diff --git a/plans/python-dsl.md b/plans/python-dsl.md
index 4a8aecd..9205a50 100644
--- a/plans/python-dsl.md
+++ b/plans/python-dsl.md
@@ -92,6 +92,144 @@ Different parts of the system need different guarantees:
 - Query planning algorithms for data pipelines
 - Time-travel and what-if analysis capabilities
 
+### Python as the Pattern Language
+
+A crucial realization: we don't need a new DSL for partition patterns - Python itself is the DSL! Python's native string handling capabilities provide everything needed:
+
+#### Built-in Pattern Matching (Python 3.10+)
+```python
+match partition_ref:
+    case f"raw/{source}/{date}" if source in ["api", "ftp"]:
+        return {"source": source, "date": date}
+    case f"model/{name}/v{version}" if version.isdigit():
+        return {"name": name, "version": int(version)}
+```
+
+#### F-String Templates as Bidirectional Patterns
+```python
+from parse import parse  # Inverse of format()
+
+# Define pattern
+pattern = "s3://bucket/{table}/date={date}/hour={hour:02d}"
+
+# Extract (pattern → values)
+result = parse(pattern, "s3://bucket/events/date=2024-01-01/hour=13")
+# {'table': 'events', 'date': '2024-01-01', 'hour': 13}
+
+# Generate (values → pattern)
+pattern.format(table='events', date='2024-01-01', hour=13)
+```
+
+#### Native Python Pattern Types
+- **Glob patterns**: `raw/*/2024-01-*` using `fnmatch` or `pathlib`
+- **Regular expressions**: Named groups for complex patterns
+- **Type annotations**: `Annotated[RawPartition, "all sources for date"]`
+- **String templates**: Standard library `string.Template`
+
+#### Why This Is Beautiful
+1. **No new syntax** - Developers already know these patterns
+2. **Full IDE support** - Autocomplete, refactoring, type checking
+3. **Composable** - Mix glob, regex, f-strings as needed
+4. **Testable** - Just Python strings and functions
+5. **Bidirectional** - Same pattern for matching and generating
+
+The key insight: Python's string formatting is already a pattern language. We don't need to build a DSL - we need to embrace Python as the DSL.
+
+### Rust as an Alternative DSL
+
+While Python offers maximum expressiveness, Rust presents an intriguing alternative that could provide compile-time guarantees without sacrificing elegance:
+
+#### Procedural Macros for Job Definition
+```rust
+#[job]
+fn process_daily(
+    date: Date,
+    #[partition("raw/{source}/{date}")] source: Partition<RawData>,
+) -> Partition<ProcessedData, "processed/{date}"> {
+    let raw_data = source.load()?;
+    transform(raw_data, date).save()
+}
+
+#[job(resources = "memory=4G,cpu=2")]
+fn aggregate_weekly(
+    week: Week,
+    #[partition("processed/[date:date+7]")] daily: PartitionSet<ProcessedData>,
+) -> Partition<WeeklyAggregate, "aggregated/week={week}"> {
+    daily.load_all()?.aggregate().save()
+}
+```
+
+#### Type-Safe Pattern Matching
+```rust
+// Compile-time pattern validation using const generics
+pub struct Partition<T, const PATTERN: &'static str> {
+    _phantom: PhantomData<T>,
+}
+
+// Pattern validated at compile time!
+impl<T, const PATTERN: &'static str> Partition<T, PATTERN> {
+    const VALIDATE: () = {
+        assert!(is_valid_pattern(PATTERN));
+    };
+}
+```
+
+#### Runtime Dependency Resolution with Compile-Time Structure
+```rust
+#[job(
+    // Pattern template at compile time
+    inputs = "raw/{source}/{date}",
+    // But resolution happens at runtime
+)]
+impl ProcessDaily {
+    fn resolve_dependencies(&self, args: &JobArgs) -> Vec<String> {
+        let date = &args.date;
+        let mut deps = vec![];
+        
+        // Dynamic resolution based on runtime state
+        for source in self.catalog.available_sources(date)? {
+            deps.push(format!("raw/{}/{}", source, date));
+        }
+        
+        // Conditional dependencies
+        if date.weekday() == Weekday::Monday {
+            deps.push(format!("raw/{}/{}", "archive", date.previous_friday()));
+        }
+        
+        deps
+    }
+}
+```
+
+#### Why Rust DSL is Compelling
+1. **Zero-cost abstractions** - Macros expand to optimal code with no runtime overhead
+2. **Compile-time validation** - Pattern syntax checked at compile time
+3. **Type safety** - Full type checking across job boundaries
+4. **Single language** - If engine is Rust, no polyglot complexity
+5. **IDE support** - Full IntelliSense, refactoring, and go-to-definition
+
+#### The Hybrid Best: Compile-Time Structure, Runtime Flexibility
+```rust
+// Macro provides syntax sugar and compile-time checks
+#[derive(DataBuildJob)]
+struct ComplexJob {
+    const INPUT_TEMPLATE: &'static str = "raw/{source}/{date}";
+    
+    // But actual resolution is dynamic
+    fn resolve_config(&self, args: &JobArgs, ctx: &Context) -> Result<JobConfig> {
+        // Full runtime flexibility for dependency calculation
+        let sources = ctx.catalog.list_sources(&args.date)?;
+        let inputs = sources.iter()
+            .map(|s| format!("raw/{}/{}", s, args.date))
+            .collect();
+        
+        Ok(JobConfig { inputs, outputs: vec![...] })
+    }
+}
+```
+
+This Rust approach offers an interesting middle ground: stronger guarantees than Python while maintaining expressiveness, and seamless integration with a Rust-based execution engine.
+
 ### Philosophical Questions
 - Is orchestration a fundamental need or an implementation detail?
 - Can we achieve both expressiveness and correctness?