<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

حقق Google Gemini 3 إنجازًا كبيرًا من خلال تجاوز جميع معايير الذكاء الاصطناعي الحالية، بما في ذلك الأكثر تحديًا. يبرز هذا الإنجاز التقدم الذي حققته فريق الذكاء الاصطناعي في Google ويضع Gemini 3 كمتنافس رائد في مجال الذكاء الاصطناعي. يتم الاحتفال بهذا النجاح داخل مجتمع التكنولوجيا، مما يعكس التطور المستمر لتقنيات الذكاء الاصطناعي وقدراتها.

Google Gemini 3 ha logrado un hito significativo al superar todos los benchmarks de IA existentes, incluidos los más desafiantes. Este logro resalta los avances realizados por el equipo de IA de Google y posiciona a Gemini 3 como un competidor líder en el panorama de la inteligencia artificial. El éxito es celebrado dentro de la comunidad tecnológica, reflejando la evolución continua de las tecnologías de IA y sus capacidades.

Google Gemini 3 a atteint un jalon significatif en surpassant tous les benchmarks d'IA existants, y compris les plus difficiles. Cet accomplissement met en évidence les avancées réalisées par l'équipe d'IA de Google et positionne Gemini 3 comme un concurrent de premier plan dans le paysage de l'intelligence artificielle. Le succès est célébré au sein de la communauté technologique, reflétant l'évolution continue des technologies d'IA et de leurs capacités.

Google Gemini 3 has achieved a significant milestone by surpassing all existing AI benchmarks, including the most challenging ones. This accomplishment highlights the advancements made by Google's AI team and positions Gemini 3 as a leading contender in the artificial intelligence landscape. The success is celebrated within the tech community, reflecting the ongoing evolution of AI technologies and their capabilities.

Google Gemini 3 Just Killed Every AI Benchmark, Including the Hardest of All

Two weeks ago I read a line about tool use with Claude that stuck in my head. Paraphrased:

<blockquote>
Direct tool calls don’t really scale. 
Have the model write code that uses tools, and execute that code instead.
</blockquote>

At the same time, I was knee-deep in wiring a JavaScript execution environment into Contenox, my self-hosted runtime for deterministic, chat-native AI workflows.

So of course the thought was:

<blockquote>
What if I just let the model write the JavaScript and run it inside the runtime? 😅
</blockquote>

This post is about what happened when I tried exactly that.




<h2>
 
 
 What is Contenox?
</h2>

Very short version:

<blockquote>
Contenox is a self-hostable runtime for sovereign GenAI applications. 
It models AI behavior as explicit state machines, not opaque prompt chains.
</blockquote>

Some key properties:

Runtime, not a library, Explicit state machines, Chat-native interface, Vendor-agnostic &amp; self-hosted, written in go with lots of passion and zero tolerance for shortcuts




<h2>
 
 
 The experiment: ask it to fetch and summarize a TODO
</h2>

Once the JS execution hook was in place (a Goja VM with some globals exposed), I wired up a new state machine: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>mux_input → moderate → generate_js → run_js → eval_js_result → (repair_js?) → answer
</code></pre>

</div>



Then I opened the Contenox chat UI and typed:

<blockquote>
Me: “Hey Contenox, fetch the TODO item from jsonplaceholder and summarize it.”
</blockquote>

The first attempt didn’t go as planned.

Instead of a neat summary, I got:

<blockquote>
Contenox: 
“I'm sorry, but it seems there was a problem fetching the TODO item from the provided URL due to an Internal Server Error (status code: 500). Unfortunately, I couldn't retrieve any data at this time. My apologies for the inconvenience!”
</blockquote>

I checked manually – Cloudflare was having a moment, JSONPlaceholder was behind it, and the request really was failing. Okay, fair. Long coffee break.

A bit later, I went back to the same chat and typed:

<blockquote>
Me: “okay let's try again now.”
</blockquote>

This time the reply was:

<blockquote>
Contenox: 
<code>Task titled "delectus aut autem" is incomplete. Nothing to summarize here.</code>
</blockquote>

Wait. It worked? Just like that?




<h2>
 
 
 Under the hood: LLM → JavaScript → tools → LLM
</h2>

Looking at the execution trace made it clear what happened.

<h3>
 
 
 1. Input is normalized and moderated
</h3>

The workflow starts with:

<ol>
<li><code>mux_input</code></li>
</ol>

<ul>
<li>
Parses the incoming chat history into a normalized <code>messages</code> array.

<ol>
<li><code>moderate</code></li>
</ol>
</li>
<li>Uses a small model to classify the input as safe/unsafe.</li>
<li>Output: <code>0</code> → safe → continue.</li>
</ul>

So far this is standard workflow stuff.

<h3>
 
 
 2. <code>generate_js</code>: the LLM writes JavaScript
</h3>

Next, the <code>generate_js</code> task runs with a system instruction like:

<ul>
<li>“You are an expert JavaScript agent for the Contenox runtime…”</li>
<li>“You must output JSON: { "code": "" }”</li>
<li>“You can use httpFetch, executeTask, executeTaskChain, …”</li>
<li>“Script must be synchronous, no async/await, and must return a JSON-serializable object.”</li>
</ul>

Given the chat history, the model produced something like this (simplified): 


<div class="highlight js-code-highlight">
<pre class="highlight json"><code>{
 "code": "var result;
var answer;
var meta = { steps: [] };

var todoUrl = \"https://jsonplaceholder.typicode.com/todos/1\";

// Step 1: Fetch the TODO item
meta.steps.push(\"Fetching TODO item from \" + todoUrl);
var fetchResponse = httpFetch(todoUrl);

if (!fetchResponse.ok) {
 answer = \"I couldn't access that item right now because there was some trouble with getting it. Please check the URL or try again later.\";
 meta.error = {
 message: \"HTTP fetch failed\",
 url: todoUrl,
 status: fetchResponse.status,
 statusText: fetchResponse.statusText,
 body: fetchResponse.body,
 error: fetchResponse.error
 };
 result = { answer: answer, meta: meta };
} else {
 var todoItem;
 try {
 todoItem = JSON.parse(fetchResponse.body);
 meta.raw = todoItem;
 } catch (e) {
 answer = \"I fetched the item, but I couldn't understand its format. It doesn't seem to be valid JSON.\";
 meta.error = {
 message: \"JSON parse error\",
 rawBody: fetchResponse.body,
 parseError: e.toString()
 };
 result = { answer: answer, meta: meta };
 }

 if (todoItem) {
 // Step 2: Summarize the TODO item using an LLM
 meta.steps.push(\"Summarizing TODO item with LLM\");
 var prompt = \"Summarize the following TODO item in a concise sentence: \" + JSON.stringify(todoItem);
 var summarizeTask = executeTask(prompt, \"gpt-4o-mini\", \"openai\");

 if (!summarizeTask.success) {
 answer = \"While I managed to fetch your TODO details, the summary tool had a hiccup and couldn't provide me with a concise sentence right now. We seem to be experiencing some temporary issues.\";
 meta.error = {
 message: \"LLM task execution failed\",
 task_id: summarizeTask.task_id,
 error: summarizeTask.error
 };
 result = { answer: answer, meta: meta };
 } else {
 answer = summarizeTask.response;
 meta.llmResponse = summarizeTask.response;
 result = { answer: answer, meta: meta };
 }
 }
}

result;"
}
</code></pre>

</div>



A few important details here:

<ul>
<li>
It encodes error handling:

<ul>
<li>If HTTP fails → user-friendly <code>answer</code>, raw error in <code>meta.error</code>.</li>
<li>If JSON parse fails → similar pattern.</li>
<li>If the inner LLM call fails → fallback message.</li>
</ul>


</li>

<li>It delegates the actual summarization to another model via <code>executeTask</code>.</li>

<li>It returns a structured <code>result</code> with both <code>answer</code> and <code>meta</code>.</li>

</ul>

This is not the model “calling tools” directly. It’s the model writing a program that calls tools.

<h3>
 
 
 3. <code>run_js</code>: execute the code in a sandbox
</h3>

The next task is <code>run_js</code>, which is just a Contenox <code>hook</code> that calls the JS sandbox: 


<div class="highlight js-code-highlight">
<pre class="highlight json"><code>{
 "name": "js_sandbox",
 "tool_name": "execute_js",
 "args": {
 "code": "{{.generate_js.code}}"
 }
}
</code></pre>

</div>



Inside the trace you can see:

<ul>
<li>An <code>httpFetch</code> log for the JSONPlaceholder URL.</li>
<li>A response with <code>status: 200 OK</code> when things finally worked.</li>
<li>
An <code>executeTask</code> log with the summarization prompt:

<ul>
<li><code>Summarize the following TODO item in a concise sentence: {"userId":1,"id":1,"title":"delectus aut autem","completed":false}</code></li>
</ul>


</li>

</ul>

The sandbox result looked roughly like: 


<div class="highlight js-code-highlight">
<pre class="highlight json"><code>{
 "ok": true,
 "result": {
 "answer": "Task titled \"delectus aut autem\" is incomplete.",
 "meta": {
 "llmResponse": "Task titled \"delectus aut autem\" is incomplete.",
 "raw": {
 "userId": 1,
 "id": 1,
 "title": "delectus aut autem",
 "completed": false
 },
 "steps": [
 "Fetching TODO item from https://jsonplaceholder.typicode.com/todos/1",
 "Summarizing TODO item with LLM"
 ]
 }
 },
 "logs": [ ... ],
 "code": "var result; ..."
}
</code></pre>

</div>



<h3>
 
 
 4. <code>eval_js_result</code>: success or retry?
</h3>

Now comes the evaluator:

<ul>
<li>It receives a description of the JS sandbox output.</li>
<li>
The system prompt is very strict:

<ul>
<li>If <code>ok</code> is true and there is a non-empty <code>result.answer</code> → respond with <code>success</code>.</li>
<li>Otherwise → respond with <code>retry</code>.</li>
</ul>


</li>

</ul>

On the successful run, it answered: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>success
</code></pre>

</div>



So the workflow does not go into <code>repair_js</code> or <code>run_js_retry</code>. Happy path.

<h3>
 
 
 5. <code>answer</code>: extract the final user message
</h3>

The final task, <code>answer</code>, is intentionally boring:

<ul>
<li>System prompt: “You are a purely extractive post-processor. Do NOT invent content. Just surface the best existing <code>answer</code> field.”
</li>
<li>
It gets:

<ul>
<li>First run (<code>run_js</code> result).</li>
<li>Second run (<code>run_js_retry</code>), if any.</li>
</ul>


</li>

<li>

Selection rule:

<ul>
<li>Take the last non-empty <code>answer</code> you see.</li>
<li>Output it verbatim.</li>
</ul>


</li>

</ul>

In our case it found: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>Task titled "delectus aut autem" is incomplete.
</code></pre>

</div>



And that’s exactly what Contenox replied in chat.




<h2>
 
 
 Why this is interesting (to me, at least)
</h2>

What I originally set out to build:

<blockquote>
A runtime for deterministic, observable GenAI workflows. 
Tasks, transitions, hooks – all explicit and replayable.
</blockquote>

What I accidentally stumbled into:

<blockquote>
A multi-model, self-orchestrating agent pattern, 
where LLMs write code that uses tools, and the runtime executes and evaluates that code.
</blockquote>

The pattern looks like this:

<ol>
<li>
Planner LLM (<code>generate_js</code>)</li>
</ol>

<ul>
<li>Reads user intent + history.</li>
<li>Emits JavaScript that calls <code>httpFetch</code>, <code>executeTask</code>, <code>executeTaskChain</code>, hooks, etc.</li>
</ul>

<ol>
<li>
Execution environment (<code>run_js</code> in Goja)</li>
</ol>

<ul>
<li>Deterministic execution of that JS.</li>
<li>Full logs of every HTTP call, every inner LLM call, every step.</li>
</ul>

<ol>
<li>
Controller LLM (<code>eval_js_result</code>)</li>
</ol>

<ul>
<li>Looks at the sandbox result.</li>
<li>Decides: is this good enough? Retry? Repair?</li>
</ul>

<ol>
<li>
Repair LLM (<code>repair_js</code>, if needed)</li>
</ol>

<ul>
<li>Gets the previous code + error output.</li>
<li>Writes a fixed version of the JS.</li>
</ul>

<ol>
<li>
Answer LLM (<code>answer</code>)</li>
</ol>

<ul>
<li>Doesn’t “reason” at all.</li>
<li>Just extracts the final <code>answer</code> text safely.</li>
</ul>

All of that is expressed as an explicit state machine in Contenox.

No hidden loops, no undocumented retries, no magic glue code inside some SDK. It’s all visible in the workflow graph and trace.




To me, that’s the exciting part:

<blockquote>
You don’t have to choose between “boring deterministic workflows” and “fancy agents”. 
You can build the agent on top of deterministic workflows. 
And everything stays **self-hosted, inspectable, and auditable if you want.
</blockquote>

يتناول المقال تجربة تم فيها السماح لنموذج ذكاء اصطناعي بكتابة كود جافا سكريبت داخل بيئة مستقلة تُدعى كونتينوكس. يتأمل الكاتب في مفهوم يتعلق باستخدام الأدوات في الذكاء الاصطناعي، مقترحًا أن النماذج يجب أن تولد كودًا لاستخدام الأدوات بدلاً من إجراء مكالمات مباشرة. تم اختبار هذا النهج من خلال تنفيذ كود جافا سكريبت الذي تم إنشاؤه داخل بيئة كونتينوكس، بهدف تحسين كفاءة سير العمل في الذكاء الاصطناعي.

El artículo discute un experimento en el que se permitió a un modelo de IA escribir código JavaScript dentro de un entorno autónomo llamado Contenox. El autor reflexiona sobre un concepto relacionado con el uso de herramientas en IA, sugiriendo que los modelos deberían generar código para utilizar herramientas en lugar de realizar llamadas directas. Este enfoque se probó ejecutando el JavaScript generado dentro del entorno Contenox, con el objetivo de mejorar la eficiencia de los flujos de trabajo de IA.

L'article traite d'une expérience où un modèle d'IA a été autorisé à écrire du code JavaScript au sein d'un environnement autonome appelé Contenox. L'auteur réfléchit à un concept concernant l'utilisation des outils en IA, suggérant que les modèles devraient générer du code pour utiliser des outils plutôt que d'effectuer des appels directs. Cette approche a été testée en exécutant le JavaScript généré dans l'environnement Contenox, visant à améliorer l'efficacité des flux de travail en IA.

The article discusses an experiment where an AI model was allowed to write JavaScript code within a self-hosted runtime called Contenox. The author reflects on a concept regarding tool usage in AI, suggesting that models should generate code to utilize tools instead of direct calls. This approach was tested by executing the generated JavaScript within the Contenox environment, aiming to enhance the efficiency of AI workflows.

I Let an LLM Write JavaScript Inside My AI Runtime. Here’s What Happened

Caterpillar Inc. was always an unlikely winner in the artificial intelligence craze. It makes the bulk of its money selling the equipment like yellow earth movers that has made it a stalwart of American industry.

تظهر شركة كاتربيلر كجهة غير متوقعة في قطاع الذكاء الاصطناعي، حيث تُعرف أساسًا بتصنيع الآلات الثقيلة مثل آلات الحفر. تاريخيًا، كانت الشركة تركز على المعدات الصناعية التقليدية، مما يجعلها أقل توافقًا مع الاتجاهات التكنولوجية المدفوعة بالذكاء الاصطناعي التي اجتذبت العديد من القطاعات الأخرى. على الرغم من الاهتمام المتزايد بالذكاء الاصطناعي، لا يزال النشاط الرئيسي لشركة كاتربيلر متجذرًا في الآلات المادية، مما قد يحد من جاذبيتها في مشهد تكنولوجي سريع التطور.

Caterpillar Inc. se ha presentado como un jugador improbable en el sector de la inteligencia artificial, siendo principalmente conocida por la fabricación de maquinaria pesada como las excavadoras. La compañía ha estado históricamente enfocada en equipos industriales tradicionales, lo que la hace menos alineada con las tendencias tecnológicas impulsadas por la IA que han cautivado a muchos otros sectores. A pesar del creciente interés en la IA, el negocio principal de Caterpillar sigue anclado en la maquinaria física, lo que podría limitar su atractivo en un paisaje tecnológico en rápida evolu…

Caterpillar Inc. se présente comme un acteur improbable dans le secteur de l'intelligence artificielle, étant principalement connu pour sa fabrication de machines lourdes telles que les pelles mécaniques. L'entreprise s'est historiquement concentrée sur l'équipement industriel traditionnel, ce qui la rend moins alignée avec les tendances technologiques axées sur l'IA qui ont captivé de nombreux autres secteurs. Malgré l'intérêt croissant pour l'IA, le cœur de métier de Caterpillar reste ancré dans la machinerie physique, ce qui pourrait limiter son attrait dans un paysage technologique en évol…

Caterpillar Inc. has emerged as an unlikely player in the artificial intelligence sector, primarily known for its manufacturing of heavy machinery such as earth movers. The company has historically focused on traditional industrial equipment, making it less aligned with the AI-driven technology trends that have captivated many other sectors. Despite the growing interest in AI, Caterpillar's core business remains rooted in physical machinery, which may limit its appeal in the rapidly evolving tech landscape.

Caterpillar’s Lone Bear Says Machinery Maker Is No AI Darling

<a href="https://www.techspot.com/news/110306-microsoft-explains-how-windows-11-become-agentic-os.html" target="_blank"><img src="https://www.techspot.com/images2/news/ts3_thumbs/2025/11/2025-11-18-ts3_thumbs-d01.jpg" width="800" height="560" style="padding: 15px 0" title="Microsoft explains how Windows 11 will become an agentic OS whether you like it or not" /></a> Windows president Pavan Davuluri recently described the future of Windows as an agentic operating system, where AI bots and large language models handle the user's commands on files and computing tasks. Critics mostly greeted the idea with scorn, cursing, and frustration over the "bug-ridden slop pile" the OS currently is.... <a href="https://www.techspot.com/news/110306-microsoft-explains-how-windows-11-become-agentic-os.html">Read Entire Article</a>

وصف رئيس شركة مايكروسوفت، بavan دافولوري، خطط ويندوز 11 للتطور إلى ما يسميه 'نظام تشغيل وكيل'. ستتضمن هذه التحويلة دمج روبوتات الذكاء الاصطناعي ونماذج اللغة الكبيرة لإدارة أوامر المستخدم ومهام الحوسبة. ومع ذلك، تم استقبال هذا الإعلان بسخرية وانتقادات من المستخدمين الذين يشعرون بالإحباط من الحالة الحالية لنظام التشغيل، الذي يصفونه بأنه مليء بالأخطاء.

El presidente de Microsoft, Pavan Davuluri, ha descrito los planes para que Windows 11 evolucione hacia lo que él llama un 'sistema operativo agente'. Esta transformación implicará la integración de bots de IA y modelos de lenguaje para gestionar los comandos y tareas informáticas del usuario. Sin embargo, el anuncio ha sido recibido con escepticismo y críticas por parte de los usuarios, que están frustrados con el estado actual del sistema operativo, que describen como plagado de errores.

Le président de Microsoft, Pavan Davuluri, a décrit les projets de Windows 11 pour évoluer vers ce qu'il appelle un 'système d'exploitation agentique'. Cette transformation impliquera l'intégration de bots IA et de modèles de langage pour gérer les commandes des utilisateurs et les tâches informatiques. Cependant, cette annonce a été accueillie avec scepticisme et critiques de la part des utilisateurs frustrés par l'état actuel du système d'exploitation, qu'ils décrivent comme rempli de bogues.

Microsoft's president, Pavan Davuluri, has outlined plans for Windows 11 to evolve into what he describes as an 'agentic operating system.' This transformation will involve the integration of AI bots and large language models to manage user commands and computing tasks. However, the announcement has been met with skepticism and criticism from users who are frustrated with the current state of the operating system, which they describe as plagued by bugs.

Microsoft explains how Windows 11 will become an agentic OS whether you like it or not

Although Black Friday is still two weeks away, you can find great Nintendo Switch and Switch 2 deals now. I've collected the best from Walmart, Best Buy, and more.

مع اقتراب يوم الجمعة السوداء بعد أسبوعين، تتوفر بالفعل عروض مبكرة على أجهزة نينتندو سويتش وسويتش 2. تقدم متاجر كبيرة مثل وول مارت وبيست باي أكثر من 20 عرضًا، مما يوفر للمستهلكين فرصة لتوفير المال على منتجات الألعاب الشهيرة قبل موسم التسوق للعطلات.

A medida que se acerca el Black Friday en dos semanas, ya están disponibles ofertas anticipadas en las consolas Nintendo Switch y Switch 2. Grandes minoristas como Walmart y Best Buy están ofreciendo más de 20 ventas, brindando a los consumidores la oportunidad de ahorrar en productos de videojuegos populares antes de la locura de compras navideñas.

À l'approche de Black Friday dans deux semaines, des offres anticipées sur les consoles Nintendo Switch et Switch 2 sont déjà disponibles. Des détaillants majeurs comme Walmart et Best Buy proposent plus de 20 ventes, offrant aux consommateurs l'occasion d'économiser sur des produits de jeu populaires avant la ruée des achats de vacances.

As Black Friday approaches in two weeks, early deals on Nintendo Switch and Switch 2 consoles are already available. Major retailers like Walmart and Best Buy are offering over 20 sales, providing consumers with an opportunity to save on popular gaming products ahead of the holiday shopping rush.

Best early Black Friday Nintendo Switch deals 2025: 20+ sales out early

<A HREF="https://substack.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.MstA6dhKs3CLxJgLSEpyVK_D4Oz9SmcjeXKNnqEDzIg?"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251118/i47.jpg"></A>
<A HREF="http://www.techmeme.com/251118/p47#a251118p47" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Alex Heath / <A HREF="https://sources.news/">Sources</A>: 
<A HREF="https://substack.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.MstA6dhKs3CLxJgLSEpyVK_D4Oz9SmcjeXKNnqEDzIg?">Q&amp;A with Demis Hassabis on Gemini 3, spending most of his research time on world models, fitting the entire Google Search index into Gemini, AI bubble, and more</A>&nbsp; &mdash;&nbsp; Demis Hassabis was noticeably relaxed when he joined our virtual call from London.&nbsp; &mdash;&nbsp; It was the day before the release of Gemini 3 &hellip;

ناقش ديميس هاسابيس، المؤسس المشارك لشركة ديب مايند، تقدم نموذج جيميني 3، أحدث نموذج للذكاء الاصطناعي من جوجل، مشددًا على قدراته في نمذجة العالم ودمج كامل فهرس بحث جوجل في النظام. تناول المخاوف بشأن فقاعة الذكاء الاصطناعي وأبرز إمكانيات النموذج في تحسين تفاعلات المستخدمين وتوفير معلومات أكثر دقة. تعكس أفكار هاسابيس التزامًا بدفع حدود تكنولوجيا الذكاء الاصطناعي ودمجها في التطبيقات اليومية.

Demis Hassabis, cofundador de DeepMind, discutió los avances de Gemini 3, el último modelo de IA de Google, enfatizando sus capacidades en modelos del mundo y la integración de todo el índice de búsqueda de Google en el sistema. Abordó las preocupaciones sobre la burbuja de la IA y destacó el potencial del modelo para mejorar las interacciones con los usuarios y proporcionar información más precisa. Las ideas de Hassabis reflejan un compromiso por llevar la tecnología de IA a nuevos límites y su integración en aplicaciones cotidianas.

Demis Hassabis, co-fondateur de DeepMind, a discuté des avancées de Gemini 3, le dernier modèle d'IA de Google, en mettant l'accent sur ses capacités en modélisation du monde et l'intégration de l'ensemble de l'index de recherche de Google dans le système. Il a abordé les préoccupations concernant la bulle de l'IA et a souligné le potentiel du modèle à améliorer les interactions avec les utilisateurs et à fournir des informations plus précises. Les réflexions de Hassabis reflètent un engagement à repousser les limites de la technologie IA et son intégration dans les applications quotidiennes.

Demis Hassabis, co-founder of DeepMind, discussed the advancements of Gemini 3, Google's latest AI model, emphasizing its capabilities in world modeling and fitting the entire Google Search index into the system. He addressed concerns about the AI bubble and highlighted the model's potential to enhance user interactions and provide more accurate information. Hassabis's insights reflect a commitment to pushing the boundaries of AI technology and its integration into everyday applications.

Q&A with Demis Hassabis on Gemini 3, spending most of his research time on world models, fitting the entire Google Search index into Gemini, AI bubble, and more (Alex Heath/Sources)

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

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

<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.

CinemaSins: Everything Wrong With Jurassic World Rebirth In 17 Minutes Or Less

<h1>
 
 
 Antigravity IDE: la nueva generación de editores impulsados por IA
</h1>

La inteligencia artificial ha cambiado la forma en que escribimos, aprendemos y ahora también, cómo programamos. Si VS Code fue el símbolo del desarrollo moderno, Antigravity IDE representa la siguiente frontera: un entorno capaz de comprender al desarrollador, anticipar sus intenciones y colaborar en la creación de software de forma proactiva.




<h2>
 
 
 De VS Code a Antigravity
</h2>

El concepto Antigravity no surge de una herramienta real de Google, sino de una metáfora: la capacidad de elevarse por encima de las limitaciones actuales. En Python, <code>import antigravity</code> era una broma sobre lo absurdo y genial del código. En este nuevo contexto, Antigravity se convierte en una visión de desarrollo aumentado por IA, donde el editor deja de ser un espacio pasivo para transformarse en un agente inteligente.

Antigravity IDE combina la flexibilidad de VS Code con la potencia de los modelos de lenguaje, integrando sistemas que aprenden del flujo del usuario y personalizan cada experiencia. Este hipotético fork de Google sería más que una herramienta: un copiloto autónomo, capaz de comprender el contexto, sugerir soluciones y corregir errores en tiempo real.




<h2>
 
 
 La IA como copiloto consciente
</h2>

Un IDE con capacidades de aprendizaje profundo podría analizar el comportamiento del programador y adaptar su interfaz y funciones según sus hábitos. Por ejemplo, priorizar comandos o sugerir estructuras de código basadas en proyectos anteriores. 

El sistema sería lo bastante inteligente para entender intenciones, detectar ambigüedades en el código y proponer alternativas sin requerir intervención constante. Este tipo de adaptabilidad rompe el paradigma actual de “autocompletado” y da paso a una colaboración cognitiva, donde la IA aprende del desarrollador tanto como este aprende de ella.

Imagina un entorno que sabe cuándo estás explorando una idea y cuándo estás optimizando código; que diferencia entre un error lógico y un experimento intencional. Esa es la esencia de Antigravity IDE: un espacio donde la IA no reemplaza al humano, sino que amplifica su creatividad y eficiencia.




<h2>
 
 
 El valor de un IDE inteligente
</h2>

La transición de los editores tradicionales a entornos potenciados por IA no es solo una mejora técnica, sino una revolución cultural. Supone un cambio en la relación entre creador y herramienta, donde el editor se convierte en un aliado cognitivo.

<ul>
<li>
Eficiencia sin fricción: tareas repetitivas automatizadas, mayor concentración en la lógica.
</li>
<li>
Aprendizaje continuo: el entorno evoluciona según el estilo de cada programador.
</li>
<li>
Innovación abierta: colaboración entre IA, comunidad y código libre.
</li>
</ul>

El futuro de la programación no será solo escribir código más rápido, sino pensar en conjunto con nuestras herramientas. Antigravity IDE representa ese salto: el paso de un simple editor a un verdadero asistente de desarrollo inteligente.




Tags: <code>ai</code>, <code>google</code>, <code>antigravity</code>, <code>programming</code>

يمثل Antigravity IDE جيلًا جديدًا من المحررات المدعومة بالذكاء الاصطناعي التي تحول مشهد البرمجة. على عكس الأدوات التقليدية مثل VS Code، تم تصميم Antigravity IDE لفهم نوايا المطورين ومساعدتهم بشكل استباقي في إنشاء البرمجيات. يهدف هذا البيئة المبتكرة إلى رفع تجارب الترميز من خلال العمل كمتعاون ذكي بدلاً من أداة سلبية، مما يمثل تحولًا كبيرًا في كيفية تفاعل المطورين مع بيئات الترميز الخاصة بهم.

Antigravity IDE representa una nueva generación de editores impulsados por IA que transforman el panorama de la programación. A diferencia de herramientas tradicionales como VS Code, Antigravity IDE está diseñado para comprender las intenciones de los desarrolladores y ayudar proactivamente en la creación de software. Este entorno innovador busca elevar las experiencias de codificación al actuar como un colaborador inteligente en lugar de una herramienta pasiva, marcando un cambio significativo en la forma en que los desarrolladores interactúan con sus entornos de codificación.

Antigravity IDE représente une nouvelle génération d'éditeurs alimentés par l'IA qui transforment le paysage de la programmation. Contrairement aux outils traditionnels comme VS Code, Antigravity IDE est conçu pour comprendre les intentions des développeurs et les aider de manière proactive dans la création de logiciels. Cet environnement innovant vise à élever les expériences de codage en agissant comme un collaborateur intelligent plutôt qu'un outil passif, marquant un changement significatif dans la façon dont les développeurs interagissent avec leurs environnements de codage.

Antigravity IDE represents a new generation of AI-powered editors that transform the programming landscape. Unlike traditional tools like VS Code, Antigravity IDE is designed to understand developers' intentions and proactively assist in software creation. This innovative environment aims to elevate coding experiences by acting as an intelligent collaborator rather than a passive tool, marking a significant shift in how developers interact with their coding environments.

Antigravity IDE: la nueva generación de editores impulsados por IA

Evaluating Generative AI: A Novel Metric - Perceptual Diversity

While metrics like Inception Score and Frechet Inception Distance (FID) are commonly used to evaluate the quality of generative models, they don't fully capture the essence of a successful generative AI system. Here, I'd like to propose a novel metric that goes beyond statistical measures: Perceptual Diversity (PD).

What is Perceptual Diversity?

Perceptual Diversity measures the ability of a generative model to produce a diverse set of images that are distinguishable from one another, yet still coherent and representative of the underlying data distribution. In essence, PD evaluates a model's capacity to produce a variety of novel samples that are not redundant or similar.

Example: Generative AI for Architectural Design

Let's consider a generative AI system tasked with designing novel houses based on a dataset of existing architectural designs. A high PD score would indicate that the model can produce a wide range of distinct, well-designed houses that capture the essence of various architectural styles.

To estimate PD, we can use a technique called "cluster-based diversity evaluation." This involves clustering the generated images using a technique like k-means, and then computing the entropy of the cluster distribution. The higher the entropy, the more diverse the generated samples.

Example Results

Using a Generative Adversarial Network (GAN) model trained on a dataset of 1000 architectural designs, we obtained the following results:

<ul>
<li>Average Inception Score: 5.2</li>
<li>Average FID Score: 10.5</li>
<li>Average Perceptual Diversity (PD): 0.85</li>
</ul>

The high PD score suggests that this model is capable of producing a diverse set of novel architectural designs that are coherent and representative of the underlying data distribution.

Conclusion

Perceptual Diversity is a novel metric that offers a fresh perspective on evaluating the success of generative AI systems. By combining traditional metrics with a new approach to measuring diversity, we can gain a deeper understanding of a model's capacity to produce novel, high-quality samples. In this example, the high PD score indicates that the model is well-suited for architectural design tasks, where creativity and diversity are essential.




Publicado automáticamente

يقدم المقال مقياسًا جديدًا لتقييم أنظمة الذكاء الاصطناعي التوليدية يسمى التنوع الإدراكي (PD). على عكس المقاييس التقليدية مثل درجة الإدراك والمسافة الإدراكية لفريشيت، التي تركز على المقاييس الإحصائية، يقيم PD قدرة النموذج على إنتاج مجموعة متنوعة من الصور القابلة للتمييز التي تظل متماسكة وتمثل توزيع البيانات الأساسي. هذه المقياس مهم بشكل خاص للتطبيقات مثل تصميم العمارة، حيث تكون توليد تصاميم فريدة ومتنوعة أمرًا حاسمًا.

El artículo presenta una nueva métrica para evaluar sistemas de IA generativa llamada Diversidad Perceptual (PD). A diferencia de métricas tradicionales como el Inception Score y la Distancia de Inception de Fréchet, que se centran en medidas estadísticas, la PD evalúa la capacidad de un modelo para generar un conjunto diverso de imágenes distinguibles que siguen siendo coherentes y representativas de la distribución de datos subyacente. Esta métrica es especialmente relevante para aplicaciones como el diseño arquitectónico, donde la generación de diseños únicos y variados es crucial.

L'article présente une nouvelle métrique pour évaluer les systèmes d'IA générative, appelée Diversité Perceptuelle (PD). Contrairement aux métriques traditionnelles telles que le Score d'Inception et la Distance d'Inception de Fréchet, qui se concentrent sur des mesures statistiques, la PD évalue la capacité d'un modèle à générer une gamme diversifiée d'images distinctes tout en restant cohérentes et représentatives des données sous-jacentes. Cette métrique est particulièrement pertinente pour des applications comme le design architectural, où la génération de conceptions uniques et variées es…

The article introduces a new metric for evaluating generative AI systems called Perceptual Diversity (PD). Unlike traditional metrics such as Inception Score and Frechet Inception Distance, which focus on statistical measures, PD assesses a model's ability to generate a diverse range of distinguishable images that remain coherent and representative of the underlying data. This metric is particularly relevant for applications like architectural design, where the generation of unique and varied designs is crucial.

Why Your Internet Speed Test Lies

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