Machine Learning FLX

The difference between a working LLM demo and a reliable production system is often enormous. Production environments require thinking about: • observability • fallback logic • evaluation • retrieval pipelines • security • inference costs As LLM adoption grows, these engineering concerns are rapidly becoming central to AI development. Useful implementation walkthrough: buff.ly/2DgIIFC #LLM #AIEngineering #GenAI #MachineLearning #ad

Woow Nvidia has just released a 2.6B open-source world model 🔥 You can turn a single image, text prompt and trajectory into controllable worlds... And on a single GPU! - Code available on GitHub - Paper as well on arxiv You can use it for many things like embodied AI and robotics research, simulations, etc. Because it can run on a single GPU (like an RTX 5090 or H100) it makes world models accessible to basically everyone!

Cursor pays engineers $1,100,000 a year to run teams of AI agents that ship code while they sleep. [The CEO of Cursor explained in 9 minutes how they ship at 100x speed using team of agents] ↓ Save this before everyone copies the playbook 1. Engineers no longer babysit one assistant. They manage dozens of agent colleagues working in parallel, each on its own remote machine 2. Validation contract before code, not after. Humans only at scoping and review. 3. The agent team handles the full loop : planning, coding, testing, shipping PRs with each agent specialised for a role. Watch the guide. Then read the guide below by @eng_khairallah1

GITHUB ACABA DE LANZAR LA CERTIFICACIÓN OFICIAL DE UNO DE LOS ROLES TECH MÁS IMPORTANTES DE 2026 → Agentic AI Developer (GH-600) Y es la primera vez que trabajar con agentes de IA se convierte oficialmente en una disciplina reconocida de ingeniería. Ya no hablamos de: • prompt engineering • vibe coding • automatizaciones simples Hablamos de un nuevo perfil técnico: → Agentic AI Developer La persona que: • coordina agentes de IA • construye workflows autónomos • integra agentes en entornos reales • supervisa fallos en producción • evita errores críticos en pipelines CI/CD • sabe cuándo un agente no es fiable Antes: → “Trabajo con agentes de IA” era difícil de validar. Ahora: → GitHub certifica oficialmente ese skillset. Y eso cambia el mercado. Las empresas van a necesitar este perfil. Pero todavía hay muy pocos developers especializados en ello. Si ya trabajas con: • Copilot • Codex • Claude Code • workflows agentic • automatizaciones con IA Probablemente ya estés haciendo este trabajo. GH-600 es la forma de demostrarlo. Guárdate esto 🔖

🚨 JUST IN - Google published a long piece about "Optimizing your website for generative AI features on Google Search" 👀 A lot in it developers.google.com/search/docs/fu… 🧵

Many AI systems depend far more on data pipeline quality than on model complexity. In distributed environments, topics such as: • partitioning • serialization • schema evolution • storage formats • query optimization have a direct impact on scalability and operational reliability. This is one reason why Spark and modern data engineering remain highly relevant even in the LLM era. Practical resource here: trk.udemy.com/c/6457882/3227… #DataEngineering #ApacheSpark #Databricks #BigData #ad

Most people talk about Agentic AI. Very few can actually design it. Here’s a simple cheat sheet to design + explain Agentic AI architecture 👇 🎯 Start here ➡️ Define the goal What exactly should the agent achieve? 1️⃣ Orchestration Layer ➡️ The control panel Decides flow, logic, and coordination 2️⃣ Agents Layer ➡️ The workforce Single or multi-agents handling specialized tasks 3️⃣ Tools Layer ➡️ Execution power APIs, web search, databases, external systems 4️⃣ Memory ➡️ The brain Short-term + long-term context storage 5️⃣ Monitoring ➡️ The eyes Track every step, detect issues in real time 6️⃣ Reliability & Failure ➡️ The safety net Retries, fallbacks, human-in-the-loop 7️⃣ Governance & Security ➡️ The guardrails Auth, compliance, audit, data protection 💡 Real insight: Agents alone don’t make systems powerful. Architecture does. If you can explain this simply, you’re already ahead of 90% in AI. ❤️ Like 🔁 Retweet 🔖 Bookmark Follow @MeenakshiYACS for more such posts #AI #ArtificialIntelligence #GenerativeAI #CareerGrowth #Upskilling

We've published a paper that explains our views on AI competition between the US and China. The US and democratic allies hold the lead in frontier AI today. Read more on what it’ll take to keep that lead: anthropic.com/research/2028-…

I just finished creating my most valuable PDF yet: "18 Claude Cowork Workflows for the Entire eBook Business" (44 pages). I might charge for this in the future, but for now... Reply "Claude" and I’ll DM it to you for free (must follow)

You've been asking for this one... Now in preview: Codex in the ChatGPT mobile app. Start new work, review outputs, steer execution, and approve next steps, all from the ChatGPT mobile app. Codex will keep running on your laptop, Mac mini, or devbox.

One of the most underestimated aspects of modern AI systems is infrastructure design. As workloads scale, issues such as: • inference latency • GPU utilization • memory constraints • concurrency • orchestration overhead can quickly become more important than the model itself. The engineering around AI systems is increasingly becoming a discipline on its own. More technical details: trk.udemy.com/c/6457882/3227… #AIInfrastructure #MLOps #LLM #AI #ad

One of the most interesting enterprise applications of Machine Learning is transaction monitoring. Traditional rule-based systems often generate large numbers of false positives, making investigation workflows inefficient and difficult to scale. ML models can help identify: • anomalous patterns • suspicious behavior • hidden correlations • dynamic risk signals This area is becoming increasingly relevant as financial institutions modernize compliance operations. Further reading: trk.udemy.com/c/6457882/3227… #MachineLearning #FinTech #AML #AI #ad

Linus Torvalds acaba de dejar claro que Linux no va a convertirse en un basurero de código generado por IA. Después de meses de debate interno, la comunidad Linux publicó sus reglas oficiales sobre el uso de herramientas como GitHub Copilot. El veredicto: se puede usar IA para programar, pero el "slop" ese código de baja calidad escupido sin pensar, no pasa el filtro. La frase que lo resume todo: "Los humanos asumen los errores." Puedes apoyarte en Copilot, en Claude, en lo que quieras. Pero si ese código entra al kernel de Linux, tú eres el responsable. Tú lo verificas. Tú corriges los fallos. Tú garantizas que cumple los estándares. Es la postura más madura que he visto en el ecosistema open source frente a la IA. Ni histeria, ni adopción ciega. Solo responsabilidad clara. El kernel tiene 30 años de historia. No lo van a arruinar por ahorrar 20 minutos con un autocomplete.

One of the most underestimated aspects of modern AI systems is infrastructure design. As workloads scale, issues such as: • inference latency • GPU utilization • memory constraints • concurrency • orchestration overhead can quickly become more important than the model itself. The engineering around AI systems is increasingly becoming a discipline on its own. More details: trk.udemy.com/c/6457882/3227… #AIInfrastructure #MLOps #LLM #AI #ad

A significant portion of ML complexity appears only after deployment. Training a model is usually just the beginning. Production environments introduce challenges around: • monitoring • reproducibility • versioning • scalability • deployment automation • cost management This operational layer is becoming one of the most important differentiators in mature AI teams. Useful implementation walkthrough: trk.udemy.com/c/6457882/3227… #MLOps #MachineLearning #AI #DataEngineering #ad

The database behind Twitter, GitHub, Snapchat, Airbnb, Pinterest, Instagram. In 2009 one Italian wrote it alone, on a MacBook Air. 🤯 Meet Salvatore Sanfilippo 🇮🇹 > Italian programmer. Born 1977 in southern Italy. Goes by "antirez" online. > Left university at 17. Self-taught coder. > 1998 ~ invented "idle scan" ~ a stealth network scanning technique now built into nmap. > 2009 ~ started building Redis alone, on a MacBook Air 11, to fix his own startup's database problem. > Redis became the in-memory database powering Twitter, GitHub, Stack Overflow, Snapchat, Airbnb, Instagram. > One of the most-used databases on Earth ~ built by one self-taught coder. > Maintained it alone as Benevolent Dictator for Life for 11 years. > Also built Kilo (a full text editor in under 1000 lines of C), Linenoise, Dump1090, Disque, Jim Tcl ~ all open source. > June 2020 ~ walked away at the peak. "My hands will be free," he wrote. > Spent two years writing a science fiction novel about artificial intelligence. > The novel described prompt engineering ~ before ChatGPT existed. 🚀 > December 2024 ~ the internet called him back. He returned to Redis. > Built the new Vector Sets data structure for AI similarity search. > 27k+ followers on GitHub. Active on BlueSky. Avoids Twitter. > Lives in Catania, Italy. Codes from home. Calls himself "the Robin Hood of open source." He built it alone. Walked away at the peak. Came back when AI needed a new way to think. No fame. No equity. Just code, novels, and home. Open source GOAT. 🐐

Stop wasting hours trying to learn AI. I have already done it for you. With one list. Zero confusion. And no fluff. 📹 Videos: 1. LLM Introduction: t.co/kyDon6qLrb 2. LLMs from Scratch: t.co/2hyMhuKoiI 3. Agentic AI Overview (Stanford): t.co/FXu6cAqITC 4. Building and Evaluating Agents: t.co/ZigR1tdOFL 5. Building Effective Agents: t.co/uYwfwO55mO 6. Building Agents with MCP: t.co/4arFTW1b3i 7. Building an Agent from Scratch: t.co/eOmveyM9Hz 8. Philo Agents: t.co/zLu7x1tx9m 🗂️ Repos 1. GenAI Agents: t.co/eXCl2YaRPv 2. Microsoft's AI Agents for Beginners: t.co/3CSW4zPAwf 3. Prompt Engineering Guide: t.co/GVzvxPYDVO 4. Hands-On Large Language Models: t.co/0rgDvhx3pI 5. AI Agents for Beginners: t.co/3CSW4zPAwf 6. GenAI Agents: lnkd.in/dEt72MEy 7. Made with ML: t.co/9z5KHF9DMe 8. Hands-On AI Engineering: t.co/dldAj5Xkr6 9. Awesome Generative AI Guide: t.co/U2WZhT4ERV 10. Designing Machine Learning Systems: t.co/sYAZX34YdQ 11. Machine Learning for Beginners from Microsoft: t.co/NjFxHbC9jZ 12. LLM Course: t.co/N34YTPu1OK 🗺️ Guides 1. Google's Agent Whitepaper: t.co/bW3Ov3vMW0 2. Google's Agent Companion: t.co/wredwWAbBA 3. Building Effective Agents by Anthropic: t.co/fxtE4alVrJ 4. Claude Code Best Agentic Coding practices: t.co/lLSwJ9pG7C 5. OpenAI's Practical Guide to Building Agents: t.co/xgkEIogGfh 📚 Books: 1. Understanding Deep Learning: t.co/CjcKpTemmV 2. Building an LLM from Scratch: t.co/DaWBxOx8o3 3. The LLM Engineering Handbook: t.co/ZA1n0N41Mf 4. AI Agents: The Definitive Guide - Nicole Koenigstein: t.co/boLkl1VlKb 5. Building Applications with AI Agents - Michael Albada: t.co/H1Xf5EkJLL 6. AI Agents with MCP - Kyle Stratis: t.co/JI3ELQZE6a 7. AI Engineering: t.co/Xk0JzMIf7o 📜 Papers 1. ReAct: t.co/QNqE4UU55w 2. Generative Agents: t.co/CwEpoJgY1U 3. Toolformer: t.co/5m9xZd5teZ 4. Chain-of-Thought Prompting: t.co/KjVlgdWi77 🧑🏫 Courses: 1. HuggingFace's Agent Course: t.co/7FSUYKxIdG 2. MCP with Anthropic: t.co/IkZGiWm2yS 3. Building Vector Databases with Pinecone: t.co/2YRoMfLdXd 4. Vector Databases from Embeddings to Apps: t.co/23A50ixbHJ 5. Agent Memory: t.co/uc3L9BrNF7 Follow @iansh04_ for more!! 👇 Comment “AI” for more resources Repost for your network ♻️ Bookmark for future.

There will be no AI jobpocalypse. The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of large-scale unemployment is irresponsible and damaging. Let’s put a stop to it. I’ve expressed skepticism about the jobpocalypse in previous posts. I’m glad to see that the popular press is now pushing back on this narrative. The image below features some recent headlines. Software engineering is the sector most affected by AI tools, as coding agents race ahead. Yet hiring of software engineers remains strong! So while there are examples of AI taking away jobs, the trends strongly suggest the net job creation is vastly greater than the job destruction — just like earlier waves of technology. Further, despite all the exciting progress in AI, the U.S. unemployment rate remains a healthy 4.3%. Why is the AI jobpocalypse narrative so popular? For one thing, frontier AI labs have a strong incentive to tell stories that make AI technology sound more powerful. At their most extreme, they promote science-fiction scenarios of AI “taking over” and causing human extinction. If a technology can replace many employees, surely that technology must be very valuable! Also, a lot of SaaS software companies charge around $100-$1000 per user/year. But if an AI company can replace an employee who makes $100,000 — or make them 50% more productive — then charging even $10,000 starts to look reasonable. By anchoring not to typical SaaS prices but to salaries of employees, AI companies can charge a lot more. Additionally, businesses have a strong incentive to talk about layoffs as if they were caused by AI. After all, talking about how they’re using AI to be far more productive with fewer staff makes them look smart. This is a better message than admitting they overhired during the pandemic when capital was abundant due to low interest rates and a massive government financial stimulus. To be clear, I recognize that AI is causing a lot of people’s work to change. This is hard. This is stressful. (And to some, it can be fun.) I empathize with everyone affected. At the same time, this is very different from predicting a collapse of the job market. Societies are capable of telling themselves stories for years that have little basis in reality and lead to poor society-wide decision making. For example, fears over nuclear plant safety led to under-investment in nuclear power. Fears of the “population bomb” in the 1960s led countries to implement harsh policies to reduce their populations. And worries about dietary fat led governments to promote unhealthy high-sugar diets for decades. Now that mainstream media is openly skeptical about the jobpocalypse, I hope these stories will start to lose their teeth (much like fears of AI-driven human extinction have). Contrary to the predictions of an AI jobpocalypse, I predict the opposite: There will be an AI jobapalooza! AI will lead to a lot more good AI engineering jobs, and I’m also optimistic about the future of the overall job market. What AI engineers do will be different from traditional software engineering, and many of these jobs will be in businesses other than traditional large employers of developers. In non-AI roles, too, the skills needed will change because of AI. That makes this a good time to encourage more people to become proficient in AI, and make sure they’re ready for the different but plentiful jobs of the future! [Original text in The Batch newsletter.]

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Ask Claude to document and describe the main flows in your app and output in a single page html + json data file. Incredibly useful for humans and the JSON file is very useful for explaining the flow to the LLM when working on new features/bugfixes.