Stix

Explore the intricate landscape of AI security and governance, from the often-overlooked security metapolicies to the need for multilateral collaboration. 

Reconciling AI Governance and Cybersecurity

Just consider how many service providers and models are in the picture. Therefore, it is time for a new and better approach — Cybersecurity Mesh Architecture.

CSMA is More Than XDR — An Introduction to Cybersecurity Mesh Architecture

The Internet of Things (IoT) has taken the entire technological industry by storm. It has not only created pathways for new technological developments but also opened new economic avenues for people across the world. Furthermore, the closer integration it enjoys with our daily lives has made it an indispensable part of our generation. Along with the internet and smartphone economies, the Internet of Things (IoT) economy has established the triumvirate of the neoclassical model of economics which will only continue to evolve from the infantile state it currently is in. From the technological maturity perspective, the IOT is still in infancy. It has successfully tackled the survival challenges that any newborn faces but now to take a leap into the adulthood it needs to overcome certain critical challenges that it&nbsp;faces.

Transforming the Internet of Things (IoT) to Value of Things (VoT)

Note: the following is a transcription of my interview with Matt Odell from Rabit Hole Recap and Neil Woodfine from Blockstream. I use Rev.com from translations and they remove ums, errs and half sentences. I have reviewed the transcription but if you find any mistakes, please feel free to <a href="http://hello@whatbitcoindid.com" target="_blank">email me</a>. You can <a href="https://www.whatbitcoindid.com/podcast/matt-odell-and-neil-woodfine-on-what-they-would-like-to-see-for-bitcoin-in-2019" target="_blank">listen to the original recording here</a>.

Matt Odell and Neil Woodfine on What They Would Like to See for Bitcoin in 2019

This is a simplified guide to an AI model called VIBE-Image-Edit [https://www.aimodels.fyi/models/huggingFace/vibe-image-edit-iitolstykh?utm_source=hackernoon&utm_medium=referral] maintained by iitolstykh [https://www.aimodels.fyi/creators/huggingFace/iitolstykh?utm_source=hackernoon&utm_medium=referral]. If you like these kinds of analysis, join AIModels.fyi [https://www.aimodels.fyi/?utm_source=hackernoon&utm_medium=referral] or follow us on Twitter [https://x.com/aimodelsfyi].


MODEL OVERVIEW

VIBE-Image-Edit is a text-guided image editing framework that combines efficiency with quality. It pairs the Sana1.5 diffusion model (1.6B parameters) with the Qwen3-VL vision-language encoder (2B parameters) to deliver fast, instruction-based image manipulation. The model handles images up to 2048 pixels and uses bfloat16 precision for optimal performance. Unlike heavier alternatives, this compact architecture maintains visual understanding capabilities while keeping computational requirements reasonable for consumer hardware. The framework builds on established foundations like diffusers and transformers, making it accessible to developers already familiar with the ecosystem.


MODEL INPUTS AND OUTPUTS

The model accepts natural language instructions paired with an image to understand both what changes should occur and where they should happen. It processes these inputs through its dual-component architecture to generate coherent edits that respect the original image composition while applying the requested modifications.


INPUTS

 * Conditioning image: The image to be edited, supporting resolutions up to 2048px
 * Text instruction: Natural language description of desired edits (e.g., "Add a cat on the sofa" or "let this case swim in the river")
 * Guidance parameters: Image guidance scale (default 1.2) and text guidance scale (default 4.5) to control edit intensity


OUTPUTS

 * Edited image: A single or multiple edited versions of the input image matching the text instruction
 * Variable quality levels: Output quality controlled through inference step count (default 20 steps)


CAPABILITIES

This model transforms images based on written instructions without requiring mask inputs or additional prompts. It handles diverse editing tasks from simple object additions to complex scene modifications. The multimodal understanding from Qwen3-VL ensures instructions align properly with visual content, reducing the gap between user intent and generated results. The linear attention mechanism in Sana1.5 enables rapid inference, generating edits in seconds rather than minutes. It maintains image coherence across different scales and aspect ratios, supporting both square and rectangular compositions.


WHAT CAN I USE IT FOR?

Content creators can use this model to prototype design changes before committing to manual edits. E-commerce platforms could enable customers to visualize product modifications in context. Marketing teams can generate multiple variations of images for A/B testing without hiring designers. Social media creators could quickly iterate on visual content. The model also supports integration into commercial applications, though it operates under SANA's original license terms. Developers building image editing tools can leverage this framework as a backend engine for their applications.


THINGS TO TRY

Experiment with varying guidance scales to control how dramatically the edits change the original image. Lower image guidance produces looser interpretations while higher values preserve more of the original composition. Test complex multi-step instructions like "add snow falling and make the trees more vibrant" to see how well the model handles compound edits. Try different image aspect ratios beyond standard square formats to explore the model's flexibility. Adjust the number of inference steps to find the balance between speed and quality for your use case—fewer steps run faster but may produce cruder results. Use style keywords in instructions (similar to how prompt engineering works in image generation) to guide the aesthetic direction of edits.

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Original post: Read on AIModels.fyi [https://www.aimodels.fyi/models/huggingFace/vibe-image-edit-iitolstykh?utm_source=hackernoon&utm_medium=referral]

The Compact Image Editor That Still Understands Your Intent: VIBE-Image-Edit

LLMs are powerful, but not universal. A practical guide to when AI adds real value—and when deterministic code is still the better choice. 

Why I Still Write Code That Will Be Obsolete in Two Years

A reflection on clean code, five years later. What still holds up, what feels incomplete, and how experience changed how I think about cleanup.

Five Years Later: Clean Code Is Still Right (But Not Always Useful)

Explore consensus algorithms on Stiefel manifolds and Siegel domains. Learn how Kuramoto models act as continuous-time algorithms to minimize disagreement.

Consensus Algorithms on Manifolds: Stiefel, Siegel, and Kuramoto Dynamics

Stop trusting the highlighter. Use AI to engineer "mental velcro" and crush your next exam.

The Highlighter Is Lying to You: Engineering "Sticky" Knowledge With AI

Why modern code reviews struggle to detect security weaknesses, and how focusing on root causes—not exploits—can improve secure software development.

Why Code Reviews Still Miss Critical Software Security Weaknesses

A 672-minute simulation of civilizational failure, and the three bugs we need to patch before we ship.

Pantheon Shows How Immortality, Infinite Compute, and Power Still End Civilizations

Hiring became AI-native the moment decision-making was designed around machines rather than humans.

The Job Market Is Now AI-Native - We’re Still Pretending It’s Human-Led

How institutions and governments are reshaping crypto adoption, replacing retail growth tactics with power, trust, and fear.

3 Lessons From Institutions Crypto Founders Still Don’t Understand About Power & Fear

Building zero-trust serverless: Lambda functions with per-function IAM, SigV4 auth, VPC endpoints. Passed SOC 2 audit in 6 weeks at $847/mo.

A $2.3M Deal, a Six-Week Deadline, and the Serverless Architecture That Saved Us

Learn how to measure marketing impact without A/B tests using causal inference, Diff-in-Diff, synthetic control, and GeoLift.

When A/B Tests Aren’t Possible, Causal Inference Can Still Measure Marketing Impact

Are Antivirus Software Still a Thing?

AI is a powerful accelerator for writing code and optimizing queries, but it lacks the contextual understanding to make high-stakes architectural trade-offs.

System Design in the Age of AI: What Still Requires Human Judgment

A grounded look at how crypto projects build trust in 2025, based on past cycles and the signals investors still misread.

The Signals Crypto Investors Still Misread in 2025

We audit Erlang/OTP’s decades-old C and NIF code with PVS-Studio, surfacing logic bugs, buffer risks, undefined behavior, and leaks in battle-tested systems.

The 1980s Code Powering Modern Reliability—and the Mistakes It Still Makes

A practical framework for setting 2026 goals across career, money, health, and relationships—designed for focus, leverage, and long-term progress.



 How to Set Goals for 2026 That Actually Stick

Latency sensitive financial systems favor predictable behavior and controlled execution paths, even as tools, infrastructure, and languages continue to evolve.

Why Low Latency Financial Systems Still Favor Determinism

A quantitative study of optimal liquidity provision in Uniswap v3, showing why LPs lose money and how dynamic strategies outperform.

In DeFi’s Most Popular Market Design, Liquidity Providers Are Still Losing

What six months of real users revealed about AI failures, JSON validation, retries, and why production—not demos—is the real teacher.

What Six Months of Real Users Taught Me About Running AI Systems in Production

A preservation-grade look at 3:2:1, fixity, provenance, and why integrity—not copy count—determines whether data survives or silently dies.

3:2:1 Is Still Necessary. It’s Just No Longer Sufficient.

Stix provides women the products, education, and community they need, starting with a better pregnancy and ovulation test

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Reconciling AI Governance and Cybersecurity

CSMA is More Than XDR — An Introduction to Cybersecurity Mesh Architecture

Transforming the Internet of Things (IoT) to Value of Things (VoT)

Matt Odell and Neil Woodfine on What They Would Like to See for Bitcoin in 2019

The Compact Image Editor That Still Understands Your Intent: VIBE-Image-Edit

Why I Still Write Code That Will Be Obsolete in Two Years

Five Years Later: Clean Code Is Still Right (But Not Always Useful)

Consensus Algorithms on Manifolds: Stiefel, Siegel, and Kuramoto Dynamics

The Highlighter Is Lying to You: Engineering "Sticky" Knowledge With AI

Why Code Reviews Still Miss Critical Software Security Weaknesses

Pantheon Shows How Immortality, Infinite Compute, and Power Still End Civilizations

The Job Market Is Now AI-Native - We’re Still Pretending It’s Human-Led

3 Lessons From Institutions Crypto Founders Still Don’t Understand About Power & Fear

A $2.3M Deal, a Six-Week Deadline, and the Serverless Architecture That Saved Us

When A/B Tests Aren’t Possible, Causal Inference Can Still Measure Marketing Impact

Are Antivirus Software Still a Thing?

System Design in the Age of AI: What Still Requires Human Judgment

The Signals Crypto Investors Still Misread in 2025

The 1980s Code Powering Modern Reliability—and the Mistakes It Still Makes

How to Set Goals for 2026 That Actually Stick

Why Low Latency Financial Systems Still Favor Determinism

In DeFi’s Most Popular Market Design, Liquidity Providers Are Still Losing

What Six Months of Real Users Taught Me About Running AI Systems in Production

3:2:1 Is Still Necessary. It’s Just No Longer Sufficient.

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