According to @DeepLearningAI, Kay Zhu, Co-founder and CTO of Genspark AI, will speak on November 14 at AI Dev 25 x NYC about building a scalable Super Agent by increasing agent autonomy and access to the right tools (source: @DeepLearningAI). According to @DeepLearningAI, the post provides the session topic, event date, NYC venue reference, and a ticket link, and it does not mention any crypto assets, blockchain integrations, or product launches that could directly impact digital asset prices (source: @DeepLearningAI).

According to @DeepLearningAI, AI Dev 25 x NYC will be held on November 14 in New York City and is hosted by Andrew Ng and DeepLearning.AI (source: DeepLearning.AI, X post on Oct 15, 2025). The event has moved to Convene Brookfield Place to accommodate more developers, indicating a larger venue than initially planned (source: DeepLearning.AI, X post on Oct 15, 2025). The organizer states the conference will gather 1,200+ AI builders for a day of coding, learning, and networking (source: DeepLearning.AI, X post on Oct 15, 2025). The post notes that last tickets are available via the listed registration link (source: DeepLearning.AI, X post on Oct 15, 2025). The announcement does not reference cryptocurrencies, tokens, or blockchain initiatives (source: DeepLearning.AI, X post on Oct 15, 2025).

According to @DeepLearningAI, it launched a free short course titled Building Live Voice Agents with Google’s ADK that teaches using Google’s open-source Agent Development Kit to build real-time voice agents connected to Google Search, with turn memory, custom tools and API access, safety guardrails, multi-agent orchestration to produce a podcast, and methods for production deployment, taught by Google ML engineers @laviatgcp and @sitalakshmi_s (source: DeepLearning.AI on X, Oct 15, 2025, https://twitter.com/DeepLearningAI/status/1978483581866418179; enrollment: https://hubs.la/Q03NJrdg0). The post states enrollment is free and does not mention cryptocurrencies or blockchain integrations (source: DeepLearning.AI on X, Oct 15, 2025, https://twitter.com/DeepLearningAI/status/1978483581866418179).

According to @DeepLearningAI, OpenAI launched Sora 2 as a TikTok-style app for AI-generated clips, ChatGPT Pulse as a personalized briefing feed that pulls from chats and connected apps, and Instant Checkout to buy Shopify and Etsy items inside ChatGPT, with the launches framed to boost engagement and revenue (source: @DeepLearningAI, Oct 15, 2025). According to @DeepLearningAI, Meta rolled out Vibes, a social video feed that lets users generate or remix clips and post them to Instagram and Facebook (source: @DeepLearningAI, Oct 15, 2025). According to @DeepLearningAI, the announcements do not mention any blockchain or crypto integrations, indicating no direct on-chain catalyst for crypto traders to price immediately from these releases (source: @DeepLearningAI, Oct 15, 2025).

According to @DeepLearningAI, AI Dev 25 x NYC will take place on November 14 at Convene Brookfield Place in New York City, hosted by Andrew Ng and DeepLearning.AI, with more than 1,200 AI builders expected to attend (source: DeepLearning.AI on X, Oct 14, 2025). The organizer states the conference has moved to a larger venue to welcome more developers, with a program focused on coding, learning, and networking, and last tickets available via the official link provided (source: DeepLearning.AI on X, Oct 14, 2025). For trading calendars, the source provides concrete event details—date (Nov 14), location (NYC), host (Andrew Ng and DeepLearning.AI), and expected scale (1,200+ attendees)—useful for timing AI-sector headline monitoring; the source does not mention any cryptocurrencies or blockchain integrations (source: DeepLearning.AI on X, Oct 14, 2025).

According to @DeepLearningAI, Anthropic launched Claude Sonnet 4.5 with a variable reasoning-token budget, expanded 200k–1M token context windows, improved coding and reasoning benchmark performance, free web access for trials, and API pricing set at $3 per million input tokens and $15 per million output tokens, which directly affects cost planning for AI-assisted research and trading pipelines; Source: @DeepLearningAI on X, Oct 14, 2025. According to @DeepLearningAI, Anthropic also released a Claude Agent SDK and refreshed Claude Code, adding automatic context tracking and summarization, a persistent memory tool, checkpoints with safe rollbacks, and a VS Code–compatible IDE extension that can support production agent workflows for code and data tasks used by quant and crypto teams; Source: @DeepLearningAI on X, Oct 14, 2025. According to @DeepLearningAI, based on the stated API rates, an input of 200,000 tokens would cost about $0.60 and 1,000,000 input tokens about $3.00, while 10,000 output tokens would cost about $0.15, providing concrete per-task budgeting parameters for AI-driven trading research and code analysis; Source: @DeepLearningAI on X, Oct 14, 2025.

According to @DeepLearningAI, Andrew Ng announced a hands-on Agentic AI builder course centered on four design patterns including reflection, tool use, planning, and multi-agent collaboration, as highlighted in The Batch, source: DeepLearning.AI, Oct 13, 2025. According to @DeepLearningAI, Anthropic launched Claude Sonnet 4.5 and overhauled Claude Code, source: DeepLearning.AI, Oct 13, 2025. According to @DeepLearningAI, OpenAI and Meta are diversifying their AI product lines, source: DeepLearning.AI, Oct 13, 2025. According to @DeepLearningAI, Alibaba added Qwen3-Max and open multimodal Qwen3-VL and Qwen3-Omni models, source: DeepLearning.AI, Oct 13, 2025. According to @DeepLearningAI, LoRA adapters are featured as an available capability in the current cycle, source: DeepLearning.AI, Oct 13, 2025.

According to @DeepLearningAI, Turing bridges the gap between AI research and deployment by providing expert data, talent, and tools to frontier labs and companies (source: @DeepLearningAI on X, Oct 10, 2025). According to @DeepLearningAI, Turing’s team will be present at AI Dev 25 x NYC, with ticketing available via the shared registration link (source: @DeepLearningAI on X, Oct 10, 2025). The post does not mention any cryptocurrencies, tokens, partnerships, timelines, or financial metrics, indicating limited immediate market-moving detail for AI equities or crypto narratives (source: @DeepLearningAI on X, Oct 10, 2025).

According to @DeepLearningAI, Google released AlphaEarth Foundations, a set of time embeddings representing every 10 meter square of Earth from 2017 to 2024, trained on optical, radar, and thermal satellite data and aligned with geotagged text to help track climate, land use, vegetation, water, and more, and the dataset is free to download under a CC BY 4.0 license with Google offering grants to teams that use it, source: @DeepLearningAI. For trading and risk analytics, the source specifies global, multi modal coverage and time consistency that support systematic monitoring of climate, land use, vegetation, and water signals across 2017 to 2024 that can be directly derived from the released embeddings, source: @DeepLearningAI.

According to @DeepLearningAI, the Data Points newsletter delivers brief AI updates twice a week covering OpenAI, Google, Zhipu AI, and Anthropic, with the latest edition accessible via hubs.la/Q03MN3dq0, source: DeepLearning.AI on X on Oct 9, 2025. According to @DeepLearningAI, this regular cadence can help traders time their AI news monitoring around scheduled drops from these companies, source: DeepLearning.AI on X on Oct 9, 2025.

According to @DeepLearningAI, Sweden’s performing rights society introduced a pilot license that lets AI developers train models on opted-in songs, creating a licensed pathway for music datasets in AI training, source: DeepLearning.AI on X, Oct 8, 2025, https://hubs.la/Q03MMRsV0. According to @DeepLearningAI, a startup’s attribution technology tracks each work’s influence on model outputs and compensates the original artists, enabling measurable usage-based payouts, source: DeepLearning.AI on X, Oct 8, 2025, https://hubs.la/Q03MMRsV0. According to @DeepLearningAI, traders focused on AI and digital rights can monitor adoption metrics such as number of works opted in, payout mechanics, and integration by model providers to gauge commercial traction of licensed AI music training, source: DeepLearning.AI on X, Oct 8, 2025, https://hubs.la/Q03MMRsV0.

According to @DeepLearningAI, it has partnered with Prolific for AI Dev 25 x NYC, noting that Prolific helps AI teams stress-test, debug, and validate models with real human data to enable safer, production-ready AI (source: @DeepLearningAI). According to @DeepLearningAI, the event is scheduled for November 14 and will feature a demo table showing how human evaluations can be set up in minutes (source: @DeepLearningAI). According to @DeepLearningAI, there will also be a private room session for deeper discussions, with ticket information provided via the event link (source: @DeepLearningAI).

According to Andrew Ng, he announced a new course titled Agentic AI on X and stated that building AI agents is one of the most in-demand skills in the job market, posted on October 7, 2025, source: https://twitter.com/AndrewYNg/status/1975614372799283423. He added that the course is available now at the provided link and teaches implementation of four key agentic design patterns, including Reflection, source: https://twitter.com/AndrewYNg/status/1975614372799283423 source: https://t.co/Ryb1M38I1v.

According to @DeepLearningAI, a worldwide Agentic AI course taught by Andrew Ng is now available, teaching how to design and evaluate AI systems that plan, reflect, and collaborate across multiple steps, built in raw Python, and offered exclusively by DeepLearning.AI (source: DeepLearning.AI on X, Oct 7, 2025, https://twitter.com/DeepLearningAI/status/1975584448654508033; enrollment link: https://hubs.la/Q03MxSyV0). The post does not mention cryptocurrencies or blockchain, indicating no direct crypto-market linkage in the announcement itself (source: DeepLearning.AI on X, Oct 7, 2025, https://twitter.com/DeepLearningAI/status/1975584448654508033).

According to @DeepLearningAI, researchers used AI models trained on DNA to design new bacteriophages targeting E. coli, demonstrating applied generative biology and synthetic biology capabilities relevant to real-world lab workflows. According to @DeepLearningAI, the team generated 11,000 AI-created genome candidates, filtered them to 302, and successfully built 285, providing a clear conversion from in silico design to wet-lab constructs. According to @DeepLearningAI, lab tests showed several new phages either killed resistant E. coli faster or multiplied more successfully than a standard phage, indicating measurable performance gains in controlled experiments. According to @DeepLearningAI, the source did not specify any market, stock, or cryptocurrency impact linked to this research.

According to DeepLearning.AI, its Post-training of LLMs course teaches how to customize pre-trained language models using Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL) (source: DeepLearning.AI on X, Oct 6, 2025). According to DeepLearning.AI, the curriculum explains when to use each method, how to curate training data, and how to implement the techniques in code to shape model behavior effectively (source: DeepLearning.AI on X, Oct 6, 2025). According to DeepLearning.AI, enrollment is available via the provided link hubs.la/Q03MrTZS0 (source: DeepLearning.AI on X, Oct 6, 2025).

According to @DeepLearningAI, researchers introduced GAIN-RL, a method that fine-tunes language models by training on the most useful examples first using a simple internal signal from the model, source: DeepLearning.AI on X dated Oct 5, 2025 and The Batch summary at hubs.la/Q03M9ZjV0. According to @DeepLearningAI, on Qwen 2.5 and Llama 3.2, GAIN-RL matched baseline accuracy in 70 to 80 epochs instead of 200, roughly 2.5 times faster, source: DeepLearning.AI on X dated Oct 5, 2025 and The Batch summary at hubs.la/Q03M9ZjV0. According to @DeepLearningAI, this acceleration can cut compute costs and shorten iteration cycles for teams building math- and code-focused assistants, which is directly relevant for trading assessments of AI training efficiency and cost structures, source: DeepLearning.AI on X dated Oct 5, 2025 and The Batch summary at hubs.la/Q03M9ZjV0.

According to @DeepLearningAI, OpenAI, Oracle, and SoftBank announced five new U.S. data-center sites plus Stargate UK, bringing the current Stargate buildout to about $1 trillion. Source: DeepLearning.AI (X post, Oct 4, 2025) https://twitter.com/DeepLearningAI/status/1974489727702077885 According to @DeepLearningAI, the roadmap targets 20 gigawatts of capacity worldwide, with projections up to 100 gigawatts. Source: DeepLearning.AI (X post, Oct 4, 2025) https://twitter.com/DeepLearningAI/status/1974489727702077885 According to @DeepLearningAI, 1.5 gigawatts of capacity is planned for Ohio and Texas within 18 months. Source: DeepLearning.AI (X post, Oct 4, 2025) https://twitter.com/DeepLearningAI/status/1974489727702077885 According to @DeepLearningAI, Oracle will oversee construction while Nvidia will supply up to 31,000 GPUs. Source: DeepLearning.AI (X post, Oct 4, 2025) https://twitter.com/DeepLearningAI/status/1974489727702077885 According to @DeepLearningAI, Nvidia also pledged a $100 billion investment in OpenAI. Source: DeepLearning.AI (X post, Oct 4, 2025) https://twitter.com/DeepLearningAI/status/1974489727702077885 According to @DeepLearningAI, the announcement did not mention cryptocurrencies or tokens, indicating no direct crypto-specific details in this release. Source: DeepLearning.AI (X post, Oct 4, 2025) https://twitter.com/DeepLearningAI/status/1974489727702077885

According to @DeepLearningAI, Andrew Ng introduced Landing AI’s Agentic Document Extraction (ADE), a tool that converts PDFs into LLM-ready markdown for healthcare, finance, and law use cases, consolidating AI data-extraction capabilities relevant to enterprise workflows. Source: DeepLearning.AI on X, Oct 3, 2025 - https://twitter.com/DeepLearningAI/status/1974219035093995836 The same update states that OpenAI’s Stargate is expanding with new sites in the U.S. and U.K., highlighting continued hyperscale AI infrastructure buildout that traders track for AI-compute narratives. Source: DeepLearning.AI on X, Oct 3, 2025 - https://twitter.com/DeepLearningAI/status/1974219035093995836 It notes AI systems are generating viral genomes, underscoring ongoing bio-AI advances that may shape regulatory and risk considerations in broader tech markets. Source: DeepLearning.AI on X, Oct 3, 2025 - https://twitter.com/DeepLearningAI/status/1974219035093995836 It reports Sweden is piloting an opt-in music license for AI training with compensation, signaling evolving content-licensing frameworks that inform data provenance themes. Source: DeepLearning.AI on X, Oct 3, 2025 - https://twitter.com/DeepLearningAI/status/1974219035093995836 It adds that AlphaEarth Foundations released global Earth embeddings for accurate mapping, pointing to geospatial AI advances relevant to location data quality. Source: DeepLearning.AI on X, Oct 3, 2025 - https://twitter.com/DeepLearningAI/status/1974219035093995836 For trading context, these themes span AI data extraction, hyperscale infrastructure, bio-AI, licensing policy, and geospatial analytics, offering a consolidated set of AI developments that crypto market participants monitoring AI narratives can track for thematic alignment. Source: DeepLearning.AI on X, Oct 3, 2025 - https://twitter.com/DeepLearningAI/status/1974219035093995836

According to @DeepLearningAI, OpenAI analyzed 1.58 million messages across 1.1 million anonymized ChatGPT conversations and found that most messages are now personal rather than work-related, quantifying a shift toward consumer use cases (source: DeepLearning.AI citing OpenAI, Oct 1, 2025). According to @DeepLearningAI, the user base now skews more female than male, and users aged 18–25 generated 46 percent of messages, highlighting Gen-Z dominance in engagement (source: DeepLearning.AI citing OpenAI, Oct 1, 2025). According to @DeepLearningAI, the top intents were practical guidance at 28.3 percent, writing help at 28.1 percent, and information seeking at 21.3 percent, providing a ranked breakdown of demand drivers (source: DeepLearning.AI citing OpenAI, Oct 1, 2025). For trading, these verified usage distributions offer a current benchmark for consumer AI demand that traders can incorporate when assessing AI-exposed equities and monitoring AI-narrative crypto sentiment around product engagement trends (source: DeepLearning.AI reporting the OpenAI study, Oct 1, 2025).