According to @DeepLearningAI, Andrew Ng identified declining public trust in AI as a major problem and urged the AI community to address legitimate concerns while building applications that benefit everyone, source: DeepLearning.AI tweet dated Dec 4, 2025. The update highlights releases including Meta’s SAM 3 that turns images into 3D scenes and people, Marble’s tool for editable 3D worlds from text, images, and video, Baidu’s open vision‑language model plus the large multimodal ERNIE 5.0, and RoboBallet for choreographing many robot arms at once, source: DeepLearning.AI tweet dated Dec 4, 2025. The source does not mention any blockchain, token, or crypto integrations, indicating no direct crypto catalyst in this communication, source: DeepLearning.AI tweet dated Dec 4, 2025. Traders tracking AI-linked assets can note continued progress in 3D generation and multimodal AI while monitoring official updates for any future digital-asset relevance cited by the source, source: DeepLearning.AI tweet dated Dec 4, 2025.

According to @AndrewYNg, a short course titled Building Coding Agents with Tool Execution, taught by @tereza_tizkova and @FraZuppichini from @e2b, shows how to build agents that write and execute code to accomplish tasks beyond predefined function calls, which is directly stated in the announcement, source: @AndrewYNg. The post does not mention cryptocurrencies or blockchain, indicating no direct crypto-market catalyst within the text of the announcement, source: @AndrewYNg.

According to @DeepLearningAI, Andrew Ng addressed whether an AI bubble exists and which parts of AI supply and demand may be affected in this week’s The Batch, source: DeepLearning.AI official X post dated Nov 28, 2025. According to @DeepLearningAI, the post reports Google dominates arena leaderboards, source: DeepLearning.AI official X post dated Nov 28, 2025. According to @DeepLearningAI, the post reports Microsoft and Anthropic formed an alliance, source: DeepLearning.AI official X post dated Nov 28, 2025. According to @DeepLearningAI, the post reports record labels back AI music, source: DeepLearning.AI official X post dated Nov 28, 2025. According to @DeepLearningAI, no cryptocurrencies were mentioned in the post, source: DeepLearning.AI official X post dated Nov 28, 2025.

According to Andrew Ng, he announced the release of a new Agentic Reviewer tool for research papers on X. Source: Andrew Ng on X https://twitter.com/AndrewYNg/status/1993001922773893273 He stated he began coding it as a weekend project and that @jyx_su significantly improved it. Source: Andrew Ng on X https://twitter.com/AndrewYNg/status/1993001922773893273 Ng said the tool was inspired by a student whose paper was rejected six times over three years, with feedback cycles of about six months each time. Source: Andrew Ng on X https://twitter.com/AndrewYNg/status/1993001922773893273 From a trading perspective, the post discloses no pricing, open-source link, partnership details, or any crypto/blockchain integration, indicating no direct token, ticker, or on-chain exposure to trade from this announcement alone. Source: Andrew Ng on X https://twitter.com/AndrewYNg/status/1993001922773893273

According to DeepLearning.AI, Andrew Ng reflects on the social energy and technical depth of AI Dev x NYC and how in-person events spark collaboration, community, and new opportunities in the AI developer ecosystem. Source: DeepLearning.AI on X, 2025-11-21, https://hubs.la/Q03Vk0V70 According to DeepLearning.AI, this edition of The Batch covers self-driving cars operating on U.S. freeways. Source: DeepLearning.AI on X, 2025-11-21, https://hubs.la/Q03Vk0V70 According to DeepLearning.AI, Moonshot releases Kimi K2 Thinking. Source: DeepLearning.AI on X, 2025-11-21, https://hubs.la/Q03Vk0V70 According to DeepLearning.AI, an Anthropic cyberattack report sparks controversy. Source: DeepLearning.AI on X, 2025-11-21, https://hubs.la/Q03Vk0V70 According to DeepLearning.AI, models learning to search their own parameters are discussed. Source: DeepLearning.AI on X, 2025-11-21, https://hubs.la/Q03Vk0V70 According to DeepLearning.AI, the post focuses on AI developments and does not include commentary on cryptocurrency market impacts. Source: DeepLearning.AI on X, 2025-11-21, https://hubs.la/Q03Vk0V70

According to @AndrewYNg, he appeared on the 20VC podcast and discussed bottlenecks in AI adoption, US–China geopolitics, and remaining opportunities to build in AI, as stated in his X post on Nov 20, 2025. Source: Andrew Ng on X. No quantitative disclosures, timelines, or sector-specific guidance were provided in the post, indicating no immediate market-moving data to act on. Source: Andrew Ng on X. Traders should monitor the release of the full 20VC episode for concrete commentary on compute constraints and policy that could inform positioning across AI equities and AI-linked crypto sectors, with actions contingent on the episode’s content. Source: Andrew Ng on X.

According to @AndrewYNg, he just returned from AI Dev x NYC and described the developer conference as a buzzing day focused on coding, learning, and connecting, source: https://twitter.com/AndrewYNg/status/1991561899088179552. He noted that at the prior AI Dev in San Francisco he met Kirsty Tan and began collaborating with her, source: https://twitter.com/AndrewYNg/status/1991561899088179552. The cited post does not mention specific product launches, funding disclosures, or crypto token references, indicating no direct trading catalyst from this update alone, source: https://twitter.com/AndrewYNg/status/1991561899088179552. Traders can treat this as a sentiment check on AI developer momentum and monitor AI-linked equities and AI-related crypto tokens for any follow-up announcements stemming from the event, source: https://twitter.com/AndrewYNg/status/1991561899088179552.

According to @AndrewYNg, DeepLearningAI engineers used AI coding to implement a basic clone of Cloudflare capabilities during a Cloudflare outage, restoring their site before many major websites; source: @AndrewYNg on X, Nov 19, 2025. For traders, this outage report highlights single-vendor infrastructure risk that can influence sentiment toward Cloudflare (NET) and AI tooling providers when availability is disrupted; source: @AndrewYNg on X, Nov 19, 2025. Crypto market impact: interruptions at core web providers can restrict access to exchange and DeFi web interfaces, so monitoring status updates and maintaining alternative access paths is prudent during such events; source: @AndrewYNg on X, Nov 19, 2025.

According to @DeepLearningAI, Andrew Ng said AI development is accelerating because coding is getting faster and teams can prototype more quickly, which supports shorter build cycles for new products, source: @DeepLearningAI. According to @DeepLearningAI, the real bottleneck is now gathering user feedback, indicating iteration cadence and time to market are governed by feedback loops rather than engineering throughput, source: @DeepLearningAI. According to @DeepLearningAI, Ng encouraged attendees to connect, collaborate, and build together, highlighting AI Aspire as an example born from prior event conversations, source: @DeepLearningAI. According to @DeepLearningAI, this shift places operational emphasis on user feedback pipelines, a factor traders can monitor when assessing execution readiness and near term deployment pace in AI focused plays, source: @DeepLearningAI.

According to DeepLearning.AI, Andrew Ng opened AI Dev 25 x NYC by emphasizing networking and citing how a prior conference meeting with Kirsty Tan led to the founding of AI Inspire, source: DeepLearning.AI. The post includes no product, model, funding, or partnership announcements, implying no immediate, verifiable trading catalyst from this update alone, source: DeepLearning.AI. More updates are scheduled throughout the day, so traders tracking AI stocks and AI-related tokens can monitor the official feed for additional details, source: DeepLearning.AI.

According to Andrew Ng, AGI remains decades away or longer, and frontier models will not eliminate most application-layer businesses without substantial customization, indicating a longer buildout cycle for tools and services rather than rapid displacement, source: Andrew Ng on Twitter dated Nov 13, 2025 and deeplearning.ai The Batch issue 327. Ng reports current LLMs are narrow versus humans, excel mainly at text, require heavy context engineering, and he would not trust a frontier model alone for calendar prioritization, resume screening, or lunch ordering, noting his team achieved a decent resume screening assistant only after significant customization, source: Andrew Ng on Twitter dated Nov 13, 2025. Ng adds that while thin wrappers may be replaced, many valuable applications will not be displaced for a long time despite rapid model progress, which counters fears that model providers will quickly wipe out app startups, source: Andrew Ng on Twitter dated Nov 13, 2025. For traders, this points to sustained demand for vertical AI integration, data pipelines, and domain-specific agents over commoditized chat fronts, aligning with Ng’s view of persistent limitations and customization needs, source: Andrew Ng on Twitter dated Nov 13, 2025 and deeplearning.ai The Batch issue 327. For crypto markets, AI-focused tokens tied to compute, data, and application ecosystems may find more durable narratives than generic LLM wrappers given customization and feedback bottlenecks highlighted by Ng, source: Andrew Ng on Twitter dated Nov 13, 2025. Ng encourages newcomers to learn to build with AI now, signaling ongoing demand for skilled builders over many years, source: Andrew Ng on Twitter dated Nov 13, 2025.

According to @AndrewYNg, DeepLearning.AI launched the course Design, Develop, and Deploy Multi-Agent Systems with CrewAI, taught by Joao Moura of CrewAI Inc, to help practitioners build and deploy production-grade multi-agent teams using the open-source CrewAI framework for complex workflow automation, with sign-ups available on the DeepLearning.AI course page; Source: Andrew Ng. According to @AndrewYNg, the curriculum covers building agents with tools, memory, and guardrails, coordinating teams that plan, reason, and collaborate, and deploying systems with tracing, evaluation, and monitoring to ensure reliability and observability; Source: Andrew Ng. According to @AndrewYNg, he disclosed a small angel investment in CrewAI to provide transparency around the announcement; Source: Andrew Ng. According to @AndrewYNg, these agentic capabilities directly address automation and orchestration needs that traders and crypto builders use in systematic execution, risk monitoring, and on-chain operations, aligning the course with production requirements for agentic trading stacks; Source: Andrew Ng.

According to @DeepLearningAI, it launched a short course titled Jupyter AI: AI Coding in Notebooks, taught by Andrew Ng and Brian Granger, that trains users to generate code, debug errors, and get explanations without leaving the notebook environment, which is directly relevant to streamlining trading research workflows; source: DeepLearning.AI on X on Nov 6, 2025 twitter.com/DeepLearningAI/status/1986453393183756511 hubs.la/Q03R_Wvf0. According to @DeepLearningAI, the curriculum includes building AI applications from scratch, explicitly featuring a stock data analysis workflow that traders can implement end-to-end in Jupyter using Jupyter AI; source: DeepLearning.AI on X on Nov 6, 2025 twitter.com/DeepLearningAI/status/1986453393183756511 hubs.la/Q03R_Wvf0. According to @DeepLearningAI, the course also teaches AI coding best practices to guide Jupyter AI with the right context, helping ensure successful project builds within the notebook stack; source: DeepLearning.AI on X on Nov 6, 2025 twitter.com/DeepLearningAI/status/1986453393183756511 hubs.la/Q03R_Wvf0.

According to @AndrewYNg on X on Nov 5, 2025, Roelof Botha is passing the baton at Sequoia, which he characterized as the end of an era. According to @AndrewYNg on X on Nov 5, 2025, he emphasized Botha’s leadership at Sequoia and his outsized influence on how investors think.

According to @AndrewYNg, the Jupyter team launched Jupyter AI to embed chat-based code generation and debugging directly inside Jupyter notebook cells, with the debut showcased at JupyterCon this week, source: Andrew Ng on X. According to @AndrewYNg, Jupyter AI is built specifically for notebooks and supports dragging cells to chat, generating cells from chat, and attaching API docs or other context so the LLM writes more accurate code, source: Andrew Ng on X. According to @AndrewYNg, Jupyter AI is integrated into the DeepLearning.AI platform via a free short course co‑taught by Andrew Ng and Jupyter co‑founder Brian Granger, and as an open‑source project it can also be installed and run locally after the course, source: Andrew Ng on X; DeepLearning.AI short course page. According to @AndrewYNg, the announcement includes no mention of cryptocurrencies, tokens, pricing, or monetization details, so no direct crypto or token catalyst was disclosed in this release, source: Andrew Ng on X.

According to @DeepLearningAI, a new course titled Jupyter AI: AI Coding in Notebooks teaches users to use Jupyter AI’s chat interface to generate, debug, and explain code directly in notebooks, build a book research assistant using the Open Library API, and create a stock-market analysis workflow that visualizes and interprets real data; source: @DeepLearningAI on X, https://twitter.com/DeepLearningAI/status/1985399396318539928. It also confirms the instructors are Andrew Ng and Brian Granger, co-founder of Project Jupyter, and provides an enrollment link at bit.ly/4qDAGjT; source: @DeepLearningAI on X, https://twitter.com/DeepLearningAI/status/1985399396318539928.

According to @DeepLearningAI, Vercel CTO Malte Ubl will join Andrew Ng, Laurence Moroney, and Fabian Hedin on a panel titled Software Development in the Age of AI at AI Dev 25 x NYC. According to @DeepLearningAI, the discussion focuses on how AI is reshaping engineering workflows and what it means for the next generation of developers. According to @DeepLearningAI, the session will be held live on November 14, with last tickets available.

According to @DeepLearningAI, DeepLearning.AI Pro is now live as a single membership that provides full access to 150+ AI courses, hands-on labs, and certificates taught by leaders including Andrew Ng, Sharon Zhou, and Laurence Moroney (source: @DeepLearningAI on X, Oct 30, 2025). The announcement highlights Andrew Ng’s message that the distance from idea to product has never been smaller and offers official links for a free trial and further details (source: @DeepLearningAI on X, Oct 30, 2025). The post does not mention cryptocurrencies, tokens, or blockchain integrations, indicating no direct on-chain or token-related catalyst associated with this launch for crypto markets (source: @DeepLearningAI on X, Oct 30, 2025).

According to @AndrewYNg, a new PyTorch for Deep Learning professional certificate taught by Laurence Moroney is now available and is described as a definitive program for learning PyTorch, which he notes is one of the main frameworks researchers use to build breakthrough AI systems (source: Andrew Ng on X, Oct 29, 2025). For trading context, this announcement underscores the ongoing centrality of PyTorch in cutting-edge AI workflows, offering a timely datapoint on developer upskilling that market participants can track when evaluating AI-exposed equities and crypto AI infrastructure themes (source: Andrew Ng on X, Oct 29, 2025).

According to Andrew Ng, DeepLearning.AI released a 5-module course on LLM post-training taught by Sharon Zhou, VP of AI at AMD, and it is available now; source: Andrew Ng on X. According to the DeepLearning.AI course page, the curriculum covers supervised fine-tuning, reward modeling, RLHF, PPO, GRPO, LoRA, and evaluation design for pre- and post-deployment; source: DeepLearning.AI course page. According to Andrew Ng, post-training is the key technique used by frontier labs to turn base LLMs into helpful, reliable assistants and to upgrade demo-level 80% reliability to consistent performance; source: Andrew Ng on X. According to the DeepLearning.AI course page, learners will gain skills to align models with RLHF, use LoRA for efficient fine-tuning without retraining entire models, prepare datasets and synthetic data, and operate LLM production pipelines with go/no-go decision points and feedback loops; source: DeepLearning.AI course page.