Regulating AI is a key societal challenge, but effective methods remain unclear. This study evaluates geographic restrictions on AI services, focusing on ChatGPT, which OpenAI blocks in several countries, including China and Russia. If restrictions were effective, ChatGPT usage in these countries should be minimal. We measured usage with a classifier trained to detect distinctive word choices (e.g., “delve”) typical of early ChatGPT outputs. The classifier, trained on pre- and post-ChatGPT “polished” abstracts, outperformed GPTZero and ZeroGPT on validation sets, including papers with self-reported AI use. Applying our classifier to preprints from Arxiv, BioRxiv, and MedRxiv revealed ChatGPT use in approximately 12.6% of preprints by August 2023, with usage 7.7% higher in restricted countries. This gap emerged before China’s first major domestic LLM became widely available. To address whether high demand could have driven even greater use without restrictions, we compared Asian countries with high expected demand (where English is not an official language) and found higher usage in countries with restrictions. ChatGPT use correlated with increased views and downloads but not with citations or journal placement. Overall, geographic restrictions on ChatGPT appear ineffective in science and potentially other domains, likely due to widespread workarounds. — Read More
Monthly Archives: May 2025
Vision Language Models (Better, Faster, Stronger)
Vision Language Models (VLMs) are the talk of the town. In a previous blog post (from April 2024), we talked a lot about VLMs. A major chunk was about LLaVA, the first successful and easily reproducible open-source vision language model, along with tips on how to discover, evaluate, and fine-tune open models.
Since then, so much has changed. Models have become smaller yet more powerful. We’ve seen the rise of new architectures and capabilities (reasoning, agency, long video understanding, etc.). In parallel, entirely new paradigms, such as multimodal Retrieval Augmented Generation (RAG) and multimodal agents have taken shape.
In this blog post, we’ll take a look back and unpack everything that happened with vision language models the past year. You’ll discover key changes, emerging trends, and notable developments. — Read More
China built hundreds of AI data centers to catch the AI boom. Now many stand unused.
A year or so ago, Xiao Li was seeing floods of Nvidia chip deals on WeChat. A real estate contractor turned data center project manager, he had pivoted to AI infrastructure in 2023, drawn by the promise of China’s AI craze.
At that time, traders in his circle bragged about securing shipments of high-performing Nvidia GPUs that were subject to US export restrictions. Many were smuggled through overseas channels to Shenzhen. At the height of the demand, a single Nvidia H100 chip, a kind that is essential to training AI models, could sell for up to 200,000 yuan ($28,000) on the black market.
Now, his WeChat feed and industry group chats tell a different story. Traders are more discreet in their dealings, and prices have come back down to earth. Meanwhile, two data center projects Li is familiar with are struggling to secure further funding from investors who anticipate poor returns, forcing project leads to sell off surplus GPUs. “It seems like everyone is selling, but few are buying,” he says. — Read More
New-type AI Storage Research Report
In 2022, the Ministry of Science and Technology and six other departments issued the “Guiding Opinions on Accelerating Scenario Innovation and Promoting High-quality Economic Development with High-level Application of Artificial Intelligence”, proposing to accelerate the research and development of artificial intelligence technology, product development and industry cultivation, explore new models and paths for the development of artificial intelligence, and promote high-quality economic development with high-level applications of artificial intelligence. In 2023, the Ministry of Industry and Information Technology and six other departments issued the “Action Plan for the High-quality Development of Computing Power Infrastructure”, proposing to strengthen the efficient and flexible guarantee of storage capacity, accelerate the research and development and application of storage capacity technology, continuously improve the storage industry capabilities, and promote the coordinated development of storage, computing and network.
In the era of big models, data determines the heights of artificial intelligence. More training data is the prerequisite for the iteration and upgrading of big models, and higher data quality also determines the effect of big model training. At present, big model technology comprehensively promotes the development of underlying infrastructure, computing power demand continues to rise, and the storage and processing demand for massive data continues to grow, which puts forward higher requirements for the performance, scalability, data security, and data paradigm of artificial intelligence storage.
This report focuses on sorting out and analyzing the concept scope, challenges, key technologies and best practices of new-type AI storage. In terms of concept scope, the basic concepts of new-type artificial intelligence storage are sorted out, and the global artificial intelligence storage strategy is analyzed. In terms of challenges, it points out that new-type AI storage is the basis for large models, but at the same time there are many challenges in terms of massive data collection, training data access efficiency, and real-time reasoning. In terms of key technologies, it explains that new-type AI storage needs to be strengthened in terms of storage media, systems, architecture, data weaving, data paradigms, and data security. In terms of best practices, it introduces practical cases of new-type AI storage in the medical, financial, cloud service providers, and AI companies. Finally, in response to the challenges of the current development of AI storage, this report puts forward suggestions for the future development of new AI storage in China. New-type AI storage-related industries and technologies are in a stage of rapid development, and the new technology ecosystem is changing rapidly. There are still many shortcomings in the report, and we sincerely invite criticism and correction from all walks of life. — Read More
Try Public APIs for free
The Public APIs repository is manually curated by community members like you and folks working at APILayer. It includes an extensive list of public APIs from many domains that you can use for your own products. Consider it a treasure trove of APIs well-managed by the community over the years. — Read More
Which LLM writes the best analytical SQL?
We asked 19 popular LLMs (+1 human) to write analytical SQL queries to filter and aggregate a 200 million row dataset. The result is the first version of the LLM SQL Generation Benchmark.
Using a set of 50 analytical questions inspired by this list from maintainers of ClickHouse®, we measure how well each model can write accurate and efficient SQL. We benchmark success rates, exactness, efficiency, query latency, and other metrics, comparing them to queries produced by an experienced human engineer.
The dataset, which contains 200 million rows of public GitHub events data (sampled from the GH Archive), is hosted in Tinybird, allowing us to run all the queries interactively and measure performance at scale. The full dashboard with results is public here. We will continually update this dashboard as new models are developed and tested (Want us to test a model? Create an issue or run the test yourself and submit a PR with new results here). — Read More
Deepfakes Now Outsmarting Detection By Mimicking Heartbeats
The assumption that deepfakes lack physiological signals, such as heart rate, is no longer valid. Recent research reveals that high-quality deepfakes unintentionally retain the heartbeat patterns from their source videos, undermining traditional detection methods that relied on detecting subtle skin color changes linked to heartbeats. Researchers suggest shifting focus from just detecting heart rate signals to analyzing how blood flow is distributed across different facial regions, providing a more accurate detection strategy. — Read More
Jinmeng 550A model claims to have hit 100% on AIME24
… Jinmeng 550A is a neuro-symbolic AI model reportedly developed by a 14-year-old Chinese prodigy named Shihao Ji. It gained attention for achieving extraordinary results on prominent AI benchmarks:
100% accuracy on AIME24 (American Invitational Mathematics Examination 2024)
99.7% accuracy on MedQA (Medical Question Answering benchmark)
These results were reported on Papers With Code and highlighted in several Chinese tech media outlets, such as Tencent Cloud and Sohu. — Read More
Ask HN: How much better are AI IDEs vs. copy pasting into chat apps?
I just wanted to hear peoples experiences with AI IDEs.
For context, I’m a heavy user of Gemini / ChatGPT for coding and Copilot. But I haven’t used Cursor / Windsurf / etc..
Copy pasting into chat apps is a first world problem: it will do the work for you, but you have to give it all the context in the prompt, which for a larger project, gets tedious.
The issue with Copilot is that it’s not as smart as the “thinking” chat apps.
This makes it clear why there’s such a need for AI IDEs. I don’t want to construct my context to a chat app. The context is already in my codebase, so the AI should pick up on it. But I also hear that it gets expensive because of the pay-per-use pricing, as opposed to effectively unlimited prompts for a thinking chat app if you pay the monthly subscription.
So I just wanted to get the lay of the land. How good are these IDEs on constructing your context to the LLMs? How much more expensive is it, and is it worth it for you? — Read More
Document My Pentest: you hack, the AI writes it up!
Tired of repeating yourself? Automate your web security audit trail. In this post I’ll introduce a new Burp AI extension that takes the boring bits out of your pen test.
Web security testing can be a grind: documenting every step, writing the same notes over and over, and repeating it all across every engagement. But what if your workflow could document itself – while you hacked?
Meet “Document My Pentest”, your silent co-analyst for security testing. It’s an open-source Burp Suite extension that watches your requests in real time, understands what you’re probing for, and automatically builds a clean, structured record of your findings – capturing exactly what you did and how you did it. When you’re ready, hand it off to AI and generate a report. No more boring note taking. Just results. — Read More
Rick's Cafe AI 