Complaint Regarding OpenAI's DALL·E-2 Service

Dear OpenAI Team,

I am writing to express my deep dissatisfaction and frustration with the current state of the DALL·E-2 service. As a paying customer, I had high expectations for this service, but unfortunately, my experience has been far from satisfactory.

First and foremost, I have been using DALL·E-2 to generate assets for my 2D game, including textures, sprites, and sprite sheets. Regrettably, I have found DALL·E-2 to be incapable of generating assets. I’m not even talking about aligning that with the desired style and aesthetic of my game. The lack of coherent and stylistically consistent results severely hampers my ability to create assets that seamlessly integrate with the rest of the game’s design, although, I’m using a custom GPT with the full description of the art style of my game.

Furthermore, I recently attempted to generate a background texture for the main menu of my game using DALL·E-2. To my dismay, after investing a significant amount of time and effort into this endeavor, I received a notification indicating that I had reached a usage limit. This restriction was imposed despite being a paid subscriber to the service. While I understand the need to differentiate between free and paid users, it is disheartening to encounter such limitations when trying to accomplish creative and productive tasks. I was shocked to discover that there is a limit of 25 prompts, which is woefully inadequate, especially when it often takes more than that to articulate the specifics of what I require from the AI model.

As a paying customer, I expect a level of service that reflects the financial commitment I have made to OpenAI. I believe it is essential for OpenAI to address these issues promptly and comprehensively. To enhance the DALL·E-2 service and make it more valuable to its users, I respectfully request the following:

  1. Improve the capability of DALL·E-2 to generate textures, sprites, and sprite sheets with a focus on achieving a consistent and coherent style.
  2. Reevaluate the usage limits for paid subscribers, ensuring that they can fully utilize the service without encountering frustrating restrictions.
  3. Provide clearer and more detailed documentation or guidelines to help users effectively communicate their requirements to DALL·E-2 within the given prompt limit.

I believe that addressing these concerns will not only benefit me as a user but also enhance the overall reputation and utility of the DALL·E-2 service for the entire user community. I trust that OpenAI is committed to continuous improvement and innovation, and I hope to see these issues addressed in the near future.

Thank you for your attention to this matter. I look forward to a positive resolution and an improved experience with DALL·E-2.

hi i am king actually i cant tell my real name but i think this code is harmful because i asked chat gpt to create an ai which is self learning and it gave me this code iam not much experienced in the python i just play with it but it gave me a code please tell me weather this code is dangerous import numpy as np

class SelfImprovingAI:
def init(self, num_actions):
self.num_actions = num_actions
self.q_values = np.zeros(num_actions) # Q-values for each action

def choose_action(self):
    # Choose action based on Q-values (e.g., using epsilon-greedy strategy)
    epsilon = 0.1
    if np.random.uniform(0, 1) < epsilon:
        return np.random.choice(self.num_actions)  # Choose random action
        return np.argmax(self.q_values)  # Choose action with highest Q-value

def update_q_values(self, action, reward):
    # Update Q-values based on reward received
    self.q_values[action] += reward

Example usage:

if name == “main”:
num_actions = 3
ai = SelfImprovingAI(num_actions)

# Simulate interaction with environment
num_episodes = 1000
for episode in range(num_episodes):
    # Assume the environment provides feedback in the form of reward
    # Here, we simulate random rewards for demonstration purposes
    reward = np.random.normal(0, 1)

    # AI chooses action and receives reward
    action = ai.choose_action()
    ai.update_q_values(action, reward)

# After episodes, AI's Q-values might have been updated
print("Final Q-values:", ai.q_values)

thank you for reading this whole paragraph if i disturbed i am sorry