy. The shocking side-by-side comparison results: Same task for each: Automate my lead qualification process. 𝗠𝗮𝗸𝗲: Great for connecting apps. Built a solid workflow between CRM and email.

## Mechanism
Lindy's Autopilot feature lets agents navigate websites without API dependencies. The others require APIs for every external interaction.

## Conditions
Holds when: the operating context matches the post's stated frame (team shape, stage, tooling, buyer type).
Fails when: the practice is lifted into a different stage or buyer context without reworking the underlying mechanism.

## Evidence
> "Here are my top 3 deployed workflows: https://resources.leadgenman.com/lindyai"

· Manthan Patel, LinkedIn, 2026-04-10

## Signals
- Make/n8n need you to think in nodes and connections.
- LangGraph needs you to think in code.
- Lindy needs you to explain what you want done with just a prompt

## Counter-evidence
No opposing view in current corpus.

## Cross-references
- (none in current corpus)