LayerX

The new capital will be used for corporate growth across talent and increasing global market presence. This round brings the company’s total investment to $34 m

LayerX Security Raises $26M For Its Browser Security Platform, Enabling Employees To Work Securely 

AI startups raised over $2.4 billion this week across enterprise infrastructure, healthcare innovation, and fintech. 

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AI startups raised over $4.1 billion this week, with watershed moments in open-source AI, legal technology, and energy infrastructure.

Weekly AI Startup Funding: October 5-11, 2025

Movie hacking ≠ reality: Real cyberattacks rely on phishing, not fast code. Moonlock, MacPaw’s cybersecurity division, reveals how films get it wrong.

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YaFSDP is an open-source tool that promises to revolutionize LLM training.

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 Dolomite will bring that same advanced infrastructure to X Layer, where it will be able to integrate many of the yield-generating assets that have accelerated 

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The first-ever browser security survey of CISOs’ security practices reveals CISOs struggles, displeasure with current security solutions and cloud concerns

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Build a transformer model with natural language processing to create new cocktail recipes from a cocktail database.

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Create a deep learning framework from scratch! 

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An overview of the current blockchain voting market around the world. 

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As the only multichain stablecoin, we are committed to <a href="https://hackernoon.com/eradicating-friction-2d4ddc4c3596" target="_blank">building stablecoins and fiat on / off ramps </a>on all of the best chains. Consequentially, we spend a lot of time researching chains and trying to decide what makes a chain “the best”.

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Ekstra AI earns a 46 Proof of Usefulness score by delivering privacy-first foot traffic intelligence to NYC small businesses—without surveillance.

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The airdop marks a major milestone in the project’s governance rollout. Goldfish has launched a social tasking leaderboard designed to provide rewards to ecosys

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Bitcoin’s layer 2 boom sells hype, not scaling. Bridges, tokens and trust replace security. True scaling stays anchored to Bitcoin’s base layer.

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Need video but only have photos? wan-effects applies popular visual effects to still images to create dynamic videos—plus tips for picking images that look best

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Bria’s structured instruction endpoint turns “edit this image” prompts into transparent, pipeline-ready instructions.

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<p><em>This is a simplified guide to an AI model called <a href="https://www.aimodels.fyi/models/huggingFace/qwen-image-layered-qwen?utm_source=hackernoon&utm_medium=referral">Qwen-Image-Layered</a> maintained by <a href="https://www.aimodels.fyi/creators/huggingFace/Qwen?utm_source=hackernoon&utm_medium=referral">Qwen</a>. If you like these kinds of analysis, join <a href="https://www.aimodels.fyi/?utm_source=hackernoon&utm_medium=referral">AIModels.fyi</a> or follow us on <a href="https://x.com/aimodelsfyi">Twitter</a>.</em></p>

<h2>Model overview</h2>
<p><code>Qwen-Image-Layered</code> decomposes images into multiple RGBA layers, creating a representation where each semantic or structural component exists in isolation. This approach differs from traditional image editing models by enabling direct manipulation of individual layers without affecting surrounding content. While <a href="https://aimodels.fyi/models/huggingFace/qwen-image-edit-plus-qwen?utm_source=hackernoon&utm_medium=referral">qwen-image-edit-plus</a> focuses on direct editing within a single image, <code>Qwen-Image-Layered</code> provides a foundational layer structure that makes subsequent edits more consistent and predictable. The model supports variable-layer decomposition, meaning you can request 3 layers, 8 layers, or any number that suits your needs.</p>
<h2>Model inputs and outputs</h2>
<p><code>Qwen-Image-Layered</code> accepts an image and configuration parameters, then generates a set of RGBA layers that reconstruct the original image when composited. The layered output enables precise control over individual elements without the consistency issues that plague traditional inpainting approaches.</p>
<h3>Inputs</h3>
<ul>
<li><strong>Image</strong>: An RGBA image to be decomposed into semantic layers</li>
<li><strong>Number of layers</strong>: The desired quantity of layers (variable and flexible)</li>
<li><strong>Resolution</strong>: Bucket resolution options (640 or 1024 pixels recommended)</li>
<li><strong>Inference steps</strong>: Number of diffusion steps (default 50)</li>
<li><strong>Guidance scale</strong>: Classifier-free guidance strength for layer quality</li>
<li><strong>Caption/Prompt</strong>: Optional text description or automatic caption generation</li>
</ul>
<h3>Outputs</h3>
<ul>
<li><strong>Layer images</strong>: Multiple RGBA images stacked as separate layers</li>
<li><strong>Composite result</strong>: The combined result showing how layers reconstruct the original</li>
<li><strong>Individual layer files</strong>: PNG files for each layer with transparency information</li>
</ul>
<h2>Capabilities</h2>
<p>The model excels at decomposing complex scenes into editable components. You can recolor individual layers while preserving others, replace specific objects (such as changing a person in one layer while keeping the background intact), modify text in isolated layers, or delete unwanted elements cleanly. Elementary operations become high-fidelity: resizing maintains quality without distortion, repositioning moves objects freely within the canvas, and recoloring affects only the target layer. The decomposition process is recursive, meaning any layer can itself be further decomposed for greater detail control.</p>
<h2>What can I use it for?</h2>
<p><code>Qwen-Image-Layered</code> suits professional and creative workflows where consistency matters. Graphic designers can edit layered compositions without accidentally affecting background elements. Content creators can swap objects in specific regions with guaranteed isolation from surrounding pixels. E-commerce platforms can modify product images by editing foreground items independently. Marketing teams can recolor branding elements across multiple assets while preserving photographic backgrounds. For monetization, agencies could offer layer-based image editing services with superior consistency guarantees compared to traditional inpainting. Integration with <a href="https://aimodels.fyi/models/huggingFace/qwen-image-edit-plus-qwen?utm_source=hackernoon&utm_medium=referral">qwen-image-edit-plus</a> allows layer-specific edits after decomposition for a complete editing pipeline.</p>
<h2>Things to try</h2>
<p>Experiment with variable-layer decomposition on the same image to see how the model interprets semantic boundaries differently. Try recursive decomposition by taking a single layer and decomposing it further—this reveals how much detail the model preserves at each level. Combine layer operations in sequences: decompose, edit multiple layers independently, then recompose. Test the model on images with overlapping objects to understand how it allocates elements across layers. Try adjusting the guidance scale to see how it affects layer separation quality, and compare results across different resolutions to balance detail against processing speed.</p>

<hr/><p><strong>Original post:</strong> <a href="https://www.aimodels.fyi/models/huggingFace/qwen-image-layered-qwen?utm_source=hackernoon&utm_medium=referral">Read on AIModels.fyi</a></p>

Meet Qwen-Image-Layered: The “Photoshop Layers” Trick That Makes AI Editing Finally Behave

Learn how the Linux kernel powers Android, managing hardware, processes, power, and secure communication beneath every app.

Android OS Architecture, Part 3: Inside the Linux Kernel Layer

If AI can optimise perfectly, it can also fail perfectly. Dynamic contracts—live guardrails with monitoring, override, and accountability—keep systems safe. 

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Users already know which documents matter for their work. So why can't they tell AI retrieval systems? Exploring the missing layer in RAG systems.

AI Retrieval Systems: What's The Missing Layer?

While teams focus on model selection, many overlook the orchestration layer, which determines whether an AI application remains economically viable at scale.

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Ivo Grigorov discusses why existing blockchains fail institutional RWA tokenization and how to solve such problems to build rails for institutional capital. 

Ivo Grigorov on Building a $500M Layer 1 Blockchain for Traditional Banks

Why AI banking needs multi-layer security, not just encryption. Buburuza’s COO explains how to stop deepfake fraud and breaches.

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BDX goes multi-chain with LayerZero and Stargate, enabling seamless cross-chain asset transfers across Ethereum, BNB Chain, Solana, Arbitrum, and Base networks.

BDX on LayerZero and Stargate: Privacy Meets Cross-Chain Interoperability

LayerX is a Japanese AI SaaS company specializing in automating back-office operations. Its flagship product, Bakuraku, streamlines corporate spending workflows, including expense management and invoice processing, serving over 15,000 companies.

LAYERX

layerx.co.jp

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Since 2018

LayerX's stories on HackerNoon

LayerX Security Raises $26M For Its Browser Security Platform, Enabling Employees To Work Securely

Weekly AI Startup Funding: October 12-19, 2025

Weekly AI Startup Funding: October 5-11, 2025

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Meet Qwen-Image-Layered: The “Photoshop Layers” Trick That Makes AI Editing Finally Behave

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