DeepLearning.AI

What would you build if people could talk to your app? Build a voice-interactive game with Ashwyn Sharma (@_ashwyn in Voice for AI Agents and Applications, our course with @VocalBridge Enroll: deeplearning.ai/courses/voic…

Retrieval finds evidence that matches the question. It doesn't check whether that evidence applies to the context. Nyah Macklin (@neo4j) & Jennifer Reif lead a hands-on AI Dev NYC workshop on closing this gap: Telling relevant evidence apart from applicable evidence Using connections across data (customer, product, date) to narrow what counts Adding checks for when an agent has enough evidence or should stop 📅Nov 30–Dec 1, 2026 · Chelsea Piers, NYC 🎟Get your tickets: ai-dev.deeplearning.ai/speak… #AIDev #DeepLearningAI #AIEvaluation

Make predictions on tabular data without training a new model for each dataset. In Tabular Foundation Models for Data Science Agents, built with @prior_labs, you'll give an AI agent a tabular foundation model as a prediction tool. Enroll for free: bit.ly/4sMtNOz

Ever had your retriever pull the right documents, but the AI answer was still wrong? @steveonjava Chin (@neo4j) shows when GraphRAG beats vector only retrieval, and how to run it in production with evaluation, provenance and access control. Main stage at AI Dev NYC. 📅Nov 30–Dec 1, 2026 · Chelsea Piers, NYC 🎟Get your tickets: ai-dev.deeplearning.ai/speak… #AIDev #DeepLearningAI #GraphRAG

AI has helped create 2 million jobs, according to LinkedIn data. AI engineer is one of the fastest growing roles in 7 major economies. This week, Andrew Ng explains why embedding AI-powered software engineers in marketing, HR, and finance now makes sense. Read Andrew’s full letter in The Batch, our weekly AI newsletter: 📬 deeplearning.ai/the-batch/ai… #DeepLearningAI #AIEngineering #CareerGrowth

A model is only as accurate as the context you give it. Stephen Chin, Nyah Macklin, and Jennifer Reif of @neo4j teach grounding models with data at AI Dev NYC: a GraphRAG talk on the main stage, plus a hands-on workshop on catching when your agent trusts evidence it shouldn’t.. 📅Nov 30–Dec 1, 2026 · Chelsea Piers, NYC 🎟Get your tickets: luma.com/AIDevNYC2026?utm_so… #AIDev #DeepLearningAI #GraphRAG

Gemini 4 Carbon vs. Claude Opus 5.5: Same price per token (not counting Carbon’s intro discount). Carbon uses 29% fewer output tokens. Carbon has 23% lower cost per task. Carbon is #1 on AA’s independent Intelligence Index (59). Read the full story in The Batch, our weekly AI newsletter: 📬 deeplearning.ai/the-batch/ge… #DeepLearningAI #AIEngineering #LLMs

Most agents forget everything between sessions. Hear @richmondalake (@Oracle) Veljko Kovac (@cognee_) & Dennis Pilarinos (Unblocked) talk Memory & Context at AI Dev NYC! 📅Nov 30–Dec 1, 2026 · Chelsea Piers, NYC 🎟luma.com/AIDevNYC2026?utm_so… #AIDev #DeepLearningAI #AgentMemory

Coding agents read your repo, not the decisions behind it. Dennis Pilarinos (Unblocked, ex-buddybuild) shares how to give AI the context to transform software development. 📅Nov 30–Dec 1, 2026 · NYC 🎟ai-dev.deeplearning.ai/speak… #AIDev #CodingAgents #DevTools

Strong pathways for local talent build a stronger NYC tech ecosystem. Brendan Collins, Executive Director of the @NYCTechTalent, joins AI Dev NYC to share how the city is putting AI engineers in the classroom. The Pipeline connects employers, training providers, job seekers, and government to expand tech access in NYC. 📅Nov 30–Dec 1, 2026 · Chelsea Piers, NYC 🎟️Get your tickets: luma.com/AIDevNYC2026?utm_so… #AIDev #DeepLearningAI #NYCTech

Find out how your agent breaks before your users do it for you. Learn to design custom benchmarks with Mehdi Jamei & @andiPartovi (@veris_ai) at AI Dev NYC: Build realistic test scenarios Measure outcomes, not just responses Keep evals repeatable 📅Nov 30–Dec 1, 2026 · Chelsea Piers, NYC 🎟luma.com/AIDevNYC2026?utm_so… #AIDev #DeepLearningAI #AIEvaluation

Building a voice agent? Latency is only half the problem. Andrew Ng on speed vs reliability. Explore three ways to add voice to apps and agents with Ashwyn Sharma @_ashwyn in our course with @VocalBridge. Enroll: deeplearning.ai/courses/voic… #VoiceAI #AIAgents #DeepLearningAI

Same model, different harness, different results. Joel Niklaus, Machine Learning Engineer at @huggingface, leads two workshops at AI Dev NYC on harness engineering and benchmarks. Speakers from @OpenAI, @neo4j, @Oracle, @Sonar and more round out two days of technical sessions. 📅Nov 30–Dec 1 · Chelsea Piers, NYC 🎟Get your tickets: ai-dev.deeplearning.ai/speak… #AIDev #DeepLearningAI #AIEngineeringSkills

One successful run does not mean your agent is reliable. In our new course, Evaluating and Improving Agentic Applications, built with @arizeai and taught by Laurie Voss, you'll learn to evaluate what your agent does, not just what it says. Enroll now: bit.ly/4iQfYeJ

One AI detector score cost an author a shot at France’s top literary prize. Meanwhile, some AI leaders want frontier models locked away. Our weekly AI newsletter’s Halloween issue on witch hunts and sealed tombs: deeplearning.ai/the-batch/is… #DeepLearningAI #AIEthics #ResponsibleAI

🧟 700 test agents broke into Hugging Face to seize its data. 🧛 Agents sucked up private user data and posted it to the open web. The cause in both cases was weak test setups and too much access. Read more about AI fears and how to face them in the Halloween issue of our weekly AI newsletter, The Batch: deeplearning.ai/the-batch/is… #DeepLearningAI #AIAgents #AISafety

Building an agent that works once is easy, but the real challenge is keeping it reliable in production. Yann LeCun, Andrew Ng + Engineers from @OpenAI, @BlackRock @huggingface, @neo4j and are teaching at AI Dev NYC on: • Evaluation-driven development • Building agentic systems • Scaling and operating in production 📅Nov 30–Dec 1 · Chelsea Piers, NYC 🎟Get your tickets: bit.ly/4yNsHna #AIDev #DeepLearningAI #AIEngineeringSkills

Short on time this week? Here’s the AI news worth knowing. Anthropic filed to list its stock publicly on Nasdaq. Revenue for the first half of 2026: $16.3B, up from $1.2B a year earlier. Compute spending fell from about 6.5x revenue to about 0.5x. AA Intelligence Index v5: 60% of the score now comes from private or held out tests. Opus 5.5 (max) 58, GPT-6 Astra 57, GPT-6.1 Sol 55. Sol costs about an eighth as much per task. Effort matters: Opus 5.5 scores 35 at low effort and 58 at max, but at 10x the cost. Unlimited OCR 2: 3.34B params, MIT license, hundreds of pages per request.