[115]

HTB{L0ST_1N_TH3_SP1R4L}

[xzin0vich]

Good share keep up

[macavitysworld]

HTB{L0ST_1N_TH3_SP1R4L}

It would be beneficial if you share the process or steps to solve the challenge rather than just flags.

[macavitysworld]

HTB{L0ST_1N_TH3_SP1R4L}

It would be beneficial if you share the process or steps to solve the challenge rather than just flags.

These bitches just trading flags. And they don't have any solutions like most of these dick-heads from "The Top Guys".
They just exchanging flags. Even for EndGames these niggers just buy or exchange flags.

I have a solution for this challenge, Solved myself and open for trades. Trading only solution -- for solution.
I miss those:

KHP Protocol
Magic Scrolls
Picture Magic
Heapify
NFTDRM
Quantum Memory Manager
ArtificialUniversity


If you have any -- i'm open for trades

I do know the solution, it's pretty easy. I'm asking for the rest of the community.

[b090134565312]

HTB{L0ST_1N_TH3_SP1R4L}

Good share, thank you

[654321123]

since nobody can be bothered to actually post it despite their selfimportant posturing

chatgpt output after some basic googling

import numpy as np import matplotlib.pyplot as plt from sklearn.decomposition import PCA # Step 1: Load data from token_embeddings.npz data = np.load('token_embeddings.npz') tokens = data['tokens']  # Assuming 'tokens' is the key for tokens array embeddings = data['embeddings']  # Assuming 'embeddings' is the key for embeddings array # Step 2: Apply PCA to reduce the dimensionality of embeddings pca = PCA(n_components=2)  # Reduce embeddings to 2 dimensions for visualization embeddings_pca = pca.fit_transform(embeddings) # Step 3: Plot tokens based on PCA-transformed embeddings plt.figure(figsize=(10, 8)) plt.scatter(embeddings_pca[:, 0], embeddings_pca[:, 1], marker='o', s=30, label='Embeddings') # Annotate each point with its token for i, token in enumerate(tokens):     plt.annotate(token, (embeddings_pca[i, 0], embeddings_pca[i, 1]), fontsize=12) plt.title('Token Embeddings Visualization using PCA') plt.xlabel('PCA Component 1') plt.ylabel('PCA Component 2') plt.legend() plt.grid(True) plt.tight_layout() plt.show()

HTB{7R4D3_M3_FL4GS_S0_1_C4N_CH347_WH1L3_F33L1NG_5UP3R10R_70_7H3_07H3R_CH3473R5}

I like your Flag content brudah  ! Unfortunately, yes 3/4 of them is just a cheaters without any solutions or even ever solving something on their own... very dissatisfactory.
But without proper understanding of the material and asking precise questions, chatgpt also generates some gibberish.