Fix titles

Author: AntoinePrv

src/components/fundable/descriptions/Float16SupportInXsimd.md

@@ -1,6 +1,6 @@
-# FP16 Support in xsimd
+#### Overview
 
-xsimd is a C++ header-only library that abstracts SIMD (vectorization) intrinsics behind a single, generic API.
+Xsimd is a C++ header-only library that abstracts SIMD (vectorization) intrinsics behind a single, generic API.
 The same code — `xsimd::batch<float>` — compiles to optimal machine code on x86 SSE/AVX, ARM NEON SVE, RISC-V, and WebAssembly, with no runtime overhead.
 When an intrinsic is missing on a given target, xsimd falls back gracefully rather than failing or leaving the developer to write platform-specific branches.
 This is why projects like Mozilla Firefox, Apache Arrow, Meta Velox, KDE Krita, and Pythran have adopted it as their vectorization layer.
@@ -13,7 +13,7 @@ xsimd currently has no FP16 support, forcing its users to drop out of the generi
 
 We propose to add vectorized FP16 support to xsimd — native FP16 operations where hardware supports them, and correct fallbacks elsewhere.
 
-## Why FP16 Matters
+#### Why FP16 Matters
 
 **Memory bandwidth is a bottleneck.** Modern CPUs and GPUs are not compute-bound — they are memory-bandwidth-bound.
 FP16 cuts data size in half versus FP32.
@@ -37,7 +37,7 @@ FP16 conversion and arithmetic are now widely available across all major SIMD fa
   This affects NEON operations on modern smartphones and all Apple silicon M-chips.
   Coverage is extended server side with both SVE and SVE2 supporting FP16.
 
-## Proposed Work
+#### Proposed Work
 
 This proposal covers foundational FP16 support: native FP16 operations on platforms that provide hardware acceleration, and correct, efficient fallbacks everywhere else.
 
@@ -46,7 +46,7 @@ Concretely, this means:
 - Support for converting from and to `batch<float>`, mapping to the optimal hardware instruction where available, and a correct SIMD algorithm elsewhere.
 - Native FP16 arithmetic operations — add, multiply, FMA, min, max, and comparison — on backends that provide hardware support, with FP32-based fallbacks on those that do not
 
-## Impact
+#### Impact
 
 Funding this development will directly open xsimd to the rapidly growing landscape of LLM and machine
 learning workflows: local inference engines, model weight processing, and embedding pipelines.