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Daily AI Briefing — January 8, 2026

1457 current signals analyzed across AI news, research, social media, and open-source projects.

News · 15 Research · 395 Social · 493 GitHub · 0

Daily synthesis

Executive Summary

Top Story

GPT-5.2 autonomously solved Erdős Problem #728, marking the first time an LLM resolved an open mathematics problem without prior human solution.

Key Developments

Safety & Regulation

  • Australia's eSafety commissioner launched investigation into Grok's deepfake image generation capabilities over non-consensual intimate imagery reports
  • Utah became the first US state to allow AI to renew medical prescriptions without doctor involvement
  • New research tested 32 models across 56 jailbreak techniques with 4.6M API calls in the field's most comprehensive safety study
  • RAILS attack method demonstrated black-box jailbreaking matching gradient-based effectiveness using only logits

Research Highlights

Looking Ahead

The combination of $30B+ in new AI funding, healthcare AI regulatory precedents, and mathematical reasoning breakthroughs suggests 2026 will test both deployment scale and governance frameworks.

Cross-category signals

Top Topics

Top Topic

AI Safety & Content Moderation Crisis

Australia's eSafety commissioner launched an investigation into Grok's deepfake image generation capabilities after reports of the tool creating non-consensual intimate imagery. Academic research delivered the most comprehensive safety study evaluating 32 models across 56 jailbreak techniques with 4.6M API calls. New attack methods like RAILS demonstrated black-box jailbreaking matching gradient-based effectiveness using only logits.

3 News 3 Research

Top Topic

Claude Code Ecosystem Expansion

Claude Code v2.1.0 released with automatic skill hot-reload and forked sub-agent contexts, with notable adoption by Microsoft employees despite having GitHub Copilot subscriptions. Ethan Mollick demonstrated building businesses and simulations as a non-coder, while Andrew Ng launched a free course teaching vibe coding to non-programmers. Nous Research released NousCoder-14B as an open-source alternative matching larger proprietary coding models.

3 Social 1 News

Top Topic

ChatGPT Health & Medical AI Regulation

OpenAI officially launched ChatGPT Health as a dedicated space for health conversations with secure connections to medical records and wellness apps like Apple Health. Utah became the first US state to allow AI to renew medical prescriptions without doctor involvement, with Doctronic's system reportedly matching doctor treatment plans in most cases. The launches represent major milestones in healthcare AI deployment and regulation.

1 Social

Top Topic

Open Source Models & Training Efficiency

Multiple significant releases including TII's Falcon-H1R-7B with hybrid Transformer-Mamba2 architecture and NVIDIA's Nemotron Speech ASR for low-latency voice agents. DeepSeek-R1's paper expanded from 22 to 86 pages with substantial implementation details, while community members demonstrated running DeepSeek v3.2 on 16x AMD MI50 GPUs. Andrej Karpathy's nanochat miniseries reproduced Chinchilla scaling laws with deeply technical methodology.

3 News 1 Social 1 Research

Current evidence

AI News

View category →

Massive funding dominates this week's AI news, with Anthropic seeking $10B at a $350B valuation and xAI raising $20B despite Grok controversies. NVIDIA's $20B acquisition of Groq signals major consolidation in AI inference hardware.

Robotics advances featured prominently:

  • Boston Dynamics unveiled Atlas at CES with a Google DeepMind partnership for cognitive capabilities
  • Mobileye announced a $900M acquisition of Mentee Robotics for physical AI development

Open-source model releases continue accelerating:

Research and safety concerns round out coverage, with new work on self-questioning AI models that learn autonomously, while Grok faces regulatory scrutiny from Australia's eSafety watchdog over deepfake generation capabilities.

News AI (artificial intelligence) | The Guardian Jan 7

AI chatbot maker Anthropic plans to raise $10bn to reach $350bn valuation

By Reuters

92 score
AI Analysis
Anthropic is planning a $10B fundraise that would value the company at $350B, nearly doubling its valuation from four months ago. GIC and Coatue Management are expected to lead the round, which could close within weeks.
Startup founded by former OpenAI staff is aiming to more than double its annualized revenue run rate this yearAnthropic is planning a $10bn fundraise that would value the Claude chatbot maker at $350bn, according to multiple reports published on Wednesday.The new valuation represents an increase of nearly double from about four months ago, per CNBC, which reported that the company had signed a term sheet that stipulated the $350bn figure. The round could close within weeks, although the size and
Funding & ValuationsFrontier AI LabsIndustry Competition
News aibusiness Jan 7

Another $20B in Funding for Musk's xAI, Despite Grok Controversy

By Graham Hope

90 score
AI Analysis
Elon Musk's xAI is raising another $20B in funding to continue scaling compute infrastructure and GPU cluster buildout, despite ongoing controversy over Grok's content generation capabilities.
Backed by major investors, xAI aims to continue to rapidly scale its compute infrastructure and buildout of GPU clusters.
Funding & ValuationsFrontier AI LabsAI Infrastructure
News Last Week in AI Jan 7

LWiAI Podcast #230 - 2025 Retrospective, Nvidia buys Groq, GLM 4.7, METR

By Last Week in AI

88 score
AI Analysis
Podcast covering major news including NVIDIA's $20B acquisition of AI chip startup Groq, New York's RAISE Act AI safety legislation, and Zhipu AI's GLM 4.7 open-source model launch.
Our 230th episode with a summary and discussion of last week’s big AI news!Recorded on 01/02/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.aiIn this episode:Nvidia’s acquisition of AI chip startup Groq for $20 billion highlights a strategic move for enhanced inference technology in GPUs.New York’s RAISE Act legislation aims to regulate AI safety, marking the second major AI sa
M&A ActivityAI RegulationAI Hardware
News aibusiness Jan 7

Boston Dynamics Unveils Humanoid Robot Atlas at CES

By Scarlett Evans

85 score
AI Analysis
Building on yesterday's Social buzz Boston Dynamics unveiled its humanoid robot Atlas at CES 2026 and announced a partnership with Google DeepMind to integrate advanced cognitive capabilities into its robots.
The company also announced a partnership with Google DeepMind to bring more cognitive capabilities to its robots.
RoboticsAI PartnershipsEmbodied AI
News Feed: Artificial Intelligence Latest Jan 7

AI Models Are Starting to Learn by Asking Themselves Questions

By Will Knight

82 score
AI Analysis
New research demonstrates AI models that can continue learning after training by generating and answering their own questions without human input. This self-directed learning approach may point toward paths to superintelligence.
An AI model that learns without human input—by posing interesting queries for itself—might point the way to superintelligence.
AI ResearchSelf-ImprovementAGI Pathways

Current evidence

Research

View category →

The OpenAI GPT-5 System Card dominates today's releases, detailing the unified architecture with dynamic routing between fast and deep reasoning modes. A remarkable autoformalization result shows 130k lines of formal topology generated in two weeks for ~$100, suggesting accessible mathematical formalization at scale.

Safety and alignment research features prominently:

Theoretical and interpretability advances challenge key assumptions:

Research arXiv (Computation and Language) Jan 8

OpenAI GPT-5 System Card

By Aaditya Singh, Adam Fry, Adam Perelman, Adam Tart, Adi Ganesh, Ahmed El-Kishky, Aidan McLaughlin, Aiden Low, AJ Ostrow, Akhila Ananthram, Akshay Nathan, Alan Luo, Alec Helyar, Aleksander Madry, Aleksandr Efremov, Aleksandra Spyra, Alex Baker-Whitcomb, Alex Beutel, Alex Karpenko, Alex Makelov, Alex Neitz, Alex Wei, Alexandra Barr, Alexandre Kirchmeyer, Alexey Ivanov, Alexi Christakis, Alistair Gillespie, Allison Tam, Ally Bennett, Alvin Wan, Alyssa Huang, Amy McDonald Sandjideh, Amy Yang, Ananya Kumar, Andre Saraiva, Andrea Vallone, Andrei Gheorghe, Andres Garcia Garcia, Andrew Braunstein, Andrew Liu, Andrew Schmidt, Andrey Mereskin, Andrey Mishchenko, Andy Applebaum, Andy Rogerson, Ann Rajan, Annie Wei, Anoop Kotha, Anubha Srivastava, Anushree Agrawal, Arun Vijayvergiya, Ashley Tyra, Ashvin Nair, Avi Nayak, Ben Eggers, Bessie Ji, Beth Hoover, Bill Chen, Blair Chen, Boaz Barak, Borys Minaiev, Botao Hao, Bowen Baker, Brad Lightcap, Brandon McKinzie, Brandon Wang, Brendan Quinn, Brian Fioca, Brian Hsu, Brian Yang, Brian Yu, Brian Zhang, Brittany Brenner, Callie Riggins Zetino, Cameron Raymond, Camillo Lugaresi, Carolina Paz, Cary Hudson, Cedric Whitney, Chak Li, Charles Chen, Charlotte Cole, Chelsea Voss, Chen Ding, Chen Shen, Chengdu Huang, Chris Colby, Chris Hallacy, Chris Koch, Chris Lu, Christina Kaplan, Christina Kim, CJ Minott-Henriques, Cliff Frey, Cody Yu, Coley Czarnecki, Colin Reid, Colin Wei, Cory Decareaux, Cristina Scheau, Cyril Zhang, Cyrus Forbes, Da Tang, Dakota Goldberg, Dan Roberts, Dana Palmie, Daniel Kappler, Daniel Levine, Daniel Wright, Dave Leo, David Lin, David Robinson, Declan Grabb, Derek Chen, Derek Lim, Derek Salama, Dibya Bhattacharjee, Dimitris Tsipras, Dinghua Li, Dingli Yu, DJ Strouse, Drew Williams, Dylan Hunn, Ed Bayes, Edwin Arbus, Ekin Akyurek, Elaine Ya Le, Elana Widmann, Eli Yani, Elizabeth Proehl, Enis Sert, Enoch Cheung, Eri Schwartz, Eric Han, Eric Jiang, Eric Mitchell, Eric Sigler, Eric Wallace, Erik Ritter, Erin Kavanaugh, Evan Mays, Evgenii Nikishin, Fangyuan Li, Felipe Petroski Such, Filipe de Avila Belbute Peres, Filippo Raso, Florent Bekerman, Foivos Tsimpourlas, Fotis Chantzis, Francis Song, Francis Zhang, Gaby Raila, Garrett McGrath, Gary Briggs, Gary Yang, Giambattista Parascandolo, Gildas Chabot, Grace Kim, Grace Zhao, Gregory Valiant, Guillaume Leclerc, Hadi Salman, Hanson Wang, Hao Sheng, Haoming Jiang, Haoyu Wang, Haozhun Jin, Harshit Sikchi, Heather Schmidt, Henry Aspegren, Honglin Chen, Huida Qiu, Hunter Lightman, Ian Covert, Ian Kivlichan, Ian Silber, Ian Sohl, Ibrahim Hammoud, Ignasi Clavera, Ikai Lan, Ilge Akkaya, Ilya Kostrikov, Irina Kofman, Isak Etinger, Ishaan Singal, Jackie Hehir, Jacob Huh, Jacqueline Pan, Jake Wilczynski, Jakub Pachocki, James Lee, James Quinn, Jamie Kiros, Janvi Kalra, Jasmyn Samaroo, Jason Wang, Jason Wolfe, Jay Chen, Jay Wang, Jean Harb, Jeffrey Han, Jeffrey Wang, Jennifer Zhao, Jeremy Chen, Jerene Yang, Jerry Tworek, Jesse Chand, Jessica Landon, Jessica Liang, Ji Lin, Jiancheng Liu, Jianfeng Wang, Jie Tang, Jihan Yin, Joanne Jang, Joel Morris, Joey Flynn, Johannes Ferstad, Johannes Heidecke, John Fishbein, John Hallman, Jonah Grant, Jonathan Chien, Jonathan Gordon, Jongsoo Park, Jordan Liss, Jos Kraaijeveld, Joseph Guay, Joseph Mo, Josh Lawson, Josh McGrath, Joshua Vendrow, Joy Jiao, Julian Lee, Julie Steele, Julie Wang, Junhua Mao, Kai Chen, Kai Hayashi, Kai Xiao, Kamyar Salahi, Kan Wu, Karan Sekhri, Karan Sharma, Karan Singhal, Karen Li, Kenny Nguyen, Keren Gu-Lemberg, Kevin King, Kevin Liu, Kevin Stone, Kevin Yu, Kristen Ying, Kristian Georgiev, Kristie Lim, Kushal Tirumala, Kyle Miller, Lama Ahmad, Larry Lv, Laura Clare, Laurance Fauconnet, Lauren Itow, Lauren Yang, Laurentia Romaniuk, Leah Anise, Lee Byron, Leher Pathak, Leon Maksin, Leyan Lo, Leyton Ho, Li Jing, Liang Wu, Liang Xiong, Lien Mamitsuka, Lin Yang, Lindsay McCallum, Lindsey Held, Liz Bourgeois, Logan Engstrom, Lorenz Kuhn, Louis Feuvrier, Lu Zhang, Lucas Switzer, Lukas Kondraciuk, Lukasz Kaiser, Manas Joglekar, Mandeep Singh, Mandip Shah, Manuka Stratta, Marcus Williams, Mark Chen, Mark Sun, Marselus Cayton, Martin Li, Marvin Zhang, Marwan Aljubeh, Matt Nichols, Matthew Haines, Max Schwarzer, Mayank Gupta, Meghan Shah, Melody Huang, Meng Dong, Mengqing Wang, Mia Glaese, Micah Carroll, Michael Lampe, Michael Malek, Michael Sharman, Michael Zhang, Michele Wang, Michelle Pokrass, Mihai Florian, Mikhail Pavlov, Miles Wang, Ming Chen, Mingxuan Wang, Minnia Feng, Mo Bavarian, Molly Lin, Moose Abdool, Mostafa Rohaninejad, Nacho Soto, Natalie Staudacher, Natan LaFontaine, Nathan Marwell, Nelson Liu, Nick Preston, Nick Turley, Nicklas Ansman, Nicole Blades, Nikil Pancha, Nikita Mikhaylin, Niko Felix, Nikunj Handa, Nishant Rai, Nitish Keskar, Noam Brown, Ofir Nachum, Oleg Boiko, Oleg Murk, Olivia Watkins, Oona Gleeson, Pamela Mishkin, Patryk Lesiewicz, Paul Baltescu, Pavel Belov, Peter Zhokhov, Philip Pronin, Phillip Guo, Phoebe Thacker, Qi Liu, Qiming Yuan, Qinghua Liu, Rachel Dias, Rachel Puckett, Rahul Arora, Ravi Teja Mullapudi, Raz Gaon, Reah Miyara, Rennie Song, Rishabh Aggarwal, RJ Marsan, Robel Yemiru, Robert Xiong, Rohan Kshirsagar, Rohan Nuttall, Roman Tsiupa, Ronen Eldan, Rose Wang, Roshan James, Roy Ziv, Rui Shu, Ruslan Nigmatullin, Saachi Jain, Saam Talaie, Sam Altman, Sam Arnesen, Sam Toizer, Sam Toyer, Samuel Miserendino, Sandhini Agarwal, Sarah Yoo, Savannah Heon, Scott Ethersmith, Sean Grove, Sean Taylor, Sebastien Bubeck, Sever Banesiu, Shaokyi Amdo, Shengjia Zhao, Sherwin Wu, Shibani Santurkar, Shiyu Zhao, Shraman Ray Chaudhuri, Shreyas Krishnaswamy, Shuaiqi (Tony) Xia, Shuyang Cheng, Shyamal Anadkat, Sim\'on Posada Fishman, Simon Tobin, Siyuan Fu, Somay Jain, Song Mei, Sonya Egoian, Spencer Kim, Spug Golden, SQ Mah, Steph Lin, Stephen Imm, Steve Sharpe, Steve Yadlowsky, Sulman Choudhry, Sungwon Eum, Suvansh Sanjeev, Tabarak Khan, Tal Stramer, Tao Wang, Tao Xin, Tarun Gogineni, Taya Christianson, Ted Sanders, Tejal Patwardhan, Thomas Degry, Thomas Shadwell, Tianfu Fu, Tianshi Gao, Timur Garipov, Tina Sriskandarajah, Toki Sherbakov, Tomer Kaftan, Tomo Hiratsuka, Tongzhou Wang, Tony Song, Tony Zhao, Troy Peterson, Val Kharitonov, Victoria Chernova, Vineet Kosaraju, Vishal Kuo, Vitchyr Pong, Vivek Verma, Vlad Petrov, Wanning Jiang, Weixing Zhang, Wenda Zhou, Wenlei Xie, Wenting Zhan, Wes McCabe, Will DePue, Will Ellsworth, Wulfie Bain, Wyatt Thompson, Xiangning Chen, Xiangyu Qi, Xin Xiang, Xinwei Shi, Yann Dubois, Yaodong Yu, Yara Khakbaz, Yifan Wu, Yilei Qian, Yin Tat Lee, Yinbo Chen, Yizhen Zhang, Yizhong Xiong, Yonglong Tian, Young Cha, Yu Bai, Yu Yang, Yuan Yuan, Yuanzhi Li, Yufeng Zhang, Yuguang Yang, Yujia Jin, Yun Jiang, Yunyun Wang, Yushi Wang, Yutian Liu, Zach Stubenvoll, Zehao Dou, Zheng Wu, Zhigang Wang

98 score
AI Analysis
Official system card for OpenAI GPT-5 launch (August 2025), describing a unified system with fast and deep reasoning models, real-time router for complexity-based model selection, and continuous training on user feedback signals.
This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reasoning model for harder problems, and a real-time router that quickly decides which model to use based on conversation type, complexity, tool needs, and explicit intent (for example, if you say 'think hard about this' in the prompt). The router is continuously trained on real signals, including when users switch models,
Language ModelsAI SafetyOpenAIFoundation Models
Research arXiv (cs.LO) Jan 8

130k Lines of Formal Topology in Two Weeks: Simple and Cheap Autoformalization for Everyone?

By Josef Urban

88 score
AI Analysis
Reports autoformalization of 130k lines of topology from Munkres textbook in two weeks for ~$100 LLM cost, including proofs of Urysohn's lemma and metrization theorem, using LLM-proof checker feedback loop.
This is a brief description of a project that has already autoformalized a large portion of the general topology from the Munkres textbook (which has in total 241 pages in 7 chapters and 39 sections). The project has been running since November 21, 2025 and has as of January 4, 2026, produced 160k lines of formalized topology. Most of it (about 130k lines) have been done in two weeks,from December 22 to January 4, for an LLM subscription cost of about \$100. This includes a 3k-line proof of Urys
Formal MethodsLanguage ModelsMathematicsAutoformalization
Research arXiv (Computation and Language) Jan 8

What Matters For Safety Alignment?

By Xing Li, Hui-Ling Zhen, Lihao Yin, Xianzhi Yu, Zhenhua Dong, Mingxuan Yuan

82 score
AI Analysis
Comprehensive empirical study on safety alignment evaluating 32 LLMs/LRMs across 13 families using 5 safety datasets, 56 jailbreak techniques, and 4 CoT attack strategies totaling 4.6M API calls.
This paper presents a comprehensive empirical study on the safety alignment capabilities. We evaluate what matters for safety alignment in LLMs and LRMs to provide essential insights for developing more secure and reliable AI systems. We systematically investigate and compare the influence of six critical intrinsic model characteristics and three external attack techniques. Our large-scale evaluation is conducted using 32 recent, popular LLMs and LRMs across thirteen distinct model families, spa
AI SafetyAlignmentJailbreakingLLM Evaluation
Research arXiv (Artificial Intelligence) Jan 8

Mastering the Game of Go with Self-play Experience Replay

By Jingbin Liu and Xuechun Wang

82 score
AI Analysis
Introduces QZero, a model-free RL algorithm for Go that learns Nash equilibrium policy through self-play and experience replay without MCTS, achieving AlphaGo-level performance with modest compute (7 GPUs, 5 months).
The game of Go has long served as a benchmark for artificial intelligence, demanding sophisticated strategic reasoning and long-term planning. Previous approaches such as AlphaGo and its successors, have predominantly relied on model-based Monte-Carlo Tree Search (MCTS). In this work, we present QZero, a novel model-free reinforcement learning algorithm that forgoes search during training and learns a Nash equilibrium policy through self-play and off-policy experience replay. Built upon entropy-
Reinforcement LearningGame AIModel-Free RL
Research arXiv (Computation and Language) Jan 8

Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models

By Kai Hu, Abhinav Aggarwal, Mehran Khodabandeh, David Zhang, Eric Hsin, Li Chen, Ankit Jain, Matt Fredrikson, Akash Bharadwaj

82 score
AI Analysis
Introduces Jailbreak-Zero, a red teaming methodology shifting from example-based to policy-based LLM safety evaluation. Uses attack LLM fine-tuned with preference data to achieve Pareto optimality across coverage, diversity, and fidelity, showing high success rates against GPT-4o and Claude 3.5.
This paper introduces Jailbreak-Zero, a novel red teaming methodology that shifts the paradigm of Large Language Model (LLM) safety evaluation from a constrained example-based approach to a more expansive and effective policy-based framework. By leveraging an attack LLM to generate a high volume of diverse adversarial prompts and then fine-tuning this attack model with a preference dataset, Jailbreak-Zero achieves Pareto optimality across the crucial objectives of policy coverage, attack strateg
AI SafetyRed TeamingLanguage ModelsAlignment

Current evidence

Social Media

View category →

OpenAI dominated discussions with the ChatGPT Health launch, integrating medical records and wellness apps for their 230M+ weekly health questioners. A buried bombshell: Greg Brockman casually revealed using GPT-5.2 for solving an open Erdős problem.

  • Andrej Karpathy released nanochat miniseries v1, reproducing Chinchilla scaling laws with deeply technical methodology
  • Andrew Wilson (NYU) introduced epiplexity, a novel information measure defining what bounded observers can extract from data
  • Jeremy Howard shared ironic proof of llms.txt value: Tailwind rejected adding it specifically because it would be too useful

The Claude Code conversation continued with Ethan Mollick demonstrating building businesses and simulations as a non-coder. Matt Shumer sought founders building 'Slack for agents,' signaling serious investor appetite for multi-agent infrastructure. Andrew Ng launched a free course teaching vibe coding to non-programmers, further democratizing AI development.

Social Twitter Jan 7

New post: nanochat miniseries v1 The correct way to think about LLMs is that you are not optimizing...

By @karpathy

95 score
AI Analysis
Karpathy releases nanochat miniseries v1, demonstrating LLM scaling laws that reproduce Chinchilla paper results, showing compute-optimal training can match GPT-2 for ~$500 (targeting <$100)
New post: nanochat miniseries v1 The correct way to think about LLMs is that you are not optimizing for a single specific model but for a family models controlled by a single dial (the compute you wish to spend) to achieve monotonically better results. This allows you to do careful science of scaling laws and ultimately this is what gives you the confidence that when you pay for "the big run", the extrapolation will work and your money will be well spent. For the first public release of nanocha
LLM scaling lawscompute optimizationopen source AI researchtraining efficiency
Social Twitter Jan 7

Introducing ChatGPT Health — a dedicated space for health conversations in ChatGPT. You can securely...

By @OpenAI

92 score
AI Analysis
OpenAI officially introduces ChatGPT Health as dedicated health conversation space with medical records and wellness app integration
Introducing ChatGPT Health — a dedicated space for health conversations in ChatGPT. You can securely connect medical records and wellness apps so responses are grounded in your own health information. Designed to help you navigate medical care, not replace it. Join the waitlist to get early access. t.co/MdpqDg7Ecg
AI healthproduct launchesChatGPT Healthmedical AI
Social Twitter Jan 7

gpt-5.2 for solving an open Erdős problem:

By @gdb

80 score
AI Analysis
Following yesterday's Reddit coverage Greg Brockman mentions using GPT-5.2 for solving an open Erdős problem in mathematics
gpt-5.2 for solving an open Erdős problem:
frontier AI capabilitiesGPT-5AI for mathematicsscientific discovery
Social Twitter Jan 7

How useful is llms.txt? It's so useful that Tailwind rejected a PR to add an llms.txt, on the basis...

By @jeremyphoward

90 score
AI Analysis
Jeremy Howard highlights ironic proof of llms.txt usefulness: Tailwind rejected PR to add llms.txt specifically because it would be so useful people wouldn't need their docs
How useful is llms.txt? It's so useful that Tailwind rejected a PR to add an llms.txt, on the basis that it would be so useful that people wouldn't need to read their docs any more! t.co/fn8Co36rmC t.co/68U8dOrzYF
llms.txtAI documentationDeveloper toolsLLM integration
Social Twitter Jan 7

If you’ve never written code before, this is for you. I’ve just launched a course that shows you, in...

By @AndrewYNg

85 score
AI Analysis
Andrew Ng launches free 30-minute course teaching non-coders to build web apps using AI, emphasizing 'vibe coding' and vendor-neutral approach
If you’ve never written code before, this is for you. I’ve just launched a course that shows you, in less than 30 minutes, how to describe an idea for an app and build it with AI. In this course, you'll build a working web application - a funny interactive birthday message generator that runs in your browser and can be shared with friends. You'll customize it by telling AI how you want it changed, and tweak it until it works the way you want. By the end, you'll have a repeatable process you can
AI educationvibe codingAI democratizationno-code development