DoEmploy

Explore the implications of hacktivism in the fight against misinformation—ethical challenges, social media moderation, and the future of Internet activism.

Misinformation, Hacktivism, and the Future of the Internet

This year's hottest startup region was Europe, with huge startup hotbeds in the UK and Germany. 

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.

The Balance Between Efficiency and Security in Zero Knowledge Proofs

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

Does AI-generated Code Need to Be Tested?

An Interview with Talk Python to Me Podcast founder, Michael Kennedy. 

"Python is eating the software development world right now" 

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.

ActiveRecord on MySQL— Iterating over large tables with conditions

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

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

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?

What is the Advantage of Using Blockchain Tech in Healthcare?

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

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

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

Even the most automated systems still need an underlying philosophy.

Playbook for Production ML: Latency Testing, Regression Validation, and Automated Deployment

Learn how to build and deploy your first AI agent using Langchain and Sevalla.


How to Build Your First AI Agent and Deploy it to Sevalla

Damian Griggs, Adaptive Systems Architect, Centaur Model, Christmas

'Twas the Night Before Deploy: A Blind Architect’s Chess Engine Journey

Avoid the "AI Slop" trap. From runaway costs to memory poisoning, here are the 7 most common failure modes of Agentic AI (and how to fix them).

Patterns That Work and Pitfalls to Avoid in AI Agent Deployment

Learn how Langchain turns a simple prompt into a fully functional AI agent that can think, act and remember.

Agents 101 — Build and Deploy AI Agents to Production using LangChain

The Ecosystem Fund will invest in strengthening ecosystem footholds across global blockchain and entertainment markets.

NEXPACE Announces Ecosystem Fund, Deploying Up To $50 Million for MSU Ecosystem Growth And Expansion

AI systems can cause harm without intent. Learn why every organization needs an AI Impact Assessment to detect bias, build trust, and scale responsibly.

Why Every AI Product Needs an Impact Assessment Before Deployment

This section defines a new, practical Instance-Incremental Learning (IIL) problem setting focused on cost-effective model promotion in deployed systems.

New IIL Setting: Enhancing Deployed Models with Only New Data

This article details how to port and run the MobileNetV3 model on the NXP i.MX8MP platform to achieve the function of handwritten digit recognition. 

Deploying MobileNetV3 on NXP i.MX8MP: A Complete Edge AI Workflow for Handwritten Digit Recognition

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