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CelaDaniel / free-ai-resources-x

Sponsor
Star 368

🌟 A curated collection of free, high quality AI tools 🤖, APIs 🔗, datasets 📊, and learning resources 📚 covering machine learning 🧠, deep learning 🧩, generative AI 🎨, NLP 💬, and data science 📈. Designed to help developers 👩‍💻, researchers 🔬, and creators ✨ explore and build with AI faster ⚡.

nlp data-science machine-learning awesome reinforcement-learning ai computer-vision deep-learning artificial-intelligence awesome-list ai-agents learning-resources ai-ethics free-resources ai-courses ai-tools large-language-models llm prompt-engineering generative-ai
  • Updated Feb 7, 2026

Anil-matcha / higgsfield-ai-scam

Star 38

Higgsfield AI scam exposed: fraud, fake unlimited plans, mass bans, predatory billing, non-consensual deepfakes, Reddit spam, unpaid creators, and reselling Kling/Minimax models at 4.5x markup. Higgsfield AI research & evidence.

machine-learning osint ai artificial-intelligence scam ai-safety ethics fraud investigation video-generation deepfakes ai-ethics deepfake consumer-protection generative-ai ai-accountability higgsfield-ai higgsfield ai-scam reddit-spam
  • Updated Feb 13, 2026

AmirhosseinHonardoust / Quiet-Machines-Minimalist-AI

Star 22

A long-form essay exploring the philosophy of minimalist AI, how future intelligent systems can be calm, ethical, and invisible. Inspired by calm technology, design minimalism, and cognitive science, Quiet Machines envisions a world where the best technology listens more than it speaks.

artificial-intelligence human-computer-interaction minimalism attention-economy ai-ethics calm-technology digital-minimalism data-storytelling design-philosophy technology-and-society ai-futures
  • Updated Nov 2, 2025

AmirhosseinHonardoust / The-Future-of-Interactive-ML

Star 21

An in-depth exploration of the rise of human-centered, interactive machine learning. This article examines how Streamlit enables collaborative AI design by merging UX, visualization, and automation. Includes theory, architecture, and design insights from the ML Playground project.

visualization python data-science machine-learning research ai scikit-learn plotly ux-design explainable-ai ai-ethics mlops human-centered-ai streamlit interactive-ml
  • Updated Nov 3, 2025

AmirhosseinHonardoust / Measuring-The-Soul-of-Data

Star 19

A narrative and technical exploration of data authenticity through the four pillars of synthetic data realism, Fidelity, Coverage, Privacy, and Utility. This thought-leadership piece combines storytelling, mathematics, and code to explain how these metrics define the ethical and functional “soul” of data in AI systems.

python data-science benchmark coverage machine-learning utility privacy evaluation gan vae copula diffusion data-quality fidelity synthetic-data explainable-ai ai-ethics responsible-ai generative-ai autocurator
  • Updated Nov 14, 2025

AmirhosseinHonardoust / The-Twin-Test-High-Stakes-ML

Star 17

A long-form article introducing the Twin Test: a practical standard for high-stakes machine learning where models must show nearest “twin” examples, neighborhood tightness, mixed-vs-homogeneous evidence, and “no reliable twins” abstention. Argues similarity and evidence packets beat probability scores for trust and safety.

product-design machine-learning risk uncertainty calibration nearest-neighbors knn governance human-in-the-loop interpretability similarity-search case-based-reasoning explainable-ai ai-ethics mlops out-of-distribution responsible-ai data-drift high-stakes decision-systems
  • Updated Dec 26, 2025

AmirhosseinHonardoust / Machine-Learning-Warning-Systems

Star 16

A long-form article and practical framework for designing machine learning systems that warn instead of decide. Covers regimes vs decimals, levers over labels, reversible alerts, anti-coercion UI patterns, auditability, and the “Warning Card” template, so ML preserves human agency while staying useful under uncertainty.

product-design machine-learning risk ux uncertainty calibration governance warnings fairness accountability human-in-the-loop interpretability explainable-ai ai-ethics mlops systems-thinking responsible-ai counterfactuals auditability decision-systems
  • Updated Dec 20, 2025

AmirhosseinHonardoust / Prediction-Fails-When-Systems-Move

Star 15

An analytical essay on why prediction-based models fail in reflexive, unstable systems. This article argues that accuracy collapses when models influence behavior, and proposes equilibrium and force-based modeling as a more robust framework for understanding pressure, instability, and transitions in AI-shaped systems.

machine-learning artificial-intelligence theory complex-systems decision-support ai-safety explainable-ai ai-ethics applied-ai systems-thinking interpretable-models uncertainty-modeling transition-risk reflexive-systems equilibrium-modeling ml-philosophy force-based-models non-stationary-systems prediction-vs-explanation modeling-instability
  • Updated Dec 13, 2025

ocatak / trustworthyai

Star 10

Trustworthy AI: From Theory to Practice book. Explore the intersection of ethics and technology with 'Trustworthy AI: From Theory to Practice.' This comprehensive guide delves into creating AI models that prioritize privacy, security, and robustness. Featuring practical examples in Python, it covers uncertainty quantification, adversarial ML

ai-ethics ethical-ai trustworthy-ai secure-ai-practices ai-model-transparency practical-ai-guide python-ai-projects
  • Updated Feb 23, 2024
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