Qwen/Qwen-Image-Layered
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Nina Larsen
ninalarsen
AI & ML interests
I’m passionate about applying artificial intelligence and machine learning to the gambling industry to enhance player insights, optimize risk management, and improve predictive accuracy. My interests lie in developing data-driven models for odds calculation, fraud detection, and player behavior analysis. I’m particularly excited by how AI can create smarter, more responsible, and personalized gaming experiences.
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a model
7 days ago
Qwen/Qwen-Image-Layered
liked
a model
7 days ago
vanta-research/scout-8b
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unmodeled-tyler's
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7 days ago
NEW MODEL: https://huggingface.co/vanta-research/scout-8b
VANTA Research is excited to share our new model, Scout-8B! This iteration of Scout is based on the RNJ-1 Instruct architecture from Essential AI, and not only improves but expands on the capabilities from https://huggingface.co/vanta-research/scout-4b
Scout is specifically designed for:
Tactical Intelligence Analysis
- Systematic problem decomposition
- Structured reconnaissance approach
- Data-driven assessment methodology
Operational Planning
- Multi-phase operation planning
- Risk assessment and mitigation
- Resource allocation guidance
Technical Assessment
- Architecture evaluation and analysis
- Performance optimization recommendations
- Security perimeter assessment
This model is great for anyone that works in security, IT, DevOps, or anyone looking for a unique, but functional AI collaborator. Check it out!
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liked
2
models
7 days ago
reacted to
unmodeled-tyler's
post with 🚀
7 days ago
Post
1974
NEW MODEL:
vanta-research/scout-8b
VANTA Research is excited to share our new model, Scout-8B! This iteration of Scout is based on the RNJ-1 Instruct architecture from Essential AI, and not only improves but expands on the capabilities from vanta-research/scout-4b
Scout is specifically designed for:
Tactical Intelligence Analysis
- Systematic problem decomposition
- Structured reconnaissance approach
- Data-driven assessment methodology
Operational Planning
- Multi-phase operation planning
- Risk assessment and mitigation
- Resource allocation guidance
Technical Assessment
- Architecture evaluation and analysis
- Performance optimization recommendations
- Security perimeter assessment
This model is great for anyone that works in security, IT, DevOps, or anyone looking for a unique, but functional AI collaborator. Check it out!
VANTA Research is excited to share our new model, Scout-8B! This iteration of Scout is based on the RNJ-1 Instruct architecture from Essential AI, and not only improves but expands on the capabilities from vanta-research/scout-4b
Scout is specifically designed for:
Tactical Intelligence Analysis
- Systematic problem decomposition
- Structured reconnaissance approach
- Data-driven assessment methodology
Operational Planning
- Multi-phase operation planning
- Risk assessment and mitigation
- Resource allocation guidance
Technical Assessment
- Architecture evaluation and analysis
- Performance optimization recommendations
- Security perimeter assessment
This model is great for anyone that works in security, IT, DevOps, or anyone looking for a unique, but functional AI collaborator. Check it out!
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dataset
7 days ago
liked
2
models
7 days ago
reacted to
elismasilva's
post with 👍
about 2 months ago
Post
2566
I'm thrilled to launch SUP Toolbox! An AI tool for image restoration & upscaling using SUPIR, FaithDiff & ControlUnion. Built with Diffusers and Gradio UI featuring 14 custom components I developed.
Space Demo: elismasilva/sup-toolbox-app
App repository: https://github.com/DEVAIEXP/sup-toolbox-app
CLI repository: https://github.com/DEVAIEXP/sup-toolbox
Feedback is welcome!
Space Demo: elismasilva/sup-toolbox-app
App repository: https://github.com/DEVAIEXP/sup-toolbox-app
CLI repository: https://github.com/DEVAIEXP/sup-toolbox
Feedback is welcome!
- 2 replies
reacted to
TravisMuhlestein's
post with 👍
about 2 months ago
Post
1404
Building Smarter AI Agents: A Tool-Based Architecture for Modularity and Trust
Over the past year, our AI engineering team at GoDaddy has been rethinking how to make agent systems more modular, transparent, and production-ready. Instead of viewing an AI agent as a monolithic process, we’ve decomposed it into four core tools that separate decision-making from execution — a design that’s proving critical for scale and observability:
🧩 MemoryTool – maintains persistent context and user continuity
✅ CompletionTool – determines when a task is truly complete
💬 UserInteractionTool – manages clarifications, approvals, and confirmations
🔁 DelegationTool – enables agents to hand off tasks to other agents or humans
This approach makes every step of an agent’s workflow explicit, testable, and auditable, allowing us to scale AI systems in production with higher confidence. We see this as a step toward a more open, composable agent ecosystem — one where frameworks can interoperate and agents can build trust through transparency and version control.
Read the full write-up here → Building AI Agents at GoDaddy – An Agent’s Toolkit https://www.godaddy.com/resources/news/building-ai-agents-at-godaddy-an-agents-toolkit
We’d love to collaborate and exchange ideas with the community:
- How are you designing modular agent architectures?
- What design patterns or abstractions have helped you manage agent complexity?
Let’s build smarter, safer agents together.
#AI #Agents #Architecture #MachineLearning #OpenSource #AgentFrameworks #TrustInAI
Over the past year, our AI engineering team at GoDaddy has been rethinking how to make agent systems more modular, transparent, and production-ready. Instead of viewing an AI agent as a monolithic process, we’ve decomposed it into four core tools that separate decision-making from execution — a design that’s proving critical for scale and observability:
🧩 MemoryTool – maintains persistent context and user continuity
✅ CompletionTool – determines when a task is truly complete
💬 UserInteractionTool – manages clarifications, approvals, and confirmations
🔁 DelegationTool – enables agents to hand off tasks to other agents or humans
This approach makes every step of an agent’s workflow explicit, testable, and auditable, allowing us to scale AI systems in production with higher confidence. We see this as a step toward a more open, composable agent ecosystem — one where frameworks can interoperate and agents can build trust through transparency and version control.
Read the full write-up here → Building AI Agents at GoDaddy – An Agent’s Toolkit https://www.godaddy.com/resources/news/building-ai-agents-at-godaddy-an-agents-toolkit
We’d love to collaborate and exchange ideas with the community:
- How are you designing modular agent architectures?
- What design patterns or abstractions have helped you manage agent complexity?
Let’s build smarter, safer agents together.
#AI #Agents #Architecture #MachineLearning #OpenSource #AgentFrameworks #TrustInAI
reacted to
unmodeled-tyler's
post with 👍
about 2 months ago
Post
1779
NEW RESEARCH MODEL:
vanta-research/atom-v1-preview-4b
We are excited to share our Atom V1 4B Preview model! This fine tuned Gemma3 4B variant has a distinct, friendly, and exploratory persona - designed to help the user think and reflect.
Atom is trained to ask questions, use approachable, yet relatable analogies in ELI5-style explanations, and engage in deep, reflective conversation.
We plan to scale Atom's persona to larger architectures, and this iteration was created as part of that R&D.
Any and all feedback is always welcome as we continue to refine our approach.
We are excited to share our Atom V1 4B Preview model! This fine tuned Gemma3 4B variant has a distinct, friendly, and exploratory persona - designed to help the user think and reflect.
Atom is trained to ask questions, use approachable, yet relatable analogies in ELI5-style explanations, and engage in deep, reflective conversation.
We plan to scale Atom's persona to larger architectures, and this iteration was created as part of that R&D.
Any and all feedback is always welcome as we continue to refine our approach.
reacted to
fd3ffff's
post with 👍
about 2 months ago
Post
1873
The methods for generating these interesting AI images:
1. Open https://imini.com/
2. Enter the instruction word.
3. Click "Generate Now".
4. Download, save or share on social media platforms.
1. Open https://imini.com/
2. Enter the instruction word.
3. Click "Generate Now".
4. Download, save or share on social media platforms.
reacted to
sequelbox's
post with 👍
about 2 months ago
Post
2533
NEW RELEASE: UML Generator is here!
- Our newest Experimental Reasoning release: create Unified Modeling Language diagrams to provide analysis and insight into your queries and situations!
- Multi-step reasoning reliably identifies diagram structure before a user response of XMI 2.5.1 code containing the UML diagram. Load the diagram into the UML tool of your choice!
- Trained in a variety of subjects for flexible analysis: software architecture, software development, business processes, systems engineering, data modeling, microservices, reverse engineering and more!
UML Generator available for multiple sizes of gpt-oss and Qwen 3, to provide increased flexibility to the user:
gpt-oss-120b: sequelbox/gpt-oss-120b-UML-Generator
gpt-oss-20b: sequelbox/gpt-oss-20b-UML-Generator
Qwen3-14B: sequelbox/Qwen3-14B-UML-Generator
Qwen3-4B-Thinking-2507: sequelbox/Qwen3-4B-Thinking-2507-UML-Generator
You can also get the UML Generator dataset, to train your own models to use UML Generator Format: sequelbox/UML-Generator-Dataset-DeepSeek-V3.2
Support our experimental open-source research efforts, models and datasets: sequelbox/SupportOpenSource
See our other Experimental Reasoning models: https://huggingface.co/collections/sequelbox/experimental-reasoning-models
with love,
allegra
- Our newest Experimental Reasoning release: create Unified Modeling Language diagrams to provide analysis and insight into your queries and situations!
- Multi-step reasoning reliably identifies diagram structure before a user response of XMI 2.5.1 code containing the UML diagram. Load the diagram into the UML tool of your choice!
- Trained in a variety of subjects for flexible analysis: software architecture, software development, business processes, systems engineering, data modeling, microservices, reverse engineering and more!
UML Generator available for multiple sizes of gpt-oss and Qwen 3, to provide increased flexibility to the user:
gpt-oss-120b: sequelbox/gpt-oss-120b-UML-Generator
gpt-oss-20b: sequelbox/gpt-oss-20b-UML-Generator
Qwen3-14B: sequelbox/Qwen3-14B-UML-Generator
Qwen3-4B-Thinking-2507: sequelbox/Qwen3-4B-Thinking-2507-UML-Generator
You can also get the UML Generator dataset, to train your own models to use UML Generator Format: sequelbox/UML-Generator-Dataset-DeepSeek-V3.2
Support our experimental open-source research efforts, models and datasets: sequelbox/SupportOpenSource
See our other Experimental Reasoning models: https://huggingface.co/collections/sequelbox/experimental-reasoning-models
with love,
allegra
liked
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about 2 months ago