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🚀Build your AI engineering skills at AI Dev NYC: knowledge graphs, agent memory, harnesses, self-improving agents, inference and security. Plus a fireside chat with Andrew Ng and Yann LeCun on world models. 📅Nov 30–Dec 1 · Chelsea Piers, NYC

Andrew Ng's framework for building AI-native engineering teams, from this week's letter in The Batch: 👥 People 🚀 Projects 🧱 Platforms 🔄 Processes His core point: building is now fast, so choosing what to build is the bottleneck. 📬 #DeepLearningAI #AIEngineering #EngineeringLeadership

🚀 Build your AI engineering skills at AI Dev NYC. Learn from OpenAI and Hugging Face speakers, plus Andrew Ng’s keynote on the AI Engineering Skills Map and his fireside chat with Yann LeCun on world models. 📍 Nov 30–Dec 1 · Chelsea Piers, NYC

📰 Missed a few days of AI news? Here's what happened this week in Data Points, our twice-weekly newsletter. 👇 🔐 PHP2Shell: GPT-5.6 Sol helped find a memory bug in software running up to 70% of sites. Patch is out. Update now. ⚡ MiniMax M3.1 Flash: 428B total / ~23B active, 1M context, 0.15/0.60 per million tokens. Weights due Oct 23. 🔌 Meta's Muse Wonder: 4/20 per million tokens, OpenAI compatible endpoint. Trying it = new base URL + model name. No full safety report yet. 🐍 Python 3.15 is out, 3.10 is end of life. Lazy imports (PEP 810), unpacking in comprehensions (PEP 798), sampling profiler in the stdlib. 🏛️ GPT-6.1 Sol achieved 97.9% byte for byte output parity migrating COBOL at higher effort. GPT-5.6 Sol only scored 5.4% at low effort. Expert review still required. 📬 Subscribe to Data Points: #DeepLearningAI #AIEngineering #SoftwareEngineering

Max reasoning should mean best results, right? 🤔 Not always. Claude Sonnet 5.5 wrote less mergeable code at max than at xhigh, one level below, while using more tokens than any model measured. 🧪 Test your settings before you trust them. 📬 #DeepLearningAI #AIEngineering #AIAgents

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Join Andrew Ng at AI Dev NYC for two days of technical sessions and workshops. Explore AI engineering skills and how to apply them, from practice to production. Get tickets ->

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Models perform worse as their context grows. In Context Engineering, built in partnership with @trychroma you'll learn to give agents the right information, in the right format, at the right time. Taught by @jeffreyhuber with guest speaker @dbreunig. Enroll for free:

💻Learn by building with an AI coding agent, right in your browser. 👀Live preview, nothing to install. 📚Try new agentic coding lab in our course, Building AI Assistants with On-Device Memory. 👉Enroll for free: #AI #AIAgents #AIEngineering

🧠 Yann LeCun helped invent the convolutional neural network and shared the 2018 Turing Award with Hinton and Bengio. Now he joins us at AI Dev NYC to share his latest work at AMI Labs. 📍 NYC 🗓️ Nov 30 to Dec 1 👉 Just. Keep. Building.

🧭In this week's letter from Andrew Ng: AI improves fastest on tasks you can verify. Judgment tasks are harder, and it's easy to overgeneralize progress in math and code to the rest of human activity. Also inside: 🤖Claude Haiku 5.5 and Sonnet 5.5 🇪🇺Mistral Large 4 💻Computers for local AI 📊CausalMix 📬 #DeepLearningAI #AIEngineering #MachineLearning

1/ 📰Missed a few days of AI news? Here's what happened in this week's Data Points 👇 2/ 🪶Claude Haiku 5.5: $0.10/$0.50 per million tokens for prompts up to 100K, ~90% cheaper than Haiku 4.5. Super fast and highly capable. 3/ 🎯Microsoft-Decision-1: calibrated probabilities over fixed options in one pass. Built for classification, routing, agent gating. This decision model costs $0.042 per million input tokens, and output is free. Check independent benchmarks to compare it with Jev and other decision models. 4/ 🔌Meta's Muse Wonder: Comparable with top models from Google and OpenAI, costs $4/$20 per million tokens. No full safety report yet. 5/ 📄Gemini 4 Argon: the official wide release has a 1M token output limit, up from 64K. Input and output share the window. $2/$10 intro pricing (per million tokens of input/output) until Nov 30. 6/ 🐍Python 3.15 is out, but 3.10 is end of life. Update your projects to support the new features where you can (3.11 remains a good backwards-compatible option). 7/ 📬Subscribe to our Data Points newsletter for authoritative, short-form, real-time AI news: #DeepLearningAI #AIEngineering #Python

🧰 Picking the most relevant tools one by one leaves gaps. HYSET scores tools as a set. 📊 Recall@5: 88.6% vs. 81.4% ✅ Tasks resolved: 69.9% vs. 61.8% Full story in our weekly newsletter for AI news and research, The Batch 📬 #DeepLearningAI #AIAgents #AIEngineering

🦾 FLUX 3 Action, a video-first world-action model, ranks first on RoboLab-120’s simulations, with a success rate of 42.9%. 🎥 It was pretrained on more than 95% video. 🔧 It adapted to a new arm with about 200 demonstrations. ⚠️ It hasn’t been tested on real robot limbs at the independent RoboArena yet. Full story in our weekly AI newsletter, The Batch 📬 #DeepLearningAI #Robotics #OpenWeights

🎯 Gemini 4 Argon has a 15% hallucination rate on AA-Omniscience, compared with 51% for GPT-6 Astra. 📊 It ranks first on the Vals Index, reflecting its high performance in legal and other knowledge work. 🔐 For now, only cybersecurity defenders in Google's Fairwind program can use it. Full story in our weekly AI newsletter, The Batch 📬 #DeepLearningAI #AIEngineering #Cybersecurity

📊 GPT-6.1 Sol: 52 on the AA Intelligence Index, at $0.72 per task. GPT-6 Astra: 53, at $3.26 per task. ⏸️ OpenAI also canceled GPT-6.1 Astra’s release after the model showed deception in internal tests. Full breakdown in The Batch 📬 #DeepLearningAI #AIEngineering #AIAgents

🖥️ Agents don't just consume tokens for input and output like humans do with early chatbots. Agents use computers: searching, storing, analyzing, transacting, at a scale no person matches. In this week's Andrew's Letter in The Batch, Andrew Ng explains why that means we need much more infrastructure, and faster tools, not just faster models. 🏗️ 📬 #DeepLearningAI #AIEngineering #AIAgents

🏗️ In this week’s letter from Andrew Ng in The Batch: One of his agents makes 5,000 to 10,000 web searches a day. Agents will need far more data centers. Also inside: 🤖 GPT-6.1 Sol ⚖️ Gemini 4 Argon 🦾 FLUX 3 Action 🧰 HYSET 📬 #DeepLearningAI #AIEngineering #AIAgents

A new agentic research model and harness called AREX checks its answers requirement by requirement, keeps what's verified, and researches only the gaps. 📰✅ 📊 82.5% BrowseComp, 82.0 F1 WideSearch-en 📈 Harness alone: up to +10 pts 🧠 Fine tuned 4B beat untuned 35B on 5 of 6 benchmarks #DeepLearningAI #AIAgents #LLMs

This assistant remembers where you left your keys. No network connection. No remote server.  Build one yourself in a course built in partnership with Qdrant and taught by Dylan Couzon.  👉 Enroll for free: #AI #VectorSearch #AIEngineering