diff --git a/README.md b/README.md
index 49b44c00..832bbd5a 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,7 @@
     <img src="https://img.shields.io/badge/python-%3E%3D3.10-blue" alt="Python">
     <img src="https://img.shields.io/badge/license-Apache%202.0-green" alt="License">
     <a href="https://discord.gg/YZZRxCAhmm"><img src="https://img.shields.io/badge/discord-join-7289da?logo=discord&logoColor=white" alt="Discord"></a>
+    <a href="https://x.com/OpenJarvisAI"><img src="https://img.shields.io/badge/X-@OpenJarvisAI-black?logo=x&logoColor=white" alt="X / Twitter"></a>
   </p>
 </div>
 
@@ -26,7 +27,7 @@
 
 Personal AI agents are exploding in popularity, but nearly all of them still route intelligence through cloud APIs. Your "personal" AI continues to depend on someone else's server. At the same time, our [Intelligence Per Watt](https://www.intelligence-per-watt.ai/) research showed that local language models already handle 88.7% of single-turn chat and reasoning queries, with intelligence efficiency improving 5.3× from 2023 to 2025. The models and hardware are increasingly ready. What has been missing is the software stack to make local-first personal AI practical.
 
-OpenJarvis is that stack. It is an opinionated framework for local-first personal AI, built around three core ideas: shared primitives for building on-device agents; evaluations that treat energy, FLOPs, latency, and dollar cost as first-class constraints alongside accuracy; and a learning loop that improves models using local trace data. The goal is simple: make it possible to build personal AI agents that run locally by default, calling the cloud only when truly necessary. OpenJarvis aims to be both a research platform and a production foundation for local AI, in the spirit of PyTorch.
+OpenJarvis is that stack. It is a framework for local-first personal AI, built around three core ideas: shared primitives for building on-device agents; evaluations that treat energy, FLOPs, latency, and dollar cost as first-class constraints alongside accuracy; and a learning loop that improves models using local trace data. The goal is simple: make it possible to build personal AI agents that run locally by default, calling the cloud only when truly necessary. OpenJarvis aims to be both a research platform and a production foundation for local AI, in the spirit of PyTorch.
 
 ## Installation
 
@@ -112,6 +113,8 @@ See the [Skills User Guide](https://open-jarvis.github.io/OpenJarvis/user-guide/
 
 ### Built-in Agents
 
+OpenJarvis ships with eight built-in agents across three execution modes (on-demand, scheduled, continuous):
+
 | Agent | Type | What it does |
 |-------|------|-------------|
 | `morning_digest` | Scheduled | Daily briefing from email, calendar, health, news — with TTS audio |
@@ -127,6 +130,13 @@ See the [User Guide](https://open-jarvis.github.io/OpenJarvis/user-guide/morning
 
 Full documentation — including Docker deployment, cloud engines, development setup, and tutorials — at **[open-jarvis.github.io/OpenJarvis](https://open-jarvis.github.io/OpenJarvis/)**.
 
+## Community
+
+- **GitHub:** [github.com/open-jarvis/OpenJarvis](https://github.com/open-jarvis/OpenJarvis)
+- **Discord:** [discord.gg/YZZRxCAhmm](https://discord.gg/YZZRxCAhmm)
+- **X / Twitter:** [@OpenJarvisAI](https://x.com/OpenJarvisAI)
+- **Docs:** [open-jarvis.github.io/OpenJarvis](https://open-jarvis.github.io/OpenJarvis/)
+
 ## Contributing
 
 We welcome contributions! See the [Contributing Guide](CONTRIBUTING.md) for incentives, contribution types, and the PR process.