clean up

README.md

@@ -30,11 +30,11 @@ CK consists of several sub-projects:
 
   * [CM interface to run MLPerf inference benchmarks](https://docs.mlcommons.org/inference)
 
-
   * [CM4ABTF](https://github.com/mlcommons/cm4abtf) - a unified CM interface and automation recipes
     to run automotive benchmark across different models, data sets, software and hardware from different vendors.
 
-* [CMX (the next generation of CM and CM4MLOps)](cm/docs/cmx) - we are developing the next generation of CM 
+* [CMX (the next generation of CM, CM4MLOps and CM4MLPerf)](cm/docs/cmx) - 
+    we are developing the next generation of CM 
     to make it simpler and more flexible based on user feedback. Please follow 
     this project [here]( https://github.com/orgs/mlcommons/projects/46 ).
 
@@ -63,8 +63,8 @@ CK consists of several sub-projects:
 
 ### Maintainers
 
-* CM/CM4Research/CM4MLPerf-results: [Grigori Fursin](https://cKnowledge.org/gfursin)
-* CM4MLOps: [Arjun Suresh](https://github.com/arjunsuresh) and [Anandhu Sooraj](https://github.com/anandhu-eng)
+* [Collective Mind (CM)](cm): [Grigori Fursin](https://cKnowledge.org/gfursin)
+* CM4MLOps (CM automation recipes): [Arjun Suresh](https://github.com/arjunsuresh) and [Anandhu Sooraj](https://github.com/anandhu-eng)
 * CMX (the next generation of CM, CM4MLOps and CM4MLPerf): [Grigori Fursin](https://cKnowledge.org/gfursin)
 
 ### Citing our project

cm/README.md

@@ -9,18 +9,20 @@
 
 ### About
 
-Collective Mind (CM) is a small, modular, cross-platform and decentralized workflow automation framework 
-with a human-friendly interface to make it easier to build, run, benchmark and optimize applications 
-across diverse models, data sets, software and hardware.
+Collective Mind (CM) is a small [Python package](https://pypi.org/project/cmind)
+with a unified CLI and API designed for creating and managing 
+portable and technology-agnostic automations for MLOps, DevOps and ResearchOps.
 
+It is intended to make it easier to build, run, benchmark and optimize applications 
+across diverse models, data sets, software and hardware.
+
 CM is a part of [Collective Knowledge (CK)](https://github.com/mlcommons/ck) - 
-an educational community project to learn how to run emerging workloads 
+an educational community project to learn how to run AI, ML and other emerging workloads 
 in the most efficient and cost-effective way across diverse 
-and continuously changing systems.
+and continuously changing systems using the MLPerf benchmarking methodology.
 
-CM includes a collection of portable, extensible and technology-agnostic automation recipes
-with a common API and CLI (aka CM scripts) to unify and automate different steps 
-required to compose, run, benchmark and optimize complex ML/AI applications 
+CM includes a [collection of portable, extensible and technology-agnostic automation recipes](https://access.cknowledge.org/playground/?action=scripts)
+(aka CM scripts) to unify and automate different steps required to compose, run, benchmark and optimize complex ML/AI applications 
 on any platform with any software and hardware. 
 
 CM scripts extend the concept of `cmake` with simple Python automations, native scripts
@@ -44,9 +46,9 @@ from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors:
   and simple JSON/YAML descriptions instead of inventing new workflow languages;
 * must have the same interface to run all automations natively, in a cloud or inside containers.
 
-### Maintainers
+### Author and maintainer
 
-* [Grigori Fursin](https://cKnowledge.org/gfursin)
+* [Grigori Fursin](https://cKnowledge.org/gfursin) (FlexAI, cTuning)
 
 ### Resources