minor thesis fixes

2 files changed, 6 insertions, 6 deletions

diff --git a/thesis/Justfile b/thesis/Justfile
index 2805cb0..4e71af3 100644
--- a/thesis/Justfile
+++ b/thesis/Justfile
@@ -1,10 +1,10 @@
 default: build
 
 build:
-  cd thesis/; latexmk -bibtex -pdf
+  latexmk -bibtex -pdf
 
 watch:
-  cd thesis/; latexmk -bibtex -pdf -pvc
+  latexmk -bibtex -pdf -pvc
 
 clean:
-  cd thesis/; latexmk -c
+  latexmk -c
diff --git a/thesis/parts/implementation.tex b/thesis/parts/implementation.tex
index 4faa8a8..884f675 100644
--- a/thesis/parts/implementation.tex
+++ b/thesis/parts/implementation.tex
@@ -20,14 +20,14 @@ each trait has its own set of benchmarks, which run different workloads
 benchmarker trait doesn't have Ns
 example benchmarks for hashmap and vec
 
-\code{candelabra::cost::benchmark} generates code which just calls candelabra_benchmarker methods
+\code{candelabra::cost::benchmark} generates code which just calls \code{candelabra\_benchmarker} methods
 Ns are set there, and vary from [...]
 
 fitting done with least squares in \code{candelabra::cost::fit}
 list other methods tried
 simple, which helps 'smooth out' noisy benchmark results
 
-profiler type in \code{primrose_library::profiler}}
+profiler type in \code{primrose\_library::profiler}
 wraps an 'inner' implementation and implements whatever operations it does, keeping track of number of calls
 on drop, creates new file in folder specified by env variable
 
@@ -37,7 +37,7 @@ each drop generates a file, so we get details of every individual collection all
 \todo{immediately aggregate these into summary statistics, for speed}
 \todo{mention benchmark repetition}
 
-estimate a cost for each candidate: op(avg_n) * op_times for each op
+estimate a cost for each candidate: op(avg\_n) * op\_times for each op
 pick the smallest one
 \todo{update for nsplit stuff}
 \todo{mention difficulties with lazy vecs}