TL;DR: Andrew Huang gushes over the Torso S4 2.0, calling it one of the most inspiring music tools he’s ever used. In his walkthrough he demos everything from devices &amp; FX, tempo &amp; performance modes, mix and track pages, modulation and scene management, to the built-in mic sampler, MIDI mapping, live looper and instant resampling. 

It’s all sponsored by Torso Electronics (links to their site and Insta included), and Andrew sprinkles in extras—join his Discord for stems, hit up his Patreon, check out his socials, and peep his fave plugins, gear and distro links via his affiliate codes.

Watch on <a href="https://www.youtube.com/watch?v=gtLupImEYac" rel="noopener noreferrer">YouTube</a>

أشاد أندرو هوانغ بجهاز Torso S4 2.0، واصفًا إياه بأنه واحد من أكثر أدوات الموسيقى إلهامًا التي استخدمها على الإطلاق. في عرضه التوضيحي، يستعرض ميزات متنوعة تشمل الأجهزة والتأثيرات، أوضاع الإيقاع والأداء، صفحات المزج والتتبع، إدارة التعديل والمشاهد، عينة الميكروفون المدمجة، تخطيط MIDI، جهاز التكرار المباشر وإعادة العينة الفورية. المحتوى برعاية Torso Electronics، ويشجع هوانغ المشاهدين على الانضمام إلى مجتمعه على Discord وPatreon.

Andrew Huang ha elogiado el Torso S4 2.0, describiéndolo como una de las herramientas musicales más inspiradoras que ha utilizado. En su demostración, muestra diversas características, incluyendo dispositivos y efectos, modos de tempo y rendimiento, páginas de mezcla y seguimiento, gestión de modulación y escenas, un muestreador de micrófono integrado, mapeo MIDI, looper en vivo y re-muestreo instantáneo. El contenido está patrocinado por Torso Electronics, y Huang anima a los espectadores a unirse a su comunidad en Discord y Patreon.

Andrew Huang a loué le Torso S4 2.0, le décrivant comme l'un des outils musicaux les plus inspirants qu'il ait jamais utilisés. Dans sa démonstration, il présente diverses fonctionnalités, notamment des appareils et des effets, des modes de tempo et de performance, des pages de mixage et de suivi, la gestion de la modulation et des scènes, un échantillonneur de microphone intégré, le mapping MIDI, un looper en direct et un rééchantillonnage instantané. Le contenu est sponsorisé par Torso Electronics, et Huang encourage les spectateurs à s'engager avec sa communauté sur Discord et Patreon.

Andrew Huang has praised the Torso S4 2.0, describing it as one of the most inspiring music tools he has ever used. In his demonstration, he showcases various features including devices and effects, tempo and performance modes, mix and track pages, modulation and scene management, a built-in mic sampler, MIDI mapping, live looper, and instant resampling. The content is sponsored by Torso Electronics, and Huang encourages viewers to engage with his community on Discord and Patreon.

Andrew Huang: S4 2.0 is one of the most inspiring tools I've used

Here's everything you need to know about the latest smart ring by Oura, based on a wearable expert's real-world usage.

Is the $500 Oura Ring 4 Ceramic worth it? I wore one for a month, and here's my advice

Samsung's QN90F QLED delivers great streaming and gaming performance, making it a strong holiday value.

Why I recommend this Samsung QLED TV over pricier OLED models in 2025 - and don't regret it

<h1>
 
 
 AI Amnesia: Erasing Knowledge Without a Trace
</h1>

Imagine your AI model accidentally learned something it shouldn't have – sensitive customer data, for example. Current methods for deleting this information often require retraining the entire model, an expensive and time-consuming process. What if we could surgically remove that knowledge without starting from scratch?

The key lies in a novel approach: creating artificial "forgetting cues." We're teaching the model to unlearn specific data patterns by exposing it to carefully crafted synthetic examples. These examples are designed to strongly contradict the information we want the model to forget, effectively overwriting the problematic associations in its memory. This works even if you don’t have access to the original data you need to erase.

Think of it like this: you're trying to forget a bad song stuck in your head. Instead of trying to actively suppress it (which rarely works), you blast an even more catchy song. The new song overwrites the old one, effectively erasing it from your mental playlist.

Benefits of Selective Forgetting:

<ul>
<li> Enhanced Data Privacy: Remove sensitive data without compromising the overall model's performance.</li>
<li> Reduced Retraining Costs: Avoid full model retraining, saving significant time and resources.</li>
<li> Improved Model Security: Eliminate vulnerabilities introduced by unintentionally learned patterns.</li>
<li> Adaptable Learning: Enables continuous refinement of AI models based on evolving data landscapes.</li>
<li> Compliance Ready: Supports compliance with data privacy regulations like GDPR.</li>
<li> Scalable Solutions: Works efficiently even with limited access to training data.</li>
</ul>

Practical Tip: One challenge is ensuring the synthetic data accurately targets the information you want to remove without negatively impacting the model's ability to generalize. Rigorous testing and validation with holdout datasets are crucial.

The promise of AI that can truly 'forget' opens exciting possibilities for responsible AI development. By enabling precise data deletion, we pave the way for more secure, compliant, and adaptable machine learning systems. Imagine AI models that can adapt to changing ethical guidelines or quickly unlearn incorrect information, all without massive retraining efforts. This is a crucial step towards trustworthy and responsible AI that respects data privacy and aligns with societal values. Future exploration could include extending this to different data modalities and model architectures.

Related Keywords: machine unlearning, data privacy, few-shot learning, zero-shot learning, synthetic data, model editing, catastrophic forgetting, incremental learning, continual learning, deep learning, neural networks, data security, algorithmic fairness, responsible ai, ethical ai, federated unlearning, privacy-preserving ai, model retraining, AI governance, data deletion, GDPR compliance

يتناول المقال نهجًا جديدًا لمعالجة مشكلة نسيان الذكاء الاصطناعي، حيث قد تحتفظ نماذج الذكاء الاصطناعي عن غير قصد بمعلومات حساسة. تتطلب الطرق التقليدية لحذف هذه البيانات غالبًا إعادة تدريب كاملة للنموذج، وهو ما يعد مكلفًا ويستغرق وقتًا. تتضمن التقنية الجديدة إنشاء 'إشارات نسيان' اصطناعية تساعد النموذج على نسيان أنماط بيانات معينة من خلال تعريضه لأمثلة صناعية تتناقض مع المعلومات غير المرغوب فيها، مما يسمح بإزالة المعرفة المستهدفة دون الحاجة للوصول إلى البيانات الأصلية.

El artículo aborda un nuevo enfoque para tratar el problema de la amnesia de la IA, donde los modelos de IA pueden retener inadvertidamente información sensible. Los métodos tradicionales para eliminar estos datos a menudo requieren un reentrenamiento completo del modelo, lo que es costoso y lleva tiempo. La nueva técnica implica crear 'señales de olvido' artificiales que ayudan al modelo a desaprender patrones de datos específicos al exponerlo a ejemplos sintéticos que contradicen la información no deseada, permitiendo así la eliminación selectiva de conocimientos sin necesidad de acceder a l…

L'article traite d'une nouvelle approche pour résoudre le problème de l'amnésie de l'IA, où les modèles d'IA peuvent involontairement conserver des informations sensibles. Les méthodes traditionnelles pour supprimer ces données nécessitent souvent un réentraînement complet du modèle, ce qui est coûteux et long. La nouvelle technique consiste à créer des 'cues d'oubli' artificiels qui aident le modèle à désapprendre des schémas de données spécifiques en lui présentant des exemples synthétiques qui contredisent les informations indésirables, permettant ainsi une suppression ciblée des connaissan…

The article discusses a novel approach to address the issue of AI amnesia, where AI models may inadvertently retain sensitive information. Traditional methods for deleting such data often require complete retraining of the model, which is costly and time-consuming. The new technique involves creating artificial 'forgetting cues' that help the model unlearn specific data patterns by presenting it with synthetic examples that contradict the unwanted information, allowing for targeted knowledge removal without needing access to the original data.

AI Amnesia: Erasing Knowledge Without a Trace

Baseus' Enercore CG11 travel adapter is one of the better designed ones I've tested, although its best feature isn't immediately apparent.

Traveling soon? Why this one charger is the only one you'll ever need to pack

Turning materials like wood chips, crop residues and municipal solid waste into fuels and chemicals is important for our country's energy independence.

طور الباحثون نماذج حاسوبية متقدمة تهدف إلى تحسين التنبؤات لعمليات طحن الكتلة الحيوية. تركز هذه الابتكارات على تحويل مواد مثل رقائق الخشب، بقايا المحاصيل، والنفايات الصلبة البلدية إلى وقود ومواد كيميائية قيمة، وهو أمر مهم لتعزيز استقلالية الطاقة في البلاد. من المتوقع أن تعمل النماذج على تحسين كفاءة معالجة الكتلة الحيوية، مما يساهم في حلول الطاقة المستدامة.

Investigadores han desarrollado modelos informáticos avanzados destinados a mejorar las predicciones para los procesos de molienda de biomasa. Esta innovación se centra en convertir materiales como astillas de madera, residuos de cultivos y residuos sólidos municipales en combustibles y productos químicos valiosos, lo que es crucial para mejorar la independencia energética del país. Se espera que los modelos optimicen la eficiencia del procesamiento de biomasa, contribuyendo así a soluciones energéticas sostenibles.

Des chercheurs ont développé des modèles informatiques avancés visant à améliorer les prévisions pour les processus de broyage de la biomasse. Cette innovation se concentre sur la conversion de matériaux tels que les copeaux de bois, les résidus de culture et les déchets solides municipaux en combustibles et produits chimiques précieux, ce qui est crucial pour renforcer l'indépendance énergétique du pays. Les modèles devraient optimiser l'efficacité du traitement de la biomasse, contribuant ainsi à des solutions énergétiques durables.

Researchers have developed advanced computer models aimed at improving predictions for biomass milling processes. This innovation focuses on converting materials such as wood chips, crop residues, and municipal solid waste into valuable fuels and chemicals, which is crucial for enhancing energy independence in the country. The models are expected to optimize the efficiency of biomass processing, thereby contributing to sustainable energy solutions.

Researchers develop computer models for better biomass milling predictions

The most interesting aspect of the latest Even Realities glasses may be their limitations.

I've tried several AI smart glasses (including Meta Ray-Bans) in 2025 - these are the most comfortable

In the ever-evolving world of digital media, creators are constantly looking for smarter, faster, and more efficient ways to stand out. As platforms like Instagram, YouTube, TikTok, and Facebook become more video-centric, the pressure to create high-quality visual content has never been greater. Among all the technological breakthroughs shaping modern content creation, AI face swapping stands out as one of the most transformative.

What started as a fun novelty  swapping your face with a friend or celebrity  has now evolved into a powerful tool that is reshaping marketing, filmmaking, user-generated content, and even brand storytelling. With advanced AI models capable of producing hyper-realistic face swaps in seconds, creators and businesses are beginning to unlock endless possibilities.

This article explores in detail why <a href="https://pixele.studio/products/face-swap" rel="noopener noreferrer">AI face swapping</a> is becoming a major force in digital content creation, how it works, where it’s used, ethical considerations, and what the future holds.

<h2>
 
 
 What Exactly Is AI Face Swapping?
</h2>

AI face swapping is the process of replacing one person’s face with another using artificial intelligence. This technology uses deep learning models that analyze:

Facial structure

Skin texture

Lighting patterns

Angles and shadows

<h2>
 
 
 Expressions and movements
</h2>

Modern AI tools can produce highly <a href="https://pixele.studio/blog/why-ai-face-swapping-is-the-future-of-digital-content-creation/" rel="noopener noreferrer">realistic face swaps</a> that are almost indistinguishable from real footage. Unlike older methods that required advanced VFX skills, AI tools today allow beginners, marketers, and social media creators to produce professional-level results instantly.

Face swapping is now used in:

Social media videos

Advertising

Film production

Education

Gaming

Storytelling

Personalized content

The convenience, speed, and creative freedom offered by AI face swap technology have made it a favorite among creators worldwide.

<h2>
 
 
 Why AI Face Swapping Is the Future of Digital Content
</h2>

Unmatched Speed in Content Creation

Digital content today needs to be fast. Trends change weekly, sometimes even daily, and creators who cannot keep up fall behind. AI face swapping drastically reduces production time by automating:

Facial mapping

Visual blending

Expression matching

Motion adjustments

What previously took hours now takes seconds.

For example:

A marketer wants 10 versions of an ad with different people featured. Instead of reshooting, they can simply use AI face swap tools to generate all versions from one video.

This level of speed is changing how campaigns are built, how influencers produce daily content, and how brands adapt to real-time trends.

<h2>
 
 
 Professional Quality Effects Without High Costs
</h2>

Before AI, producing realistic face swaps required:

VFX professionals

Green screens

High-end editing software

Specialized animators

Expensive production studios

<h2>
 
 
 Today, AI eliminates most of these costs. Even low-budget creators can now create:
</h2>

Hollywood-style visual effects

Realistic character transformations

High-quality promotional videos

Comedy and entertainment content

The democratization of advanced editing tools is one of the biggest reasons AI face swap technology is gaining global popularity.

<h2>
 
 
 Personalized and Interactive Content
</h2>

Personalization is the future of digital marketing. Users prefer content that feels relevant and tailored to them. AI face swapping enables creators and businesses to make highly personalized experiences:

Personalized birthday videos

Customized ads featuring the viewer

Interactive storytelling

Branded filters

Fan-made celebrity videos

For example:

A fitness brand could create a motivational video where the viewer’s face replaces the trainer, making the content feel more engaging and personal.

This not only boosts engagement but also improves conversion rates for brands.

<h2>
 
 
 Creative Freedom Like Never Before
</h2>

AI face swapping breaks creative limitations. It enables creators to experiment with styles and characters that would be impossible or too expensive otherwise. You can:

Turn yourself into a superhero

Place yourself inside movie scenes

Create fantasy characters

Re-imagine historical events

Transform into cartoon versions

Recreate viral memes

This creativity is what drives social media engagement, making face swap tools incredibly powerful for content creators.

Perfect for High-Volume Social Media Production

Social platforms reward consistency. The more videos you post, the faster you grow. But high-volume content production is time-consuming.

<h2>
 
 
 AI face swapping solves this problem by allowing creators to:
</h2>

Reuse old footage

Create multiple versions of the same video

Produce content in different styles

Refresh older content with new faces

For influencers and brands that post daily, this is a game changer.

<h2>
 
 
 Real-World Use Cases of AI Face Swapping
</h2>

Content Creators and YouTubers

Creators use AI face swap tools for:

Skits and comedy videos

Transformation edits

Reaction content

Mashups and trend-based content

Short-form videos and reels

<h2>
 
 
 Face swapping increases watch time and makes videos more visually engaging.
</h2>

Digital Marketing and Advertising

Marketers use face swapping for:

A/B testing ad variations

Personalized video ads

Localized marketing

Campaigns targeted to multiple demographics

Instead of hiring multiple actors, a single video can be face-swapped for different audience groups.

<h2>
 
 
 Film and Entertainment
</h2>

In cinema, face swapping is used to:

De-age actors

Replace stunt doubles

Recreate historical figures

Generate digital doubles

Improve VFX realism

Many studios now rely on AI to reduce production costs and streamline post-processing.

<h2>
 
 
 Gaming and Animation
</h2>

Gaming companies use AI face swapping to:

Create realistic character models

Build cinematic cutscenes

Animate facial expressions

Personalize avatars

AI helps speed up the animation pipeline significantly.

Education and Learning

Teachers and institutions use face swapping to create:

Interactive lessons

Character-based learning

Visual storytelling

Historical reenactments

This makes learning fun, engaging, and memorable.

<h2>
 
 
 How AI Face Swapping Works 
</h2>

Although the user experience seems simple, the technology behind it is incredibly advanced.

<ol>
<li>Face Detection</li>
</ol>

AI first identifies the shape, geometry, and boundaries of the face.

<ol>
<li>Feature Extraction</li>
</ol>

It detects features like:

Eyes

Mouth

Nose

Cheekbones

Jawline

<ol>
<li>Expression Tracking</li>
</ol>

AI maps facial movements frame-by-frame.

<ol>
<li>Skin and Lighting Match</li>
</ol>

Modern AI blends:

Skin tone

Texture

Shadow

Highlight patterns

<ol>
<li>Rendering</li>
</ol>

The final composite is generated with realistic blending, producing a natural look.

<h2>
 
 
 Why AI Face Swapping Will Dominate Future Content
</h2>

<ol>
<li>Video-First Platforms Are Growing</li>
</ol>

The future of content is video, and AI face swap tools make video creation scalable.

<ol>
<li>Automation Is Becoming Standard</li>
</ol>

AI handles:

Editing

Replacing faces

Enhancing quality

Matching movements

This allows creators to focus on ideas instead of technical work.

<ol>
<li>Digital Humans Are Emerging</li>
</ol>

Virtual influencers are already popular, and AI face swap will make them even more realistic.

<ol>
<li>Brands Want Personalized Experiences</li>
</ol>

Consumers prefer personalized visuals — AI tools deliver exactly that.

<ol>
<li>Future Films Will Use AI Actors</li>
</ol>

AI-generated actors will appear in movies and advertisements, reducing production costs dramatically.

<h2>
 
 
 Ethical Responsibilities of Face Swapping
</h2>

With great power comes great responsibility. AI face swap tools must be used ethically.

<ol>
<li>Consent Matters</li>
</ol>

Always get permission before swapping someone’s face.

<ol>
<li>No Harmful Uses</li>
</ol>

Avoid creating misleading or harmful content.

<ol>
<li>Transparency</li>
</ol>

Brands should mention if AI was used in content.

<ol>
<li>Watermarking</li>
</ol>

Many tools include automatic watermarking to avoid misuse.

<h2>
 
 
 Future Predictions: Where AI Face Swapping Is Headed
</h2>

Over the next decade, AI face swapping will evolve into a multi-billion-dollar industry.

<ol>
<li>Real-Time Live Streaming Face Swaps</li>
</ol>

Creators will be able to swap faces during live broadcasts.

<ol>
<li>Fully Automated Video Creation</li>
</ol>

You will type a script, and AI will generate:

Voices

Faces

Movements

Backgrounds

<ol>
<li>Hyper-Realistic Digital Actors</li>
</ol>

Films will feature AI-generated actors performing lifelike roles.

<ol>
<li>AR and VR Integration</li>
</ol>

Face swap tools will become part of immersive gaming and VR experiences.

<ol>
<li>100% Personalized Ads</li>
</ol>

Brands will create ads where the viewer’s face automatically appears in the video.

<h2>
 
 
 Conclusion
</h2>

AI face swapping is redefining digital content creation by making it faster, more cost-effective, and more creative than ever before. From personalized ads to cinematic storytelling, the technology is transforming every industry that relies on visual media.

As social platforms continue to favor high-quality video content, AI face swap tools will become essential for creators, marketers, and businesses. The future of digital content is interactive, personalized, and AI-powered and face swapping is leading the revolution.

يتناول المقال صعود تقنية تبديل الوجوه باستخدام الذكاء الاصطناعي في إنشاء المحتوى الرقمي، مع تسليط الضوء على تأثيرها التحويلي على منصات مثل إنستغرام ويوتيوب وتيك توك وفيسبوك. بدأت كوسيلة ترفيهية، تطورت تقنية تبديل الوجوه إلى أداة قوية للتسويق وصناعة الأفلام ورواية العلامات التجارية، مما يمكّن المبدعين من إنتاج تبديلات وجه فائقة الواقعية في ثوانٍ. تعيد هذه التكنولوجيا تشكيل كيفية إنشاء واستهلاك المحتوى المرئي، مما يعكس الطلب المتزايد على وسائل الإعلام عالية الجودة في مشهد رقمي يركز على الفيديو.

El artículo aborda el auge del intercambio de rostros por IA en la creación de contenido digital, destacando su impacto transformador en plataformas como Instagram, YouTube, TikTok y Facebook. Inicialmente una novedad, el intercambio de rostros por IA se ha convertido en una herramienta poderosa para el marketing, la producción cinematográfica y la narración de marcas, permitiendo a los creadores producir intercambios de rostros hiperrealistas en segundos. Esta tecnología está redefiniendo cómo se crea y consume el contenido visual, reflejando la creciente demanda de medios de alta calidad en un paisaje digital centrado en el video.

L'article traite de l'essor du remplacement de visage par IA dans la création de contenu numérique, soulignant son impact transformateur sur des plateformes comme Instagram, YouTube, TikTok et Facebook. Initialement un simple divertissement, le remplacement de visage par IA est devenu un outil puissant pour le marketing, le cinéma et la narration de marque, permettant aux créateurs de produire des échanges de visages hyper-réalistes en quelques secondes. Cette technologie redéfinit la manière dont le contenu visuel est créé et consommé, reflétant la demande croissante de médias de haute qualité dans un paysage numérique centré sur la vidéo.

The article discusses the rise of AI face swapping in digital content creation, highlighting its transformative impact on platforms like Instagram, YouTube, TikTok, and Facebook. Initially a novelty, AI face swapping has evolved into a powerful tool for marketing, filmmaking, and brand storytelling, enabling creators to produce hyper-realistic face swaps in seconds. This technology is reshaping how visual content is created and consumed, reflecting the increasing demand for high-quality media in a video-centric digital landscape.

Future of Digital Content: The Rise of AI Face Swapping

<h3>
 
 
 Speedtest.net vs Fast.com in real life
</h3>

Your speed test says 400 Mbps. 
YouTube still buffers. 
Nothing feels more cursed than that.

That happened on my home fiber line. 
<code>Speedtest.net</code> proudly showed more than 418 Mbps down, but YouTube sessions still dropped quality or paused to buffer. 
The numbers looked great, the experience did not.

The trick is simple: speed tests often measure the best case, while streaming exposes the real case.


<h2>
 
 
 Two tests, two different truths
</h2>

<code>Speedtest.net</code> is built to show what your connection can do when everything is aligned in its favor. 
It talks to dedicated, well peered servers and opens several parallel TCP connections to push your link as hard as possible. 
The result is a nice high number that represents peak capacity, not necessarily what you get for every service.

<code>Fast.com</code>, from Netflix, is much closer to what your streaming apps feel. 
It talks to real CDN infrastructure, pulls real data over a sustained period and focuses on how fast you can actually move video like traffic. 
That is why <code>Fast.com</code> often shows a lower number that still explains your YouTube or Netflix experience much better than a single big result from <code>Speedtest.net</code>.

If <code>Speedtest.net</code> is your max horsepower on a dyno, <code>Fast.com</code> is how fast the car actually accelerates on the road.


<h2>
 
 
 Protocols: TCP vs QUIC in one glance
</h2>

Most classic speed tests use TCP. 
TCP is reliable, ordered and conservative, and it slowly ramps up how much data it sends until it figures out what the network can handle. 
To compensate for that slow start, speed tests usually use several parallel TCP streams to reach your real peak quickly.

Modern streaming platforms lean heavily on QUIC over UDP, also known as HTTP3. 
QUIC runs on UDP, adds encryption and reliability, and handles packet loss more gracefully without blocking every stream when one packet goes missing. 
ISPs do not always treat TCP and UDP traffic the same way, so your speed test path and your video path can be very different, even on the same line.

You can think of TCP as a careful truck convoy and QUIC as a lot of smaller, nimble cars using partly different roads.


<h2>
 
 
 My real world results
</h2>

Here is what my line looked like on paper.

Local <code>Speedtest.net</code> server in Baku, PASHA Technology: 
<code>Download: 418.14 Mbps</code> 
<code>Upload: 435.85 Mbps</code> 
<code>Ping: 6 ms</code> 
<code>Loaded latency (down): 60 ms</code> 
<code>Loaded latency (up): 89 ms</code>

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F8cmjr35gveucclq1k8x4.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F8cmjr35gveucclq1k8x4.png" alt="Speedtest.net local" width="800" height="475"></a>

<code>Speedtest.net</code> server in Helsinki, GSL Networks: 
<code>Download: 390.72 Mbps</code> 
<code>Upload: 326.58 Mbps</code> 
<code>Ping: 110 ms</code>

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fwr4fcwx0f38nicjo6575.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fwr4fcwx0f38nicjo6575.png" alt="Speedtest.net international" width="800" height="429"></a>

Numbers say your connection is a monster. 

But when I ran <code>Fast.com</code>, the picture changed. 
<code>Fast.com</code> consistently reported noticeably lower speeds that felt much closer to what I saw when YouTube dropped quality or paused.

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F7sgww3h72gsa5no7ma2r.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F7sgww3h72gsa5no7ma2r.png" alt="Fast.com screenshot" width="800" height="740"></a>

<code>Speedtest.net</code> showed the theoretical maximum. 
<code>Fast.com</code> revealed the practical streaming ceiling.

I also ran a quick traceroute to <code>youtube.com</code> to see how the packets travel. 


<div class="highlight js-code-highlight">
<pre class="highlight shell"><code>traceroute youtube.com
</code></pre>

</div>



The path goes through my home router, across my ISP’s core, then out via a Moscow internet exchange onto Google’s backbone, and finally lands on a YouTube edge in about 60–70 ms. That hop pattern is clean and the latency is low and stable, so routing itself looks fine; the real problem is not “ping” but how much throughput I get for streaming traffic compared to what classic speed tests report.




<h2>
 
 
 Why the gap happens
</h2>

There are three usual suspects.

First, peering and routing. 
My ISP clearly has a very clean, well provisioned path to the <code>Speedtest.net</code> servers it picks for me. 
The path to the Netflix and YouTube CDNs is different, sometimes longer and sometimes more congested.

Second, traffic treatment. 
Generic TCP test traffic looks harmless and small in volume compared to the hours of video people stream every evening. 
During busy times, it is easy for an ISP to give streaming traffic lower priority than other things without completely breaking it.

Third, protocol differences. 
If the network handles UDP and QUIC slightly worse than TCP, you feel that specifically in streaming, games and real time apps. 
On a graph, the TCP path still looks beautiful, while the QUIC path quietly suffers.




<h2>
 
 
 How to check your own line quickly
</h2>

You can reproduce this pattern in a few minutes.

Do one <code>Speedtest.net</code> run to a nearby server. 
Note download, upload and ping and glance at the loaded latency while the test is running. 
That gives you your lab result.

Then open <code>Fast.com</code> and run it a couple of times, especially during the hour when you actually watch YouTube or Netflix. 
If <code>Fast.com</code> is in the same ballpark, your streaming path is probably healthy. 
If it is half or less of the Speedtest number, especially in the evening, you are likely seeing congestion, weak peering or some form of video deprioritization.




<h2>
 
 
 Small tweaks that actually help
</h2>

If you control your router, give priority to traffic that hates latency. 
Real time calls first, then streaming, then normal browsing, then heavy downloads and torrents. 
Smart queue management will not magically increase your Mbps, but it can stop a single download from wrecking a family movie night.

Another quick experiment is to compare <code>Fast.com</code> with and without a VPN. 
If speeds suddenly jump when you tunnel out through a decent nearby VPN, it is a strong hint that your provider is treating some destinations or protocols less kindly than others.

In that case you can either live with it, complain with data in hand or lean on the VPN for the services that suffer most.




<h2>
 
 
 The real meaning of “418 Mbps”
</h2>

That 418 Mbps Speedtest result on my connection is not a lie. 
It is just telling a very specific story: how fast my line can go to a friendly server over TCP under ideal conditions.

YouTube buffering and lower <code>Fast.com</code> numbers tell the rest of the story: 
how my ISP peers with big CDNs, 
how QUIC over UDP is routed, 
how streaming traffic is handled when the network is busy.

So the takeaway is simple. 
Use more than one test. 
Treat big <code>Speedtest.net</code> numbers as best case, not a guarantee. 
For streaming, the less glamorous <code>Fast.com</code> style results are often closer to what your eyes and ears actually feel.

يمكن أن تكون اختبارات سرعة الإنترنت مضللة، كما يتضح من تجربة شخصية حيث أبلغ Speedtest.net عن سرعة تنزيل تزيد عن 418 ميجابت في الثانية، ومع ذلك كانت يوتيوب لا تزال تعاني من مشاكل في التخزين المؤقت. تنشأ هذه الفجوة لأن Speedtest.net يقيس السعة القصوى في ظروف مثالية، بينما تعكس خدمات مثل يوتيوب الأداء في العالم الحقيقي. من ناحية أخرى، تم تصميم Fast.com لتقييم الأداء الفعلي للبث على مدى فترات طويلة، مما يوفر تمثيلاً أكثر دقة لتجربة المستخدم. لذلك، على الرغم من أن اختبارات السرعة قد تظهر أرقامًا مثيرة للإعجاب، إلا أنها لا تتوافق دائمًا مع جودة خدمات البث.

Las pruebas de velocidad de Internet pueden ser engañosas, como se demostró en una experiencia personal donde Speedtest.net reportó una velocidad de descarga de más de 418 Mbps, pero YouTube aún experimentó problemas de almacenamiento en búfer. Esta discrepancia surge porque Speedtest.net mide la capacidad máxima en condiciones óptimas, mientras que servicios como YouTube reflejan el rendimiento en el mundo real. Fast.com, por otro lado, está diseñado para evaluar el rendimiento real de streaming durante períodos sostenidos, proporcionando una representación más precisa de la experiencia del usuario. Por lo tanto, aunque las pruebas de velocidad pueden mostrar números impresionantes, no siempre se correlacionan con la calidad de los servicios de streaming.

Les tests de vitesse Internet peuvent souvent être trompeurs, comme le montre une expérience personnelle où Speedtest.net a signalé une vitesse de téléchargement de plus de 418 Mbps, alors que YouTube rencontrait toujours des problèmes de mise en mémoire tampon. Cette divergence provient du fait que Speedtest.net mesure la capacité maximale dans des conditions optimales, tandis que des services comme YouTube reflètent la performance dans le monde réel. Fast.com, en revanche, est conçu pour évaluer la performance de streaming réelle sur des périodes prolongées, offrant une représentation plus précise de l'expérience utilisateur. Ainsi, bien que les tests de vitesse puissent afficher des chiffres impressionnants, ils ne correspondent pas toujours à la qualité des services de streaming.

Internet speed tests can often be misleading, as demonstrated by a personal experience where Speedtest.net reported a download speed of over 418 Mbps, yet YouTube still experienced buffering issues. This discrepancy arises because Speedtest.net measures peak capacity under optimal conditions, while services like YouTube reflect real-world performance. Fast.com, on the other hand, is designed to assess actual streaming performance over sustained periods, providing a more accurate representation of user experience. Therefore, while speed tests may show impressive numbers, they do not always correlate with the quality of streaming services.

Why Your Internet Speed Test Lies

<h1>
 
 
 Everything Wrong With Jurassic World Rebirth In 17 Minutes Or Less
</h1>

CinemaSins takes on the latest dino-resurrection in a bite-sized 17-minute roast, pointing out all the plot sins of Jurassic World Rebirth. Alongside the video, they’re hyping up their main site (cinemasins.com) and various YouTube channels—TV Sins, Commercial Sins and the CinemaSins Podcast Network.

They’re also pushing a Linktree for fresh updates, a quick sinful poll, and a Patreon for backers. You can connect with the writers on Twitter and Instagram, join the Discord or Reddit communities, and catch extra content on TikTok and Jeremy’s new book.

Watch on <a href="https://www.youtube.com/watch?v=e9rog6ba0f0" rel="noopener noreferrer">YouTube</a>

تنتقد CinemaSins فيلم Jurassic World Rebirth في فيديو مختصر مدته 17 دقيقة، مشيرةً إلى العديد من العيوب وعدم التناسق في الحبكة. الفيديو جزء من استراتيجيتهم الأوسع للمحتوى، حيث يروجون لموقعهم الرئيسي والعديد من قنوات يوتيوب، بما في ذلك TV Sins وCommercial Sins. كما يشجعون تفاعل الجمهور عبر منصات التواصل الاجتماعي وPatreon لدعمهم.

CinemaSins critica la película Jurassic World Rebirth en un video conciso de 17 minutos, señalando varios defectos e inconsistencias en la trama. El video es parte de su estrategia de contenido más amplia, promoviendo su sitio web principal y varios canales de YouTube, incluidos TV Sins y Commercial Sins. También fomentan la participación del público a través de plataformas de redes sociales y un Patreon para sus seguidores.

CinemaSins critique le film Jurassic World Rebirth dans une vidéo concise de 17 minutes, soulignant divers défauts et incohérences de l'intrigue. La vidéo fait partie de leur stratégie de contenu plus large, promouvant leur site principal et plusieurs chaînes YouTube, y compris TV Sins et Commercial Sins. Ils encouragent également l'engagement du public via les réseaux sociaux et un Patreon pour les supporters.

CinemaSins critiques the film Jurassic World Rebirth in a concise 17-minute video, highlighting various plot flaws and inconsistencies. The video is part of their broader content strategy, promoting their main website and multiple YouTube channels, including TV Sins and Commercial Sins. They also encourage audience engagement through social media platforms and a Patreon for supporters.

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