Remove API reference section completely

- Removed API reference content from index page generation
- Index page now contains only README and reproducibility content
- Clean documentation structure with direct category navigation
- Documentation builds successfully with minimal warnings

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

Author: ChrisRackauckas

docs/make.jl

@@ -24,49 +24,6 @@ open(joinpath(@__DIR__, "src", "index.md"), "w") do io
         println(io, line)
     end
 
-    # Add API reference content
-    println(io, "")
-    println(io, "## API Reference")
-    println(io, "")
-    println(io, "```@docs")
-    println(io, "FiniteDiff")
-    println(io, "```")
-    println(io, "")
-    println(io, "FiniteDiff.jl provides fast, non-allocating finite difference calculations with support for sparsity patterns and various array types. The API is organized into several categories:")
-    println(io, "")
-    println(io, "### Function Categories")
-    println(io, "")
-    println(io, "- **[Derivatives](@ref derivatives)**: Single and multi-point derivatives of scalar functions")
-    println(io, "- **[Gradients](@ref gradients)**: Gradients of scalar-valued functions with respect to vector inputs")
-    println(io, "- **[Jacobians](@ref jacobians)**: Jacobian matrices of vector-valued functions, including sparse Jacobian support")
-    println(io, "- **[Hessians](@ref hessians)**: Hessian matrices of scalar-valued functions")
-    println(io, "- **[Jacobian-Vector Products](@ref jvp)**: Efficient computation of directional derivatives without forming full Jacobians")
-    println(io, "- **[Utilities](@ref utilities)**: Internal utilities and helper functions")
-    println(io, "")
-    println(io, "### Quick Start")
-    println(io, "")
-    println(io, "All functions follow a consistent API pattern:")
-    println(io, "")
-    println(io, "- **Cache-less versions**: `finite_difference_*` - convenient but allocate temporary arrays")
-    println(io, "- **In-place versions**: `finite_difference_*!` - efficient, non-allocating when used with caches")
-    println(io, "- **Cache constructors**: `*Cache` - pre-allocate work arrays for repeated computations")
-    println(io, "")
-    println(io, "### Method Selection")
-    println(io, "")
-    println(io, "Choose your finite difference method based on accuracy and performance needs:")
-    println(io, "")
-    println(io, "- **Forward differences**: Fast, `O(h)` accuracy, requires `O(n)` function evaluations")
-    println(io, "- **Central differences**: More accurate `O(h²)`, requires `O(2n)` function evaluations")
-    println(io, "- **Complex step**: Machine precision accuracy, `O(n)` evaluations, requires complex-analytic functions")
-    println(io, "")
-    println(io, "### Performance Tips")
-    println(io, "")
-    println(io, "1. **Use caches** for repeated computations to avoid allocations")
-    println(io, "2. **Consider sparsity** for large Jacobians with known sparsity patterns")
-    println(io, "3. **Choose appropriate methods** based on your accuracy requirements")
-    println(io, "4. **Leverage JVPs** when you only need directional derivatives")
-    println(io, "")
-    
     for line in eachline(joinpath(@__DIR__, "src", "reproducibility.md"))
         println(io, line)
     end