fix urls

developers/inference/abstractmcmc-turing/index.qmd

@@ -262,7 +262,7 @@ $$
 
 with $\theta_{\text{prop}}$ a sample from the proposal and $x_{\text{obs}}$ the observed data.
 
-This begs the question: how can these functions access model information during sampling? Recall that the model is stored as an instance `m` of `Model`. One of the attributes of `m` is the model evaluation function `m.f`, which is built by compiling the `@model` macro. Executing `f` runs the tilde statements of the model in order, and adds model information to the sampler (the instance of `Sampler` that stores information about the ongoing sampling process) at each step (see [here](https://turinglang.org/dev/docs/for-developers/compiler) for more information about how the `@model` macro is compiled). The DynamicPPL functions `assume` and `observe` determine what kind of information to add to the sampler for every tilde statement.
+This begs the question: how can these functions access model information during sampling? Recall that the model is stored as an instance `m` of `Model`. One of the attributes of `m` is the model evaluation function `m.f`, which is built by compiling the `@model` macro. Executing `f` runs the tilde statements of the model in order, and adds model information to the sampler (the instance of `Sampler` that stores information about the ongoing sampling process) at each step (see [here]({{<meta dev-model-manual>}}) for more information about how the `@model` macro is compiled). The DynamicPPL functions `assume` and `observe` determine what kind of information to add to the sampler for every tilde statement.
 
 Consider an instance `m` of `Model` and a sampler `spl`, with associated `VarInfo` `vi = spl.state.vi`. At some point during the sampling process, an AbstractMCMC function such as `step!` calls  `m(vi, ...)`, which calls the model evaluation function `m.f(vi, ...)`.
 

faq/index.qmd

@@ -55,7 +55,7 @@ Turing.jl fully supports sampling multiple chains in parallel:
 - **Multithreaded sampling**: Use `MCMCThreads()` to run one chain per thread
 - **Distributed sampling**: Use `MCMCDistributed()` for distributed computing
 
-See the [Core Functionality guide]({{< meta core-functionality >}}/#sampling-multiple-chains) for examples.
+See the [Core Functionality guide]({{<meta core-functionality>}}#sampling-multiple-chains) for examples.
 
 ### 2. Threading Within Models
 Using threads inside your model (e.g., `Threads.@threads`) requires more care:

tutorials/bayesian-logistic-regression/index.qmd

@@ -152,7 +152,7 @@ chain
 ::: {.callout-warning collapse="true"}
 ## Sampling With Multiple Threads
 The `sample()` call above assumes that you have at least `nchains` threads available in your Julia instance. If you do not, the multiple chains 
-will run sequentially, and you may notice a warning. For more information, see [the Turing documentation on sampling multiple chains.](https://turinglang.org/dev/docs/using-turing/guide/#sampling-multiple-chains)
+will run sequentially, and you may notice a warning. For more information, see [the Turing documentation on sampling multiple chains.]({{<meta core-functionality>}}#sampling-multiple-chains)
 :::
 
 ```{julia}

tutorials/bayesian-poisson-regression/index.qmd

@@ -172,7 +172,7 @@ chain
 ::: {.callout-warning collapse="true"}
 ## Sampling With Multiple Threads
 The `sample()` call above assumes that you have at least `nchains` threads available in your Julia instance. If you do not, the multiple chains 
-will run sequentially, and you may notice a warning. For more information, see [the Turing documentation on sampling multiple chains.](https://turinglang.org/dev/docs/using-turing/guide/#sampling-multiple-chains)
+will run sequentially, and you may notice a warning. For more information, see [the Turing documentation on sampling multiple chains.]({{<meta core-functionality>}}#sampling-multiple-chains)
 :::
 
 # Viewing the Diagnostics