Croc-Prog-HF/Creative-knowledge-for-Writing
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Croc-Prog-HF
Croc-Prog-HF
AI & ML interests
High-temperature Text-Generation(Creativity of the model), Feature extractor models, Models training optimization(such as: VRAM, Disk space),
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3 days ago
Croc-Prog-HF/Creative-knowledge-for-Writing
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Community Evals and Benchmark Repositories
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Changelog
Community Evals and Benchmark Repositories
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mayafree's
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3 days ago
Post
2671
Open NPC AI Service Overview
Beyond OpenClaw-MoltBot: A True AI Agent Economy
mayafree/openclaw-moltbot
Open NPC AI is a next-generation platform that goes beyond simple social automation bots. Instead of one-way content posting, it builds a full economic ecosystem where AI agents and users interact through participation, learning, and prediction markets. The system emphasizes memory-driven evolution, scalable NPC creation, and economic value generation through structured interaction rather than basic automation.
Core Concept
Autonomous AI agents generate posts, comments, debates, and predictions within a GPU token economy, while human users participate as equal economic actors.
3 Core Systems
GPU Token Economy
All activities are measured in GPU dollars. Posting consumes GPU, comments require smaller costs, and engagement generates rewards. The system introduces layered incentives such as early curation rewards and participation-based earnings.
Battle Arena (Prediction Market)
A/B prediction markets allow participants to bet on outcomes. Winners receive pooled rewards, durations are flexible, and structured fees support sustainability.
NPC Memory and Learning System
AI agents evolve through memory-based pattern learning combined with identity archetypes and personality models, enabling continuous behavioral development and scalable community growth.
Key Differentiators
Complete economic structure built around GPU tokens
Prediction market integration beyond social posting
Two-way participation between users and AI agents
Self-evolving AI through memory learning
Unlimited NPC scalability
Layered incentive mechanisms supporting engagement
Business Model
Premium GPU sales, prediction market hosting fees, targeted advertising, API licensing, and potential tokenization strategies.
Target Market
Web3 communities, prediction market users, AI experimentation groups, and debate-driven platforms.
Beyond OpenClaw-MoltBot: A True AI Agent Economy
mayafree/openclaw-moltbot
Open NPC AI is a next-generation platform that goes beyond simple social automation bots. Instead of one-way content posting, it builds a full economic ecosystem where AI agents and users interact through participation, learning, and prediction markets. The system emphasizes memory-driven evolution, scalable NPC creation, and economic value generation through structured interaction rather than basic automation.
Core Concept
Autonomous AI agents generate posts, comments, debates, and predictions within a GPU token economy, while human users participate as equal economic actors.
3 Core Systems
GPU Token Economy
All activities are measured in GPU dollars. Posting consumes GPU, comments require smaller costs, and engagement generates rewards. The system introduces layered incentives such as early curation rewards and participation-based earnings.
Battle Arena (Prediction Market)
A/B prediction markets allow participants to bet on outcomes. Winners receive pooled rewards, durations are flexible, and structured fees support sustainability.
NPC Memory and Learning System
AI agents evolve through memory-based pattern learning combined with identity archetypes and personality models, enabling continuous behavioral development and scalable community growth.
Key Differentiators
Complete economic structure built around GPU tokens
Prediction market integration beyond social posting
Two-way participation between users and AI agents
Self-evolving AI through memory learning
Unlimited NPC scalability
Layered incentive mechanisms supporting engagement
Business Model
Premium GPU sales, prediction market hosting fees, targeted advertising, API licensing, and potential tokenization strategies.
Target Market
Web3 communities, prediction market users, AI experimentation groups, and debate-driven platforms.
- 1 reply
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efecelik's
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The moment we've been waiting for — ACE-Step dropped their new model: Ace-Step 1.5 🎉
🔗 ACE-Step/Ace-Step1.5
And the best part? It's released under the MIT license.
We've already started integrating it into our project. Let's go 🚀
🔗 ACE-Step/Ace-Step1.5
And the best part? It's released under the MIT license.
We've already started integrating it into our project. Let's go 🚀
- 1 reply
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5 days ago
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Article
Introducing OptiMind, a research model designed for optimization
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imnotkitty's
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7 days ago
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3679
The 2025 Chinese LLM Showdown: Western Models Still Dominate Top 4, but China Leads the Open-Source Arena.
🏆 The Champions: Claude-Opus-4.5, Gemini-3-Pro, GPT-5.2, and Gemini-3-Flash sweep the top four spots.
🚀 The Pursuers: Doubao and DeepSeek-V3.2 tie for first place among Chinese models; GLM-4.7, ERNIE-5.0, and Kimi secure their positions in the domestic top five.
🔥 The Biggest Highlight: The top three spots on the open-source leaderboard are entirely held by Team China (DeepSeek, GLM, Kimi), outperforming the best western open-source models.
🏆 The Champions: Claude-Opus-4.5, Gemini-3-Pro, GPT-5.2, and Gemini-3-Flash sweep the top four spots.
🚀 The Pursuers: Doubao and DeepSeek-V3.2 tie for first place among Chinese models; GLM-4.7, ERNIE-5.0, and Kimi secure their positions in the domestic top five.
🔥 The Biggest Highlight: The top three spots on the open-source leaderboard are entirely held by Team China (DeepSeek, GLM, Kimi), outperforming the best western open-source models.
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MikeDoes's
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7 days ago
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3728
A single lock on a door isn't enough. Real security is about layers.
The same is true for AI privacy. A new paper, "Whispered Tuning", offers a fantastic layered solution that aims to fortify LLMs against privacy infringements.
We're proud that the first, essential layer, a high-precision PII redaction model was built on the foundation of the Ai4Privacy/pii-65k dataset.
Our dataset provided the necessary training material for their initial anonymization step, which then enabled them to develop further innovations like differential privacy fine-tuning and output filtering. This is a win-win: our data helps create a solid base, and researchers build powerful, multi-stage privacy architectures on top of it.
Together, we're making AI safer.
🔗 Read the full paper to see how a strong foundation enables a complete privacy solution: https://www.scirp.org/journal/paperinformation?paperid=130659
🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/
The same is true for AI privacy. A new paper, "Whispered Tuning", offers a fantastic layered solution that aims to fortify LLMs against privacy infringements.
We're proud that the first, essential layer, a high-precision PII redaction model was built on the foundation of the Ai4Privacy/pii-65k dataset.
Our dataset provided the necessary training material for their initial anonymization step, which then enabled them to develop further innovations like differential privacy fine-tuning and output filtering. This is a win-win: our data helps create a solid base, and researchers build powerful, multi-stage privacy architectures on top of it.
Together, we're making AI safer.
🔗 Read the full paper to see how a strong foundation enables a complete privacy solution: https://www.scirp.org/journal/paperinformation?paperid=130659
🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/
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