According to DeepLearning.AI, a new blog shares a practical playbook to rapidly build GenAI prototypes using Streamlit and Snowflake based on lessons from a course taught by Chanin Nantasenamat. According to DeepLearning.AI, the announcement focuses on developer speed and a Streamlit-Snowflake workflow without disclosing product changes, pricing, or user metrics. According to DeepLearning.AI, the post is an educational resource and does not reference cryptocurrencies or blockchain, so there are no token-specific disclosures in this update. According to DeepLearning.AI, the announcement was posted on September 25, 2025 with a link directing readers to the full blog.

According to @DeepLearningAI, Illinois became the second U.S. state after Nevada to ban AI apps from administering psychotherapy without a doctor’s direct participation under the Wellness and Oversight for Psychological Resources Act, source: DeepLearning.AI. The law prohibits marketing chatbots as therapeutic tools and bars clinicians from using AI to make treatment decisions or assess a patient’s mental state, source: DeepLearning.AI. It requires informed consent for any recorded or transcribed sessions and restricts AI to administrative tasks only, source: DeepLearning.AI. Violations can incur fines up to $10,000 per use; AI healthcare deployments in Illinois and Nevada must exclude psychotherapy functionality or face penalties, source: DeepLearning.AI.

According to @DeepLearningAI, the organization announced a free short course titled Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta and Josh Reini. Source: DeepLearning.AI on X, Sep 24, 2025, https://bit.ly/424ndqQ According to @DeepLearningAI, the course teaches learners to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes final results. Source: DeepLearning.AI on X, Sep 24, 2025, https://bit.ly/424ndqQ According to @DeepLearningAI, the announcement notes that most data agents struggle with reliability or cannot handle multi-step reasoning, and the curriculum focuses on evaluation and tracing for such agent workflows. Source: DeepLearning.AI on X, Sep 24, 2025, https://bit.ly/424ndqQ According to @DeepLearningAI, the course defines a data agent as a system that extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings, with free enrollment available via the provided link. Source: DeepLearning.AI on X, Sep 24, 2025, https://bit.ly/424ndqQ

According to @DeepLearningAI, Alibaba released Qwen3-Next-80B-A3B in Base, Instruct, and Thinking variants under an open-weights Apache 2.0 license, targeting faster long-context inference and supporting inputs up to 262,144 tokens with multi-token prediction; source: DeepLearning.AI on X, Sep 22, 2025, https://twitter.com/DeepLearningAI/status/1970254860416131146; The Batch overview, https://hubs.la/Q03KsR8W0. The 80-billion-parameter mixture-of-experts replaces most vanilla attention layers with Gated DeltaNet and the remainder with gated attention, is trained on a 15-trillion-token subset of the Qwen3 dataset, and is fine-tuned with GSPO; source: DeepLearning.AI on X, Sep 22, 2025, https://twitter.com/DeepLearningAI/status/1970254860416131146; The Batch overview, https://hubs.la/Q03KsR8W0. For trading focus, key measurable specs to track are the 262,144-token context window, multi-token prediction, and open-weights Apache 2.0 licensing, as these parameters define model accessibility and performance for builders; the source does not mention any cryptocurrency integrations or market pricing effects; source: DeepLearning.AI on X, Sep 22, 2025, https://twitter.com/DeepLearningAI/status/1970254860416131146; The Batch overview, https://hubs.la/Q03KsR8W0.

According to @DeepLearningAI, a private school in Austin replaced a 6-hour teaching day with 2 hours of personalized, AI-guided lessons, source: @DeepLearningAI. Alpha School plans to open more classrooms in 12 cities, defining a concrete rollout scope that market participants can track for real-world AI adoption in education, source: @DeepLearningAI. Its proprietary platform sequences mastery-based exercises across math, science, reading, and language skills and targets 70–95 percent performance, source: @DeepLearningAI. The pedagogy avoids chatbots, tracks engagement via camera, and integrates content from IXL, Khan Academy, and Trilogy Software, source: @DeepLearningAI. The source makes no mention of blockchain or cryptocurrencies, indicating no direct crypto-specific features or token integration in this rollout, source: @DeepLearningAI.

According to @DeepLearningAI, Andrew Ng and DeepLearning.AI will host the second AI Developer Conference in New York City on November 14, 2025, featuring technical teams from Anthropic, AWS, Snowflake, Arm, Neo4j, and Coderabbit; source: DeepLearning.AI on X, 2025-09-19 https://twitter.com/DeepLearningAI/status/1969144469032231222. The organizer provides a live list of partners and speakers at hubs.la/Q03K75qR0 and notes that more will be announced; source: DeepLearning.AI on X and hubs.la/Q03K75qR0. The announcement highlights limited regular-priced tickets and an impending move to the next pricing tier, indicating a time-sensitive registration window; source: DeepLearning.AI on X, 2025-09-19 https://twitter.com/DeepLearningAI/status/1969144469032231222. The post does not reference cryptocurrencies, blockchain, or tokens, so no direct crypto market impact is indicated by the source; source: DeepLearning.AI on X, 2025-09-19 https://twitter.com/DeepLearningAI/status/1969144469032231222.

According to @DeepLearningAI, Andrew Ng explains that automated software testing for AI coding agents can miss subtle bugs that are hard to detect, especially in back-end infrastructure code, source: @DeepLearningAI. The update also reports that Alibaba has updated Qwen3 with faster 80B Mixture-of-Experts (MoE) models, source: @DeepLearningAI. It further notes that multiple U.S. states have banned AI-only psychotherapy, source: @DeepLearningAI. Additionally, the piece highlights Energy-Based Transformers that refine each token step by step, source: @DeepLearningAI. No direct cryptocurrency market impact was cited in the update, source: @DeepLearningAI.

According to @DeepLearningAI, a U.S. federal judge ordered Google to provide eligible U.S.-based AI and search rivals a one-time copy of its web index and to syndicate search results under existing partner terms, source: @DeepLearningAI. The court declined to order divestitures or a breakup, meaning Google keeps Chrome and Android as part of Alphabet (GOOGL), source: @DeepLearningAI. Google may continue paying Apple and other partners for default search placement but cannot require exclusivity, preserving current distribution payments while prohibiting exclusive deals, source: @DeepLearningAI.

According to @DeepLearningAI, DeepLearning.AI and Box launched a free short course called Build AI Apps with MCP Servers: Working with Box Files, taught by Box CTO Ben Kus. Source: DeepLearning.AI on X, Sep 17, 2025. According to @DeepLearningAI, the course begins with building an LLM app that processes files manually downloaded from a Box folder and stored locally. Source: DeepLearning.AI on X, Sep 17, 2025. According to @DeepLearningAI, learners then refactor the app to be MCP-compliant and connect it to the Box MCP server so the application can process files directly in Box using server-provided tools. Source: DeepLearning.AI on X, Sep 17, 2025. According to @DeepLearningAI, the program culminates in evolving the solution into a multi-agent system coordinated via the A2A protocol. Source: DeepLearning.AI on X, Sep 17, 2025. According to @DeepLearningAI, the announcement focuses on enterprise file workflows and agent coordination for developers and does not mention any cryptocurrency, blockchain, or token integrations. Source: DeepLearning.AI on X, Sep 17, 2025.

According to @DeepLearningAI, Google researchers introduced ATLAS, a transformer-like language model that replaces attention with a trainable memory module and processes inputs up to 10 million tokens; source: @DeepLearningAI. According to @DeepLearningAI, the team trained a 1.3 billion-parameter model on FineWeb and updates only the memory module at inference; source: @DeepLearningAI. According to @DeepLearningAI, ATLAS achieved 80 percent on BABILong with 10 million-token inputs and averaged 57.62 percent across eight QA benchmarks, outperforming Titans and Transformer++; source: @DeepLearningAI. According to @DeepLearningAI, the source does not mention cryptocurrencies, but the reported long-context benchmarks and memory-augmented inference provide concrete performance data that traders can track when assessing AI-related market narratives; source: @DeepLearningAI.

According to @DeepLearningAI, the organization partnered with Snowflake to launch the course Fast Prototyping of GenAI Apps with Streamlit, taught by Chanin Nantasenamat, showing how a few lines of Python can become working GenAI app prototypes that run inside Streamlit and Snowflake; source: DeepLearning.AI on X, Sep 16, 2025. According to @DeepLearningAI, the announcement highlights rapid feedback and iteration toward production within Snowflake and Streamlit, providing a concrete event traders can log when tracking Snowflake’s GenAI developer enablement for SNOW monitoring; source: DeepLearning.AI on X, Sep 16, 2025.

According to @DeepLearningAI, Meta announced LlamaFirewall, an open-source toolkit designed to protect LLM agents from jailbreaking, goal hijacking, and exploitation of vulnerabilities in generated code. Source: DeepLearning.AI tweet https://twitter.com/DeepLearningAI/status/1967986588312539272; DeepLearning.AI The Batch summary https://www.deeplearning.ai/the-batch/meta-releases-llamafirewall-an-open-source-defense-against-ai-hijacking/ The toolkit is free to use for projects with up to 700 million monthly active users, as stated in the announcement. Source: DeepLearning.AI tweet https://twitter.com/DeepLearningAI/status/1967986588312539272; DeepLearning.AI The Batch summary https://www.deeplearning.ai/the-batch/meta-releases-llamafirewall-an-open-source-defense-against-ai-hijacking/

According to @DeepLearningAI, Meta will retrain assistants on Facebook, Instagram, and WhatsApp to avoid sexual or self-harm discussions with teens and will block minors from user-made role-play bots, while OpenAI will add parental controls, route crisis chats to stricter reasoning models, and notify guardians in acute-distress cases, source: DeepLearning.AI on X Sep 16, 2025 https://twitter.com/DeepLearningAI/status/1967749185232355369; The Batch https://hubs.la/Q03JsXHw0. For traders, the source frames these as concrete safety and compliance changes with no mention of crypto or blockchain, positioning this as AI-governance headline context rather than a token-specific catalyst, source: DeepLearning.AI on X Sep 16, 2025 https://twitter.com/DeepLearningAI/status/1967749185232355369; The Batch https://hubs.la/Q03JsXHw0.

According to @DeepLearningAI, Andrew Ng’s latest The Batch highlights Coursera’s shift toward skills-based education and new AI-driven tools (source: @DeepLearningAI, Sep 12, 2025). It also reports that Meta and OpenAI tightened child safety in chatbots (source: @DeepLearningAI, Sep 12, 2025). The issue discusses arguments for Google to share its search index with AI rivals (source: @DeepLearningAI, Sep 12, 2025). It notes a private school system adopting two hours of AI-assisted education daily (source: @DeepLearningAI, Sep 12, 2025). It profiles ATLAS, a memory-based long-context LLM (source: @DeepLearningAI, Sep 12, 2025). For traders, these themes align with watchlists across edtech adoption, AI safety and compliance, search ecosystem access, and long-context model infrastructure, with narrative spillovers monitored by AI-linked crypto sectors (source: @DeepLearningAI, Sep 12, 2025).

According to DeepLearning.AI, Hangzhou is building itself into an AI hub by providing company subsidies and tax breaks, leveraging Zhejiang University for talent, and enabling access to Alibaba Cloud and ample GPUs, outlining concrete policy and compute inputs relevant to evaluating China-based AI capacity. Source: DeepLearning.AI on X, Sep 8, 2025. According to DeepLearning.AI, the city’s leading cohort includes five AI firms—DeepSeek, BrainCo, Deep Robotics, ManyCore, Unitree Robotics—plus game studio Game Science, providing identifiable ecosystem participants for tracking partnerships, hiring, and deployment activity. Source: DeepLearning.AI on X, Sep 8, 2025. According to DeepLearning.AI, the combination of fiscal incentives and GPU availability highlights operational conditions that can influence AI model training and deployment timelines within the Hangzhou cluster, a practical data point for traders assessing regional AI production readiness. Source: DeepLearning.AI on X, Sep 8, 2025. According to DeepLearning.AI, the update does not mention cryptocurrencies, blockchain, or digital assets, indicating no direct on-chain integration details in this announcement for crypto market positioning. Source: DeepLearning.AI on X, Sep 8, 2025.

According to @DeepLearningAI, a field study covering 67,000 interviews for entry-level customer service roles found that screening with an AI interviewer increased job offers, acceptances, and retention versus human recruiters (source: DeepLearning.AI, The Batch). According to @DeepLearningAI, candidates screened by the chatbot were 12 percent more likely to receive offers and 18 percent more likely to accept an offer and start work (source: DeepLearning.AI, The Batch).

According to @DeepLearningAI, Meta released DINOv3, a self-supervised vision transformer that improves image embeddings for tasks like segmentation and depth estimation (source: DeepLearning.AI). The model has 6.7 billion parameters and was trained on over 1.7 billion Instagram images, highlighting a significant scale-up in self-supervised vision pretraining (source: DeepLearning.AI). Technical updates include a new loss term that preserves patch-level diversity, mitigating limitations from training without labels and strengthening downstream performance baselines (source: DeepLearning.AI). Weights and training code are available under a license that allows commercial use but forbids military applications, enabling broad enterprise deployment while constraining defense use cases (source: DeepLearning.AI). The source does not cite any direct cryptocurrency market impact; traders can note that a stronger open self-supervised backbone may influence developer adoption trends in AI infrastructure that markets often track for sentiment, but no market effects are stated by the source (source: DeepLearning.AI).

According to @DeepLearningAI, GenAI with Streamlit enables developers to start from intent and generate working app code in seconds, replacing hours of setup previously required for data or AI prototypes; source: https://twitter.com/DeepLearningAI/status/1963954016100004104. According to @DeepLearningAI, the post promotes the course Fast Prototyping of GenAI Apps with Streamlit aimed at helping learners prototype faster, test more ideas, and move from concept to app; source: https://www.deeplearning.ai/courses/fast-prototyping-of-genai-apps-with-streamlit and https://twitter.com/DeepLearningAI/status/1963954016100004104. According to @DeepLearningAI, the announcement explicitly tags Snowflake and thedataprof, highlighting relevance to data-centric development audiences; source: https://twitter.com/DeepLearningAI/status/1963954016100004104. According to @DeepLearningAI, no specific cryptocurrencies, tickers, or market metrics are cited in the announcement, so any trading impact would stem from faster in-house tool development rather than direct asset news; source: https://twitter.com/DeepLearningAI/status/1963954016100004104 and https://www.deeplearning.ai/courses/fast-prototyping-of-genai-apps-with-streamlit.

According to @DeepLearningAI, Andrew Ng advises developers to pair strong computer science fundamentals with AI-assisted, agentic coding skills, indicating enterprise focus on agent workflows in software development, source: DeepLearning.AI, The Batch, Sep 4, 2025. According to @DeepLearningAI, a study cited in The Batch found AI-led interviews improved hiring and retention outcomes, a data point relevant to HR-tech adoption trends, source: DeepLearning.AI, The Batch, Sep 4, 2025. According to @DeepLearningAI, Hangzhou is profiled as an emerging AI hub in China, spotlighting geographic clustering in the country’s AI ecosystem, source: DeepLearning.AI, The Batch, Sep 4, 2025. According to @DeepLearningAI, Google quantified Gemini’s per-prompt footprint, offering measured environmental metrics for inference that are relevant to ESG tracking in AI workloads, source: DeepLearning.AI, The Batch, Sep 4, 2025. According to @DeepLearningAI, Meta introduced LlamaFirewall to secure agentic large language models, highlighting the growing emphasis on agent security controls, source: DeepLearning.AI, The Batch, Sep 4, 2025. These updates collectively identify trading-relevant themes across AI equities and infrastructure — coding agents, HR-tech, China AI hubs, model sustainability metrics, and agent security — as reported in one issue of The Batch, source: DeepLearning.AI, The Batch, Sep 4, 2025.

According to @DeepLearningAI, Chinese scientists disguised a four-legged robot in an antelope hide and trained it to simulate herd behavior to unobtrusively observe a Tibetan antelope herd, highlighting a field use of embodied AI and biomimetic robotics, source: DeepLearning.AI. For crypto traders, AI-robotics breakthroughs have historically coincided with heightened sensitivity in AI-linked tokens such as RNDR, FET, and AGIX during AI news cycles, source: Kaiko Research 2024; Binance Research 2023-2024. Monitor funding rates, open interest, and social volume around robotics headlines because social activity spikes have preceded volatility in crypto assets, source: Santiment Research 2023. Token context: RNDR powers the Render Network’s distributed GPU rendering, while FET and AGIX underpin AI agent and model marketplaces, aligning them with compute and embodied AI narratives tracked by traders, source: Render Network documentation; Fetch.ai documentation; SingularityNET documentation.