Skip to content

fixed a misspelling and a typo #5

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

fixed a misspelling and a typo #5

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -88,7 +88,7 @@ Here’s a clear, beginner-friendly way to see **n8n as an agentic AI platform**

![](compare.jpg)

Agentic AI platforms can be introduced as a continuum—**no-code**, **low-code**, and **full-code**—that aligns delivery speed with architectural control as solutions mature. **No-code** platforms provide visual builders, templates, and managed connectors so non-developers can assemble agent workflows quickly and safely. **Low-code** platforms retain a visual canvas but add programmable “escape hatches” (custom logic, APIs, components) to handle real-world variability while preserving rapid iteration for internal tools and orchestration. **Full-code** platforms expose full SDKs and runtime control, enabling engineers to implement bespoke agent behaviors, enforce testing and observability, integrate with existing services, and meet performance, security, and compliance requirements. A pragmatic adoption path for developers is to ideate in low-code for fastest validation and prototying,and graduate the durable or business-critical workloads to full-code for long-term reliability and scale.
Agentic AI platforms can be introduced as a continuum—**no-code**, **low-code**, and **full-code**—that aligns delivery speed with architectural control as solutions mature. **No-code** platforms provide visual builders, templates, and managed connectors so non-developers can assemble agent workflows quickly and safely. **Low-code** platforms retain a visual canvas but add programmable “escape hatches” (custom logic, APIs, components) to handle real-world variability while preserving rapid iteration for internal tools and orchestration. **Full-code** platforms expose full SDKs and runtime control, enabling engineers to implement bespoke agent behaviors, enforce testing and observability, integrate with existing services, and meet performance, security, and compliance requirements. A pragmatic adoption path for developers is to ideate in low-code for fastest validation and prototying, and graduate the durable or business-critical workloads to full-code for long-term reliability and scale.

Here’s a crisp way to tell them apart and know when to use which.

Expand Down Expand Up @@ -155,7 +155,7 @@ In short: we bet on the winners in each category to move faster now and scale sa

---

## Comparision of n8n Skills vs OpenAI Agents SDK skills for Enterprise Development, Startups, and Freelancing
## Comparison of n8n Skills vs OpenAI Agents SDK skills for Enterprise Development, Startups, and Freelancing

Let’s do a **three-way comparison** of **n8n skills** vs **OpenAI Agents SDK skills**, and examine **how useful they are in enterprise development, startups, and freelancing**.

Expand Down