A practical guide to offloading performance-critical code to C without abandoning Python.
The post <a href="https://towardsdatascience.com/make-python-up-to-150x-faster-with-c/">Run Python Up to 150× Faster with C</a> appeared first on <a href="https://towardsdatascience.com">Towards Data Science</a>.

دليل جديد نُشر على موقع Towards Data Science يوضح كيفية تحسين أداء بايثون من خلال نقل الشيفرة الحرجة إلى C، مما قد يجعلها أسرع حتى 150 مرة. تتيح هذه الطريقة للمطورين الحفاظ على سهولة استخدام بايثون مع الاستفادة من سرعة C، وهو أمر مهم للتطبيقات التي تتطلب أداءً عاليًا.

Una nueva guía publicada en Towards Data Science describe cómo mejorar el rendimiento de Python trasladando código crítico a C, lo que puede hacerlo hasta 150 veces más rápido. Este enfoque permite a los desarrolladores mantener la facilidad de uso de Python mientras aprovechan la velocidad de C, lo cual es significativo para aplicaciones que requieren un alto rendimiento.

Un nouveau guide publié sur Towards Data Science explique comment améliorer les performances de Python en déchargeant le code critique vers C, ce qui peut le rendre jusqu'à 150 fois plus rapide. Cette approche permet aux développeurs de conserver la simplicité de Python tout en tirant parti de la rapidité de C, ce qui est significatif pour les applications nécessitant des performances élevées.

A new guide published on Towards Data Science outlines how to enhance Python's performance by offloading critical code to C, potentially making it up to 150 times faster. This approach allows developers to maintain the ease of Python while leveraging the speed of C, which is significant for performance-intensive applications.

Run Python Up to 150× Faster with C

<A HREF="https://fortune.com/2025/11/19/exclusive-doppel-raises-70-million-series-c-at-more-than-600-million-valuation-to-fight-ai-powered-social-engineering-attacks/"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251119/i39.jpg"></A>
<A HREF="http://www.techmeme.com/251119/p39#a251119p39" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Allie Garfinkle / <A HREF="http://www.fortune.com/">Fortune</A>: 
<A HREF="https://fortune.com/2025/11/19/exclusive-doppel-raises-70-million-series-c-at-more-than-600-million-valuation-to-fight-ai-powered-social-engineering-attacks/">Doppel, which makes an AI social engineering detection service, raised a $70M Series C led by Bessemer at a $600M+ valuation, up from $205M in May</A>&nbsp; &mdash;&nbsp; Senior Finance Reporter And Author Of Term Sheet&nbsp; &mdash;&nbsp; I tried to fool my brother, sort of.&nbsp; &mdash;&nbsp; Next to him and his Pekingese on the couch &hellip;

دوبل، وهي شركة متخصصة في خدمات الكشف عن الهندسة الاجتماعية باستخدام الذكاء الاصطناعي، نجحت في جمع 70 مليون دولار في جولة تمويل من السلسلة C بقيادة بيسمر. وقد زادت هذه الجولة من تقييم الشركة إلى أكثر من 600 مليون دولار، ارتفاعًا من 205 مليون دولار في مايو.

Doppel, una empresa que se especializa en servicios de detección de ingeniería social mediante IA, ha recaudado con éxito 70 millones de dólares en una ronda de financiación Serie C liderada por Bessemer. Esta ronda ha elevado la valoración de la empresa a más de 600 millones de dólares, un aumento significativo desde los 205 millones en mayo.

Doppel, une entreprise spécialisée dans les services de détection d'ingénierie sociale par IA, a levé avec succès 70 millions de dollars lors d'un tour de financement de série C dirigé par Bessemer. Ce tour de financement a porté la valorisation de l'entreprise à plus de 600 millions de dollars, contre 205 millions en mai.

Doppel, a company specializing in AI social engineering detection services, has successfully raised $70 million in a Series C funding round led by Bessemer. This funding round has increased the company's valuation to over $600 million, a significant rise from $205 million in May.

Doppel, which makes an AI social engineering detection service, raised a $70M Series C led by Bessemer at a $600M+ valuation, up from $205M in May (Allie Garfinkle/Fortune)

The service is customized for teachers' needs and includes added security and privacy, a collaborative workspace, and more.

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The agent offers one-click buying for all your holiday needs and will be free for all US-based users.

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<a href="https://fetch.ai/">Fetch AI</a>, a startup founded and led by former DeepMind founding investor, Humayun Sheikh, <a href="https://www.businesswire.com/news/home/20251119088395/en/Fetch-Combines-Personalized-AI-with-Multi-Agent-Collaboration-to-Handle-Complex-Consumer-Tasks-Launches-Claim-Your-Agent-to-Fight-Brand-Knock-Offs">today announced the release</a> of three interconnected products designed to provide the trust, coordination, and interoperability needed for large-scale AI agent ecosystems. The launch includes <a href="https://asi1.ai/">ASI:One</a>, a personal-AI orchestration platform; <a href="https://business.fetch.ai/">Fetch Business</a>, a verification and discovery portal for brand agents; and <a href="https://agentverse.ai/?sort=relevancy&amp;page=1&amp;recommended=true">Agentverse</a>, an open directory hosting more than two million agents. Together, the system positions Fetch as an infrastructure provider for what it calls the “Agentic Web”—a layer where consumer AIs and brand AIs collaborate to complete tasks instead of merely suggesting them.The company says the tools address a central limitation in current consumer AI: models can provide recommendations but cannot reliably execute multi-step actions that require coordination across businesses. Fetch’s approach centers on enabling agents from different organizations to interoperate securely, using verified identities and shared context to complete end-to-end workflows.“We’re creating the same foundation for agents that Google created for websites,” said Humayun Sheikh, Founder and CEO of Fetch AI, and an early investor in DeepMind, in a press release provided to VentureBeat. “Instead of just finding information, your personal AI coordinates with verified brand agents to get things done.”<h2>Background: Fetch’s Founding and DeepMind Connection </h2>Fetch AI was founded in 2017 by Humayun Sheikh, an entrepreneur whose early investment in DeepMind helped support the company’s commercial development before its acquisition by Google. “I was one of the first five people at DeepMind and its first investor. My check was the first one in,” Sheikh said, reflecting on the period when advanced machine learning research was still largely inaccessible outside major technology companies.His early experience helped shape Fetch’s direction. “Even in 2013, it was clear to me that agentic systems were going to be the ones that worked. That’s where I focused—on the agentic web,” Sheikh noted. Fetch built on this thesis by developing infrastructure for autonomous software agents, focusing on verifiable identity, secure data exchange, and multi-agent coordination. Over the past several years, the company has expanded to a 70-person team across Cambridge and Menlo Park, raised approximately $60 million, and accumulated more than one million users interacting with its model—data that informed the design of the newly launched products.Sheikh added that his decision to bootstrap the company initially came directly from the proceeds of the DeepMind exit, noting in the interview that while the sale to Google was “a good exit,” he believed the team could have held out for a higher valuation. The early self-funding period allowed Fetch to begin work in 2015—well before transformer architectures went mainstream—on the hypothesis that agentic infrastructure would become foundational to applied AI.<h2>ASI:One — A Platform for Multi-Agent Orchestration</h2>At the core of the launch is ASI:One, a language model interface designed specifically for coordinating multiple agents rather than addressing isolated queries. Fetch describes it as an “intelligence layer” that handles context sharing, task routing, and preference modeling.The system stores user-level signals such as favored airlines, dietary constraints, budget ranges, loyalty program identifiers, and calendar availability. When a user requests a complex task—such as planning a trip with flights, hotels, and restaurant reservations—ASI:One retrieves those preferences and delegates work to the appropriate verified agents. The agents then return actionable outputs, including inventory and booking options, rather than generic recommendations.In practice, ASI:One functions as a workflow generator across organizational boundaries. By contrast with conventional LLM applications, which often rely on APIs or RAG techniques to surface information, ASI:One is built to coordinate autonomous agents that can complete transactions. Fetch notes that personalization improves over time as the model accumulates structured preference data.Sheikh emphasized the distinction between orchestrated execution and traditional AI output. “This isn’t searching for options separately and hoping they work together,” he said. “It’s orchestration.” He added that Fetch’s architecture is intentionally modular: “Our architecture is a mix of agentic and expert models. One large model isn’t enough—you need specialists. That’s why we built ASI1, tuned specifically for agentic systems.”The interview also revealed new details about ASI:One’s personalization systems: the platform uses multiple user-owned knowledge graphs to store preferences, travel history, social connections, and contextual constraints. These knowledge graphs are siloed per user and not co-mingled with any Fetch-operated data. Sheikh described this as a “deterministic backbone” that gives the personal AI a stable memory layer beyond the probabilistic output of a single large model.ASI:One launches in Beta today, with a broader release planned for early 2026. Fetch also offers ASI:One Mobile, released earlier this year, giving users access to the same agent-orchestration capabilities on iOS and Android. The mobile app connects directly to Agentverse and the user’s knowledge graphs, enabling on-the-go task execution and real-time interaction with registered agents.<h2>Fetch Business — Verified Identity and Brand Control</h2>To enable reliable coordination between consumers and companies, Fetch is introducing a verification and discovery portal called Fetch Business. The platform allows organizations to verify their identity and claim an official Brand Agent handle—for example, @Hilton or @Nike—regardless of which tools they use to build the underlying agent.Fetch positions the product as an analogue to ICANN domain registration and SSL certificate systems for websites. Verified status is intended to protect consumers from interacting with counterfeit or untrusted agents, a problem the company describes as a major barrier to widespread agent adoption.The system includes low-code tools for small businesses to create agents in a few steps and connect real-time APIs such as inventory, booking systems, or CRM platforms. “With Fetch, you can create an agent in one minute. It gets a handle, like a Twitter username, and you can personalize it completely—even give it your social media permissions to post on your behalf,” Sheikh said. Once a brand claims its namespace, its agent becomes discoverable to consumer AIs and other agents inside Agentverse.The company has pre-reserved thousands of brand namespaces in anticipation of demand. Verification status persists across any platform that integrates with Agentverse, creating a portable identity layer for business agents.The interview highlighted that Fetch Business inherits web-trust primitives directly: domain owners verify their identity by inserting a short code snippet into their existing website backend, allowing the system to pass a cryptographic challenge and grant the agent an authenticity badge similar to a “blue check” for agent identities. Sheikh framed this as “reusing the trust layer the web already spent decades building.”Companies can begin claiming agents now at <a href="https://business.fetch.ai/">business.fetch.ai</a>.<h2>Agentverse — An Open Directory of More Than Two Million Agents</h2>The final component of the release is <a href="https://agentverse.ai/">Agentverse</a>, an open directory and cloud platform that hosts agents and enables cross-ecosystem discoverability. Fetch states that millions of agents have already registered, spanning travel, retail, entertainment, food service, and enterprise categories.Agentverse provides metadata, capability descriptions, and routing logic that ASI:One uses to identify appropriate agents for specific tasks. It also supports secure communication and data exchange between agents. The company notes that the directory is platform-agnostic: agents built with any framework can join and interoperate.According to Sheikh, the lack of a discovery layer is one reason most AI agents see little or no usage. “Ninety percent of AI agents never get used because there’s no discovery layer,” he said. He framed the role of Agentverse in more technical terms: “Right now, if you build an agent, there’s no universal way for others to discover it. That’s what AgentVerse solves—it’s like DNS for agents.” He also described the system as an essential component of the emerging agent economy: “Fetch is building the Google of agents. Just like websites needed search, agents need discovery, trust, and interaction—Fetch provides all of that.”The interview further underscored that Agentverse is cloud-agnostic by design. Sheikh contrasted this with competing agent ecosystems tied to specific cloud providers, arguing that a universal registry is only viable if independent of proprietary cloud environments. He said the open architecture enables an LLM to query any agent “within one minute of deployment,” turning agent publication into a near-instantaneous process similar to registering a domain.Agentverse also integrates payment pathways, enabling agents to execute purchases using partners such as Visa, Skyfire, and supported stablecoins. Consumers can configure spending limits or require explicit approval for transactions.<h2>Industry Context and Implications</h2>Fetch’s launch comes at a time when consumer AI platforms are exploring the shift from static chat interfaces toward autonomous agents capable of completing actions. However, most agent systems remain limited by siloed architectures, limited interoperability, and weak verification standards.Fetch positions its infrastructure as a response to these limitations by providing a cross-platform coordination layer, identity system, and directory service. The company argues that an agent ecosystem requires consistent verification mechanisms to ensure that consumers interact with authentic brand representatives rather than imitations. By establishing namespace control and portable trust indicators, Fetch Business aims to fill a gap similar to early web domain verification.At the same time, ASI:One attempts to centralize user preference data in a way that enables more efficient personalization and multi-agent coordination. This approach differs from generalist LLM applications, which often lack persistent preference architectures or direct access to brand-controlled agents.The interview also made clear that micropayments and digital transaction infrastructure are central to Fetch’s long-term vision. Sheikh referenced integrations with protocols such as Coinbase’s 402 and AP2, positioning these capabilities as essential for autonomous agents to complete end-to-end tasks that include financial execution.<h2>Takeaway</h2>Fetch’s combined release of ASI:One, Fetch Business, and Agentverse introduces an interconnected stack designed to support large-scale deployment and usage of AI agents. The company frames the system as foundational infrastructure for an agentic ecosystem, where consumer AIs can coordinate with verified brand agents to complete tasks reliably and securely. The additions to its identity, discovery, and orchestration layers reflect Fetch’s long-standing thesis—rooted partly in lessons from DeepMind’s early development—that intelligence becomes meaningful only when paired with the capacity to act.

أطلقت شركة Fetch AI، وهي شركة ناشئة يقودها هميون شيخ، ثلاثة منتجات مترابطة تهدف إلى تعزيز نظام وكلاء الذكاء الاصطناعي. تشمل العروض الجديدة ASI:One، وهي منصة تنسيق ذكاء اصطناعي شخصي، وFetch Business، وهو بوابة للتحقق والاكتشاف لوكلاء العلامات التجارية، وAgentverse، وهو دليل مفتوح يضم أكثر من مليوني وكيل. تسعى هذه المبادرة إلى إنشاء بنية تحتية قوية لما تصفه Fetch بـ 'الويب الوكالي.'

Fetch AI, una startup dirigida por Humayun Sheikh, ha lanzado tres productos interconectados destinados a mejorar el ecosistema de agentes de IA. Las nuevas ofertas incluyen ASI:One, una plataforma de orquestación de IA personal, Fetch Business, un portal de verificación y descubrimiento para agentes de marca, y Agentverse, un directorio abierto con más de dos millones de agentes. Esta iniciativa busca establecer una infraestructura sólida para lo que Fetch describe como la 'Web Agente.'

Fetch AI, une startup dirigée par Humayun Sheikh, a lancé trois produits interconnectés visant à améliorer l'écosystème des agents IA. Les nouvelles offres comprennent ASI:One, une plateforme d'orchestration d'IA personnelle, Fetch Business, un portail de vérification et de découverte pour les agents de marque, et Agentverse, un annuaire ouvert avec plus de deux millions d'agents. Cette initiative vise à établir une infrastructure robuste pour ce que Fetch décrit comme le 'Web Agentique.'

Fetch AI, a startup led by Humayun Sheikh, has launched three interconnected products aimed at enhancing the ecosystem of AI agents. The new offerings include ASI:One, a personal-AI orchestration platform, Fetch Business, a verification and discovery portal for brand agents, and Agentverse, an open directory with over two million agents. This initiative seeks to establish a robust infrastructure for what Fetch describes as the 'Agentic Web.'

The Google Search of AI agents? Fetch launches ASI:One and Business tier for new era of non-human web

If aesthetics and efficiency top your list of needs, there are several Linux distributions that are right up your alley. Both Ubuntu Budgie and Pop!_OS should top that list.

Ubuntu Budgie vs. Pop!_OS: I've used both Linux distros - here's how to choose

I built my first Zapier workflow because I wanted to save time.

Now I have:

<ul>
<li>19 workflows</li>
<li>4 broken triggers</li>
<li>3 unstable tokens</li>
<li>1 webhook that only runs when I’m not looking</li>
<li>and the unshakable suspicion that I’m now just working for Zapier, unpaid</li>
</ul>

Welcome to automation.




<h2>
 
 
 Rule #1: The Automation Always Works Until You Need It To
</h2>

Automation never breaks when you’re testing. 
 It only breaks when:

<ul>
<li>A client is waiting</li>
<li>You're traveling</li>
<li>You're bragging about it to a friend</li>
<li>Or it's just been running perfectly for 3 months and decides it deserves a vacation</li>
</ul>


<h2>
 
 
 Rule #2: OAuth Expiration Is the Final Boss of Solopreneurship
</h2>

I have fought API keys. 
 I have fought rate limits. 
 I have fought Notion’s “internal server error” with the calmness of a monk.

But OAuth token refresh? 
 That one broke me spiritually.


<h2>
 
 
 Rule #3: Every Automation System Can Be Classified Into Two Stages
</h2>

<ol>
<li>“This is amazing I am a genius”</li>
<li>“Why did I ever do this everything ispain”</li>
</ol>

There is no Stage 3.


<h2>
 
 
 Tech Stack? They Asked Me About My Tech Stack?
</h2>

Okay here it is: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>Notion API
FastAPI
Make (formerly Integromat)
Zapier
GitHub Actions
Python scripts I refuse to document
Cron jobs I forgot exist
One workflow that only runs if the moon is in retrograde
</code></pre>

</div>



Yes, it is technically “working.” 
 No, I do not trust it.




<h2>
 
 
 The Real Automation Was the Anxiety We Made Along the Way
</h2>

Everyone talks about saving 10 hours per month through automation. 
 Nobody talks about losing 10 hours trying to debug a Make scenario you don’t remember creating.

But honestly? 
 I’d still choose this over manually copying data between Google Sheets.

Because if my life is going to be chaos either way— 
 at least this version of chaos feels like engineering.




<h1>
 
 
 🏷 Hashtags
</h1>

#Automation #Zapier #Make #NotionAPI #SolopreneurDev #TechHumor #APIFirst

يتناول المقال تجربة الكاتب مع تدفقات العمل الآلي، وخاصة مع زابير. تم إنشاؤه في البداية لتوفير الوقت، يدير الكاتب الآن 19 تدفق عمل، ويواجه مشكلات مثل 4 مشغلات معطلة و3 رموز غير مستقرة، ويشعر أنه يعمل لصالح زابير دون تعويض. يبرز الكاتب عدم القدرة على التنبؤ بالآلية، مشيرًا إلى أنها تميل إلى الفشل في أكثر الأوقات غير الملائمة، ويعبر عن إحباطه من التحديات المتعلقة بتحديث الرموز OAuth، التي أثبتت أنها صعبة بشكل خاص.

El artículo aborda la experiencia del autor con flujos de trabajo de automatización, especialmente con Zapier. Inicialmente creado para ahorrar tiempo, el autor ahora gestiona 19 flujos de trabajo, enfrenta problemas como 4 desencadenadores rotos y 3 tokens inestables, y siente que está trabajando para Zapier sin compensación. El autor destaca la imprevisibilidad de la automatización, señalando que tiende a fallar en los momentos más inconvenientes, y expresa frustración con los desafíos de la actualización de tokens OAuth, que han demostrado ser particularmente difíciles.

L'article traite de l'expérience de l'auteur avec les flux de travail d'automatisation, en particulier avec Zapier. Initialement créé pour gagner du temps, l'auteur gère désormais 19 flux de travail, fait face à des problèmes tels que 4 déclencheurs cassés et 3 jetons instables, et a l'impression de travailler pour Zapier sans compensation. L'auteur souligne l'imprévisibilité de l'automatisation, notant qu'elle a tendance à échouer aux moments les plus inopportuns, et exprime sa frustration face aux défis des rafraîchissements de jetons OAuth, qui se sont révélés particulièrement difficiles.

The article discusses the author's experience with automation workflows, particularly using Zapier. Initially created to save time, the author now manages 19 workflows, faces issues like 4 broken triggers and 3 unstable tokens, and feels as though they are working for Zapier without compensation. The author highlights the unpredictability of automation, noting that it tends to fail at the most inconvenient times, and expresses frustration with the challenges of OAuth token refreshes, which have proven to be particularly difficult.

When Your Automation Workflow Becomes Your Full-Time Job (And You Don’t Get Paid For It)

Which is actually how some people do it
The post <a href="https://towardsdatascience.com/how-to-build-an-overengineered-retrieval-system/">How to Build an Over-Engineered Retrieval System</a> appeared first on <a href="https://towardsdatascience.com">Towards Data Science</a>.

How to Build an Over-Engineered Retrieval System

LLMs are a seamless way to find value in your unstructured data, but the truth is, there is so much more value hidden within your structured data. This post explores what LLMs are (and aren’t) optimized for and how the industry is approaching AI over structured business datasets – including one approach developed by my team and me.
The post <a href="https://towardsdatascience.com/why-llms-arent-a-one-size-fits-all-solution-for-enterprises/">Why LLMs Aren’t a One-Size-Fits-All Solution for Enterprises</a> appeared first on <a href="https://towardsdatascience.com">Towards Data Science</a>.

Why LLMs Aren’t a One-Size-Fits-All Solution for Enterprises

Introduction Automatic plant leaf detection is a remarkable innovation in computer vision and machine learning, enabling the identification of plant species by examining a photograph of the leaves. Deep learning is applied to extract meaningful features from an image of leaves and convert them into small, numerical representations known as embeddings. These embeddings capture the [&#8230;]
The post <a href="https://towardsdatascience.com/how-deep-feature-embeddings-and-euclidean-similarity-power-automatic-plant-leaf-recognition/">How Deep Feature Embeddings and Euclidean Similarity Power Automatic Plant Leaf Recognition</a> appeared first on <a href="https://towardsdatascience.com">Towards Data Science</a>.

Run Python Up to 150× Faster with C

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