标签：#ai

Plaud is trying to make a mark in a crowded market full of AI-powered meeting notetakers.

By this time next year, Fox Corporation CEO Lachlan Murdoch intends to have added Roku to his already expansive media empire. Should the acquisition go through, Fox will gain control of Roku's modest library of original programming, and the newly combined company will become "the third-largest player in U.S. television" in terms of viewing share. […]

Unlike many of his tech industry peers who have cut thousands of jobs citing the need to restructure to make the most of AI, Robinhood's CEO Vlad Tenev conspicuously made no mention of AI in his note about layoffs.

SpaceX is buying Cursor to bolster the capabilities of xAI.

SpaceX's valuation has increased by $1 trillion since its shares started trading on Friday.

Probably wants to prevent hallucinations and factual errors from reaching users, and achieve accuracy on par with deterministic systems.

There is a persistent myth that to build a worthy code assistant, you absolutely must use GPT or Claude. This is false. You don't need a 1-trillion parameter model. You need a small local model and extremely rigorous engineering around it. This is the direction history is taking for companies. As Mark Zuckerberg mentioned, the future isn't a single omniscient model, but "every company having its own specialized AI" . And this specialization necessarily involves fine-tuning and local deployment (or on sovereign servers) to guarantee data security. The thesis behind the construction of Vibrisse Agent can be summed up in one sentence: Small models, Great tools. In this article, I will detail the technical stack and concrete engineering solutions I implemented to tame a local model and make it reliable in production: LangGraph, Ollama, FastAPI, React (no build step, with embedded custom CSS) , all running on a machine with 32 GB of RAM. For the curious who want to run the agent on their machine right now: // MacOs / Linux curl -sSL https://agent.vibrisse-studio.dev/install.sh | bash // Windows irm https://agent.vibrisse-studio.dev/install.ps1 | iex Architecture: Why a State Machine (LangGraph)? At first, when building an LLM application, we tend to think in sequential chains: Input -> Prompt -> Tool -> Output . The problem is that if one node fails, the whole chain stops without us being able to catch the error or understand the context of the crash. That's where LangGraph comes in. Vibrisse's architecture isn't a chain, it's a state machine . Every node in the graph has a very precise responsibility, shares a global conversation state, and uses conditional transitions to move to the next node. I implemented the Supervisor / Worker pattern: The Supervisor analyzes the user's intent. It does nothing else but route. It dispatches the task to specialized Workers (the RAG Worker, the Search Worker, the Ghost Worker...). If a Worker fails or needs more information, it can se

Il y a un mythe persistant selon lequel pour construire un assistant de code digne de ce nom, il faut absolument utiliser GPT ou Claude. C'est faux. Vous n'avez pas besoin d'un modèle à 1 trillion de paramètres. Vous avez besoin d'un modèle local de taille réduite et d'une ingénierie extrêmement rigoureuse autour de lui. C'est d'ailleurs le sens de l'histoire pour les entreprises. Comme l'évoquait Mark Zuckerberg, l'avenir n'est pas à un modèle omniscient unique, mais à "chaque entreprise avec sa propre IA spécialisée" . Et cette spécialisation passe obligatoirement par le fine-tuning et le déploiement local (ou sur serveurs souverains) pour garantir la sécurité des données. La thèse derrière la construction de Vibrisse Agent tient en une phrase : Small models, Great tools. Dans cet article, je vais détailler la stack technique et les solutions d'ingénierie concrètes que j'ai mises en place pour dompter un modèle local et le rendre fiable en production : LangGraph, Ollama, FastAPI, React (sans build step, avec CSS custom embarqué) , le tout tournant sur une machine avec 32 Go de RAM. Pour les curieux qui souhaitent lancer l'agent sur leur machine dès maintenant : // MacOs / Linux curl -sSL https://agent.vibrisse-studio.dev/install.sh | bash // Windows irm https://agent.vibrisse-studio.dev/install.ps1 | iex L'Architecture : Pourquoi une Machine à États (LangGraph) ? Au début, quand on construit une application LLM, on a tendance à penser en chaîne séquentielle : Input -> Prompt -> Outil -> Output . Le problème, c'est que si un nœud échoue, toute la chaîne s'arrête sans qu'on puisse rattraper l'erreur ou comprendre le contexte du plantage. C'est là qu'intervient LangGraph . L'architecture de Vibrisse n'est pas une chaîne, c'est une machine à états . Chaque nœud du graphe a une responsabilité très précise, partage un état global de la conversation, et utilise des transitions conditionnelles pour passer au nœud suivant. J'ai implémenté le pattern Supervisor / Worker : L

🚀 Hello, DEV Community! I'm Nader Al Shawki , a final-year AI Engineering student at Al-Razi University, Yemen. This is my first post here, and I'm excited to start sharing my journey with this amazing community. 🎯 Who Am I? I'm passionate about building production-grade AI systems that solve real-world problems. My main areas of focus are: 🖼️ Computer Vision & Deep Learning 🤖 ML Model Deployment (Docker, FastAPI, REST APIs) 🧠 LLMs, RAG, and AI Agents (currently learning) 📊 Data Visualization & Analytics (Power BI) 💡 What I've Built So Far 1. 🍅 Tomato Leaf Disease Detection Platform Tech: YOLOv8, PyTorch, FastAPI, Docker What it does: Detects tomato leaf diseases from images with real-time inference. Containerized with Docker for easy deployment. 2. 🫁 Pneumonia Detection System Tech: PyTorch, CNN Architecture, Medical Imaging What it does: A deep learning model that detects pneumonia from chest X-ray images. 3. 📊 Sales Profit Analysis Dashboard Tech: Power BI, DAX, Data Analysis What it does: Interactive dashboard for tracking sales KPIs. 4. 😀 Face Detection & Emotion Recognition Tech: OpenCV, Deep Learning What it does: Real-time face detection, age estimation, emotion recognition, and gender classification. 5. 🍽️ Restaurant Website Tech: HTML5, CSS3, JavaScript What it does: Fully responsive restaurant website with interactive UI. 🌱 What I'm Currently Learning LLMs (Large Language Models) RAG (Retrieval-Augmented Generation) LangChain & AI Agents Workflow automation with n8n 🔗 Let's Connect 🐙 GitHub: Naderalshawki 💼 LinkedIn: in/nader-al-shawky 📫 Email: naderalshawki@gmail.com Thanks for reading! I'll be posting regularly about AI projects, tutorials, and lessons learned. Stay tuned! 🚀

I use a form validation problem as a technical interview question. It's deceptively simple — and the solutions people reach for reveal a lot about how they think. Then I tried it on Claude, ChatGPT, and Gemini. The results were illuminating, but not for the reasons I expected. The Problem Many form libraries share a common convention: form data is represented as a plain nested object, and the validation function returns an object of the same shape containing the errors. You'll find this pattern in Formik and React Final Form in React, and — full disclosure — in Inglorious Web , my own framework, which ships form handling built in without any extra dependencies. const values = { productName : ' VR Visor ' , quantity : 1 , homeAddress : { street : ' Long St ' , zip : ' 00666 ' }, shippingAddress : { street : ' Short St ' , zip : ' 00777 ' , co : ' Inglorious Coderz ' }, billingAddress : { street : ' Wide Plaza ' , zip : ' 00888 ' , vat : ' 1142042 ' }, } The validation function should return an object containing all errors found. A starting example: function validate ( values ) { const errors = {} if ( ! values . productName ) { errors . productName = ' required ' } return errors } The ask: extend this to validate every field . Notice that the three address types aren't identical. shippingAddress requires a co field. billingAddress requires a vat . These differences matter — and how you handle them reveals a lot. Four Solutions, Four Instincts 1. The Flag — the average human The most common approach I see in interviews is a single validateAddress function with a type parameter: function validateAddress ( values = {}, type ) { const errors = {} if ( ! values . street ) errors . street = ' required ' if ( ! values . zip ) errors . zip = ' required ' if ( type === ' shipping ' && ! values . co ) errors . co = ' required ' if ( type === ' billing ' && ! values . vat ) errors . vat = ' required ' return errors } It works. But every new address type, every new special rule,

TL;DR — A few weeks ago I tested four AI tools on a build job: a website for my son's cricket academy. This time the job had nothing to do with code. The coach just wanted a banner he could post. Same four tools, totally different result. ChatGPT made the best image, Grok made the best video, Gemini wouldn't make anything, and Claude tried to solve a graphics problem by writing HTML. If you read the last post , you've met my son's cricket coach. He runs MMCA — Maverick Master's Cricket Academy. Started in 2020, based in Bengaluru, genuinely good with the kids. The website is live now and parents have started messaging him on WhatsApp. So last weekend he came back with the next thing he needed, which is the thing every small academy actually runs on: "Can you make me a weekend batch banner? Something I can post in the parent groups." Now, this is a completely different job from the last one. That first experiment was design and development — agents writing real code, running tests, deploying to Cloudflare. This one is just graphics. No repo, no deploy, nobody reviewing a pull request. Just: here's my logo, here's a sample I like, make me something I'd be happy to send out. So I figured I'd run the same four tools again and see what happened. Same brief, same logo, everything on the default model with no special settings : ChatGPT, Claude, Gemini, Grok. Here's roughly what I typed, the way a normal client would brief you: Similar to this banner, make one for MMCA Academy (since 2020, logo attached). Weekend batch Sat 4:30—7, Sun 7—9:30pm. Add a small phrase like the sample. Be creative, keep it simple, but don't copy the sample exactly. The whole test really came down to one instruction: be creative, but don't copy. Whatever each tool did with that told me everything. Round 1: the static banner ChatGPT got it on the first go. "WEEKEND BATCH. TRAIN. PLAY. GROW." Logo top-left, the "Since 2020" bit kept, timings in clean little cards, an enrol number, three badges acros

Coinbase has published a detailed postmortem of its May 7, 2026, outage, revealing how a localized cooling failure inside an AWS data center escalated into a multi-hour disruption that halted nearly all trading activity across the cryptocurrency exchange By Craig Risi

Days after its massive IPO, SpaceX says it is spending $60 billion to buy Cursor - a bet designed to help Elon Musk's sprawling rocket / AI / social media behemoth win over lucrative enterprise customers and close the gap with AI rivals like Anthropic and OpenAI. The takeover was not entirely unexpected: SpaceX announced […]

Instead of heating metals, Foundation Alloy beats them into submission. The startup has raised $22 million to scale up production of its alloys.

The deal is supposed to help SpaceX's struggling AI division. The company told IPO investors it sees a $26 trillion addressable market in AI.

SearchLeak exploit shows why the industry's approach to LLM security fails over and over.

Originally published on rikuq.com . Republished here for Dev.to's readers. I dropped my $100/month Claude Max subscription and migrated entirely back to Antigravity. If you want the verdict upfront: Claude Desktop is still the best tool for beginners who need the AI to guess their intent from clumsy prompts. But if you have solid documentation discipline and cost efficiency is a serious factor for your SaaS, Antigravity is now the clear winner. I'm a Chartered Accountant by trade with zero formal coding experience. I’ve shipped three production AI SaaS— Prism , Citare , and BatchWise —relying entirely on AI tools. I started with VSCode, moved to Antigravity (when it was just an IDE), and eventually landed on the Claude Desktop App. Claude was incredible; it operated in the background, handled my stack, and I didn't need to know what was happening under the hood. But the bills started stacking up. When my Claude usage consistently hit $100 a month, efficiency became a priority. I fired up the new version of Antigravity and found the recent updates had completely transformed it. It is no longer just an IDE—it is a full agentic desktop experience that mirrors what made Claude so good. TL;DR — The 2026 Reality Feature Claude Desktop App Antigravity (New Update) Best for Beginners, unlimited budgets, "pure performance" Experienced AI directors, cost-conscious solo founders Pricing $100+/mo (Claude Max) $20/mo (Gemini Advanced) Agentic Workflow Exceptional. The benchmark. Identical. Background execution, zero friction. Context Handling Better at anticipating intent from messy prompts Huge total memory, but requires tighter prompting MCP Support Native Native (handles them just as well) Verdict Keep it if cost doesn't matter Switch to it if efficiency is the goal The Catalyst for Switching My path to Antigravity wasn't a calculated feature comparison. It was pure economics combined with a pleasant surprise. I had previously dropped Antigravity when it was just an IDE. When

So here's what happened: i Wish I Knew AI Recommendation Sooner — Here's the Full Breakdown Last quarter I burned through about three billable hours debugging a recommendation pipeline for a Shopify client. The thing was — it shouldn't have taken that long. I had the data. I had the API keys. What I didn't have was a clear-eyed picture of what AI recommendation systems actually cost in 2026 when you're paying the bills yourself. If you freelance like I do, every line item matters. My "office" is a kitchen table, my "PM" is a Slack ping at 11pm, and my CFO is whatever's left in my checking account after software subscriptions. So when I say I've been digging into the numbers on AI recommendation systems for the last six weeks, I mean I've been doing it the way I do everything: with a calculator open in one tab and a client invoice in the other. This post is the writeup I wish I'd had before I started. Consider it the field guide for anyone building recommendation features on a budget, on a deadline, or just for fun. Why I Even Cared About Recommendation Systems I took on a small retainer back in February for an indie e-commerce shop that sells specialty coffee beans. They wanted "AI-powered product recommendations" on their storefront — you know, the classic "customers who bought this also bought..." thing, but smarter. The owner had been quoted $15,000 by a "full-service AI agency" to build it. He doesn't have $15,000. He has $15,000 in revenue per month and a wife who is deeply skeptical of his side-hustle energy. So he came to me. And I said yes, because I'm a sucker and also because I knew it should cost a tiny fraction of that quote. The math was never going to support five figures for a recommendation widget. Not when the underlying API calls are fractions of a cent. That's when I started really paying attention to the pricing landscape. The 184-Model Elephant in the Room Here's the thing nobody tells you when you start shopping for LLMs: there are a lot of the

I'm not here to trash Sololearn. Sololearn taught millions of people how to code. It was one of the first apps to make programming education feel mobile-native. That's a real achievement. I respect it. But I'm building Codino — a Python learning app — and I'd be lying if I said I didn't study Sololearn carefully before writing a single line of code. I looked at what they got right. I looked at where users complained. And I made decisions based on both. This is that honest breakdown. What Sololearn Got Right 1. The Community Feel Sololearn built a genuine community. The code playground where users share their projects, comment on each other's code, and get likes — that was smart. Learning feels less lonely when other people are doing it alongside you. It created a social loop that kept people coming back even when they weren't actively doing lessons. I haven't built this yet in Codino. The leaderboard is a start, but a full community layer is something I'm thinking about for a future update. 2. Multi-Language Support Sololearn didn't bet on just one language. Python, JavaScript, C++, SQL, HTML — they covered everything. That gave them a massive addressable audience. Codino is Python-only right now. That's intentional — going deep on one language is better than going shallow on ten. But I understand why multi-language eventually matters for scale. 3. The Code Playground The ability to write and run real code inside the app — without going to a browser — was ahead of its time when Sololearn launched it. That feature alone brought back users who had finished all the lessons. Codino has a full offline IDE powered by Sora Editor. I'd argue ours is actually more capable — real syntax highlighting, autocompletion, offline Python execution — but Sololearn deserves credit for proving this feature matters. 4. Bite-Sized Lessons That Actually Work Sololearn understood that people learn on the bus, in bed, waiting in line. Their lessons are short, digestible, and don't demand 45

Every day, startups rent expensive GPUs to power AI applications. The problem is that most of those GPUs spend a surprising amount of time doing nothing. Imagine renting an apartment and only using one room while paying for the entire building. That's effectively what many AI teams do with GPU infrastructure. The Hidden Cost of GPU Rentals When you rent a GPU, you're usually paying for uptime. Whether your application is processing requests or sitting idle at 3 AM, the bill keeps running. For many early-stage products: Traffic is inconsistent Usage spikes are unpredictable Most requests arrive in short bursts As a result, GPU utilization can be far lower than expected. The Utilization Problem A startup might rent a GPU for an entire month. But how much of that compute is actually being used? During development: Developers test occasionally Demos happen a few times a day Customer requests arrive sporadically The GPU remains available 24/7, but actual inference workloads often occupy only a small fraction of that time. Yet the infrastructure bill reflects full-time usage. Why This Matters For startups, infrastructure costs directly affect runway. Every dollar spent on idle compute is a dollar that cannot be spent on: Product development Customer acquisition Hiring Experiments Reducing wasted infrastructure spend can significantly improve efficiency. A Different Model Instead of paying for GPU uptime, what if developers only paid when inference actually occurred? For example: Pay per token generated Pay per image generated Pay per second of video generated This approach aligns cost with actual usage rather than reserved capacity. The Future of AI Infrastructure As AI adoption grows, efficiency becomes increasingly important. The next generation of AI infrastructure may look less like traditional server rentals and more like utilities: Use what you need. Pay for what you use. Nothing more. What has your experience been with GPU utilization and AI infrastructure costs? I