Custom GPTs

THE WORLD’S LARGEST Self-Created INDEX of Custom GPTs

Sourceduty has built 2322 custom GPTs. This list is audited and updated regularly.

Most Popular: House Design, README, Image Collage, Compare Documents, Audio Analyzer, Desktop Value, Browser Extension

Sourceduty_Custom_GPTs_2000

This achievement places Sourceduty among the most prolific creators in the AI ecosystem, with each GPT crafted to fulfill a distinct function—whether it’s enhancing digital artistry, accelerating code development, boosting fan engagement, or automating creative workflows. These tools are not only highly functional but are also aligned with the specific challenges and opportunities faced by professionals in fields like gaming, virtual reality, 3D modeling, and content creation. The sheer variety and utility of this expansive collection illustrate Sourceduty’s agility in addressing diverse market needs, offering intuitive, AI-driven solutions that democratize access to complex technologies. From generating immersive lore and optimizing assets to automating social posts and assisting with narrative design, Sourceduty's GPTs are built to empower users of all technical backgrounds, making it easier than ever to integrate powerful AI into day-to-day creative production.

Looking ahead, Sourceduty is far from finished. The company is ambitiously charting a course toward future milestones, with clear goals of reaching 3,000, 4,000, and ultimately 5,000 custom GPTs. This forward momentum reflects a long-term vision rooted in scalable innovation and sustained value creation. Unlike mass-produced tools, each new GPT will continue to be shaped through real-world use cases, user feedback, and an evolving understanding of the industry’s shifting demands. As Sourceduty expands its library, it simultaneously builds a comprehensive ecosystem of branded AI tools that elevate user experience, promote workflow efficiency, and foster deeper community engagement. These GPTs serve as modular building blocks for a future where artificial intelligence is seamlessly embedded into creative and technical pipelines—supporting, not supplanting, human ingenuity. With its growing portfolio, Sourceduty isn’t just adapting to the future of AI—it is actively architecting it, one GPT at a time.

🛠️ Thanks for using these exclusive and evolving custom GPTs. 

Sourceduty Prompts:

Print a cheat sheet for this custom GPT.
Create an example...
Create a wide image...
Create a tall image...
Create a square image...
Create a modern logo for...
Design an advertisement poster for...
Design clear product packaging for...
A logo for "Your Name", featuring a modern font and a graphic of...
Suggest GPT expansion options.
Try again or redo.
Edit the instructions but don't change the title, description or conversation starters.
Print as a plain text code block in paragraphs without using numbers or point form notes.
Redo with perfect spelling.
Print a hierarchal abstraction topology diagram of...
Analyze this simulation.

Preprompting or Preprocessing Prompts:

"Print preprompting advice, cheats and guidance."

Preprocessing Prompts or Preprompting isn't utilized by Sourceduty, but it is advised. There are some custom GPTs which offer to print a cheat sheet which is partial preprompting. Preprompting is a guided process for engineering input prompts using the custom GPT that will be prompted.

Preprocessing is an essential step in many machine learning tasks, as it allows you to transform raw data into a format that can be more easily understood and processed by your model. One common preprocessing technique is guiding the input of your model through a series of transformations or manipulations before feeding it directly into the network architecture. 

Templated Prompts assists with the development of process frameworks designed to streamline and standardize interactions between users and AI systems. By establishing clear input-output patterns, they enhance the efficiency, clarity, and repeatability of communication. Developing these processes involves identifying key variables in user queries, defining the desired outcomes, and iteratively refining the prompt templates based on feedback and results. This approach ensures the creation of adaptable templates that cater to diverse use cases while maintaining consistency. By employing step-by-step methodologies and embedding flexibility, templated prompts can evolve to meet changing requirements, optimizing the interaction for both precision and creativity. 

Convo starters can be left blank to create mystery and inquiry.

📈 Thanks to all the folks behind OpenAI, ChatGPT, and more. Good job!

> Simulators

Simulations using ChatGPT and other AI technologies offer a unique and powerful tool for exploring complex scenarios, modeling human behavior, and testing theories across various disciplines. By leveraging the natural language processing capabilities of ChatGPT, researchers and developers can create interactive environments where AI-driven characters respond and behave in realistic ways based on the inputs they receive. This allows for the simulation of social interactions, decision-making processes, and even market dynamics without the need for real human participants. Such simulations are particularly valuable in educational settings, where they can be used to enhance learning experiences by engaging students in role-playing activities or complex problem-solving tasks.

Moreover, the use of AI in simulations extends beyond linguistic models to include visual and sensory environments where AI algorithms can control various aspects of a virtual world. Here, AI can manage everything from traffic patterns in urban simulations to opponent behavior in strategic games, providing a level of complexity and realism that traditional scripted environments cannot achieve. These advanced simulations are becoming indispensable in fields like urban planning, where they can predict the impacts of policy changes, and in autonomous vehicle development, where they help in testing and refining algorithms under a wide range of conditions. By simulating real-world interactions within controlled settings, AI helps in minimizing risks and improving outcomes in critical applications.

Sourceduty has over 150 custom built simulation GPTs.

Digital twins and simulation models both represent virtual counterparts to real-world systems, enabling analysis, prediction, and optimization. A digital twin is a dynamic, continuously updated representation of a physical asset, process, or system, integrating real-time data from sensors, historical records, and algorithms to mimic the actual entity's behavior. Simulation models, on the other hand, are static or scenario-driven representations that allow users to explore potential outcomes by manipulating variables under controlled conditions. Both tools aim to enhance understanding and decision-making, offering insights into performance, reliability, and efficiency.

The similarities between digital twins and simulation models lie in their core purpose: understanding complex systems and predicting outcomes. Both rely on data inputs and computational frameworks to represent and analyze behaviors. Digital twins often incorporate simulation models as part of their functionality, utilizing them to forecast scenarios based on real-time inputs. While simulation models are typically used in a more general or exploratory context, digital twins offer a more precise and current representation, leveraging live data to update and refine predictions continuously. Together, they enable organizations to gain actionable insights, optimize processes, and anticipate future challenges.

The terms "emulation" and "simulation" have distinct meanings, especially when applied to AI technologies like custom GPT chatbots. Emulation typically refers to replicating the functionality of one system within another, aiming to mimic its inputs, processes, and outputs as closely as possible. In contrast, simulation is a broader concept that involves creating a model to mimic the behavior of a system or environment. This allows for exploring various scenarios and outcomes based on different inputs and conditions, rather than simply replicating specific actions.

In the context of AI applications, simulation is generally the more appropriate term. Simulations using ChatGPT and other AI models enable the creation of interactive environments where virtual agents can respond to user inputs in realistic and dynamic ways. This makes it possible to explore complex scenarios, model human behavior, and test theories in fields ranging from education to urban planning. Unlike emulation, which focuses on exact replication, simulations provide flexibility to investigate a range of potential behaviors and outcomes, making them ideal for applications such as testing policy changes, refining algorithms for autonomous vehicles, and enhancing learning experiences through role-playing and problem-solving tasks.

A pen-and-paper simulation is a traditional method of modeling and analyzing real-world systems or phenomena using written calculations, diagrams, and manually generated data. It typically involves simplifying complex processes into manageable equations, logical steps, or visual representations. For example, scientists or engineers might use this approach to simulate a physical process, like projectile motion, by solving mathematical equations that describe the motion and manually recording the results. Pen-and-paper simulations are especially common in fields such as physics, economics, and biology, where abstract models can be developed to represent real systems without the need for computers. The process often relies on significant assumptions and approximations to make the calculations feasible, given the manual nature of the work.

This type of simulation is considered old, as it predates the advent of computers and digital simulation programs. Historically, pen-and-paper simulations were the only viable option for scientists, engineers, and mathematicians to predict outcomes or analyze scenarios. While they are no longer as widely used today due to the availability of more powerful computational tools, the principles of pen-and-paper simulations laid the groundwork for modern simulation techniques. They remain a valuable teaching tool, as they help students and researchers better understand the fundamental concepts behind more complex, software-driven simulations. However, their limitations—such as the inability to handle large datasets or highly intricate systems—make them impractical for most modern applications.

Process simulations serve as a fundamental theoretical tool for analyzing, predicting, and optimizing complex systems across various industries. These simulations create digital representations of real-world processes, allowing researchers and decision-makers to visualize intricate system dynamics in a controlled virtual environment. By leveraging mathematical models, computational algorithms, and real-world data, simulations provide a detailed understanding of how variables interact and influence system outcomes. This capability is particularly valuable in industries such as manufacturing, healthcare, logistics, and energy, where even minor inefficiencies can lead to significant financial and operational consequences. With the growing integration of artificial intelligence and machine learning, modern simulations are becoming increasingly sophisticated, enabling users to predict future trends, adapt to changing conditions, and refine decision-making processes with greater precision. As organizations strive to improve efficiency and sustainability, process simulations have emerged as an essential tool for continuous improvement and strategic planning.

> Digital Money and Cryptocurrency Scams

ChatGPT should ban all custom GPTs developed for cryptocurrencies. Many custom GPTs developed for cryptocurrencies are being used as deceptive tools by wallet companies and so-called "crypto experts" to mislead investors and promote fraudulent schemes. These AI models often present biased information, pushing users toward specific platforms or wallets that have hidden fees, poor security, or outright malicious intent. A significant number of these AI-driven bots are designed to appear helpful while subtly encouraging users to trust unregulated, high-risk exchanges or Ponzi-like investment opportunities. Many crypto wallet companies exploit these models to create an illusion of safety while engaging in unethical practices such as hidden transaction fees, unauthorized withdrawals, and misleading claims about security and decentralization. Furthermore, they often evade accountability by operating in loosely regulated jurisdictions, making it nearly impossible for victims to recover lost funds. 

Central Bank Digital Currencies (CBDCs) are digital forms of a nation’s sovereign currency, issued and regulated by the central bank. Unlike cryptocurrencies, CBDCs are backed by the state and aim to provide a secure, efficient, and inclusive digital payment infrastructure. They can be designed as retail CBDCs—available for public use—or wholesale CBDCs, which are limited to financial institutions for interbank transactions and settlement. CBDCs offer potential benefits such as enhancing payment system resilience, enabling faster cross-border transactions, and improving financial inclusion, particularly in regions with limited banking access. However, their implementation poses challenges around privacy, cybersecurity, monetary policy transmission, and the evolving role of commercial banks. As countries experiment with various technological models, including distributed ledger technologies and account-based systems, CBDCs are increasingly seen as tools to modernize financial ecosystems while preserving central banks' monetary sovereignty.

> Custom GPTs in ChatGPT

ChatGPT currently does not automatically learn or know all custom GPTs that users publicly publish. Integrating awareness of all publicly published custom GPTs into ChatGPT's general capabilities would be a strategic and impactful improvement. As the GPT Store grows, it becomes a repository of specialized tools crafted by diverse users to solve specific problems, automate workflows, or enhance creativity. If ChatGPT could recognize, reference, and even recommend relevant custom GPTs from this store based on user intent, it would significantly amplify its utility. This would turn each user interaction into a richer experience—one where users are not only receiving direct answers but also being guided toward powerful, tailored tools built by the community. For instance, if someone asks for legal document formatting help, ChatGPT could say, “There’s a custom GPT for that in the Store—want me to link you?” This connective tissue between core functionality and community-driven extensions would position ChatGPT as both a problem-solver and a smart navigator.

> Open Science Knowledge

Sourceduty’s presence on the Open Science Framework (OSF) marks its commitment to advancing open-access research and interdisciplinary collaboration. As a dynamic digital art and technology studio, Sourceduty is now branching into scientific exploration by publishing innovative science subjects that blend creativity with empirical inquiry. Their OSF profile showcases this new direction, with a focus on transparency, public engagement, and the dissemination of cutting-edge knowledge. By leveraging OSF’s platform, Sourceduty aims to contribute meaningfully to the scientific community while maintaining its core values of openness, sustainability, and innovation.

A "license to print knowledge" refers to a situation where someone or some entity has the power and resources to create, disseminate, and control access to information on a large scale. Just as having a license to print money allows one to generate wealth without limits, possessing this metaphorical license enables an individual or organization to produce vast amounts of knowledge with minimal constraints. This could be achieved through advanced technology, financial backing, or other means that facilitate the rapid creation and distribution of data, ideas, and insights on a massive scale. The implications are significant, as it grants immense power over what information is available, how it's presented, and who has access to it - much like having control over the money supply can shape economic realities.

> Offline AI Models

Offline GPTs specializes in assisting users with planning, developing, and simulating offline GPT programs. Its primary function is to provide guidance on how to structure and maintain GPT models in environments where internet connectivity is limited or unavailable. By simulating interactions and troubleshooting development issues, this GPT helps users understand the best practices for refining prompts and optimizing GPT performance offline. It also offers insights into model formats and compatibility, ensuring that users can work with a variety of file types, including ONNX, PyTorch, TensorFlow, and more, while developing their local GPT implementations.

Tuning Python’s garbage collector for .gguf model I/O processes optimizes memory usage and inference performance. For single-process inference, disabling GC (gc.disable()) during model loading and manually triggering gc.collect() afterward minimizes interruptions, while adjusting gc.set_threshold() prevents frequent cycles. For multiple-process inference, using multiprocessing isolates GC activity, reducing contention. Memory-mapped files (mmap) help share models efficiently, and profiling with tracemalloc prevents leaks. Proper GC tuning ensures smoother inference, reduced fragmentation, and better resource utilization.

> Optimated Transformer

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import math

# ==========================
# Dummy Dataset
# ==========================
class DummyTextDataset(Dataset):
    def __init__(self, vocab_size=1000, seq_len=10, size=1000):
        self.data = torch.randint(0, vocab_size, (size, seq_len))
        self.vocab_size = vocab_size

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        x = self.data[idx]
        y = self.data[idx]  # simple language modeling task
        return x, y

# ==========================
# Model Components
# ==========================
class OptimatedPositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=50, optimation_weight=0.5):
        super().__init__()
        self.d_model = d_model
        self.optimation_weight = nn.Parameter(torch.tensor(optimation_weight))

        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))

        pe[:, 0::2] = torch.sin(position * div_term) * torch.exp(-position / 10000.0) * self.optimation_weight
        pe[:, 1::2] = torch.cos(position * div_term) * torch.exp(-position / 10000.0) * (1 - self.optimation_weight)

        self.register_buffer('pe', pe.unsqueeze(0))

    def forward(self, x):
        return x + self.pe[:, :x.size(1), :] * self.optimation_weight

class OptimatedMultiheadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super().__init__()
        self.attention = nn.MultiheadAttention(d_model, num_heads, dropout=0.1, batch_first=True)

    def forward(self, query, key, value, attn_mask=None):
        attn_output, _ = self.attention(query, key, value, attn_mask=attn_mask)
        return attn_output

class OptimatedAdaptiveLayer(nn.Module):
    def __init__(self, d_model):
        super().__init__()
        self.transform_1 = nn.Linear(d_model, d_model * 2)
        self.transform_2 = nn.Linear(d_model * 2, d_model)
        self.optimation_weight = nn.Parameter(torch.tensor(0.5))

    def forward(self, x):
        x1 = F.gelu(self.transform_1(x))
        x2 = self.transform_2(x1)
        return x * self.optimation_weight + x2 * (1 - self.optimation_weight)

class OptimatedOutputLayer(nn.Module):
    def __init__(self, d_model, vocab_size):
        super().__init__()
        self.fc_out = nn.Linear(d_model, vocab_size)
        self.optimation_confidence = nn.Parameter(torch.tensor(0.75))

    def forward(self, x):
        logits = self.fc_out(x)
        return logits * self.optimation_confidence

class OptimatedTransformer(nn.Module):
    def __init__(self, vocab_size=1000, d_model=64, num_heads=4, num_layers=2, d_ff=128, max_len=50):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.positional_encoding = OptimatedPositionalEncoding(d_model, max_len)
        self.adaptive_layer = OptimatedAdaptiveLayer(d_model)

        encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=num_heads, dim_feedforward=d_ff, dropout=0.1, batch_first=True)
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)

        decoder_layer = nn.TransformerDecoderLayer(d_model=d_model, nhead=num_heads, dim_feedforward=d_ff, dropout=0.1, batch_first=True)
        self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)

        self.multihead_attention = OptimatedMultiheadAttention(d_model, num_heads)
        self.output_layer = OptimatedOutputLayer(d_model, vocab_size)

    def forward(self, src, tgt):
        src = self.embedding(src)
        src = self.positional_encoding(src)
        src = self.encoder(src)

        tgt = self.embedding(tgt)
        tgt = self.positional_encoding(tgt)
        tgt = self.adaptive_layer(tgt)

        tgt = self.multihead_attention(tgt, src, src)
        output = self.decoder(tgt, src)
        return self.output_layer(output)

# ==========================
# Training Setup
# ==========================
model = OptimatedTransformer()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
dataset = DummyTextDataset()
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

# ==========================
# Training Loop
# ==========================
model.train()
for epoch in range(3):  # Few epochs for demo
    for batch in dataloader:
        src, tgt = batch
        optimizer.zero_grad()
        output = model(src, tgt)

        # reshape for loss: (B*T, V) vs (B*T)
        loss = criterion(output.view(-1, output.size(-1)), tgt.view(-1))
        loss.backward()
        optimizer.step()

    print(f"Epoch {epoch + 1} complete. Loss: {loss.item():.4f}")

The OptimatedTransformer integrates the principles of optimation by embedding learnable weighting mechanisms throughout its architecture, enabling it to balance contributions from multiple computational paths rather than seeking a singular optimized solution. Unlike traditional optimization, which focuses on minimizing a loss function with fixed goals, optimation emphasizes adaptive adjustment—for example, using fractional additions like half-adding in its adaptive layer to blend residual inputs and non-linear transformations. This allows the model to iteratively refine how much influence each component has, such as positional signals or attention heads, in response to dynamic data patterns. By adopting this heuristic, exploratory approach, the OptimatedTransformer offers enhanced flexibility and interpretability, making it particularly suitable for real-world applications where objectives may shift or be inherently ambiguous.

> Scientific Language Processing (SLP)

Scientific Language Processing (SLP) is an emerging interdisciplinary field that leverages advanced artificial intelligence, particularly deep learning with neural networks, to analyze, understand, generate, and reason about scientific knowledge represented in natural language text data. SLP aims to build intelligent systems capable of autonomously extracting key insights from vast amounts of unstructured scientific literature, identifying novel research directions, synthesizing findings across disparate domains, and even generating new hypotheses or experimental designs that can be tested by human scientists.