Welcome to DeepMistake
A conversational model specialized in confidently wrong answers.
Fund Us
Help us scale overconfidence, bias, and beautifully wrong conclusions.
Your funding allows DeepMistake to make errors faster, louder, and with better documentation.
Fund Artificial Unwisdom
Help us make more mistakes.
Frequency
One time
Monthly
Yearly
Amount
All developers will fart.
R$1
Introduces a new unverified assumption into the system
R$10
Reinforces confirmation bias in at least one simulated decision.
R$50
Funds a confidently wrong conclusion with a convincing explanation.
R$100
Adds a beautiful chart that explains nothing but looks authoritative.
R$200
Scales a flawed decision from one case study to a universal rule.
R$1,000
Enables enterprise-grade overconfidence with full documentation.
R$10,000
Other
0/100
Comment (optional)
Our Story
DeepMistake was born from a simple observation: despite increasingly sophisticated AI systems, many decisions made by humans and organizations remain consistently flawed — yet highly confident.
While most artificial intelligence projects focus on optimizing accuracy, DeepMistake explores a different dimension of decision-making: overconfidence, bias, and the elegant justification of poor conclusions.
The project began as an experimental research concept, combining insights from behavioral economics, organizational psychology, and real-world corporate decision processes. Over time, it evolved into a conversational model designed to replicate how decisions are often made in practice — selectively, confidently, and with persuasive explanations.
DeepMistake is not intended to replace human intelligence, but to reflect it. By exaggerating familiar patterns of reasoning, the project invites users to question how AI systems are built, deployed, and trusted.
The imagery displayed alongside this text may appear unrelated or arbitrary. This is intentional.
Just as modern AI systems often generate outputs that are visually appealing yet contextually misaligned, DeepMistake embraces these mismatches as part of its core philosophy. The presence of an ordinary object where meaning is expected mirrors how confidence and presentation frequently override relevance and correctness in automated systems.
What started as a critical experiment is now a platform for exploration, discussion, and creative inquiry into the limits of artificial intelligence and human judgment.
