diff --git a/README.md b/README.md
index 175feee..d26f9de 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,16 @@
-# lecture-dps
+# Advanced Dynamic Programming
 
-Dynamic Programming Squared
+This website presents a set of lectures on advanced topics in dynamic programming.
+
+## Jupyter notebooks
+
+Jupyter notebook versions of each lecture are available for download
+via the website.
+
+## Contributions
+
+To comment on the lectures please add to or open an issue in the issue tracker (see above).
+
+We welcome pull requests!  
+
+Please read the [QuantEcon style guide](https://manual.quantecon.org/intro.html) first, so that you can match our style.
diff --git a/environment.yml b/environment.yml
index 8571e57..b26998d 100644
--- a/environment.yml
+++ b/environment.yml
@@ -3,7 +3,7 @@ channels:
   - default
 dependencies:
   - python=3.11
-  - anaconda=2023.09
+  - anaconda=2024.02
   - pip
   - pip:
     - jupyter-book==0.15.1
@@ -18,5 +18,3 @@ dependencies:
     - sphinx-togglebutton==0.3.2
     - arviz==0.13.0
     - kaleido
-    # Docker Requirements
-    - pytz
diff --git a/lectures/_static/lecture_specific/opt_tax_recur/recursive_allocation.py b/lectures/_static/lecture_specific/opt_tax_recur/recursive_allocation.py
index 2ce9387..c197f11 100644
--- a/lectures/_static/lecture_specific/opt_tax_recur/recursive_allocation.py
+++ b/lectures/_static/lecture_specific/opt_tax_recur/recursive_allocation.py
@@ -131,8 +131,8 @@ def simulate(self, b0, s0, T, sHist=None):
         # Time 1 onward
         for t in range(1, T):
             s, x = sHist[t], xHist[t-1]
-            cHist[t] = interp(self.x_grid, self.c1[:, s], x)
-            nHist[t] = interp(self.x_grid, self.n1[:, s], x)
+            cHist[t] = np.interp(x, self.x_grid, self.c1[:, s])
+            nHist[t] = np.interp(x, self.x_grid, self.n1[:, s])
 
             τHist[t] = self.τ(cHist[t], nHist[t])
 
@@ -140,8 +140,8 @@ def simulate(self, b0, s0, T, sHist=None):
 
             c, n = np.empty((2, self.S))
             for sprime in range(self.S):
-                c[sprime] = interp(x_grid, self.c1[:, sprime], x)
-                n[sprime] = interp(x_grid, self.n1[:, sprime], x)
+                c[sprime] = np.interp(x, x_grid, self.c1[:, sprime])
+                n[sprime] = np.interp(x, x_grid, self.n1[:, sprime])
             Euc = π[sHist[t-1]] @ Uc(c, 1-n)
             RHist[t-1] = Uc(cHist[t-1], 1-nHist[t-1]) / (self.pref.β * Euc)
 
@@ -150,7 +150,7 @@ def simulate(self, b0, s0, T, sHist=None):
             
             if t < T-1:
                 sprime = sHist[t+1]
-                xHist[t] = interp(self.x_grid, self.xprime1[:, s, sprime], x)
+                xHist[t] = np.interp(x, self.x_grid, self.xprime1[:, s, sprime])
 
         return [cHist, nHist, Bhist, τHist, gHist, yHist, xHist, RHist]
 
@@ -209,7 +209,7 @@ def obj_V(z_sub, x, s, V, pref, π, g, x_grid, b0=None):
     # prepare Vprime vector
     Vprime = np.empty(S)
     for sprime in range(S):
-        Vprime[sprime] = interp(x_grid, V[:, sprime], xprime[sprime])
+        Vprime[sprime] = np.interp(xprime[sprime], x_grid, V[:, sprime])
 
     # compute the objective value
     obj = U(c, l) + β * π[s] @ Vprime
diff --git a/lectures/opt_tax_recur.md b/lectures/opt_tax_recur.md
index c0ab6de..34f7fe7 100644
--- a/lectures/opt_tax_recur.md
+++ b/lectures/opt_tax_recur.md
@@ -31,7 +31,6 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 tags: [hide-output]
 ---
 !pip install --upgrade quantecon
-!pip install interpolation
 ```
 
 ## Overview
@@ -73,7 +72,6 @@ import matplotlib.pyplot as plt
 from scipy.optimize import root
 from quantecon import MarkovChain
 from quantecon.optimize.nelder_mead import nelder_mead
-from interpolation import interp
 from numba import njit, prange, float64
 from numba.experimental import jitclass
 ```
diff --git a/lectures/status.md b/lectures/status.md
index 24b489c..3ada25f 100644
--- a/lectures/status.md
+++ b/lectures/status.md
@@ -18,5 +18,17 @@ This table contains the latest execution statistics.
 
 (status:machine-details)=
 
-These lectures are built on `linux` instances through `github actions` so are
-executed using the following [hardware specifications](https://docs.github.com/en/actions/reference/specifications-for-github-hosted-runners#supported-runners-and-hardware-resources)
\ No newline at end of file
+These lectures are built on `linux` instances through `github actions`. 
+
+These lectures are using the following python version
+
+```{code-cell} ipython
+!python --version
+```
+
+and the following package versions
+
+```{code-cell} ipython
+:tags: [hide-output]
+!conda list
+```
\ No newline at end of file