diff --git a/.envrc b/.envrc
new file mode 100644
index 0000000..3550a30
--- /dev/null
+++ b/.envrc
@@ -0,0 +1 @@
+use flake
diff --git a/.gitignore b/.gitignore
deleted file mode 100644
index 0c19cf9..0000000
--- a/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-benchmarks/**
diff --git a/Lab1/.gitignore b/Lab1/.gitignore
new file mode 100644
index 0000000..55576fd
--- /dev/null
+++ b/Lab1/.gitignore
@@ -0,0 +1,3 @@
+benchmarks/**
+dist/**
+results.txt
diff --git a/benchmark.py b/Lab1/benchmark.py
similarity index 62%
rename from benchmark.py
rename to Lab1/benchmark.py
index 4f6b9a7..7a5c8bb 100644
--- a/benchmark.py
+++ b/Lab1/benchmark.py
@@ -7,6 +7,8 @@ def run_benchmark(program_name, array):
     start = time()
     process = subprocess.Popen(program_name, stdin=subprocess.PIPE)
     process.communicate((f"{len(array)}\n" + "\n".join(map(str, array))).encode())
+    if process.returncode == 1:
+        raise RuntimeError("Error while benchmarking")
     end = time()
     return end - start
 
@@ -25,23 +27,38 @@ def generate_sequence(sequence_type, array_size):
         raise ValueError("Invalid sequence type")
 
 def plot(program_name, sequences, array_size):
+    sequence_names = {
+        "random": "Losowy",
+        "sorted": "Posortowany",
+        "reversed": "Odwrócony",
+        "constant": "Stały",
+        "v_shaped": "V-kształtny",
+    }
     plt.figure(figsize=(10, 6))
     for sequence_type, timings in sequences.items():
-        plt.plot(array_size, timings, label=sequence_type)
-    plt.xlabel("")
+        plt.plot(array_size, timings, label=sequence_names[sequence_type])
+    plt.xlabel("Rozmiar tablicy")
     plt.ylabel("Czas (ms)")
-    plt.title(f"{program_name}")
+    plt.title(program_name.capitalize())
     plt.legend()
-    plt.savefig(f"benchmarks/{program_name}.png")
-    # plt.show()
+    plt.savefig(f"./benchmarks/{program_name}.png")
 
 if __name__ == "__main__":
-    program_names = ["./insertion", "./selection"]
+    # program_names = ["insertion", "selection", "heapsort", "mergesort"]
+    program_names = ["heapsort"]
     sequence_types = ["random", "sorted", "reversed", "constant", "v_shaped"]
     array_sizes = [i for i in range(1000, 15001, 1000)]
+    tests = 10
 
     with open("results.txt", "w") as f:
         for program_name in program_names:
+            if program_name == "heapsort" or program_name == "mergesort":
+                array_sizes = [i for i in range(15000, 40001, 1000)]
+                tests  = 20
+            else:
+                array_sizes = [i for i in range(1000, 15001, 1000)]
+                tests = 10
+
             sequenceTiming = {
                 "random": [],
                 "sorted": [],
@@ -54,16 +71,18 @@ if __name__ == "__main__":
                 print(f" - running {sequence_type}")
                 for array_size in array_sizes:
                     sequence = generate_sequence(sequence_type, array_size)
-                    print(f"   - running {sequence_type} with size {array_size}")
                     durations = []
-                    for i in range(10):
-                        duration = run_benchmark(program_name, sequence)
+                    for i in range(tests):
+                        print(f"     - running {sequence_type} with size {array_size} {i+1}/{tests}", end="\r")
+                        duration = run_benchmark(f"./dist/{program_name}", sequence)
                         duration *= 1000
                         durations.append(duration)
-                        sleep(0.1)
+                        if program_name == "heapsort" or program_name == "mergesort":
+                            sleep(0.1)
+                    print()
                     duration = sum(durations) / len(durations)
-                    f.write(f"{program_name} {sequence_type} {array_size} {duration}\n")
                     sequenceTiming[sequence_type].append(duration)
-                    sleep(0.5)
+                    if program_name == "heapsort" or program_name == "mergesort":
+                        sleep(0.1)
             plot(program_name, sequenceTiming, array_sizes)
     print("Benchmark finished")
diff --git a/heapsort.c b/Lab1/heapsort.c
similarity index 100%
rename from heapsort.c
rename to Lab1/heapsort.c
diff --git a/insertion.c b/Lab1/insertion.c
similarity index 57%
rename from insertion.c
rename to Lab1/insertion.c
index 95c834a..c4bb052 100644
--- a/insertion.c
+++ b/Lab1/insertion.c
@@ -4,13 +4,23 @@
 // Capture the pipe of array contents, first number is the size
 int main() {
   int s;
-  scanf("%d", &s);
-  int *array = malloc(sizeof(int) * s);
-  for (int i = 0; i < s; i++) {
-    scanf("%d", &array[i]);
+  int res = scanf("%d", &s);
+  if (res != 1) {
+    fprintf(stderr, "Error reading size\n");
+    return 1;
   }
 
-  for (int j = 1; j < s; j++) {
+  int *array = malloc(sizeof(int) * s);
+  for (int i = 0; i < s; i++) {
+    res = scanf("%d", &array[i]);
+    if (res != 1) {
+      fprintf(stderr, "Error reading array element\n");
+      free(array);
+      return 1;
+    }
+  }
+
+  for (int j = 0; j < s; j++) {
     int key = array[j];
     int i = j - 1;
     while (i >= 0 && array[i] > key) {
diff --git a/Lab1/mergesort.c b/Lab1/mergesort.c
new file mode 100644
index 0000000..303785d
--- /dev/null
+++ b/Lab1/mergesort.c
@@ -0,0 +1,53 @@
+#include <stdio.h>
+#include <stdlib.h>
+
+void MergeSort(int A[], int l, int r, int B[]) {
+  int m = (l + r) / 2;
+  if (m - l > 0) {
+    MergeSort(A, l, m, B);
+  }
+
+  if (r - m > 0) {
+    MergeSort(A, m + 1, r, B);
+  }
+
+  int i = l;
+  int j = m + 1;
+  for (int k = l; k <= r; k++) {
+    if ((i <= m && j > r) || ((i <= m && j <= r) && A[i] <= A[j])) {
+      B[k] = A[i];
+      i++;
+    } else {
+      B[k] = A[j];
+      j++;
+    }
+  }
+
+  for (int k = l; k <= r; k++) {
+    A[k] = B[k];
+  }
+}
+
+int main() {
+  int size;
+  int res = scanf("%d", &size);
+  if (res != 1) {
+    fprintf(stderr, "Error reading size\n");
+    return 1;
+  }
+
+  int *arrayA = malloc(sizeof(int) * size);
+  for (int i = 0; i < size; i++) {
+    res = scanf("%d", &arrayA[i]);
+    if (res != 1) {
+      fprintf(stderr, "Error reading array element\n");
+      free(arrayA);
+      return 1;
+    }
+  }
+
+  int *arrayB = malloc(sizeof(int) * size);
+  MergeSort(arrayA, 0, size - 1, arrayB);
+
+  return 0;
+}
diff --git a/Lab1/quicksort.c b/Lab1/quicksort.c
new file mode 100644
index 0000000..f2e3687
--- /dev/null
+++ b/Lab1/quicksort.c
@@ -0,0 +1,80 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <time.h>
+
+enum PivotStrategy { RIGHTMOST = 0, MIDDLE = 1, RANDOM = 2 };
+
+void swap(int *a, int *b) {
+  int temp = *a;
+  *a = *b;
+  *b = temp;
+}
+
+int partition(int A[], int left, int right, int pivot_strategy, int length) {
+  int pivot_index;
+  if (pivot_strategy == RIGHTMOST) {
+    pivot_index = right;
+  } else if (pivot_strategy == MIDDLE) {
+    pivot_index = (left + right) / 2;
+  } else if (pivot_strategy == RANDOM) {
+    pivot_index = (rand() % (length + 1));
+  }
+
+  int pivot = A[pivot_index];
+  swap(&A[pivot_index], &A[right]);
+  int i = left - 1;
+
+  for (int j = left; j < right; j++) {
+    if (A[j] <= pivot) {
+      i++;
+      swap(&A[i], &A[j]);
+    }
+  }
+  swap(&A[i + 1], &A[right]);
+  return i + 1;
+}
+
+void quicksort(int A[], int p, int r, int pivot_strategy, int length) {
+  if (p < r) {
+    int q = partition(A, p, r, pivot_strategy, length);
+    quicksort(A, p, q - 1, pivot_strategy, length);
+    quicksort(A, q + 1, r, pivot_strategy, length);
+  }
+}
+
+int main() {
+  int size, pivot_strategy;
+
+  if (scanf("%d", &size) != 1) {
+    fprintf(stderr, "Error reading size\n");
+    return 1;
+  }
+
+  if (scanf("%d", &pivot_strategy) != 1) {
+    fprintf(stderr, "Error reading pivot strategy\n");
+    return 1;
+  }
+  // Init the random number generator when needed
+  if (pivot_strategy == RANDOM) {
+    srand(size);
+  }
+
+  int *array = malloc(sizeof(int) * size);
+  if (array == NULL) {
+    fprintf(stderr, "Memory allocation failed\n");
+    return 1;
+  }
+
+  for (int i = 0; i < size; i++) {
+    if (scanf("%d", &array[i]) != 1) {
+      fprintf(stderr, "Error reading array element\n");
+      free(array);
+      return 1;
+    }
+  }
+
+  quicksort(array, 0, size - 1, pivot_strategy, size);
+
+  free(array);
+  return 0;
+}
diff --git a/Lab1/quicksort_bench.py b/Lab1/quicksort_bench.py
new file mode 100644
index 0000000..7bdc945
--- /dev/null
+++ b/Lab1/quicksort_bench.py
@@ -0,0 +1,66 @@
+import subprocess
+from time import time, sleep
+import matplotlib.pyplot as plt
+
+pivot_strategies = {
+    "rightmost": 0,
+    "middle": 1,
+    "random": 2
+}
+
+def run_benchmark(array, pivot_strategy):
+    input_data = f"{len(array)}\n{pivot_strategies[pivot_strategy]}\n" + "\n".join(map(str, array)) + "\n"
+
+    start = time()
+    process = subprocess.Popen("./dist/quicksort", stdin=subprocess.PIPE)
+    process.communicate(input_data.encode())
+    if process.returncode != 0:
+        raise RuntimeError("Error while benchmarking")
+    end = time()
+
+    return end - start
+
+def generate_v_shaped(array_size):
+    return [i for i in range(array_size // 2, 0, -1)] + [i for i in range(array_size // 2)]
+
+def plot(sequences, array_size):
+    pivot_names = {
+        "rightmost": "Pivot prawy",
+        "middle": "Pivot środkowy",
+        "random": "Pivot losowy"
+    }
+    plt.figure(figsize=(10, 6))
+    for pivot_type, timings in sequences.items():
+        plt.plot(array_size, timings, label=pivot_names[pivot_type])
+    plt.xlabel("Rozmiar tablicy")
+    plt.ylabel("Czas (ms)")
+    plt.title("Quicksort")
+    plt.legend()
+    plt.savefig("./benchmarks/quicksort.png")
+
+if __name__ == "__main__":
+    array_sizes = [i for i in range(1000, 15001, 1000)]
+
+    with open("results.txt", "w") as f:
+        sequenceTiming = {
+                "rightmost": [],
+                "middle": [],
+                "random": [],
+            }
+        for pivot_strategy in pivot_strategies:
+            print(f"Running with pivot strategy {pivot_strategy}")
+            for array_size in array_sizes:
+                sequence = generate_v_shaped(array_size)
+                durations = []
+                for i in range(10):
+                    print(f"     - with size of {array_size} {i+1}/10", end="\r")
+                    duration = run_benchmark(sequence, pivot_strategy)
+                    duration *= 1000
+                    durations.append(duration)
+                    sleep(0.1)
+                print()
+                duration = sum(durations) / len(durations)
+                sequenceTiming[pivot_strategy].append(duration)
+                sleep(0.1)
+        plot(sequenceTiming, array_sizes)
+    print("Benchmark finished")
diff --git a/selection.c b/Lab1/selection.c
similarity index 99%
rename from selection.c
rename to Lab1/selection.c
index 1499d9b..7ba936e 100644
--- a/selection.c
+++ b/Lab1/selection.c
@@ -8,6 +8,7 @@ int main() {
     fprintf(stderr, "Error reading size\n");
     return 1;
   }
+
   int *array = malloc(sizeof(int) * s);
   for (int i = 0; i < s; i++) {
     res = scanf("%d", &array[i]);
diff --git a/Lab1/test_data.txt b/Lab1/test_data.txt
new file mode 100644
index 0000000..37bb752
--- /dev/null
+++ b/Lab1/test_data.txt
@@ -0,0 +1,6 @@
+5
+12
+65
+233
+87
+23
diff --git a/flake.lock b/flake.lock
new file mode 100644
index 0000000..1d0fc86
--- /dev/null
+++ b/flake.lock
@@ -0,0 +1,25 @@
+{
+  "nodes": {
+    "nixpkgs": {
+      "locked": {
+        "lastModified": 1739736696,
+        "narHash": "sha256-zON2GNBkzsIyALlOCFiEBcIjI4w38GYOb+P+R4S8Jsw=",
+        "rev": "d74a2335ac9c133d6bbec9fc98d91a77f1604c1f",
+        "revCount": 754461,
+        "type": "tarball",
+        "url": "https://api.flakehub.com/f/pinned/NixOS/nixpkgs/0.1.754461%2Brev-d74a2335ac9c133d6bbec9fc98d91a77f1604c1f/01951426-5a87-7b75-8413-1a0d9ec5ff04/source.tar.gz"
+      },
+      "original": {
+        "type": "tarball",
+        "url": "https://flakehub.com/f/NixOS/nixpkgs/0.1.%2A.tar.gz"
+      }
+    },
+    "root": {
+      "inputs": {
+        "nixpkgs": "nixpkgs"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}
diff --git a/flake.nix b/flake.nix
new file mode 100644
index 0000000..2a1088b
--- /dev/null
+++ b/flake.nix
@@ -0,0 +1,35 @@
+{
+  description = "A Nix-flake-based C/C++ development environment";
+
+  inputs.nixpkgs.url = "https://flakehub.com/f/NixOS/nixpkgs/0.1.*.tar.gz";
+
+  outputs = { self, nixpkgs }:
+    let
+      supportedSystems = [ "x86_64-linux" ];
+      forEachSupportedSystem = f:
+        nixpkgs.lib.genAttrs supportedSystems
+        (system: f { pkgs = import nixpkgs { inherit system; }; });
+    in {
+      devShells = forEachSupportedSystem ({ pkgs }: {
+        default = pkgs.mkShell.override {
+          # Override stdenv in order to change compiler:
+          # stdenv = pkgs.clangStdenv;
+        } {
+          packages = with pkgs;
+            [
+              clang-tools
+              cmake
+              codespell
+              conan
+              cppcheck
+              doxygen
+              gtest
+              lcov
+              vcpkg
+              vcpkg-tool
+              python312Packages.matplotlib
+            ] ++ (if system == "aarch64-darwin" then [ ] else [ gdb ]);
+        };
+      });
+    };
+}
diff --git a/heapsort b/heapsort
deleted file mode 100755
index 6258740..0000000
Binary files a/heapsort and /dev/null differ
diff --git a/insertion b/insertion
deleted file mode 100755
index 6135dce..0000000
Binary files a/insertion and /dev/null differ
diff --git a/insertion_benchmark.png b/insertion_benchmark.png
deleted file mode 100644
index 3d99f95..0000000
Binary files a/insertion_benchmark.png and /dev/null differ
diff --git a/results.txt b/results.txt
deleted file mode 100644
index 50d2643..0000000
--- a/results.txt
+++ /dev/null
@@ -1,150 +0,0 @@
-./insertion random 1000 1.0615110397338867
-./insertion random 2000 1.5358448028564453
-./insertion random 3000 2.274918556213379
-./insertion random 4000 3.3971309661865234
-./insertion random 5000 4.603886604309082
-./insertion random 6000 6.020760536193848
-./insertion random 7000 7.8598737716674805
-./insertion random 8000 9.824132919311523
-./insertion random 9000 12.162041664123535
-./insertion random 10000 14.610838890075684
-./insertion random 11000 17.46840476989746
-./insertion random 12000 20.206165313720703
-./insertion random 13000 23.66960048675537
-./insertion random 14000 27.488040924072266
-./insertion random 15000 30.75859546661377
-./insertion sorted 1000 0.8951187133789062
-./insertion sorted 2000 0.9664773941040039
-./insertion sorted 3000 1.1182308197021484
-./insertion sorted 4000 1.237630844116211
-./insertion sorted 5000 1.397252082824707
-./insertion sorted 6000 1.5844106674194336
-./insertion sorted 7000 1.7046451568603516
-./insertion sorted 8000 1.8734216690063477
-./insertion sorted 9000 2.349853515625
-./insertion sorted 10000 2.4387359619140625
-./insertion sorted 11000 2.462148666381836
-./insertion sorted 12000 2.5963783264160156
-./insertion sorted 13000 3.156876564025879
-./insertion sorted 14000 3.132033348083496
-./insertion sorted 15000 3.238677978515625
-./insertion reversed 1000 1.2768268585205078
-./insertion reversed 2000 2.137279510498047
-./insertion reversed 3000 3.491377830505371
-./insertion reversed 4000 5.584287643432617
-./insertion reversed 5000 7.988524436950684
-./insertion reversed 6000 10.77868938446045
-./insertion reversed 7000 14.134597778320312
-./insertion reversed 8000 18.278932571411133
-./insertion reversed 9000 22.71718978881836
-./insertion reversed 10000 27.739286422729492
-./insertion reversed 11000 33.2233190536499
-./insertion reversed 12000 39.423227310180664
-./insertion reversed 13000 45.474839210510254
-./insertion reversed 14000 52.727437019348145
-./insertion reversed 15000 60.09998321533203
-./insertion constant 1000 0.9403467178344727
-./insertion constant 2000 0.9927988052368164
-./insertion constant 3000 1.1275291442871094
-./insertion constant 4000 1.2822389602661133
-./insertion constant 5000 1.5305280685424805
-./insertion constant 6000 1.5430212020874023
-./insertion constant 7000 1.8325090408325195
-./insertion constant 8000 1.804804801940918
-./insertion constant 9000 2.2058486938476562
-./insertion constant 10000 2.1335840225219727
-./insertion constant 11000 2.322697639465332
-./insertion constant 12000 2.5031328201293945
-./insertion constant 13000 2.8443336486816406
-./insertion constant 14000 2.675914764404297
-./insertion constant 15000 2.9070377349853516
-./insertion v_shaped 1000 1.075124740600586
-./insertion v_shaped 2000 1.6106843948364258
-./insertion v_shaped 3000 2.419257164001465
-./insertion v_shaped 4000 3.438878059387207
-./insertion v_shaped 5000 4.725170135498047
-./insertion v_shaped 6000 6.513190269470215
-./insertion v_shaped 7000 7.992100715637207
-./insertion v_shaped 8000 10.184979438781738
-./insertion v_shaped 9000 12.364602088928223
-./insertion v_shaped 10000 15.029740333557129
-./insertion v_shaped 11000 18.083977699279785
-./insertion v_shaped 12000 21.661686897277832
-./insertion v_shaped 13000 24.31178092956543
-./insertion v_shaped 14000 27.93755531311035
-./insertion v_shaped 15000 32.60822296142578
-./selection random 1000 1.4104366302490234
-./selection random 2000 2.2107839584350586
-./selection random 3000 3.5886764526367188
-./selection random 4000 5.592441558837891
-./selection random 5000 7.922983169555664
-./selection random 6000 11.436033248901367
-./selection random 7000 14.230155944824219
-./selection random 8000 18.193602561950684
-./selection random 9000 22.896504402160645
-./selection random 10000 29.131484031677246
-./selection random 11000 33.37256908416748
-./selection random 12000 39.19968605041504
-./selection random 13000 47.031211853027344
-./selection random 14000 53.545570373535156
-./selection random 15000 61.37092113494873
-./selection sorted 1000 1.2252330780029297
-./selection sorted 2000 2.236461639404297
-./selection sorted 3000 3.660869598388672
-./selection sorted 4000 5.638456344604492
-./selection sorted 5000 8.042407035827637
-./selection sorted 6000 11.208224296569824
-./selection sorted 7000 14.447546005249023
-./selection sorted 8000 18.74523162841797
-./selection sorted 9000 22.982478141784668
-./selection sorted 10000 28.246402740478516
-./selection sorted 11000 34.0437650680542
-./selection sorted 12000 39.82250690460205
-./selection sorted 13000 46.31156921386719
-./selection sorted 14000 53.86967658996582
-./selection sorted 15000 61.89534664154053
-./selection reversed 1000 1.210784912109375
-./selection reversed 2000 2.1828174591064453
-./selection reversed 3000 3.7517547607421875
-./selection reversed 4000 5.427956581115723
-./selection reversed 5000 8.042573928833008
-./selection reversed 6000 10.962772369384766
-./selection reversed 7000 14.444756507873535
-./selection reversed 8000 18.53172779083252
-./selection reversed 9000 22.98440933227539
-./selection reversed 10000 28.051161766052246
-./selection reversed 11000 33.19251537322998
-./selection reversed 12000 39.997243881225586
-./selection reversed 13000 45.667243003845215
-./selection reversed 14000 53.35855484008789
-./selection reversed 15000 60.373663902282715
-./selection constant 1000 1.2602806091308594
-./selection constant 2000 2.1334409713745117
-./selection constant 3000 3.5085439682006836
-./selection constant 4000 5.34212589263916
-./selection constant 5000 7.747673988342285
-./selection constant 6000 10.555744171142578
-./selection constant 7000 14.265799522399902
-./selection constant 8000 18.23742389678955
-./selection constant 9000 22.236275672912598
-./selection constant 10000 27.255654335021973
-./selection constant 11000 32.57889747619629
-./selection constant 12000 38.65196704864502
-./selection constant 13000 45.708537101745605
-./selection constant 14000 51.95658206939697
-./selection constant 15000 59.54868793487549
-./selection v_shaped 1000 1.179647445678711
-./selection v_shaped 2000 2.0728111267089844
-./selection v_shaped 3000 3.5805463790893555
-./selection v_shaped 4000 5.700540542602539
-./selection v_shaped 5000 8.303594589233398
-./selection v_shaped 6000 10.987281799316406
-./selection v_shaped 7000 14.623737335205078
-./selection v_shaped 8000 18.853092193603516
-./selection v_shaped 9000 22.894835472106934
-./selection v_shaped 10000 27.98130512237549
-./selection v_shaped 11000 33.84420871734619
-./selection v_shaped 12000 39.37234878540039
-./selection v_shaped 13000 46.07586860656738
-./selection v_shaped 14000 53.040337562561035
-./selection v_shaped 15000 60.929179191589355
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diff --git a/selection_benchmark.png b/selection_benchmark.png
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