Here are the free ChatGPT alternatives I genuinely recommend. They are fast, reliable, and useful for research, coding, brainstorming, and creative work.

مقالة حديثة تسلط الضوء على سبع بدائل مجانية لـ ChatGPT، مشددة على سرعتها وموثوقيتها وفائدتها لمهام متنوعة مثل البحث والترميز والعصف الذهني والعمل الإبداعي. هذه المعلومات مهمة لأنها توفر للمستخدمين خيارات تتجاوز ChatGPT، مما يلبي احتياجات متنوعة في مجال الذكاء الاصطناعي.

Un artículo reciente destaca siete alternativas gratuitas a ChatGPT, enfatizando su rapidez, fiabilidad y utilidad para diversas tareas como investigación, programación, lluvia de ideas y trabajo creativo. Esta información es significativa ya que proporciona a los usuarios opciones más allá de ChatGPT, atendiendo a diversas necesidades en el panorama de la IA.

Un article récent met en avant sept alternatives gratuites à ChatGPT, soulignant leur rapidité, leur fiabilité et leur utilité pour diverses tâches telles que la recherche, le codage, le brainstorming et le travail créatif. Cette information est importante car elle offre aux utilisateurs des options au-delà de ChatGPT, répondant à des besoins divers dans le paysage de l'IA.

A recent article highlights seven free alternatives to ChatGPT, emphasizing their speed, reliability, and usefulness for various tasks such as research, coding, brainstorming, and creative work. This information is significant as it provides users with options beyond ChatGPT, catering to diverse needs in the AI landscape.

Top 7 ChatGPT Alternatives You Can Try For Free

The story of the Ghost in the Shell’s main villain the Puppet Master hinted at a future where governments use hackers for espionage, at a time when most of the world had never connected to the internet.

الأنمي الكلاسيكي 'Ghost in the Shell' يقدم شخصية Puppet Master، التي تتنبأ بمستقبل تستخدم فيه الحكومات القراصنة للتجسس. ظهرت هذه التنبؤات في وقت كانت فيه غالبية سكان العالم لم تتصل بعد بالإنترنت، مما يبرز رؤية العرض لمشكلات الأمن السيبراني.

El clásico anime 'Ghost in the Shell' presenta al Puppet Master, un personaje que anticipa un futuro en el que los gobiernos utilizan hackers para el espionaje. Esta predicción surgió en un momento en que la mayoría de la población mundial aún no estaba conectada a Internet, destacando la previsión del programa sobre los problemas de ciberseguridad.

L'anime classique 'Ghost in the Shell' présente le Puppet Master, un personnage qui préfigure un avenir où les gouvernements utilisent des hackers pour l'espionnage. Cette prédiction est survenue à une époque où la majorité de la population mondiale n'était pas encore connectée à Internet, soulignant la prévoyance de l'émission concernant les problèmes de cybersécurité.

The classic anime 'Ghost in the Shell' features the Puppet Master, a character that foreshadows a future where governments utilize hackers for espionage. This prediction emerged at a time when the majority of the global population had yet to connect to the internet, highlighting the show's foresight regarding cybersecurity issues.

How the classic anime ‘Ghost in the Shell’ predicted the future of cybersecurity 30 years ago

<p>Text-to-image diffusion models have become the workhorses of generative imaging. They can paint photorealistic scenes, mimic art styles, and blend concepts in ways that were science fiction a few years ago. Yet they stumble embarrassingly on a skill that even small children master: basic spatial reasoning.</p>

<p>Ask a state-of-the-art model for “a dog to the right of a teddy bear” and you often get:</p>

<ul>
<li>The dog on the left</li>
<li>One of the objects missing</li>
<li>Or a bizarre hybrid where dog and teddy are fused into a single creature</li>
</ul>

<p><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%2F49rtb08366xdl284o4z0.jpg" 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%2F49rtb08366xdl284o4z0.jpg" alt=" " width="800" height="532"></a></p>

<p>These failures become more severe for unusual compositions like “a giraffe above an airplane”. Traditional fixes range from expensive fine-tuning to brittle, hand-written loss functions at inference time—but both options come with significant downsides.</p>

<p>NVIDIA’s Learn-to-Steer framework (accepted to WACV 2026) proposes a different path: instead of hard-coding spatial rules or retraining the entire model, it learns a data-driven objective that can “steer” diffusion at inference time. The method reads the model’s own cross-attention maps, trains a lightweight classifier to detect spatial relations, and then uses that classifier’s gradient as a learned loss to nudge the generation towards layouts that match the prompt.</p>

<p>In this blog, we’ll unpack:</p>

<ul>
<li>What makes spatial reasoning so fragile in current diffusion models</li>
<li>How Learn-to-Steer learns spatial constraints from the model itself</li>
<li>How it steers images during generation without changing model weights</li>
<li>The top gains on spatial benchmarks like GenEval and T2I-CompBench</li>
<li>The trade-offs in compute cost and generality, and what this implies for future generative systems</li>
</ul>

<h1>
  
  
  Why Spatial Reasoning Fails in Text-to-Image Diffusion
</h1>

<h2>
  
  
  What Makes Spatial Relations So Difficult for Diffusion Models?
</h2>

<p>Modern diffusion models (e.g., Stable Diffusion, Flux) are excellent at what should appear in an image—objects, styles, textures—but much less reliable at where those objects should be.</p>

<p>Several factors contribute:</p>

<h3>
  
  
  Weak supervision of spatial language
</h3>

<ul>
<li>Training data rarely comes with precise annotations like “object A is left of object B”.
</li>
<li>Captions often describe content loosely, so phrases like “on top of” or “to the right of” are under-specified.</li>
</ul>

<h3>
  
  
  Entangled visual concepts
</h3>

<ul>
<li>When two objects frequently co-occur, models may treat them as a single visual blob.</li>
<li>This leads to object fusion, where a “cat on a bookshelf” becomes a cat-bookshelf chimera.</li>
</ul>

<h3>
  
  
  Benchmark saturation without spatial coverage
</h3>

<ul>
<li>Many standard text-to-image benchmarks emphasize realism and style, not relational accuracy.</li>
<li>Models can score highly while still being spatially confused.</li>
</ul>

<p>Empirical studies confirm three recurring failure modes on spatial benchmarks:</p>

<ul>
<li>Incorrect placement: Objects appear in the wrong relative position.</li>
<li>Missing entities: One or more requested objects never appear.</li>
<li>Merged entities: Two objects get mashed into a single, incoherent form.</li>
</ul>

<p>The model “knows” the objects you asked for, but it doesn’t reliably understand where to place them.</p>

<h1>
  
  
  Why Fine-Tuning and Handcrafted Losses Are Not Enough
</h1>

<p>Two broad strategies have tried to patch this gap:</p>

<h2>
  
  
  Fine-tuning for spatial awareness
</h2>

<ul>
<li>Retrain the diffusion model on datasets with explicit layouts or spatial annotations.</li>
<li>Methods like COMPASS show that this can significantly improve spatial accuracy.</li>
<li>But this comes at a cost: expensive retraining, sensitivity to dataset bias, and often regressions in other capabilities such as color fidelity or counting.</li>
</ul>

<h2>
  
  
  Handcrafted test-time losses
</h2>

<ul>
<li>At inference, inject extra loss terms that penalize spatial errors (e.g., overlapping activation maps, incorrect ordering).</li>
<li>These losses must be manually designed to approximate relations like “left of” or “above”.</li>
<li>In practice, these heuristics are fragile, often over-fitting simple cases and failing on more complex layouts.</li>
</ul>

<p>In short, we’ve lacked a solution that is:</p>

<ul>
<li>Data-driven rather than rule-based</li>
<li>Plug-and-play at inference time (no full retraining)</li>
<li>Targeted enough to improve spatial reasoning without damaging other strengths</li>
</ul>

<p>This is where Learn-to-Steer enters.</p>

<h1>
  
  
  How Learn-to-Steer Works: Data-Driven Steering at Inference
</h1>

<h2>
  
  
  How Cross-Attention Maps Provide a Spatial Signal
</h2>

<p>During diffusion, at each denoising step, the model computes cross-attention maps that connect text tokens to image regions. For a prompt like “a dog to the right of a teddy bear”, you can think of:</p>

<ul>
<li>One set of attention maps for “dog”</li>
<li>Another set for “teddy bear”</li>
<li>Additional context around words like “right” or “of”</li>
</ul>

<p>These maps form a rich, high-dimensional signal describing where in the image the model currently believes each word should manifest. Prior work has used cross-attention to locate objects or edit images; Learn-to-Steer goes further by treating them as a feature space in which spatial relations can be learned.</p>

<h2>
  
  
  How a Relation Classifier Becomes a Learned Loss
</h2>

<p>The core idea of Learn-to-Steer is to train a small relation classifier that takes cross-attention maps for two objects and predicts the spatial relation between them (left-of, right-of, above, below, etc.).</p>

<p>The pipeline looks like this:</p>

<h3>
  
  
  Collect supervision
</h3>

<ul>
<li>Use images where the true relation between object A and object B is known (from datasets like GQA and synthetic layouts).</li>
<li>For each image, invert it through the diffusion model with a descriptive prompt to recover cross-attention maps for the relevant tokens.</li>
</ul>

<p><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%2Fi9dbjsdc4c8yjz2r88k4.jpg" 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%2Fi9dbjsdc4c8yjz2r88k4.jpg" alt=" " width="800" height="446"></a></p>

<h3>
  
  
  Train a classifier on attention patterns
</h3>

<ul>
<li>Input: attention maps for object A and object B.</li>
<li>Output: predicted relation (e.g., “A is left of B”).</li>
</ul>

<p>Naively, however, this leads to a subtle but serious issue: relation leakage.</p>

<h2>
  
  
  How Dual Inversion Solves the “Relation Leakage” Problem
</h2>

<p>If you always invert images with a correct prompt (e.g., “a dog to the left of a cat”), hints about the word “left” can leak into the attention patterns. A naïve classifier might then “cheat” by reading out linguistic artefacts instead of learning genuine visual geometry.</p>

<p>To prevent this, Learn-to-Steer uses a dual inversion strategy:</p>

<ul>
<li>For each image with a true relation (say, dog left of cat), create two prompts:

<ul>
<li>A positive prompt with the correct relation (“dog to the left of a cat”).</li>
<li>A negative prompt with an incorrect relation (“dog above a cat”).</li>
</ul>


</li>

<li>Run inversion with both prompts, obtaining two sets of attention maps.</li>

<li>Label both sets with the true relation (left-of), because that is what the image actually depicts.</li>

</ul>

<p>The classifier sees pairs of attention maps that share the same underlying geometry but differ in the relation words used in the prompt. To succeed, it must ignore the unreliable linguistic cue and zero in on the geometric evidence in the attention patterns. This breaks the leakage shortcut and yields a classifier that actually understands “left-of” in terms of where things appear in the model’s internal vision.</p>

<p>To improve robustness, NVIDIA combines:</p>

<ul>
<li>Real images (complex, natural scenes)</li>
<li>Synthetic images (simpler, cleaner attention patterns akin to generation scenarios)</li>
</ul>

<h1>
  
  
  How Learn-to-Steer Guides Images During Generation
</h1>

<h2>
  
  
  Step-by-Step: From Prompt to Steered Latent
</h2>

<p>Once the relation classifier is trained, Learn-to-Steer uses it at inference time as a learned objective:</p>

<h3>
  
  
  Parse the spatial prompt
</h3>

<ul>
<li>Extract subject, relation, and object from the text (e.g., subject = dog, relation = right-of, object = teddy bear).</li>
</ul>

<h3>
  
  
  Run diffusion as usual—but with checkpoints
</h3>

<ul>
<li>As the model denoises latent noise into an image, periodically extract cross-attention maps for the subject and object tokens.</li>
</ul>

<h3>
  
  
  Evaluate spatial correctness
</h3>

<ul>
<li>Feed these maps into the relation classifier, which outputs a probability distribution over relations.</li>
<li>Compare this distribution to the desired relation from the prompt, and compute a loss (e.g., cross-entropy).</li>
</ul>

<h3>
  
  
  Backpropagate into the latent
</h3>

<ul>
<li>Compute the gradient of this loss with respect to the latent representation at that timestep.</li>
<li>Nudge the latent in the direction that increases the classifier’s confidence in the correct relation.</li>
</ul>

<h3>
  
  
  Continue the diffusion process
</h3>

<ul>
<li>Let the denoising proceed from the adjusted latent.</li>
<li>Repeat this steering a number of times (often during the earlier half of the diffusion steps).</li>
</ul>

<h2>
  
  
  Support for Multiple Architectures and Relations
</h2>

<p><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%2F578a9bjc7gmtemh0jbsj.jpg" 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%2F578a9bjc7gmtemh0jbsj.jpg" alt=" " width="800" height="477"></a></p>

<p>A key advantage of Learn-to-Steer is that it’s architecture-agnostic:</p>

<ul>
<li>It has been demonstrated on both UNet-based models (like Stable Diffusion 1.4/2.1) and MMDiT-style models (like Flux).</li>
<li>The only requirement is access to a text-image alignment signal (cross-attention or similar).</li>
</ul>

<p>It can also handle prompts with multiple constraints, such as:</p>

<p>“A frog above a sneaker below a teapot.”</p>

<p>Here, Learn-to-Steer alternates attention between relations:</p>

<ul>
<li>At one timestep, optimize the frog–sneaker relation.</li>
<li>At another, optimize the sneaker–teapot relation.</li>
</ul>

يهدف Learn-to-Steer من NVIDIA إلى معالجة قيد كبير في نماذج الانتشار من النص إلى الصورة، التي تعاني من ضعف في التفكير المكاني الأساسي. يمكن لهذه النماذج إنشاء صور فوتوغرافية واقعية، لكنها غالبًا ما تضع الأشياء في غير موضعها، مثل وضع كلب على اليسار بدلاً من اليمين بجانب دمية دب. تهدف هذه الخطوة إلى تحسين دقة الصور المولدة من خلال تعزيز الفهم المكاني.

El Learn-to-Steer de NVIDIA busca abordar una limitación significativa en los modelos de difusión de texto a imagen, que luchan con el razonamiento espacial básico. Estos modelos pueden crear imágenes fotorealistas, pero a menudo colocan mal los objetos en relación entre sí, como poner un perro a la izquierda de un oso de peluche en lugar de a la derecha. Este avance tiene como objetivo mejorar la precisión de las imágenes generadas al mejorar la comprensión espacial.

Le Learn-to-Steer de NVIDIA vise à résoudre une limitation importante des modèles de diffusion texte-image, qui ont du mal avec le raisonnement spatial de base. Ces modèles peuvent créer des images photoréalistes mais placent souvent mal les objets les uns par rapport aux autres, comme mettre un chien à gauche d'un ours en peluche au lieu de la droite. Cette avancée vise à améliorer l'exactitude des images générées en renforçant la compréhension spatiale.

NVIDIA's Learn-to-Steer is set to address a significant limitation in text-to-image diffusion models, which struggle with basic spatial reasoning. These models can create photorealistic images but often misplace objects in relation to one another, such as placing a dog to the left of a teddy bear instead of the right. This advancement aims to enhance the accuracy of generated images by improving spatial understanding.

What Is Learn-to-Steer? NVIDIA’s 2025 Spatial Fix for Text-to-Image Diffusion

<p>The $5tn firm handily beat expectations, but analysts are awaiting projections for future demand for firm’s AI chips</p><p>Nvidia shares are rising in after-market trading after the company posted third quarter earnings that beat Wall Street estimates.<strong> </strong>All eyes were on Nvidia, the bellwether for the AI industry and the most valuable publicly traded company in the world, as analysts and investors hoped the chipmaker’s third-quarter earnings would assuage concerns about whether the high-flying valuations of AI firms have peaked.</p><p>“Blackwell sales are off the charts, and cloud GPUs are sold out,” said Jensen Huang, founder and CEO of Nvidia in a press release. “Compute demand keeps accelerating and compounding across training and inference – each growing exponentially. We’ve entered the virtuous cycle of AI. The AI ecosystem is scaling fast – with more new foundation model makers, more AI startups, across more industries, and in more countries. AI is going everywhere, doing everything, all at once.”</p> <a href="https://www.theguardian.com/technology/2025/nov/19/nvidia-earning-report">Continue reading...</a>

تجاوزت شركة إنفيديا توقعات وول ستريت مع نتائجها للربع الثالث، مما أظهر طلبًا قويًا على شرائح الذكاء الاصطناعي الخاصة بها. ارتفعت أسهم الشركة في التداول بعد السوق، مما يعكس ثقة المستثمرين وسط مخاوف بشأن تقييم سوق الذكاء الاصطناعي. وأبرز الرئيس التنفيذي جينسن هوانغ مبيعات قياسية ونظامًا بيئيًا سريع التوسع في مجال الذكاء الاصطناعي، مما يشير إلى نظرة إيجابية لمستقبل الشركة.

Nvidia superó las expectativas de Wall Street con sus ganancias del tercer trimestre, mostrando una fuerte demanda por sus chips de IA. Las acciones de la compañía aumentaron en el comercio posterior al cierre, reflejando la confianza de los inversores en medio de preocupaciones sobre la valoración del mercado de IA. El CEO Jensen Huang destacó las ventas récord y un ecosistema de IA en rápida expansión, indicando una perspectiva positiva para el futuro de la empresa.

Nvidia a dépassé les attentes de Wall Street avec ses résultats du troisième trimestre, montrant une forte demande pour ses puces d'IA. Les actions de l'entreprise ont augmenté lors des échanges après la clôture, reflétant la confiance des investisseurs face aux préoccupations concernant la valorisation du marché de l'IA. Le PDG Jensen Huang a souligné des ventes record et un écosystème IA en pleine expansion, indiquant une perspective positive pour l'avenir de l'entreprise.

Nvidia exceeded Wall Street expectations with its third-quarter earnings, showcasing strong demand for its AI chips. The company's shares rose in after-market trading, reflecting investor confidence amid concerns about the AI market's valuation. CEO Jensen Huang highlighted record sales and a rapidly expanding AI ecosystem, indicating a positive outlook for the company's future.

‘AI is going everywhere, doing everything:’ Nvidia beats Wall Street estimates amid market selloff and AI bubble fears

The SanDisk ExtremeFit USB-C flash drive is barely three grams, but offers 1TB of external storage and impressive speeds.

I refused to believe this coin-sized gadget was a storage drive, until I tried it for myself

<p>Swift 6.3 is bringing significant enhancements to Embedded Swift, the subset of Swift designed for resource-constrained environments like microcontrollers. Here's what's new:</p>

<h2>
  
  
  Key Improvements
</h2>

<h3>
  
  
  Libraries &amp; Standard Library
</h3>

<ul>
<li>
<strong>Floating-point printing</strong>: The <code>description</code> and <code>debugDescription</code> properties now work for Float, Double, and other floating-point types with a new all-Swift implementation</li>
<li>
<strong>Better diagnostics</strong>: New <code>EmbeddedRestrictions</code> diagnostic group warns about unsupported language constructs</li>
<li>
<strong>Swift MMIO 0.1.x</strong>: Includes code generation from SVD files and improved debugging with SVD2LLDB plugin</li>
</ul>

<h3>
  
  
  C Interoperability
</h3>

<ul>
<li>
<strong><code>@c</code> attribute</strong>: Define C-compatible functions and enums (from SE-0495)
</li>
</ul>

<div class="highlight js-code-highlight">
<pre class="highlight swift"><code><span class="kd">@c</span><span class="p">(</span><span class="kt">MyLib_initialize</span><span class="p">)</span>
<span class="kd">public</span> <span class="kd">func</span> <span class="nf">initialize</span><span class="p">()</span> <span class="p">{</span> <span class="o">...</span> <span class="p">}</span>
</code></pre>

</div>



<ul>
<li>
<strong>Improved type matching</strong>: Better tolerance for mismatching C signatures, eliminating cryptic deserialization errors</li>
</ul>

<h3>
  
  
  Debugging
</h3>

<ul>
<li>
<strong>Enhanced LLDB support</strong>: Better value printing for Embedded Swift types</li>
<li>
<strong>Core dump inspection</strong>: Dictionary, Array, and other common types now inspectable without a live process</li>
<li>
<strong>ARMv7m exception unwinding</strong>: Complete backtraces through exception frames</li>
</ul>

<h3>
  
  
  Linking &amp; Compilation
</h3>

<ul>
<li>
<strong><code>@section</code> and <code>@used</code> attributes</strong>: Control where globals are emitted and ensure symbols aren't stripped (SE-0492)</li>
<li>
<strong>Weak symbol definitions</strong>: Fixes duplicate symbol errors in diamond dependencies</li>
<li>
<strong><code>@export</code> attribute</strong>: Better control over function visibility (SE-0497)</li>
</ul>




<p><em>Want to dive deeper? Read the <a href="https://www.swift.org/blog/embedded-swift-improvements-coming-in-swift-6.3/" rel="noopener noreferrer">full announcement</a> on Swift.org</em></p>

تقدم Swift 6.3 تحسينات كبيرة على Embedded Swift، مما يعزز وظيفته في البيئات ذات الموارد المحدودة مثل المتحكمات الدقيقة. تشمل التحسينات الرئيسية قدرات جديدة لطباعة الأعداد العشرية، وتشخيصات أفضل مع مجموعة EmbeddedRestrictions، وإدخال Swift MMIO 0.1.x لتوليد الشيفرة وتصحيح الأخطاء.

Swift 6.3 presenta mejoras significativas en Embedded Swift, aumentando su funcionalidad para entornos con recursos limitados como microcontroladores. Las mejoras clave incluyen nuevas capacidades de impresión de números de punto flotante, mejores diagnósticos con el grupo EmbeddedRestrictions y la introducción de Swift MMIO 0.1.x para la generación de código y la depuración.

Swift 6.3 apporte des améliorations significatives à Embedded Swift, renforçant sa fonctionnalité pour les environnements à ressources limitées comme les microcontrôleurs. Les principales améliorations comprennent de nouvelles capacités d'impression de nombres à virgule flottante, de meilleurs diagnostics avec le groupe EmbeddedRestrictions, et l'introduction de Swift MMIO 0.1.x pour la génération de code et le débogage.

Swift 6.3 introduces significant upgrades to Embedded Swift, enhancing its functionality for resource-constrained environments like microcontrollers. Key improvements include new floating-point printing capabilities, better diagnostics with the EmbeddedRestrictions group, and the introduction of Swift MMIO 0.1.x for code generation and debugging.

Embedded Swift Gets Major Upgrades in Swift 6.3

<a href="https://www.techspot.com/news/110317-judge-dismisses-lawsuit-twice-due-alleged-deepfake-video.html" target="_blank"><img src="https://www.techspot.com/images2/news/ts3_thumbs/2025/11/2025-11-19-ts3_thumbs-252.jpg" width="800" height="560" style="padding: 15px 0" title="Judge dismisses lawsuit twice due to alleged deepfake video testimony" /></a><br />A California housing dispute is getting media attention over allegations that lawyers presented a deepfake video as witness testimony. NBC News reports that Judge Victoria Kolakowski became suspicious after the supposed witness showed signs that something was not right, including a monotone voice, fuzzy facial features, and repeated facial expressions....<br /><br /><a href="https://www.techspot.com/news/110317-judge-dismisses-lawsuit-twice-due-alleged-deepfake-video.html">Read Entire Article</a><br /><br />

تجذب نزاع سكني في كاليفورنيا الانتباه الإعلامي بعد ظهور مزاعم بأن المحامين قدموا فيديو مزيف كدليل شهود. أعربت القاضية فيكتوريا كولاكوفسكي عن شكوكها بشأن الفيديو، مشيرة إلى صوت الشاهد الأحادي، وملامح الوجه غير الواضحة، وتكرار التعبيرات. أدى ذلك إلى رفض الدعوى القضائية مرتين.

Una disputa de vivienda en California ha llamado la atención de los medios tras las alegaciones de que los abogados presentaron un video deepfake como testimonio. La jueza Victoria Kolakowski expresó su escepticismo sobre el video, señalando la voz monótona del testigo, rasgos faciales borrosos y expresiones repetitivas. Esto llevó al desestimado de la demanda en dos ocasiones.

Un litige immobilier en Californie suscite l'attention des médias après des allégations selon lesquelles des avocats auraient présenté une vidéo deepfake comme témoignage. La juge Victoria Kolakowski a exprimé des doutes sur la vidéo, notant la voix monotone du témoin, des traits faciaux flous et des expressions répétitives. Cela a conduit à l'annulation de la poursuite à deux reprises.

A California housing dispute has drawn attention after allegations surfaced that lawyers presented a deepfake video as witness testimony. Judge Victoria Kolakowski expressed skepticism about the video, noting the witness's monotone voice, unclear facial features, and repetitive expressions. This led to the dismissal of the lawsuit on two occasions.

Judge dismisses lawsuit twice due to alleged deepfake video testimony

<p>(My actual stack: simple, powerful, and beginner-friendly)</p>

<p>People overestimate what they need to start an AI-driven business.<br>
They think they need paid tools, fancy dashboards, or enterprise software.</p>

<p>Truth is, I scaled ReThynk AI, wrote 42+ books, built audiences across platforms, and automated huge parts of my workflow using free tools.</p>

<p>Here are the best ones I relied on every week, with the exact use cases that helped me run multiple brands without chaos. (Note: As I run a research company, I keep switching between tools for research purposes. I use many premium tools as well. )</p>

<p><strong>1. ChatGPT Free Tier: The Core Engine</strong></p>

<p>Yes, the free tier.<br>
The paid version is amazing, but even the free tier handles:</p>

<ul>
<li>Outlining content</li>
<li>Structuring ideas</li>
<li>Generating variations</li>
<li>Drafting documentation</li>
<li>Writing internal notes</li>
<li>Debugging simple code</li>
<li>Creating mindmaps</li>
</ul>

<p>The trick isn’t the tool.<br>
The trick is how you think with it.</p>

<p><strong>2. GitHub: My Brain’s Second Hard Drive</strong></p>

<p>I host:</p>

<ul>
<li>Prompt libraries</li>
<li>Documentation</li>
<li>Templates</li>
<li>Frameworks</li>
<li>Experiments</li>
<li>AI workflow scripts</li>
</ul>

<p>GitHub is not a coder’s tool anymore; it’s an intelligence repository.<br>
Once you start versioning your thinking, you never go back.</p>

<p><strong>3. Notion (Free Plan): My “AI HQ”</strong></p>

<p>I use Notion for:</p>

<ul>
<li>Knowledge base</li>
<li>Content calendar</li>
<li>Prompt archives</li>
<li>Research notes</li>
<li>Project management</li>
</ul>

<p>AI workflows become 10× clearer when you centralise information.<br>
Notion does that beautifully, even on the free plan.</p>

<p><strong>4. Canva Free: For Fast Brand Assets</strong></p>

<p>I create:</p>

<ul>
<li>Book covers</li>
<li>Thumbnails</li>
<li>Presentation slides</li>
<li>Simple diagrams</li>
<li>Social graphics</li>
</ul>

<p>Not perfect for advanced design, but perfect for speed.<br>
Speed compounds more than perfection.</p>

<p><strong>5. YouTube Studio: For Running My AI Education Channel</strong></p>

<p>I use it to:</p>

<ul>
<li>Manage lectures</li>
<li>Analyse audience insights</li>
<li>Test titles/thumbnails</li>
<li>Track engagement loops</li>
</ul>

<p>YouTube is one of the best free feedback engines on the planet.</p>

<p><strong>6. Google Drive: Storage + Fast Sharing</strong></p>

<p>Free tools inside Drive = gold:</p>

<ul>
<li>Docs (collaboration)</li>
<li>Sheets (data, logs, planning)</li>
<li>Slides (presentations)</li>
<li>Keep (micro-notes)</li>
</ul>

<p>Most of my ideas start in Google Docs before becoming articles, books, or videos.</p>

<p><strong>7. Readwise (Free Tier): My “Learning Memory”</strong></p>

<p>I use it to collect:</p>

<ul>
<li>Quotes</li>
<li>Research snippets</li>
<li>Highlights</li>
<li>Insights from books</li>
</ul>

<p>Then I reuse these for:</p>

<ul>
<li>Prompt engineering</li>
<li>Books</li>
<li>Dev.to posts</li>
<li>Courses</li>
<li>Workshops</li>
</ul>

<p>Readwise makes your learning compounding instead of disposable.</p>

<p><strong>8. VS Code (Free): For Testing AI-Generated Code</strong></p>

<p>I quickly validate:</p>

<ul>
<li>Scripts</li>
<li>Prototypes</li>
<li>Debug suggestions</li>
<li>Refactored code</li>
<li>Micro-tools</li>
</ul>

<p>AI writes the first version. VS Code helps me check the rest.</p>

<p><strong>Why This Stack Works</strong></p>

<p>Because it’s lightweight, fast, flexible, and zero-friction.</p>

<p>Most people fail because they complicate their stack before they even start.</p>

<p>My philosophy is simple:</p>

<blockquote>
<p>Tools shouldn’t slow you down.<br>
Tools should disappear into your workflow.</p>
</blockquote>

<p>The best AI operators run lean, not heavy.</p>

<p><strong>My Core Insight</strong></p>

<p>People think “paid tools give leverage.”<br>
Wrong.</p>

<p>Systems give leverage.<br>
Thinking gives leverage.<br>
Your workflow gives leverage.</p>

<p>The tools are just containers.<br>
What you put inside them, your clarity, reasoning, and consistency, is what creates value.</p>

<p><strong>Final Thought</strong></p>

<p>You don’t need a $500/month tool stack to build an AI-driven career.<br>
You need:</p>

<ul>
<li>Free tools</li>
<li>Clear thinking</li>
<li>Repeatable workflows</li>
<li>And the courage to publish and ship</li>
</ul>

<p>Everything else is noise.</p>

<p>Start simple.<br>
Scale intelligently.<br>
Win consistently.</p>

<p><strong>Next Article</strong></p>

<p>Tomorrow we go deeper:</p>

<p>“Why Prompt Templates Are the New APIs.”</p>

<p>This one will shift how developers think about the next decade of software and AI.</p>

يتناول المقال تجربة الكاتب في إدارة عمل مدفوع بالذكاء الاصطناعي باستخدام أدوات مجانية. ويؤكد أن العديد من رواد الأعمال يبالغون في تقدير الحاجة إلى أدوات مدفوعة وبرامج معقدة لبدء مشاريعهم. يبرز الكاتب فعالية الأدوات المجانية مثل ChatGPT وGitHub وNotion في توسيع أعماله وكتابة أكثر من 42 كتابًا وأتمتة سير العمل. يعمل المقال كدليل لأولئك الذين يسعون للاستفادة من الموارد المتاحة لبناء وإدارة علاماتهم التجارية بكفاءة.

El artículo aborda la experiencia del autor en la gestión de un negocio impulsado por IA utilizando herramientas gratuitas. Se enfatiza que muchos emprendedores sobreestiman la necesidad de herramientas pagadas y software complejo para iniciar sus proyectos. El autor destaca la efectividad de herramientas gratuitas como ChatGPT, GitHub y Notion para escalar su negocio, escribir más de 42 libros y automatizar flujos de trabajo. El artículo sirve como una guía para aquellos que buscan aprovechar recursos accesibles para construir y gestionar sus marcas de manera eficiente.

L'article traite de l'expérience de l'auteur dans la gestion d'une entreprise alimentée par l'IA en utilisant des outils gratuits. Il souligne que de nombreux entrepreneurs surestiment la nécessité d'outils payants et de logiciels complexes pour démarrer leurs projets. L'auteur met en avant l'efficacité d'outils gratuits comme ChatGPT, GitHub et Notion pour développer son entreprise, écrire plus de 42 livres et automatiser les flux de travail. L'article sert de guide pour ceux qui cherchent à tirer parti de ressources accessibles pour construire et gérer leurs marques efficacement.

The article discusses the author's experience in running an AI-driven business using free tools. It emphasizes that many entrepreneurs overestimate the necessity of paid tools and complex software to start their ventures. The author highlights the effectiveness of free tools like ChatGPT, GitHub, and Notion in scaling their business, writing over 42 books, and automating workflows. The article serves as a guide for those looking to leverage accessible resources to build and manage their brands efficiently.

The Best Free Tools I Use to Run an AI-Driven Business

This infographic illustrates what sets these agents apart, how they operate, and why they represent a foundational leap for AI.

تسلط الرسوم البيانية بعنوان 'فك شفرة الذكاء الاصطناعي الوكيل: صعود الأنظمة المستقلة' الضوء على الخصائص الفريدة للذكاء الاصطناعي الوكيل، موضحة كيفية عمل هذه الأنظمة المستقلة وأهميتها في مجال الذكاء الاصطناعي. تؤكد الرسوم البيانية على التقدم الأساسي الذي يمثله الذكاء الاصطناعي الوكيل، مما يميزه عن أنظمة الذكاء الاصطناعي التقليدية. المحتوى مستمد من KDnuggets، وهي منصة مرموقة لمعلومات الذكاء الاصطناعي وعلوم البيانات.

La infografía titulada 'Decodificando la IA Agente: El Auge de los Sistemas Autónomos' destaca las características únicas de la IA agente, detallando cómo funcionan estos sistemas autónomos y su importancia en el campo de la inteligencia artificial. Se enfatizan los avances fundamentales que representa la IA agente, diferenciándola de los sistemas de IA tradicionales. El contenido proviene de KDnuggets, una plataforma de renombre en información sobre IA y ciencia de datos.

L'infographie intitulée 'Décoder l'IA agentique : L'essor des systèmes autonomes' met en lumière les caractéristiques uniques de l'IA agentique, détaillant comment ces systèmes autonomes fonctionnent et leur importance dans le domaine de l'intelligence artificielle. Elle souligne les avancées fondamentales que représente l'IA agentique, la distinguant des systèmes d'IA traditionnels. Le contenu provient de KDnuggets, une plateforme réputée pour les informations sur l'IA et la science des données.

The infographic titled 'Decoding Agentic AI: The Rise of Autonomous Systems' highlights the unique characteristics of agentic AI, detailing how these autonomous systems function and their significance in the field of artificial intelligence. It emphasizes the foundational advancements that agentic AI represents, distinguishing it from traditional AI systems. The content is sourced from KDnuggets, a reputable platform for AI and data science insights.

Decoding Agentic AI: The Rise of Autonomous Systems

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