I rely on ElevenReader several times a week for quick text to voice on snippets of text I’m working on or sometimes on full web pages when I hand it a url. It’s quick and easy to use and the performance and quality is high.
Late Sunday night, I gained access to OpenAI’s newly launched Deep Research and immediately tested it on a draft blog post about Uniform Electronic Transactions Act (UETA) compliance and AI-agent error handling [1]. Here’s what I found:
Within minutes, it generated a detailed, well-cited research report that significantly expanded my original analysis, covering:
* Legal precedents & case law interpretations (including a nuanced breakdown of UETA Section 10).
* Comparative international frameworks (EU, UK, Canada).
* Real-world technical implementations (Stripe’s AI-driven transaction handling).
* Industry perspectives & business impact (trust, risk allocation, compliance).
* Emerging regulatory standards (EU AI Act, FTC oversight, ISO/NIST AI governance).
What stood out most was its ability to:
- Synthesize complex legal, business, and technical concepts into clear, actionable insights.
- Connect legal frameworks, industry trends, and real-world case studies.
- Maintain a business-first focus, emphasizing practical benefits.
- Integrate 2024 developments with historical context for a deeper analysis.
The depth and coherence of the output were comparable to what I would expect from a team of domain experts—but delivered in a fraction of the time.
From the announcement: Deep Research leverages OpenAI’s next-generation model, optimized for multi-step research, reasoning, and synthesis. It has already set new performance benchmarks, achieving 26.6% accuracy on Humanity’s Last Exam (the highest of any OpenAI model) and a 72.57% average accuracy on the GAIA Benchmark, demonstrating advanced reasoning and research capabilities.
Currently available to Pro users (with up to 100 queries per month), it will soon expand to Plus and Team users. While OpenAI acknowledges limitations—such as occasional hallucinations and challenges in source verification—its iterative deployment strategy and continuous refinement approach are promising.
My key takeaway: This LLM agent-based tool has the potential to save hours of manual research while delivering high-quality, well-documented outputs. Automating tasks that traditionally require expert-level investigation, it can complete complex research in 5–30 minutes (just 6 minutes for my task), with citations and structured reasoning.
I don’t see any other comments yet from people who have actually used it, but it’s only been a few hours.I’d love to hear how it’s performing for others. What use cases have you explored? How did it do?
(Note: This review is based on a single use case. I’ll provide further updates as I conduct broader testing.)
The second was a task to do an industry analysis on a space in which I worked for about ten years. I think its overall synthesis was good (it accorded with my understanding of the space), but there were a number of errors in the statistics and supporting evidence it compiled, based upon my random review of the source material.
I think the product is cool and will definitely be helpful, but I would still recommend verifying its outputs. I think the process of verification is less time-consuming than the process of researching and writing, so that is likely an acceptable compromise in many cases.
I’ve been using Sublime Text many times a day since around 2010 for my daily and weekly logs. My Mac is set to use it as the default for .py, .md, and .txt files, so I find myself in Sublime all the time—and I absolutely love it! Sure, I develop in VS Code, but I live in sublime. It’s fast, clean, and dependable. Thanks to the team for building such a fantastic editor!
China is leveraging open-source AI models to gain global influence, potentially creating dependencies on its technology and values. This article argues that U.S. policymakers are focusing on the wrong threats by restricting open AI models and suggests that embracing open-source innovation is key to maintaining U.S. leadership in AI. It outlines strategies like investing in open development, promoting adoption, and avoiding restrictive policies to counter China's growing AI dominance. A compelling call for 'open-source diplomacy' in the AI race. This was written over a week ago, and now with the advent of the R1 model out of China it seems even more relevant and timely.
Another thought: Focusing resources on promoting creation of the most powerful open source models rather than continuing the overwrought emphasis on risks and safety is a vastly wiser course. The R1 models recently out of China are a solid case in point. The real risks will follow from failing to compete, and the real rewards will follow from succeeding. you can’t regulate and restrict your way into successful competition. In fact, it is quite the opposite.
This guide compares top general-purpose AI models like ChatGPT, Claude, and Gemini, highlighting recent advancements, strengths, and use cases. It offers practical advice on choosing the right AI for various needs, making it I think a useful resource for anyone navigating the fast-changing AI landscape.
Honestly, LLM-related posts are usually a topic I’m more likely to click on, depending on the article. I find some of the discussions pretty interesting and useful, though I get why the saturation might feel like too much for others.
This article does a great job summarizing the rapid advancements in reasoning and AI agents. Models like OpenAI's o1/o3 and DeepSeek's r1 demonstrate how inference-time compute and structured Chain of Thought (CoT) are pushing LLM capabilities in STEM and coding tasks. The speculation about pivot words and backtracking behavior learned through reinforcement learning is particularly intriguing—it could be transformative for reasoning in domains with external verification.
The discussion on agents and moving beyond chat interfaces toward workflows like Cursor resonated with me. A shift in Human-AI interaction paradigms feels essential to unlock the full potential of autonomous agents. However, as the author notes, error rates and cost remain significant hurdles.
I've been experimenting with multi-agent systems in Python for the last year and find measuring performance and success one of the hardest parts. While today's LLM agents are still primitive, they already show immense potential. Even without advances in base models, creative agent design patterns could unlock more functionality, and with better reasoning and larger context windows, the possibilities expand even further.
Just a thought – could the existence of something like dark matter actually be causing the differences in the rate of time in different parts of the universe? If dark matter affects gravity in ways we don't fully understand, maybe it's also influencing time dilation across the cosmic web. That might reconcile these two ideas – with dark matter being the underlying cause of the time variations that timescape cosmology talks about. I'm curious if anyone has explored this connection or if it's completely off base?
Come to think of it, another angle to consider – could it work the other way around? Instead of dark matter causing time dilation, could time dilation itself be a root cause that leads to the accumulation or creation of something like dark matter? If time moves differently in certain regions, maybe it affects how matter and energy interact over cosmic timescales, creating the conditions we interpret as dark matter. Just wondering if this has been explored or if it’s way out there as an idea? I’m definitely not a cosmologist!
The idea here (timescapes) is specifically about dark energy, not dark matter.
EDIT: But, yes, if there's lots of dark matter not uniformly distributed in the universe, then that dark matter would have differing effects on time dilation in different parts of the universe. Questions: Do the great voids contain much dark matter? What about non-void areas that also have very little DM somehow?
Thanks for clarifying that timescape cosmology focuses on dark energy, not dark matter—makes sense they’re addressing different puzzles. That said, I’m curious: could there be any indirect connection between the two? For instance, if dark matter’s influence on gravity shapes the 'lumpiness' of the universe, could that, in turn, amplify the time dilation effects discussed in timescapes? I realize the authors didn’t delve into dark matter, but wondering if anyone has explored how these two phenomena might interact. Definitely out of my depth here, but this potential connection is what sprung to mind and on rare occasions my ill-informed intuitive curiosities turn out to have unexpected merit, so I’m asking if there could be anything to it?
> could there be any indirect connection between the two?
Unlikely. DE and DM are very different problems, sharing only a word in their casual names: dark :)
The galaxy rotation problem requires either modified gravity (MOND, etc.), actual dark matter, finding that the measurements and/or their interpretation are wrong, or something else. There are some GR effects to consider, mainly frame dragging, but frame dragging doesn't seem to be anywhere near enough because the frame dragging effect diminishes with the same proportion as gravity: inverse square distance.
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