DoEmploy

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Startups of The Year 2023 Winners: Europe

In summary, various techniques can be used to improve the efficiency and security of zero-knowledge proofs, such as preprocessing, batching, recursion, etc.

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We will see that AI-powered tools for writing code cannot guarantee anything regarding the functional correctness and security of the code they suggest.

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In this article, I’m going to demonstrate performance differences between two ways of iterating over the records in a MySQL database table with millions of records. In a high volume analytics system, tables with millions of records are quite common and iterating over the full table or a subset of these tables becomes often necessary — whether it’s to perform computations, run a migration, or create parallelized background jobs on the records. At <a href="https://www.airpr.com/" target="_blank">AirPR</a>, we have many database tables with 100s of millions of records, and it becomes important to write efficient code for iterations because there is often an order of magnitude difference between a good and not-so-good approach.

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Learn how to boost query performance, operations, and manage large datasets effectively. From basic to advanced tips for developers looking to enhance their EF 

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Temporal tables are a type of database table that store historical data, allowing users to query the data as it existed at specific points in time.

Golang: a RESTful API Using Temporal Table With MariaDB

 Adopt SOLID design principles in GO for better code quality and easier software maintenance.

GO Design Patterns: An Introduction to SOLID

Are traditional data management practices becoming obsolete?

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Simplify New Relic deployments track with a custom Laravel. Track deployments easily without GitHub Actions by using 'php artisan app:deploy-mark'. 

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EU proposes AI Omnibus to delay high-risk AI rules to 2027 and centralize enforcement, reshaping how companies comply with the AI Act.

EU’s AI Omnibus: Pivoting from Regulation to Active Deployment

A practical, no-nonsense guide to getting your vibe-coded app live with a PaaS, without falling into DevOps rabbit holes.

A Vibe Coder’s Guide to Deployment using a PaaS

Secure your cloud at commit time. Learn how to protect Terraform, CI/CD pipelines, IAM roles, and state files with policy-as-code and DevSecOps best practices. 

Securing the Cloud Control Plane: A Practical Guide to Secure IaC Deployments

AlphaTON Capital adds 504 NVIDIA Blackwell B200 GPUs in Canada, scaling Telegram AI infrastructure and targeting $3M+ monthly revenue.

AlphaTON Capital Deploys 504 NVIDIA Blackwell B200 GPUs to Power Telegram’s AI Infrastructure

Legacy industries don’t fear technology, they fear costly mistakes. They face important challenges, and a founder's role is also to help navigate them.

The Reality of Deploying Tech in Legacy Industries

Staging environments use fake data. Canary releases expose real users. Shadow deployments sit upstream and catch regressions before anyone notices.

Ship Code Fearlessly With Shadow Deployments

Linux upgrades don’t have to be risky. Learn how dual-root partitions enable safe testing, instant rollback, and stress-free OS migrations.

Dual-Root Linux: Blue-Green Deployments for Your Operating System

A step-by-step guide to containerising a FastAPI application with Docker and deploying it to the cloud for consistent, production-ready delivery.

How to Dockerize Your Application and Deploy It to Sevalla

Learn to build a log analyzing agent using Langchain and Sevalla

How to Build and Deploy a LogAnalyzer Agent using Langchain and Sevalla

MomentProof’s patented technology certifies images, video, voice recordings, and associated metadata at the moment of capture.

MomentProof Deploys Patented Digital Asset Protection

A practical LTX-2 guide: inputs/outputs, text-to-video and image-to-video, the 19B and 8-step variants, BF16 training, LoRA tips, and how to get cleaner results

The Creator’s Shortcut to Sounded AI Video: LTX-2, Distilled and Deployable

<p><em>This is a simplified guide to an AI model called <a href="https://www.aimodels.fyi/models/huggingFace/nunchaku-qwen-image-edit-2509-nunchaku-ai?utm_source=hackernoon&utm_medium=referral">nunchaku-qwen-image-edit-2509</a> maintained by <a href="https://www.aimodels.fyi/creators/huggingFace/nunchaku-ai?utm_source=hackernoon&utm_medium=referral">nunchaku-ai</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>nunchaku-qwen-image-edit-2509</code> is a quantized version of Qwen-Image-Edit-2509, an image-editing model developed by the Nunchaku Team. This model is based on <a href="https://aimodels.fyi/models/huggingFace/nunchaku-qwen-image-nunchaku-tech?utm_source=hackernoon&utm_medium=referral">Qwen-Image</a> and represents advances in complex text rendering for image manipulation tasks. The quantization process reduces model size while maintaining performance quality, making it suitable for efficient inference on consumer hardware. The model comes in multiple variants with different data types (INT4 and NVFP4) and rank configurations to balance speed and quality based on your hardware requirements.</p>
<h2>Model inputs and outputs</h2>
<p>The model accepts images and text instructions as inputs and produces edited images as outputs. It operates as an image-to-image transformer, allowing users to modify existing images based on textual descriptions or editing prompts. The quantized variants preserve the core functionality of the original while reducing computational demands.</p>
<h3>Inputs</h3>
<ul>
<li><strong>Source image</strong>: The image to be edited</li>
<li><strong>Text instruction</strong>: A description of the desired edit or modification</li>
</ul>
<h3>Outputs</h3>
<ul>
<li><strong>Edited image</strong>: The modified image reflecting the requested changes</li>
</ul>
<h2>Capabilities</h2>
<p>The model handles text-guided image editing with support for complex text rendering. It can process various editing scenarios from simple modifications to more intricate transformations. Multiple versions exist for different use cases: standard inference models for general editing, 4-step lightning models for rapid generation, and 8-step variants for cases where quality matters over speed. The lightning models are fused with specialized LoRA weights to accelerate inference while minimizing quality loss.</p>
<h2>What can I use it for?</h2>
<p>Image editing applications benefit from this model's efficiency and quality balance. Content creators can use it for rapid prototyping of visual edits, marketers can automate product image modifications, and developers can integrate it into applications requiring fast image manipulation. The quantized nature makes it accessible for deployment on standard GPUs without requiring high-end hardware. The 4-step lightning variants are particularly useful when response time is critical, such as in interactive web applications or real-time editing interfaces.</p>
<h2>Things to try</h2>
<p>Experiment with different rank configurations (r32 for speed, r128 for quality) to find the optimal balance for your specific use case. Test the lightning variants with 4 or 8 steps to observe how inference speed improves with fewer diffusion steps. Compare results between INT4 and NVFP4 data types if you have access to different GPU architectures. Try combining editing instructions with various image styles to understand how the model handles complex artistic transformations and maintains coherence with detailed text prompts.</p>

<hr/><p><strong>Original post:</strong> <a href="https://www.aimodels.fyi/models/huggingFace/nunchaku-qwen-image-edit-2509-nunchaku-ai?utm_source=hackernoon&utm_medium=referral">Read on AIModels.fyi</a></p>

A No-Drama Guide to Deploying Qwen Image Editing on Standard GPUs

Learn how to turn any blog post or text into a clear, natural-sounding audio file using OpenAI's text-to-speech API.

How to Build and Deploy a Blog-to-Audio Service Using OpenAI

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DoEmploy's stories on HackerNoon

Misinformation, Hacktivism, and the Future of the Internet

Startups of The Year 2023 Winners: Europe

The Balance Between Efficiency and Security in Zero Knowledge Proofs

Does AI-generated Code Need to Be Tested?

"Python is eating the software development world right now"

ActiveRecord on MySQL— Iterating over large tables with conditions

Essential Entity Framework Core Tips: How to Optimize Performance, Streamline Queries, and More

Golang: a RESTful API Using Temporal Table With MariaDB

GO Design Patterns: An Introduction to SOLID

What is the Advantage of Using Blockchain Tech in Healthcare?

You Can Track Code Deployments in New Relic With Laravel Commands: Here's How

EU’s AI Omnibus: Pivoting from Regulation to Active Deployment

A Vibe Coder’s Guide to Deployment using a PaaS

Securing the Cloud Control Plane: A Practical Guide to Secure IaC Deployments

AlphaTON Capital Deploys 504 NVIDIA Blackwell B200 GPUs to Power Telegram’s AI Infrastructure

The Reality of Deploying Tech in Legacy Industries

Ship Code Fearlessly With Shadow Deployments

Dual-Root Linux: Blue-Green Deployments for Your Operating System

How to Dockerize Your Application and Deploy It to Sevalla

How to Build and Deploy a LogAnalyzer Agent using Langchain and Sevalla

MomentProof Deploys Patented Digital Asset Protection

The Creator’s Shortcut to Sounded AI Video: LTX-2, Distilled and Deployable

A No-Drama Guide to Deploying Qwen Image Editing on Standard GPUs

How to Build and Deploy a Blog-to-Audio Service Using OpenAI

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