As cloud project tracking software <a href="https://monday.com/">monday.com</a>’s engineering organization scaled past 500 developers, the team began to feel the strain of its own success. Product lines were multiplying, microservices proliferating, and code was flowing faster than human reviewers could keep up. The company needed a way to review thousands of pull requests each month without drowning developers in tedium — or letting quality slip.That’s when Guy Regev, VP of R&amp;D and head of the Growth and monday Dev teams, started experimenting with a new AI tool from <a href="https://www.qodo.ai/">Qodo</a>, an Israeli startup focused on developer agents. What began as a lightweight test soon became a critical part of monday.com’s software delivery infrastructure, as a <a href="https://www.qodo.ai/blog/monday-com-accelerates-review-cycles-and-improves-code-quality-with-qodo/">new case study</a> released by both Qodo and monday.com today reveals. “Qodo doesn’t feel like just another tool—it’s like adding a new developer to the team who actually learns how we work,&quot; Regev told VentureBeat in a recent video call interview, adding that it has &quot;prevented over 800 issues per month from reaching production—some of them could have caused serious security vulnerabilities.&quot;Unlike code generation tools like GitHub Copilot or Cursor, Qodo isn’t trying to write new code. Instead, it specializes in reviewing it — using what it calls context engineering to understand not just what changed in a pull request, but why, how it aligns with business logic, and whether it follows internal best practices. &quot;You can call Claude Code or Cursor and in five minutes get 1,000 lines of code,&quot; said Itamar Friedman, co-founder and CEO of Qodo, in the same video call interview as with Regev. &quot;You have 40 minutes, and you can&#x27;t review that. So you need Qodo to actually review it.”For monday.com, this capability wasn’t just helpful — it was transformative.<h2>Code Review, at Scale</h2>At any given time, monday.com’s developers are shipping updates across hundreds of repositories and services. The engineering org works in tightly coordinated teams, each aligned with specific parts of the product: marketing, CRM, dev tools, internal platforms, and more.That’s where Qodo came in. The company’s platform uses AI not just to check for obvious bugs or style violations, but to evaluate whether a pull request follows team-specific conventions, architectural guidelines, and historical patterns. It does this by learning from your own codebase — training on previous PRs, comments, merges, and even Slack threads to understand how your team works.&quot;The comments Qodo gives aren’t generic—they reflect our values, our libraries, even our standards for things like feature flags and privacy,&quot; Regev said. &quot;It’s context-aware in a way traditional tools aren’t.&quot;<h2>What “Context Engineering” Actually Means</h2>Qodo calls its secret sauce context engineering — a system-level approach to managing everything the model sees when making a decision. This includes the PR code diff, of course, but also prior discussions, documentation, relevant files from the repo, even test results and configuration data.The idea is that language models don’t really “think” — they predict the next token based on the inputs they’re given. So the quality of their output depends almost entirely on the quality and structure of their inputs.As Dana Fine, Qodo’s community manager, put it in a<a href="https://www.qodo.ai/blog/context-engineering/"> blog post</a>: “You’re not just writing prompts; you’re designing structured input under a fixed token limit. Every token is a design decision.”This isn’t just theory. In monday.com’s case, it meant Qodo could catch not only the obvious bugs, but the subtle ones that typically slip past human reviewers — hardcoded variables, missing fallbacks, or violations of cross-team architecture conventions.One example stood out. In a recent PR, Qodo flagged a line that inadvertently exposed a staging environment variable — something no human reviewer caught. Had it been merged, it might have caused problems in production. &quot;The hours we would spend on fixing this security leak and the legal issue that it would bring would be much more than the hours that we reduce from a pull-request,&quot; said Regev.<h2>Integration into the Pipeline</h2>Today, Qodo is deeply integrated into monday.com’s development workflow, analyzing pull requests and surfacing context-aware recommendations based on prior team code reviews. “It doesn’t feel like just another tool... It feels like another teammate that joined the system — one who learns how we work,&quot; Regev noted. Developers receive suggestions during the review process and remain in control of final decisions — a human-in-the-loop model that was critical for adoption.Because Qodo integrated directly into GitHub via pull request actions and comments, Monday.com’s infrastructure team didn’t face a steep learning curve.“It’s just a GitHub action,” said Regev. “It creates a PR with the tests. It’s not like a separate tool we had to learn.”“The purpose is to actually help the developer learn the code, take ownership, give feedback to each other, and learn from that and establish the standards,&quot; added Friedman.<h2>The Results: Time Saved, Bugs Prevented</h2>Since rolling out Qodo more broadly, monday.com has seen measurable improvements across multiple teams.Internal analysis shows that developers save roughly an hour per pull request on average. Multiply that across thousands of PRs per month, and the savings quickly reach thousands of developer hours annually.These aren’t just cosmetic issues — many relate to business logic, security, or runtime stability. And because Qodo’s suggestions reflect monday.com’s actual conventions, developers are more likely to act on them.The system’s accuracy is rooted in its data-first design. Qodo trains on each company’s private codebase and historical data, adapting to different team styles and practices. It doesn’t rely on one-size-fits-all rules or external datasets. Everything is tailored.<h2>From Internal Tool to Product Vision</h2>Regev’s team was so impressed with Qodo’s impact that they’ve started planning deeper integrations between Qodo and Monday Dev, the developer-focused product line monday.com is building.The vision is to create a workflow where business context — tasks, tickets, customer feedback — flows directly into the code review layer. That way, reviewers can assess not just whether the code “works,” but whether it solves the right problem.“Before, we had linters, danger rules, static analysis... rule-based... you need to configure all the rules,&quot; Regev said. &quot;But it doesn’t know what you don’t know... Qodo... feels like it’s learning from our engineers.”This aligns closely with Qodo’s own roadmap. The company doesn’t just review code. It’s building a full platform of developer agents — including Qodo Gen for context-aware code generation, Qodo Merge for automated PR analysis, and Qodo Cover, a regression-testing agent that uses runtime validation to ensure test coverage.All of this is powered by Qodo’s own infrastructure, including its new open-source embedding model, Qodo-Embed-1-1.5B, which outperformed offerings from OpenAI and Salesforce on code retrieval benchmarks.<h2>What’s Next?</h2>Qodo is now offering its platform under a freemium model — free for individuals, discounted for startups through Google Cloud’s Perks program, and enterprise-grade for companies that need SSO, air-gapped deployment, or advanced controls.The company is already working with teams at NVIDIA, Intuit, and other Fortune 500 companies. And thanks to a recent partnership with Google Cloud, Qodo’s models are available directly inside Vertex AI’s Model Garden, making it easier to integrate into enterprise pipelines.&quot;Context engines will be the big story of 2026,&quot; Friedman said. &quot;Every enterprise will need to build their own second brain if they want AI that actually understands and helps them.&quot;As AI systems become more embedded in software development, tools like Qodo are showing how the right context — delivered at the right moment — can transform how teams build, ship, and scale code across the enterprise.

بينما توسعت monday.com لتتجاوز 500 مطور، واجهت الشركة تحديات مع زيادة عدد خطوط الإنتاج والخدمات الصغيرة، مما أدى إلى صعوبة إدارة مراجعات الكود. لمعالجة ذلك، بدأت في تجربة أداة ذكاء اصطناعي من Qodo، والتي أصبحت منذ ذلك الحين جزءًا أساسيًا من عملية تسليم البرمجيات الخاصة بها. لقد حسنت هذه الأداة الكفاءة بشكل كبير، حيث منعت أكثر من 800 مشكلة شهريًا من الوصول إلى الإنتاج. تسلط دراسة حالة حديثة الضوء على فعالية Qodo، حيث أشار نائب الرئيس غاي ريجيف إلى أنها تشبه إضافة مطور جديد يفهم سير العمل الخاص بهم. هذه الابتكار ضروري للحفاظ على الجودة مع استمرار نمو الشركة.

A medida que monday.com escaló a más de 500 desarrolladores, la empresa enfrentó desafíos con un número creciente de líneas de productos y microservicios, lo que dificultó la gestión de las revisiones de código. Para abordar esto, comenzaron a experimentar con una herramienta de IA de Qodo, que desde entonces se ha vuelto integral en su proceso de entrega de software. Esta herramienta ha mejorado significativamente la eficiencia, evitando que más de 800 problemas al mes lleguen a producción. Un estudio de caso reciente destaca la efectividad de Qodo, con el VP Guy Regev señalando que se siente como agregar un nuevo desarrollador que entiende su flujo de trabajo. Esta innovación es crucial para mantener la calidad a medida que la empresa sigue creciendo.

Alors que monday.com a dépassé les 500 développeurs, l'entreprise a rencontré des défis liés à l'augmentation du nombre de lignes de produits et de microservices, rendant la gestion des revues de code difficile. Pour y remédier, ils ont commencé à expérimenter un outil d'IA de Qodo, qui est devenu essentiel à leur processus de livraison de logiciels. Cet outil a considérablement amélioré l'efficacité, empêchant plus de 800 problèmes par mois d'atteindre la production. Une étude de cas récente met en lumière l'efficacité de Qodo, le VP Guy Regev notant qu'il ressemble à l'ajout d'un nouveau développeur qui comprend leur flux de travail. Cette innovation est cruciale pour maintenir la qualité à mesure que l'entreprise continue de croître.

As monday.com scaled to over 500 developers, the company faced challenges with an increasing number of product lines and microservices, leading to difficulties in managing code reviews. To address this, they began experimenting with an AI tool from Qodo, which has since become integral to their software delivery process. This tool has significantly improved efficiency, preventing over 800 issues per month from reaching production. A recent case study highlights the effectiveness of Qodo, with VP Guy Regev noting that it feels like adding a new developer who understands their workflow. This innovation is crucial for maintaining quality as the company continues to grow.

How context engineering can save your company from AI vibe code overload: lessons from Qodo and Monday.com

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 what appeared to be a bid to soak up some of Google&#x27;s limelight prior to the <a href="https://venturebeat.com/ai/google-unveils-gemini-3-claiming-the-lead-in-math-science-multimodal-and">launch of its new Gemini 3 flagship AI model </a>— now recorded as the most powerful LLM in the world by multiple independent evaluators — Elon Musk&#x27;s rival AI startup xAI last night unveiled its newest large language model, <a href="https://x.ai/news/grok-4-1">Grok 4.1.</a>The model is now live for consumer use on Grok.com, social network X (formerly Twitter), and the company’s iOS and Android mobile apps, and it arrives with major architectural and usability enhancements, among them: faster reasoning, improved emotional intelligence, and significantly reduced hallucination rates. xAI also commendably published a white paper on its evaluations and including a small bit on training process <a href="https://data.x.ai/2025-11-17-grok-4-1-model-card.pdf">here</a>. Across public benchmarks, Grok 4.1 has vaulted to the top of the leaderboard, outperforming rival models from Anthropic, OpenAI, and Google — at least, Google&#x27;s pre-Gemini 3 model (Gemini 2.5 Pro). It builds upon the success of xAI&#x27;s Grok-4 Fast, which <a href="https://venturebeat.com/ai/what-to-know-about-grok-4-fast-for-enterprise-use-cases">VentureBeat covered favorably</a> shortly following its release back in September 2025.However, enterprise developers looking to integrate the new and improved model Grok 4.1 into production environments will find one major constraint: it&#x27;s not yet available through <a href="https://docs.x.ai/docs/models?cluster=us-east-1#detailed-pricing-for-all-grok-models">xAI’s public API</a>. Despite its high benchmarks, Grok 4.1 remains confined to xAI’s consumer-facing interfaces, with no announced timeline for API exposure. At present, only older models—including Grok 4 Fast (reasoning and non-reasoning variants), Grok 4 0709, and legacy models such as Grok 3, Grok 3 Mini, and Grok 2 Vision—are available for programmatic use via the xAI developer API. These support up to 2 million tokens of context, with token pricing ranging from $0.20 to $3.00 per million depending on the configuration.For now, this limits Grok 4.1’s utility in enterprise workflows that rely on backend integration, fine-tuned agentic pipelines, or scalable internal tooling. While the consumer rollout positions Grok 4.1 as the most capable LLM in xAI’s portfolio, production deployments in enterprise environments remain on hold.<h3>Model Design and Deployment Strategy</h3>Grok 4.1 arrives in two configurations: a fast-response, low-latency mode for immediate replies, and a “thinking” mode that engages in multi-step reasoning before producing output. Both versions are live for end users and are selectable via the model picker in xAI’s apps.The two configurations differ not just in latency but also in how deeply the model processes prompts. Grok 4.1 Thinking leverages internal planning and deliberation mechanisms, while the standard version prioritizes speed. Despite the difference in architecture, both scored higher than any competing models in blind preference and benchmark testing.<h3>Leading the Field in Human and Expert Evaluation</h3>On the <a href="https://lmarena.ai/leaderboard/text">LMArena Text Arena leaderboard</a>, Grok 4.1 Thinking briefly held the top position with a normalized Elo score of 1483 — then was dethroned a few hours later with <a href="https://venturebeat.com/ai/google-unveils-gemini-3-claiming-the-lead-in-math-science-multimodal-and">Google&#x27;s release of Gemini 3 </a>and its incredible 1501 Elo score. The non-thinking version of Grok 4.1 also fares well on the index, however, at 1465. These scores place Grok 4.1 above Google’s Gemini 2.5 Pro, Anthropic’s Claude 4.5 series, and OpenAI’s GPT-4.5 preview.In creative writing, Grok 4.1 ranks second only to Polaris Alpha (an early GPT-5.1 variant), with the “thinking” model earning a score of 1721.9 on the Creative Writing v3 benchmark. This marks a roughly 600-point improvement over previous Grok iterations. Similarly, in the Arena Expert leaderboard, which aggregates feedback from professional reviewers, Grok 4.1 Thinking again leads the field with a score of 1510.The gains are especially notable given that Grok 4.1 was released only two months after Grok 4 Fast, highlighting the accelerated development pace at xAI.<h3>Core Improvements Over Previous Generations</h3>Technically, Grok 4.1 represents a significant leap in real-world usability. Visual capabilities—previously limited in Grok 4—have been upgraded to enable robust image and video understanding, including chart analysis and OCR-level text extraction. Multimodal reliability was a pain point in prior versions and has now been addressed.Token-level latency has been reduced by approximately 28 percent while preserving reasoning depth. In long-context tasks, Grok 4.1 maintains coherent output up to 1 million tokens, improving on Grok 4’s tendency to degrade past the 300,000 token mark.xAI has also improved the model&#x27;s tool orchestration capabilities. Grok 4.1 can now plan and execute multiple external tools in parallel, reducing the number of interaction cycles required to complete multi-step queries. According to internal test logs, some research tasks that previously required four steps can now be completed in one or two.Other alignment improvements include better truth calibration—reducing the tendency to hedge or soften politically sensitive outputs—and more natural, human-like prosody in voice mode, with support for different speaking styles and accents.<h3>Safety and Adversarial Robustness</h3>As part of its risk management framework, xAI evaluated Grok 4.1 for refusal behavior, hallucination resistance, sycophancy, and dual-use safety.The hallucination rate in non-reasoning mode has dropped from 12.09 percent in Grok 4 Fast to just 4.22 percent — a roughly 65% improvement.The model also scored 2.97 percent on FActScore, a factual QA benchmark, down from 9.89 percent in earlier versions.In the domain of adversarial robustness, Grok 4.1 has been tested with prompt injection attacks, jailbreak prompts, and sensitive chemistry and biology queries. Safety filters showed low false negative rates, especially for restricted chemical knowledge (0.00 percent) and restricted biological queries (0.03 percent). The model’s ability to resist manipulation in persuasion benchmarks, such as MakeMeSay, also appears strong—it registered a 0 percent success rate as an attacker.<h3>Limited Enterprise Access via API</h3>Despite these gains, Grok 4.1 remains unavailable to enterprise users through xAI’s API. According to the company’s <a href="https://docs.x.ai/docs/models">public documentation</a>, the latest available models for developers are Grok 4 Fast (both reasoning and non-reasoning variants), each supporting up to 2 million tokens of context at pricing tiers ranging from $0.20 to $0.50 per million tokens. These are backed by a 4M tokens-per-minute throughput limit and 480 requests per minute (RPM) rate cap.By contrast, Grok 4.1 is accessible only through xAI’s consumer-facing properties—X, Grok.com, and the mobile apps. This means organizations cannot yet deploy Grok 4.1 via fine-tuned internal workflows, multi-agent chains, or real-time product integrations.<h3>Industry Reception and Next Steps</h3>The release has been met with strong public and industry feedback. Elon Musk, founder of xAI, posted a brief endorsement, calling it “a great model” and congratulating the team. AI benchmark platforms have praised the leap in usability and linguistic nuance.For enterprise customers, however, the picture is more mixed. Grok 4.1’s performance represents a breakthrough for general-purpose and creative tasks, but until API access is enabled, it will remain a consumer-first product with limited enterprise applicability.As competitive models from OpenAI, Google, and Anthropic continue to evolve, xAI’s next strategic move may hinge on when—and how—it opens Grok 4.1 to external developers.

أطلقت شركة xAI التابعة لإيلون ماسك Grok 4.1، وهو نموذج لغوي كبير مصمم لتحسين تجربة المستخدم على Grok.com والشبكة الاجتماعية X. يتميز هذا النموذج الجديد بتحسينات مثل التفكير الأسرع، والذكاء العاطفي الأفضل، ومعدل هلاوس منخفض بشكل كبير. حاليًا، لا يوجد وصول لواجهة برمجة التطبيقات متاح للمطورين. يأتي هذا الإطلاق في الوقت الذي تستعد فيه جوجل للكشف عن نموذجها Gemini 3، الذي تم التعرف عليه كأقوى نموذج لغوي كبير من قبل مقيمين مستقلين.

La startup de Elon Musk, xAI, ha lanzado Grok 4.1, un modelo de lenguaje diseñado para mejorar la experiencia del usuario en Grok.com y en la red social X. Este nuevo modelo presenta mejoras como un razonamiento más rápido, mejor inteligencia emocional y una tasa de alucinaciones significativamente reducida. Actualmente, no hay acceso a API disponible para desarrolladores. Este lanzamiento se produce mientras Google se prepara para presentar su modelo Gemini 3, reconocido como el modelo de lenguaje más potente por evaluadores independientes.

La startup d'Elon Musk, xAI, a lancé Grok 4.1, un modèle de langage conçu pour améliorer l'expérience utilisateur sur Grok.com et le réseau social X. Ce nouveau modèle présente des améliorations telles qu'un raisonnement plus rapide, une meilleure intelligence émotionnelle et un taux de hallucination considérablement réduit. Actuellement, aucun accès API n'est disponible pour les développeurs. Ce lancement intervient alors que Google se prépare à dévoiler son modèle Gemini 3, reconnu comme le modèle de langage le plus puissant par des évaluateurs indépendants.

Elon Musk's AI startup xAI has launched Grok 4.1, a large language model designed to enhance user experience on Grok.com and the social network X. This new model features improvements such as faster reasoning, better emotional intelligence, and a significantly reduced hallucination rate. Currently, there is no API access available for developers. The launch comes as Google prepares to unveil its Gemini 3 model, which has been recognized as the most powerful large language model by independent evaluators.

Musk's xAI launches Grok 4.1 with lower hallucination rate on the web and apps — no API access (for now)

<a href="https://writer.com/">Writer</a>, a San Francisco-based artificial intelligence startup, is launching a unified AI agent platform designed to let any employee automate complex business workflows without writing code — a capability the company says distinguishes it from consumer-oriented tools like <a href="https://copilot.microsoft.com/">Microsoft Copilot</a> and <a href="https://chatgpt.com/">ChatGPT</a>.The platform, called <a href="https://writer.com/agents/">Writer Agent</a>, combines chat-based assistance with autonomous task execution in a single interface. Starting Tuesday, enterprise customers can use natural language to instruct the AI to create presentations, analyze financial data, generate marketing campaigns, or coordinate across multiple business systems like Salesforce, Slack, and Google Workspace—then save those workflows as reusable &quot;Playbooks&quot; that run automatically on schedules.The announcement comes as enterprises struggle to move AI initiatives beyond pilot programs into production at scale. Writer CEO May Habib has been outspoken about this challenge, recently revealing that 42% of Fortune 500 executives surveyed by her company said AI is &quot;<a href="https://venturebeat.com/ai/ai-is-tearing-companies-apart-writer-ai-ceo-slams-fortune-500-leaders-for">tearing their company apart</a>&quot; due to coordination failures between departments.&quot;We&#x27;re delivering an agent interface that is both incredibly powerful and radically simple to transform individual productivity into organizational impact,&quot; Habib said in a statement. &quot;Writer Agent is the difference between a single sales rep asking a chatbot to write an outreach email and an enterprise ensuring that 1,000 reps are all sending on-brand, compliant, and contextually-aware messages to target accounts.&quot;<h2>How Writer is putting workflow automation in the hands of non-technical workers</h2>The platform&#x27;s core innovation centers on making workflow automation accessible to non-technical employees—what Writer executives call &quot;democratizing who gets to be a builder.&quot;In an exclusive interview with VentureBeat, Doris Jwo, Writer&#x27;s director of product management, demonstrated how the system works: A user types a request in plain English — for example, &quot;Create a two-page partnership proposal between [Company A] and [Company B], make it a branded deck, include impact metrics and partnership tiers.&quot;The AI agent then breaks down that request into discrete steps, conducts web research, generates graphics and charts on the fly, creates individual slides with sourced information, and assembles a complete presentation. The entire process, which might take an employee hours or days, can be completed in 10-12 minutes.&quot;The agent basically looks at the request, breaks it down, does research, understands what pieces it needs, creates a detailed plan at a step-by-step level,&quot; Jwo explained during a product demonstration. &quot;It might say, &#x27;I need to do web research,&#x27; or &#x27;This user needs information from Gong or Slack,&#x27; and it reaches out to those connectors, grabs the data, and executes the plan.&quot;Crucially, users can save these multi-step processes as Playbooks—reusable templates that colleagues can deploy with a single click. Routines allow those Playbooks to run automatically at scheduled intervals, essentially putting knowledge work &quot;on autopilot.&quot;<h2>Security and compliance controls: Writer&#x27;s answer to enterprise IT concerns</h2>Writer positions these enterprise-focused controls as a key differentiator from competitors. While <a href="https://microsoft.com/">Microsoft</a>, <a href="https://openai.com/">OpenAI</a>, and <a href="https://www.anthropic.com/">Anthropic</a> offer powerful AI capabilities, Writer&#x27;s executives argue those tools weren&#x27;t designed from the ground up for the security, compliance, and governance requirements of large regulated organizations.&quot;All of the products you mentioned are great products, but even Copilot is very much focused on personal productivity—summarizing email, for example, which is important, but that&#x27;s not the component we&#x27;re focusing on,&quot; said Matan-Paul Shetrit, Writer&#x27;s director of product management, in an exclusive interview with VentureBeat.Shetrit emphasized Writer&#x27;s &quot;trust, security, and interoperability&quot; approach. IT administrators can granularly control what the AI can access — for instance, preventing market research agents from mentioning competitors, or restricting which employees can use web search capabilities. All activity is logged with detailed audit trails showing exactly what data the agent touched and what actions it took.&quot;These fine-grained controls are what make products enterprise-ready,&quot; Shetrit said. &quot;We can deploy to tens of thousands or hundreds of thousands of employees while maintaining the security and guardrails you need for that scale.&quot;This architecture reflects <a href="https://writer.com/company/about/">Writer&#x27;s origin story</a>. Unlike OpenAI or Anthropic, which started as research labs and later added enterprise offerings, Writer has targeted Fortune 500 companies since its 2020 founding. &quot;We&#x27;re not a research lab that went to consumer and is dabbling in enterprise,&quot; Shetrit said. &quot;We are first and foremost targeting the Global 2000 and Fortune 500, and our research is in service of these customers&#x27; needs.&quot;<h2>Inside Writer&#x27;s strategy to connect AI agents across enterprise software systems</h2>A critical technical component is Writer&#x27;s approach to system integrations. The platform includes pre-built connectors to more than a dozen enterprise applications—<a href="https://workspace.google.com/lp/business/?utm_source=google&amp;utm_medium=cpc&amp;utm_campaign=1710046-Workspace-DR-NA-US-en-Google-BKWS-EXA-na&amp;utm_content=c-Hybrid+%7C+BKWS+-+EXA+%7C+Txt-Google+Workspace-Core-346911454270&amp;utm_term=google%20workspace&amp;gclsrc=aw.ds&amp;gad_source=1&amp;gad_campaignid=20159848966&amp;gclid=CjwKCAiAz_DIBhBJEiwAVH2XwDbhidrT22spa5fU8uNyySir49xeTOAji42tQEy7zvzTZpUAL4TFgRoCLz8QAvD_BwE">Google Workspace</a>, <a href="https://www.microsoft.com/en-us/microsoft-365">Microsoft 365</a>, <a href="https://www.snowflake.com/en/">Snowflake</a>, <a href="https://asana.com/">Asana</a>, <a href="https://slack.com/">Slack</a>, <a href="https://www.gong.io/">Gong</a>, <a href="https://www.hubspot.com">HubSpot</a>, <a href="https://www.atlassian.com/">Atlassian</a>, <a href="https://www.databricks.com/">Databricks</a>, <a href="https://pitchbook.com/">PitchBook</a>, and <a href="https://www.factset.com/">FactSet</a>—allowing the AI to retrieve information and take actions across those systems.Writer built these connectors using the <a href="https://www.anthropic.com/news/model-context-protocol">Model Context Protocol (MCP)</a>, an emerging standard for AI system integrations, but added what Shetrit described as an &quot;enterprise-ready&quot; layer on top.&quot;We took a first-principle approach of: You have this MCP connector infrastructure—how do you build it in a way that&#x27;s enterprise-ready?&quot; Shetrit explained. &quot;What we have today in the industry is definitely not it.&quot;The system can write and execute code on the fly to handle unexpected scenarios. If a user uploads an unfamiliar file format, for instance, the agent will generate code to extract and process the text without requiring a human to intervene.Jwo demonstrated this capability with a daily workflow she runs: Every morning at 10 a.m., a Routine automatically summarizes her Google Calendar meetings, identifies external participants, finds their LinkedIn profiles, and sends the summary to her via Slack — all without her involvement.&quot;This was pretty simple, but you can imagine for a salesperson it might say, &#x27;At the end of the day, wrap up a summary of all the calls I had, send me action items, post it to the account-specific Slack channel, and tag these folks so they can accomplish those workflows,&#x27;&quot; Jwo said. &quot;That can run continuously each day, each week, or on demand.&quot;<h2>From mortgage lenders to CPG brands: Real-world AI agent use cases across industries</h2>The platform is attracting customers across multiple industries. <a href="https://www.newamericanfunding.com/">New American Funding</a>, a mortgage lender, uses Writer Agent to automate marketing workflows. Senior Content Marketing Manager Karen Rodriguez uploads Asana project tickets with creative briefs, and the AI executes tasks like updating email campaigns or transforming articles into social media carousels, video scripts, and captions.Other use cases span financial services teams creating investment dashboards with <a href="https://pitchbook.com/">PitchBook</a> and <a href="https://www.factset.com/">FactSet</a> data, consumer packaged goods companies brainstorming new product lines based on social media trends, and marketing teams generating partnership presentations with branded assets.Writer has added customers including <a href="https://www.tiktok.com/en/">TikTok</a>, <a href="https://www.cmcsa.com/">Comcast</a>, <a href="https://www.keurigdrpepper.com/">Keurig Dr Pepper</a>, <a href="https://www.caa.com/">CAA</a>, and <a href="https://www.aptitudehealth.com/">Aptitude Health</a>, joining an existing base that includes <a href="https://www.accenture.com/">Accenture</a>, <a href="https://www.qualcomm.com/">Qualcomm</a>, <a href="https://www.uber.com/">Uber</a>, <a href="https://investor.vanguard.com/corporate-portal">Vanguard</a>, and <a href="https://www.marriott.com/default.mi">Marriott</a>. The company now serves more than 300 enterprises and has secured over $50 million in signed contracts, with projections to double that to $100 million this year.The startup&#x27;s net retention rate — a measure of how much existing customers expand their usage — stands at 160%, meaning customers on average increase their spending by 60% after initial contracts. Twenty customers who started with $200,000-$300,000 contracts now spend about $1 million annually, according to company data.<h2>&#x27;Vibe working&#x27;: Writer&#x27;s vision for AI-powered productivity beyond coding</h2>Writer executives frame the platform as enabling what they call &quot;vibe working&quot; — a playful reference to the popular term &quot;vibe coding,&quot; which describes AI tools like Cursor that dramatically accelerate software development.&quot;We used to call it transformation when we took 12 steps and made them nine. That&#x27;s optimizing the world as it is,&quot; Habib said at Writer&#x27;s AI Leaders Forum earlier this month, according to <a href="https://www.forbes.com/sites/stevenwolfepereira/2025/11/08/what-writers-ai-leaders-forum-revealed-about-enterprise-ai/">Forbes</a>. &quot;We can now create a new world. That is the greenfield mindset.&quot;Shetrit echoed this framing: &quot;Vibe coding is the theme of 2025. Our view is that ‘vibe working’ is the theme of 2026. How do you bring the same productivity gains you&#x27;ve seen with coding agents into the workspace in a way that non-technical users can maximize them?&quot;The platform is powered by <a href="https://writer.com/engineering/long-context-palmyra-x5/">Palmyra X5</a>, Writer&#x27;s proprietary large language model featuring a one-million-token context window — among the largest commercially available. Writer trained the model for approximately $700,000, a fraction of the estimated $100 million OpenAI spent on GPT-4, by using synthetic data and techniques that halt training when returns diminish.The model can process one million tokens in about 22 seconds and costs 60 cents per million input tokens and $6 per million output tokens — significantly cheaper than comparable offerings, according to company specifications.<h2>Making AI Decisions Visible: Writer&#x27;s Approach to Trust and Transparency</h2>A distinctive aspect of Writer&#x27;s approach is transparency into the AI&#x27;s decision-making process. The interface displays the agent&#x27;s step-by-step reasoning, showing which data sources it accessed, what code it generated, and how it arrived at outputs.&quot;There&#x27;s a very clear exhibition of how the agent is thinking, what it&#x27;s doing, what it&#x27;s touching,&quot; Shetrit said. &quot;This is important for the end user to trust it, but also important for the IT person or security professional to see what&#x27;s going on.&quot;This &quot;supervision&quot; model goes beyond simple observability of API calls to encompass what Shetrit described as &quot;a superset of observability&quot; — giving organizations the ability to not just monitor but control AI behavior through policies and permissions.Session logs capture all agent activity when enabled by administrators, and users can submit feedback on every output to help improve system performance. The platform also emphasizes providing sources and citations for generated content, allowing users to verify information.&quot;With any sort of chat assistant, agentic or not, trust but verify is really important,&quot; Jwo said. &quot;That&#x27;s part of the pillars of us building this and making it enterprise-grade.&quot;<h2>What Writer Agent Costs—and Why It&#x27;s Included in the Base Platform</h2>Writer is including all the new capabilities—Playbooks, Routines, Connectors, and Personality customization—as part of its core platform without additional charges, according to Jwo.&quot;This is fully included as part of the Writer platform,&quot; she said. &quot;We&#x27;re not charging additional for using Writer Agent.&quot;The &quot;Personality&quot; feature allows individual users, teams, or entire organizations to customize the AI&#x27;s communication style, ensuring generated content matches brand voice and tone guidelines. This works alongside company-level controls that enforce terminology and style requirements.For highly structured, repetitive tasks, Writer also offers a library of more than 100 pre-built agents and an AI Studio for building custom multi-agent systems aligned with specific business use cases.<h2>The Race to Define Enterprise AI: Can Purpose-Built Platforms Beat Tech Giants?</h2>The launch crystallizes a fundamental tension in how enterprises will adopt AI at scale. While consumer-facing AI tools emphasize individual productivity gains, companies need systems that work reliably across thousands of employees, integrate with existing software infrastructure, maintain regulatory compliance, and deliver measurable business impact.Writer&#x27;s wager is that these requirements demand purpose-built enterprise platforms rather than consumer tools adapted for business use. The company&#x27;s <a href="https://techcrunch.com/2024/11/12/generative-ai-startup-writer-raises-200m-at-a-1-9b-valuation/">$1.9 billion valuation</a> — achieved in a November 2024 funding round that raised $200 million — suggests investors see merit in this thesis. Backers include <a href="https://www.premjiinvest.com/">Premji Invest</a>, <a href="https://radical.vc/">Radical Ventures</a>, <a href="https://www.iconiqcapital.com/growth">ICONIQ Growth</a>, <a href="https://salesforceventures.com/">Salesforce Ventures</a>, and <a href="https://www.adobe.com/ventures.html">Adobe Ventures</a>.Yet the competitive landscape remains formidable. Microsoft and Google command enormous distribution advantages through their existing enterprise software relationships. OpenAI and Anthropic possess research capabilities that have produced breakthrough models. Whether Writer can maintain its differentiation as these giants expand their enterprise offerings will test the startup&#x27;s core premise: that serving Fortune 500 companies from day one creates advantages that research labs turned enterprise vendors cannot easily replicate.&quot;We&#x27;re entering an era where if you can describe a better way to work, you can build it,&quot; Jwo said. &quot;The new Writer Agent democratizes who gets to be a builder, empowering the operational experts and creative problem-solvers in every department to become the architects of their own transformation. That&#x27;s how you unlock innovation that competitors can&#x27;t replicate.&quot;The promise is alluring — AI capabilities powerful enough to transform how work gets done, accessible enough for any employee to use, and controlled enough for enterprises to deploy safely at scale. Whether Writer can deliver on that promise at the speed and scale required will determine if its vision of &quot;vibe working&quot; becomes the 2026 theme Shetrit predicts, or just another ambitious attempt to solve enterprise AI&#x27;s execution problem.But one thing is certain: In a market where 85% of AI initiatives fail to escape pilot purgatory, Writer is betting that the winners won&#x27;t be the companies with the most powerful models—they&#x27;ll be the ones that make those models actually work inside the enterprise.

تقوم شركة Writer، وهي شركة ناشئة في مجال الذكاء الاصطناعي مقرها في سان فرانسيسكو، بإطلاق منصة موحدة لوكلاء الذكاء الاصطناعي تهدف إلى تمكين الموظفين من أتمتة سير العمل التجاري المعقد دون الحاجة إلى كتابة كود. تُسمى المنصة Writer Agent، وتجمع بين المساعدة القائمة على الدردشة وتنفيذ المهام بشكل مستقل. اعتبارًا من يوم الثلاثاء، يمكن للعملاء من الشركات استخدام اللغة الطبيعية لتوجيه الذكاء الاصطناعي لإنشاء العروض التقديمية وتحليل البيانات المالية، وحفظ هذه العمليات كـ 'Playbooks' قابلة لإعادة الاستخدام تعمل تلقائيًا.

Writer, una startup de inteligencia artificial con sede en San Francisco, está lanzando una plataforma unificada de agentes de IA que permite a los empleados automatizar flujos de trabajo comerciales complejos sin necesidad de programar. La plataforma, llamada Writer Agent, combina asistencia basada en chat con ejecución autónoma de tareas. A partir del martes, los clientes empresariales pueden usar lenguaje natural para instruir a la IA en tareas como crear presentaciones y analizar datos financieros, guardando estos flujos de trabajo como 'Playbooks' reutilizables que se ejecutan automáticam…

Writer, une startup d'IA basée à San Francisco, lance une plateforme d'agents IA unifiée permettant aux employés d'automatiser des flux de travail commerciaux complexes sans coder. La plateforme, nommée Writer Agent, intègre une assistance par chat avec une exécution autonome des tâches. À partir de mardi, les clients d'entreprise peuvent utiliser un langage naturel pour demander à l'IA de créer des présentations et d'analyser des données financières, en sauvegardant ces flux de travail sous forme de 'Playbooks' réutilisables qui s'exécutent automatiquement.

Writer, a San Francisco-based AI startup, is launching a unified AI agent platform that allows employees to automate complex business workflows without coding. The platform, named Writer Agent, integrates chat-based assistance with autonomous task execution. Starting Tuesday, enterprise customers can use natural language to instruct the AI for tasks such as creating presentations and analyzing financial data, saving these workflows as reusable 'Playbooks' that run automatically.

Writer's AI agents can actually do your work—not just chat about it

أطلقت جوجل رسميًا نموذج Gemini 3، أحدث نماذج الذكاء الاصطناعي لديها، بعد فترة من التكهنات المكثفة. هذا النموذج الجديد هو جزء من خط Gemini، الذي تم تقديمه في عام 2023، وهو مصمم للتفوق في معايير الرياضيات والعلوم والذكاء الاصطناعي متعدد الوسائط. يتوفر Gemini 3 حصريًا من خلال منتجات جوجل ومنصاتها، بما في ذلك Google AI Studio وVertex AI، ويهدف إلى تحسين تفاعل المستخدم ووظائفه عبر تطبيقات متنوعة.

Google ha lanzado oficialmente Gemini 3, su último modelo de IA, tras un período de intensa especulación. Este nuevo modelo forma parte de la línea Gemini, que se presentó en 2023, y está diseñado para sobresalir en métricas de matemáticas, ciencias y IA multimodal. Gemini 3 está disponible exclusivamente a través de productos y plataformas de Google, incluyendo Google AI Studio y Vertex AI, y tiene como objetivo mejorar la interacción y funcionalidad del usuario en diversas aplicaciones.

Google a officiellement lancé Gemini 3, son dernier modèle d'IA, après une période de spéculation intense. Ce nouveau modèle fait partie de la ligne Gemini, qui a été introduite en 2023, et est conçu pour exceller dans les benchmarks en mathématiques, sciences et IA multimodale. Gemini 3 est disponible exclusivement via les produits et plateformes de Google, y compris Google AI Studio et Vertex AI, et vise à améliorer l'interaction et la fonctionnalité des utilisateurs à travers diverses applications.

Google has officially launched Gemini 3, its latest AI model, following extensive speculation. This new model is part of the Gemini line, which debuted in 2023, and is designed to excel in math, science, and multimodal AI benchmarks. Gemini 3 is available exclusively through Google products and platforms, including Google AI Studio and Vertex AI, and aims to enhance user interaction and functionality across various applications.

How context engineering can save your company from AI vibe code overload: lessons from Qodo and Monday.com

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