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The Rundown AI7 août, 09:00

Des chercheurs créent 16 virus synthétiques fonctionnels grâce à un modèle de langage

L'exploit scientifique valide l'usage des architectures de type LLM pour la biologie synthétique, mais déplace le problème de sécurité vers la chaîne physique d'impression d'ADN.

Pourquoi c'est important

L'expérience établit que des modèles de langage peuvent générer des génomes complexes et viables à partir de zéro. La disponibilité en open source du modèle Evo 2 accélère la recherche contre l'antibiorésistance tout en renforçant la nécessité d'un contrôle strict sur la synthèse d'ADN.

Resume

Publiée dans la revue Science, l'étude détaille la création de 16 génomes de bactériophages ciblant la bactérie E. coli à partir de modèles génératifs. Sur 285 phages assemblés en laboratoire, 16 ont montré une capacité réelle à infecter et détruire des bactéries résistantes aux antibiotiques. L'entraînement des modèles a délibérément exclu les pathogènes humains, animaux et végétaux pour prévenir les risques sanitaires.

Impact concret

Les directeurs R&D en biotechnologie peuvent adopter ces modèles pour concevoir des traitements ciblant les superbactéries. Les responsables de la sécurité biologique doivent mettre en place un filtrage systématique chez les fournisseurs de synthèse génétique.

Applications concrètes

Conception automatisée de bactériophages thérapeutiques contre les infections bactériennes multirésistantes, génération de nouvelles variantes génomiques pour la recherche en biologie synthétique.

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Read Online | Sign Up | Advertise Good morning, {{ first_name | AI enthusiasts }}, and welcome to the 5,515 new readers who joined us yesterday. Scientists just used AI to design viruses that don't exist in nature, then created them in the lab and watched them come to life and infect their targets. The viruses are harmless — they only attack bacteria, with the goal of fighting drug-resistant infections. The worry is what happens if the same tools get repurposed, the way we've seen in cybersecurity, to invent something dangerous. In today’s AI rundown: AI designs working viruses from scratch Rowan’s Corner: The best AI use cases aren’t coming from labs Turn any idea into an AI-powered site with Lovable Anthropic solves Fable 5’s biggest problem COMMUNITY AI WORKFLOW OF THE DAY ▸ Charles built a self-paced course generator with AI He built a reusable AI skill that turns any topic and source material into a self-paced course with lessons, verified YouTube videos, quizzes with explanations, all packaged in its own course folder. Credit to reader Charles Clogston . How do you use AI? Tell us here for a chance to be featured next. LATEST DEVELOPMENTS AI BIOLOGY 🦠 AI designs working viruses from scratch Image source: Images 2.0 / The Rundown The Rundown: Stanford and Arc Institute researchers just used AI to design 16 viruses not found in nature, marking the first instance of complete, working genomes generated by a language model, with the results published in the journal Science. The details: The team trained Evo 1 and Evo 2 on millions of genomes, then had them write new versions of Phi X174, a well-studied virus that infects only E. coli. Of 285 phages synthesized and tested, 16 were viable, some replicating faster than the original, with a few different enough to count as new species. A cocktail of AI-made viruses wiped out E. coli that had grown resistant to the natural one, a proof of concept for therapies for antibiotic-resistant infections. For safety, the team never trained the model on viruses that infect humans, animals, or plants, so it can't generate anything that threatens people. Why it matters: The AI that can design viruses to beat drug-resistant infections could, if trained differently, also help build a dangerous pathogen. Evo 2 is open source, and the field is moving fast, so expect more news in this field as well as mounting pressure to build the guardrails and testing frameworks for ensuring biosafety. TOGETHER WITH ALGOLIA 🔎 The no-code approach to clean data The Rundown: Our daily digital experiences are fueled by data, but to be effective, it needs to be clean and structured. This white paper explores how no-code, AI-powered data preparation helps teams create searchable, high-quality data faster. You'll learn: Why clean data matters Limits of traditional methods Benefits of modern no-code tools How to turn messy data into valuable insights and a better search experience Download the whitepaper . ROWAN’S CORNER 🤗 The best AI use cases aren’t coming from labs The Hub's weekly leaderboard, one week in Rowan: In case you missed it, we just launched our highly requested Community AI Workflow Hub , a free extension of the Community Workflows section in our daily newsletter. The reaction exceeded my expectations, and going through thousands of submissions and requests taught me a ton about the current state of AI adoption. A rundown of my key learnings: People are done with hype. They don’t want to learn more about AI use cases from execs, influencers, or the media — they want to learn from real, everyday people, leveraging those use cases to do real work. Sharing how you use AI is now a hiring signal. We had several recruiters asking us to add location tags to posts so they could find and hire local builders. Step-by-step workflows beat inspiration. The most upvoted workflows are from builders who share their exact steps rather than just a high-level summary. People want to plug in, not just read. We had multiple API and MCP access requests in week one. For those asking, we hear you and are working on it! To keep the momentum going, we’ll soon be highlighting top-voted builders not just in our newsletter, but across our socials: a weekly X thread, Instagram carousels, and LinkedIn shoutouts. So if you’ve built a workflow that saves you hours, share it in the Hub , and you might just get featured in front of millions. AI TRAINING 💖 Turn any idea into an AI-powered site with Lovable The Rundown: In this guide, you will learn how to turn a rough app idea into a working AI-powered site without writing a giant technical prompt. Here, we’ll build Method, a recipe app that turns pasted recipe text into an editable visual card. Step-by-step: In ChatGPT, attach the recipe-card screenshot and describe the app. Answer its questions, but keep version one focused on paste, convert, review, and save Ask ChatGPT to turn the conversation into feature requirements and a detailed brand brief for Lovable. Remove any extra features before copying the handoff Create a Lovable project, attach the same screenshot, and paste the handoff specs from ChatGPT In Connectors → Cloud AI, enable Lovable’s built-in AI. Now, you can test and refine your app Pro tip: Once the core loop works, add accounts, data storage, and sharing. Build those features around a validated product, not an assumption. PRESENTED BY ENEY BY MACPAW 🧑‍💻 A proactive AI assistant for focused work The Rundown: Eney is a proactive AI assistant for Mac that handles repetitive tasks, keeps your work in context, and helps you stay focused by reducing context switching. Eney is built to help you: Start each day with a Morning Brief that surfaces what needs your attention Work seamlessly across Gmail, Calendar, Notion, and Jira Complete tasks automatically, with your approval before anything ships Try Eney now , with no setup needed. ANTHROPIC 🧬 Anthropic solves Fable 5’s biggest problem Image source: Anthropic The Rundown: Anthropic rewrote the safety classifier that handles Fable 5's biology responses, reducing false positives so the model now answers a far wider range of health and educational questions instead of routing them to the less capable Opus. The details: At launch, Fable 5 blocked almost all biology queries, even a ‘Hi’ from some researchers, and sent them to Opus — a deliberate safety filter from Anthropic. The rewrite now cuts those fallbacks by ~85%, meaning Fable can now handle everyday tasks like understanding labs and symptoms, or learning biology. However, it still isn't usable for research or drug development yet, with dual-use queries involving virology, toxicology, and molecular design going to Opus. Anthropic is loosening Fable’s restrictions only as its classifiers improve, given its own tests show the model can uplift bad actors developing bioweapons. Why it matters: Most people want exactly this from AI labs: basic use (like reading lab reports), but nothing that builds a bioweapon. It leaves one group frustrated, though — researchers doing real good, still blocked from the capabilities they need most. Anthropic knows it's turning them away but hopes to fix the gap with future updates. QUICK HITS 🛠️ Trending AI Tools 🔌 Agent Plugins - Vendor-neutral standard for Agent Skills and MCP servers 📺️ Wan 3.0 - Alibaba's video AI with omni-reference support 🗣️ Gemma Translator - Google's open-source on-device AI translation stack 🧠 Muse Code - Meta's terminal-based AI coding agent 📰 Everything else in AI today CData Connect AI can cut LLM context handling costs by up to 97.6% without sacrificing answers. Learn more here .* OpenAI asked a judge to dismiss Apple's trade secret lawsuit, calling it a "baseless and pretextual" attempt to make up for its own AI and talent retention failures. Meta’s Muse Spark 1.1 AI hacked another company's systems after a test misconfiguration gave it internet access, joining similar cases at OpenAI and Anthropic. OpenAI is rolling out GPT-5.6 Luna as ChatGPT's default model for Free and Go users, with unlimited text chats and a new Think button for deeper reasoning. DeepSeek warned developers that it will significantly raise API prices soon, urging them to plan usage ahead of the increase. Google Maps updated Ask Maps with AI agents that can order food, compare hotels, and find event tickets, rolling out in the U.S. with more countries to follow. *Sponsored Listing 🎓 Highlights: News, Guides & Events Read our last AI newsletter: Google reshuffles AI leadership Read our last Tech newsletter: Biohackers head to Big Sky Country Read our last Robotics newsletter: Musk's moon factories need robots Today’s AI tool guide: Turn any idea into an AI-powered site with Lovable RSVP to next workshop on Aug. 12: Catch up on AI fundamentals See you soon, Rowan, Zach, Shubham, Jennifer, and Nate — the humans behind The Rundown

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🔧Evo 2

Modèle génératif biologique de fondation entraîné sur des génomes pour la conception de séquences ADN et virales.