Refresh readme

Author: shikokuchuo

README.Rmd

@@ -51,9 +51,9 @@ install.packages("mirai")
 
 ### Quick Start
 
-→ `mirai()`: Evaluate an R expression asynchronously in a parallel process.
+- `mirai()`: Evaluate an R expression asynchronously in a parallel process.
 
-→ `daemons()`: Set and launch persistent background processes, local or remote, on which to run mirai tasks.
+- `daemons()`: Set and launch persistent background processes, local or remote, on which to run mirai tasks.
 
 ```{r}
 #| label: exec
@@ -79,6 +79,20 @@ mp[.flat]
 daemons(0)
 ```
 
+### Key Features
+
+1. Distributed Execution: Run tasks across networks and clusters using various deployment methods (SSH, HPC clusters using Slurm, SGE, Torque, PBS, LSF, etc.)
+
+1. Compute Profiles: Manage different sets of daemons independently, allowing tasks with different requirements to be executed on appropriate resources.
+
+1. Promises Integration: An event-driven implementation performs actions on returned values as soon as tasks complete, with minimal latency.
+
+1. Serialization Support: Native serialization support for reference objects such as Arrow Tables, Polars DataFrames or torch tensors.
+
+1. Error Handling: Robust error handling and reporting, with full stack traces for debugging.
+
+1. RNG Management: L'Ecuyer-CMRG RNG streams for reproducible parallel random number generation.
+
 ### Design Concepts
 
 mirai is designed from the ground up to provide a production-grade experience.
@@ -103,20 +117,6 @@ mirai is designed from the ground up to provide a production-grade experience.
 - Capacity for millions of tasks over thousands of connections
 - Proven track record for heavy-duty workloads in the life sciences industry
 
-### Key Features
-
-→ Distributed Execution: Run tasks across networks and clusters using various deployment methods (SSH, HPC clusters using Slurm, SGE, Torque, PBS, LSF, etc.)
-
-→ Compute Profiles: Manage different sets of daemons independently, allowing tasks with different requirements to be executed on appropriate resources.
-
-→ Promises Integration: An event-driven implementation performs actions on returned values as soon as tasks complete, ensuring minimal latency.
-
-→ Serialization Support: Native serialization support for reference objects such as Arrow Tables, Polars DataFrames or torch tensors.
-
-→ Error Handling: Robust error handling and reporting, with full stack traces for debugging.
-
-→ RNG Management: L'Ecuyer-CMRG RNG streams for reproducible parallel random number generation.
-
 ### Powering the Ecosystem
 
 mirai serves as a foundation for asynchronous and parallel computing in the R ecosystem:

README.md

@@ -39,11 +39,11 @@ install.packages("mirai")
 
 ### Quick Start
 
-→ `mirai()`: Evaluate an R expression asynchronously in a parallel
-process.
+- `mirai()`: Evaluate an R expression asynchronously in a parallel
+  process.
 
-→ `daemons()`: Set and launch persistent background processes, local or
-remote, on which to run mirai tasks.
+- `daemons()`: Set and launch persistent background processes, local or
+  remote, on which to run mirai tasks.
 
 ``` r
 library(mirai)
@@ -72,6 +72,29 @@ mp[.flat]
 daemons(0)
 ```
 
+### Key Features
+
+1.  Distributed Execution: Run tasks across networks and clusters using
+    various deployment methods (SSH, HPC clusters using Slurm, SGE,
+    Torque, PBS, LSF, etc.)
+
+2.  Compute Profiles: Manage different sets of daemons independently,
+    allowing tasks with different requirements to be executed on
+    appropriate resources.
+
+3.  Promises Integration: An event-driven implementation performs
+    actions on returned values as soon as tasks complete, with minimal
+    latency.
+
+4.  Serialization Support: Native serialization support for reference
+    objects such as Arrow Tables, Polars DataFrames or torch tensors.
+
+5.  Error Handling: Robust error handling and reporting, with full stack
+    traces for debugging.
+
+6.  RNG Management: L’Ecuyer-CMRG RNG streams for reproducible parallel
+    random number generation.
+
 ### Design Concepts
 
 mirai is designed from the ground up to provide a production-grade
@@ -103,28 +126,6 @@ experience.
 - Proven track record for heavy-duty workloads in the life sciences
   industry
 
-### Key Features
-
-→ Distributed Execution: Run tasks across networks and clusters using
-various deployment methods (SSH, HPC clusters using Slurm, SGE, Torque,
-PBS, LSF, etc.)
-
-→ Compute Profiles: Manage different sets of daemons independently,
-allowing tasks with different requirements to be executed on appropriate
-resources.
-
-→ Promises Integration: An event-driven implementation performs actions
-on returned values as soon as tasks complete, ensuring minimal latency.
-
-→ Serialization Support: Native serialization support for reference
-objects such as Arrow Tables, Polars DataFrames or torch tensors.
-
-→ Error Handling: Robust error handling and reporting, with full stack
-traces for debugging.
-
-→ RNG Management: L’Ecuyer-CMRG RNG streams for reproducible parallel
-random number generation.
-
 ### Powering the Ecosystem
 
 mirai serves as a foundation for asynchronous and parallel computing in