update docs

README.md

@@ -5,9 +5,9 @@
 [<img alt="docs.rs" src="https://img.shields.io/badge/docs.rs-gen_rs-66c2a5?style=for-the-badge&labelColor=555555&logo=docs.rs" height="20">](https://docs.rs/gen-rs)
 [<img alt="status" src="https://img.shields.io/github/actions/workflow/status/agarret7/gen-rs/test.yml?branch=main&style=for-the-badge" height="20">](https://github.com/agarret7/gen-rs/actions?query=branch%3Amain)
 
-gen-rs is an experimental, research crate for probabilistic programming in Rust. gen-rs supports many features of other Rust-native probabilistic computing frameworks such as [ferric](https://github.com/ferric-ai/ferric), and is written at a lower-level than languages such as [Gen.jl](https://github.com/probcomp/Gen.jl).
+gen-rs is an experimental, research crate for probabilistic programming in Rust. gen-rs supports many features of other Rust-native probabilistic computing frameworks such as [ferric](https://github.com/ferric-ai/ferric), and is written at a lower-level than languages such as [Gen.jl](https://github.com/probcomp/Gen.jl). It was also strongly inspired by [GenTL](https://github.com/OpenGen/GenTL/tree/main), but with Rust-native constructs.
 
-This library implements the Generative Function Interface [[GFI]](https://github.com/agarret7/gen-rs/blob/main/gen-rs/src/gfi.rs) as specified in the [Gen.jl whitepaper](https://dl.acm.org/doi/10.1145/3314221.3314642) and [Marco Cusumano-Towner's thesis](https://www.mct.dev/assets/mct-thesis.pdf)), and a basic, yet complete set of inference procedures.
+This library implements the Generative Function Interface [[GFI]](https://github.com/agarret7/gen-rs/blob/main/gen-rs/src/gfi.rs) as specified in the [Gen.jl whitepaper](https://dl.acm.org/doi/10.1145/3314221.3314642) and [Marco Cusumano-Towner's thesis](https://www.mct.dev/assets/mct-thesis.pdf)) and a basic, yet complete set of inference procedures.
 
 
 ## Modeling Features
@@ -31,8 +31,6 @@ cargo test && python visualization/visualizer.py
 
 ## Disclaimer
 
-Unlike most modern ML systems, probabilistic programming doesn't require a differentiable likelihood; a fast (possibly parallelized) CPU-bound iterator is often sufficient for inference. This aligns well with Rust's principle of "fearless concurrency". However, most embodied (read: practical) modeling efforts still require extensive parameter tuning and Langevin or Hamiltonian Monte Carlo inference moves, to effectively leverage numerical gradients of the local energy landscape in top-down or supervised data processing.
+Unlike most modern ML systems, probabilistic programming doesn't require a differentiable likelihood; a fast (possibly parallelized) CPU-bound iterator is often sufficient for inference. This aligns well with Rust's principle of "fearless concurrency".
 
-Despite Rust being a delightful experience to program in, AD support and GPU acceleration is still somewhat shaky (given the lack of first-class Rust-native tensor libraries), limiting these applications.
-
-This project was heavily inspired by [GenTL](https://github.com/OpenGen/GenTL/tree/main) and several more fully-featured projects in the OpenGen ecosystem such as [Gen.jl](https://github.com/probcomp/Gen.jl/tree/master), [GenParticleFilters](https://github.com/probcomp/GenParticleFilters.jl), [SMCP3](https://github.com/probcomp/aistats2023-smcp3), [Bayes3D](https://github.com/probcomp/bayes3d/tree/main), and GenJax.
+AD support is not currently planned for this library. If you'd like native AD in a probabilistic language you might want to check out some more fully-featured projects like [GenTorch](https://github.com/OpenGen/GenTorch/), [Gen.jl](https://github.com/probcomp/Gen.jl/tree/master), [GenParticleFilters](https://github.com/probcomp/GenParticleFilters.jl), [SMCP3](https://github.com/probcomp/aistats2023-smcp3), [Bayes3D](https://github.com/probcomp/bayes3d/tree/main), or GenJax.